Beyond BRCA deficiency: Clinical and molecular predictors of survival in patients with BRCA-deficient tubo-ovarian high-grade serous carcinoma

Beyond BRCA deficiency: Clinical and molecular predictors of survival in patients with BRCA-deficient tubo-ovarian high-grade serous carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Beyond BRCA deficiency: Clinical and molecular predictors of survival in patients with BRCA-deficient tubo-ovarian high-grade serous carcinoma Dale Garsed, Tibor Zwimpfer, Sian Fereday, Ahwan Pandey, Dinuka Ariyaratne, and 143 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7572112/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract BRCA -associated homologous recombination deficiency (HRD) is present in ~ 50% of high-grade serous carcinomas (HGSC) and predicts sensitivity to platinum-based therapy. However, there is little understanding of why some patients with BRCA -deficient tumors experience unexpectedly poor outcomes. We profiled 154 tumors, enriched for patients with BRCA -deficient tumors that experienced short overall survival (≤ 3 years, n = 42), using whole-genome, transcriptome, and methylation analyses. All but one BRCA -deficient tumor exceeded an accepted HRD genomic scarring threshold. However, patients with BRCA1 -deficient HGSC with a more elevated HRD score survived significantly longer. Patients with BRCA2 -deficient HGSC and loss of NF1 survived twice as long as those without NF1 loss, whereas PIK3CA or RAD21 amplification defined BRCA2 -deficient HGSC with exceptionally short survival. BRCA1 -deficient tumors in short survivors had evidence of immunosuppressive c-kit signaling and EMT. In a large HGSC cohort (n = 1,389) including 282 individuals with pathogenic germline BRCA variants (g BRCA pv), the location of the mutation within functional domains stratified clinical outcomes. Notably, residual disease after primary surgery had limited prognostic effect in g BRCA pv-carriers compared to non-carriers. Our findings indicate that tumor HR proficiency in the context of therapy response and survival is not a binary property, and highlight genomic and immune modifiers of outcomes in BRCA -deficient HGSC. Biological sciences/Cancer/Gynaecological cancer/Ovarian cancer Health sciences/Medical research/Genetics research Biological sciences/Biological techniques/Genomic analysis Biological sciences/Molecular biology/Transcriptomics Biological sciences/Genetics/Genetic association study/Genome-wide association studies Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION The identification of clinical and molecular determinants of survival in patients with cancer has the dual benefits of finding biomarkers that may guide patient management or provide novel therapeutic opportunities. Until relatively recently, the identification of prognostic biomarkers in ovarian cancer has been confounded by a lack of appreciation of the distinctly different molecular characteristics of the various histologic subtypes that make up epithelial ovarian cancer 1 . Evaluating histologically homogenous sets of ovarian tumors has been critical in deciphering the prognostic importance of proteins such as p53 2,3 and WT1 4 , and identifying genetic risk loci 5 – 12 . High-grade serous carcinoma (HGSC) is the most common histotype, accounting for approximately 70% of ovarian cancer deaths in Western countries 13 – 16 . Homologous recombination-mediated DNA repair deficiency (HRD) is frequent in HGSC and is most often associated with mutations in BRCA1 and BRCA2 17–19 . Approximately fifty percent of HGSC are regarded to have HRD, a feature that can be inferred through specific patterns of genomic scarring in tumor cells 13 , 20 – 25 . HRD leads to genomic instability and tumorigenesis, providing a vulnerability in tumor cells with increased sensitivity to double-strand DNA breaks that can be exploited therapeutically 26 – 28 . As a result, platinum-based chemotherapy and poly (ADP-ribose) polymerase inhibitor (PARPi) maintenance therapy are generally more effective in patients with HRD tumors 28 – 33 . While HRD status is informative, accurate prediction of treatment response and survival in HGSC cannot be simply determined by the presence or absence of mutations in genes associated with HR DNA repair. The initial survival advantage for carriers of pathogenic germline BRCA1 variants (g BRCA1 pv) diminishes over time, with fewer g BRCA1 pv-carriers surviving 10 years after diagnosis than either g BRCA2 pv-carriers or non-carriers 33 – 35 . Factors associated with survival outcome in HGSC include residual disease following cytoreductive surgery 16 , 36 – 38 , the molecular subtype of the tumor 39 , age at diagnosis 40 , and the extent of T- and B-cell infiltration into tumors 41 , 42 . In germline pathogenic variant carriers, the location of mutations within BRCA1 or BRCA2 or the retention of the wildtype allele in the tumor can result in a hypomorphic phenotype associated with resistance to platinum-based therapy 43 – 47 . Furthermore, revertant mutations restoring BRCA1 and BRCA2 function contribute to acquired resistance to platinum-based therapy and PARPis, impacting treatment response and patient outcomes 48 – 50 . Comparing patients who represent the extremes of survival outcomes may provide increased sensitivity to identify prognostic biomarkers that are relevant to a wider patient population 51 . Using this approach, we have recently shown that plasma cell infiltration and other molecular changes, including co-loss of BRCA and the tumor suppressor RB1 , are associated with especially long-term survival in HGSC 22 , 52 , 53 . The current study evaluates BRCA -deficient HGSC by first focusing on g BRCA pv-carriers and then expanding to include somatic mutations and promoter methylation in BRCA1/2 , and other key HR genes, as well as evaluating tumor HRD status. We focus on patients with either poor or favorable survival outcomes, harnessing the value of analyzing patients with exceptional survival outcomes while comparing cohorts that are as similar as possible in other respects. RESULTS Association of residual disease with prognosis is attenuated in g BRCA pv-carriers Pathogenic germline BRCA variants (g BRCA pv) were identified in 20% of patients in the Australian Ovarian Cancer Study (AOCS) cohort ( n = 282/1389) (Table 1 , Supplementary Tables S1 and S2). In applying a survival model, there was evidence that the proportional hazards assumption did not hold ( P 1 indicates longer time to progression or death, and a TR < 1 indicates shorter survival or time to progression. Patients with g BRCA pvs exhibited improved overall survival (OS; TR: 1.53, 95% CI: 1.33–1.76, P < 0.001) and progression-free survival (PFS; TR: 1.34 95% CI: 1.28–1.53, P < 0.001) compared with non-carriers (Supplementary Tables S3 and S4). Table 1 Baseline characteristics of the clinicopathological features from patients with high-grade serous ovarian cancer (HGSC) of the Australian Ovarian Cancer Study (AOCS) cohort. Characteristics n = 1,389 n (%) Age at diagnosis (years) Median 61 Range 24–87 Unknown 7 (0.5) Germline BRCA status Wildtype 1,107 (79.7) g BRCA1 pv 175 (12.6) g BRCA2 pv 107 (7.7) Grade G3 1,100 (79.2) G2 237 (17.1) Unknown 52 (3.7) FIGO stage III-IV 1,193 (85.9) I-II 134 (9.6) Unknown 62 (4.5) Primary site Ovary 1,008 (72.6) Peritoneum 215 (15.5) Fallopian tube 140 (10.1) Unknown 26 (1.9) Surgery Primary cytoreductive surgery 991 (71.3) Interval cytoreductive surgery 299 (21.5) Other 70 (5) Unknown 29 (2.1) Residual disease status Residual disease 829 (59.7) No residual disease 467 (33.6) Unknown 93 (6.7) Neoadjuvant chemotherapy No 1,060 (76.3) Yes 322 (23.2) Unknown 7 (0.5) PARP inhibitor 1st line No 1,350 (97.2) Yes 39 (2.8) Progression-free survival (months) Median 15 Range 0-285 Unknown 11 (0.8) Overall survival (months) Median 38 Range 1-290 Unknown 11 (0.8) Status Deceased 984 (70.8) Alive 393 (28.3) Unknown 12 (0.9) We considered whether clinical characteristics differed by germline BRCA status and found a statistically significant interaction with residual disease status ( P -interaction = 0.011; Supplementary Table S3 ). Using this interaction term, we found that the negative effect of residual disease after cytoreductive surgery on OS was less pronounced in g BRCA pv-carriers (TR: 0.87, 95% CI: 0.72–1.06, P = 0.162) than in non-carriers (TR: 0.51, 95% CI: 0.44–0.59, P < 0.001; Fig. 1 a, Table 2 ) . The importance of residual disease for survival in non-carriers was confirmed in the independent OTTA cohort ( n = 1004, g BRCA pv-carriers = 221, 22%; Fig. 1 b, Extended Data Figs. 1 and 2 a). Table 2 Multivariable Accelerated Failure Time (AFT) model of BRCA and residual disease status and clinicopathological predictive features on overall survival in patients from the Australian Ovarian Cancer Study (AOCS) cohort. The model was fitted using a log-logistic distribution. Results are expressed as Time Ratios (TR) with corresponding 95% confidence intervals (CI) and p-values derived from Wald tests. A TR > 1 indicates a longer survival time, whereas a TR < 1 indicates a shorter survival time. Age at diagnosis was modeled using restricted cubic splines with 3 knots and is presented as two spline terms. Univariable Multivariable 95%CI 95%CI Feature Factor Number TR lower upper P -value TR lower upper P -value g BRCA pv & Residual status Non carriers & R0 356 - - - - - - - - Non carriers & R 649 0.42 0.37 0.48 < 0.001 0.51 0.44 0.59 < 0.001 g BRCA pv carriers & R0 105 1.31 1.03 1.66 0.028 1.18 0.92 1.50 0.191 g BRCA pv carriers & R 174 0.82 0.68 0.98 0.028 0.87 0.72 1.06 0.162 FIGO stage I + II 134 - - - - - - - - III + IV 1183 0.34 0.28 0.42 < 0.001 0.59 0.47 0.74 < 0.001 Primary site Ovary 1002 - - - - - - - - FT 135 1.28 1.04 1.58 0.023 1.10 0.89 1.36 0.364 Peritoneum 215 0.66 0.57 0.77 < 0.001 0.82 0.71 0.94 0.005 Age at diagnosis Years Spline 1 1370 0.99 0.98 1.00 0.069 1.00 0.99 1.01 0.669 Years Spline 2 0.99 0.97 1.00 0.142 0.98 0.97 1.00 0.029 Surgery Primary CS 980 - - - - - - - - Interval CS 299 0.83 0.72 0.95 0.007 0.89 0.48 1.64 0.703 Other 69 1.27 0.99 1.63 0.065 1.13 0.84 1.53 0.418 Neoadjuvant CHT No 1048 - - - - - - - - Yes 322 0.83 0.72 0.95 0.006 0.96 0.53 1.75 0.897 Grade G2 237 - - - - - - - - G3 1088 1.22 1.05 1.41 0.009 1.07 0.93 1.22 0.347 PARP inhibitor 1st line No 1338 - - - - - - - - Yes 39 1.40 0.90 2.12 0.119 1.25 0.81 1.92 0.321 R = Residual disease, R0 = No residual disease, G2 = Grade 2, G3 = Grade 3, OS = Overall survival, gBRCApv = pathogenic germline BRCA variant, TR = Time ratio, CI = confidence interval, CHT = chemotherapy, CS = cytoreductive surgery, FT = fallopian tube We examined the relationship of residual disease and BRCA status to known immune and molecular features associated with survival, including tumor-infiltrating lymphocytes (TIL) 42 , 55 , RB1 loss 22 , 52 , 56 , and transcriptional molecular subtypes 39 . Non-carriers with residual disease had an inverse association with high CD8 + TIL density ( P = 0.016), with 38.3% of tumors classified as having low or no TIL (Extended Data Fig. 2 b, Supplementary Table S5). This group also showed an inverse association with the C4/differentiated (C4.DIF) molecular subtype ( P = 0.010; Extended Data Fig. 2 b). We observed an association between the C1/mesenchymal (C1.MES) molecular subtype and residual disease as previously reported 57 , but this was only statistically significant among non-carriers ( P = 0.005). RB1 loss was associated with g BRCA pv-carriers without residual disease ( P < 0.001; Extended Data Fig. 2 b). Although no statistically significant interaction between neoadjuvant chemotherapy (NACT) and BRCA status was observed ( P -interaction = 0.12; Supplementary Table S3 ), there was evidence of heterogeneity of effect in these subgroups. Among participants who did not receive NACT, g BRCA pv-carriers showed a survival benefit compared to non-carriers (TR: 1.60, 95% CI: 1.37–1.87, P < 0.001; Supplementary Table S6, Extended Data Fig. 3 ). In contrast, the overall survival benefit in g BRCA pv-carriers versus non-carriers was not statistically significant in the NACT group (TR: 1.39 and 1.17, 95% CI: 0.75–2.60 and 0.62–2.21, P = 0.298 and P = 0.634 respectively, compared to non-carriers who did not receive NACT). g BRCA pv location and type are associated with survival and therapy response Mutations located in various functional domains of BRCA1 and BRCA2 have been associated with differences in survival and responses to PARPi in ovarian cancer 43 , 44 . The mutation type and location of g BRCA pvs was ascertained for 240 of the patients in the AOCS cohort from their clinical records and/or previous sequencing analyses 22 , 56 , 58 , 59 (Extended Data Figs. 4 a,b and Supplementary Table S2 ). Following adjustment for FIGO stage, residual disease status, primary site, age, and first-line treatment, patients with g BRCA1 pvs in exon 10 had a statistically significant improved OS and PFS (TR: 1.54 and 1.49, 95% CI: 1.19-2.00 and 1.16–1.91, P < 0.001 and P = 0.002, respectively ), but the association was attenuated for those with variants outside exon 10 (TR: 1.21 and 1.18, 95% CI: 0.97–1.51 and 0.96–1.46, P = 0.09 and P = 0.12, respectively) compared to non-carriers (Table 3 ). More specifically, pathogenic variants in the DNA binding domain (DBD) of BRCA1 , located in exon 10, were associated with an OS and PFS benefit compared to non-carriers (TR: 1.60 and 1.58, 95% CI: 1.14–2.25 and 1.15–2.18, P = 0.005 and P = 0.006, respectively; Table 3 ). In contrast, the OS and PFS benefit was not statistically significant for patients with pathogenic variants in the Really Interesting New Gene (RING) (TR: 1.28 and 1.15, 95% CI: 0.87–1.90 and 0.82–1.61, P = 0.216 and P = 0.419, respectively) and C-terminal domains of BRCA1 (BRCT) (TR: 1.35 and 1.43, 95% CI: 0.83–2.20 and 0.90–2.26, P = 0.222 and P = 0.126, respectively), located outside of exon 10. Table 3 Adjusted Accelerated Failure Time (AFT) model analysis of germline BRCA pathogenic variant (g BRCA pv) location and progression-free survival and overall survival in patients from the Australian Ovarian Cancer Study (AOCS) cohort. Models were adjusted for FIGO stage, residual disease status, primary tumor site, type of surgery, age at diagnosis (modelled with restricted cubic splines, 3 knots), use of neoadjuvant chemotherapy, tumor grade, and PARP inhibitor use in first-line treatment. AFT models were fitted using a log-logistic distribution. Results are presented as Time Ratios (TR) with 95% confidence intervals (CI) and P -values derived from Wald tests. A TR > 1 indicates an association with longer time to progression or death, while a TR < 1 reflects shorter survival. The reference group for all comparisons is non-carriers of g BRCA pv. Progression-free survival Overall survival 95%CI 95%CI Feature Factor Number TR lower upper P -value TR lower upper P -value g BRCA pv exon Non carriers 1096 - - - - - - - - g BRCA1 pv Exon 10 68 1.49 1.16 1.91 0.002 1.54 1.19 2.00 < 0.001 g BRCA1 pv outside Exon 10 81 1.18 0.96 1.46 0.12 1.21 0.97 1.51 0.093 g BRCA2 pv Exon 11 52 1.52 1.16 2.00 0.002 1.67 1.26 2.23 < 0.001 g BRCA2 pv outside Exon 11 38 1.66 1.15 2.42 0.007 1.90 1.31 2.76 < 0.001 g BRCA pv domain Non carriers 1096 - - - - - - - - g BRCA1 pv BRCT 17 1.43 0.90 2.26 0.126 1.35 0.83 2.20 0.222 g BRCA1 pv DBD 40 1.58 1.15 2.18 0.005 1.60 1.14 2.25 0.006 g BRCA1 pv outside domain 54 1.41 1.09 1.81 0.007 1.38 1.06 1.79 0.017 g BRCA1 pv RING 27 1.15 0.82 1.61 0.419 1.28 0.87 1.90 0.216 g BRCA2 pv DBD 13 0.81 0.43 1.51 0.506 0.79 0.39 1.63 0.528 g BRCA2 pv outside domain 35 2.10 1.46 3.00 < 0.001 2.03 1.44 2.87 < 0.001 g BRCA2 pv RAD51-BD 39 1.37 1.01 1.85 0.04 1.58 1.14 2.21 0.006 DBD = DNA Binding Domain, RING = Really Interesting New Gene, RAD51-BD = RAD51 Binding Domain, BRCT = BRCA1 C-Terminal Patients with BRCA1 variants in exon 10 have been reported to have poorer outcomes 46 due to expression of an alternative splice isoform called BRCA1 -delta11q (Δ11q) that bypasses almost all of exon 10 of BRCA1 (historically referred to as exon 11). To explore this further, we assessed BRCA1 isoform expression in our multi-omics cohort ( n = 154) using the bulk RNA sequencing reads spanning the exon 10 to exon 11 junction (Fig. 2 a, Supplementary Tables S7 and S8, Supplementary Information). The Δ11q isoform was widely expressed regardless of BRCA- status, but patients with BRCA1 variants in exon 10 had significantly higher proportions of Δ11q transcripts relative to canonical transcripts ( P = 0.011; Fig. 2 b). Patients with BRCA1 variants in exon 10 were classified as having high ( n = 10) or low (n = 9) BRCA1 Δ11q expression, according to the median. Patients with high Δ11q expression had a shorter survival (median OS 2.74 years) compared to those with low Δ11q expression (median OS not reached), although this was not statistically significant ( P = 0.083) and was not associated with differences in the HRD sum score (Figs. 2 c,d and Supplementary Table S9). Overall, patients with g BRCA2 pv had an improved OS compared to non-carriers, regardless of mutation location (Table 3 ). The only exception was the small group ( n = 13) with pathogenic variants in the DNA binding domain (DBD) of BRCA2 , located outside of exon 11, who did not show a statistically significant OS or PFS benefit compared to non-carriers (TR: 0.79 and 0.81, 95% CI: 0.39–1.63 and 0.43–1.51, P = 0.528 and P = 0.506, respectively). The type of mutation in BRCA1 and BRCA2 also plays a predictive role in response to PARPi therapy in ovarian cancer 43 . In our analysis, pathogenic variants in BRCA1 exon 10 and BRCA2 exon 11 were more likely to be truncating (98.6% and 92.3%) than those outside these exons (60% and 76.3%, P < 0.001 and P = 0.032 respectively; Figs. 2 e,f). BRCA1 and BRCA2 domains associated with prolonged survival were more likely to have truncating variants than missense or splice site variants ( P < 0.001 and P = 0.067, respectively; Extended Data Figs. 4 c,d). NF1 gene alterations are associated with improved survival in BRCA2 -deficient HGSC To identify genomic features associated with short survival in HRD tumors, we compared tumor genomes and transcriptomes between short (OS ≤ 3 years, STS) and long-term (OS > 3 years, LTS) survival groups (Fig. 3 a). Tumor genomes were classified as either BRCA1 -deficient, BRCA2 -deficient or BRCA -proficient, which incorporated germline and somatic alterations in BRCA1 and BRCA2 , as well as other well-defined HR genes, and tumor HRD status as determined by a mutational signature-based classifier (CHORD, Classifier of HOmologous Recombination Deficiency) 60 (Supplementary Information and Supplementary Tables S10-S12). CCNE1 amplifications (gene level copy number ≥ 7) were associated with BRCA -proficiency, and particularly the short-survival BRCA- proficient group (50%, P adj <0.001; Fig. 3 b). BRCA -proficient tumors had less genomic scarring and were associated with an older age at diagnosis compared to BRCA1 -deficient and BRCA2 -deficient tumors (Extended Data Figs. 5 a,b). Gene methylation has been identified as a prognostic factor in HGSC 61 , but no significantly differentially methylated genes with corresponding up- or down-regulated gene expression were observed between STS and LTS groups in BRCA1 - and BRCA2 -deficient tumors (Supplementary Table S13 and Supplementary Information). Alterations in NF1 were most common in BRCA -deficient tumors, regardless of survival group ( BRCA1 STS 43.8%, BRCA1 LTS 33.3%, BRCA2 STS 30%, BRCA2 LTS 37.5%, BRCA -P STS 21.4%, BRCA -P LTS 14.3%, P adj =0.061; Fig. 3 c and Supplementary Table S14). Notably, gene breakage caused by large-scale deletions was enriched in BRCA2 -deficient tumors in the LTS group. We hypothesized that not all alteration types equivalently disrupt gene function. Indeed, only 54.2% (26/48) of NF1 alterations showed a locus-specific loss of heterozygosity (LOH) suggesting a loss-of-function (Supplementary Table S14 and Supplementary Information). Concordantly, NF1 mRNA expression varied in tumors according to the type of NF1 alteration and was particularly depleted in those with locus-specific LOH ( P < 0.0001; Extended Data Fig. 6a). Patients with tumors that harbored loss-of-function NF1 alterations showed an improved survival compared to non-loss-of-function NF1 alterations (median OS 11.92 years vs 5.17 years, P = 0.032; Extended Data Fig. 6b). In particular, the combination of both BRCA2 -deficiency and loss-of-function NF1 alteration ( n = 11) was associated with the best survival outcome (median OS 16.96 years), almost twice as long as those with BRCA2 -deficient tumors with no loss-of-function NF1 alteration (median OS 8.84 years; Extended Data Fig. 6c and Supplementary Table S9). NF1 protein expression was assessed by IHC in a larger cohort enriched for long-term survivors ( n = 658; Extended Data Fig. 1 ). NF1 protein loss was observed in 13.37% ( n = 88/658) of patients and was associated with improved survival compared to retained NF1 expression (median OS 4.70 vs. 3.58 years, P = 0.028; Extended Data Fig. 7a). Although there were few patients with NF1 protein loss and germline BRCA1 ( n = 21) or BRCA2 ( n = 6) pathogenic variants, NF1 loss was associated with better survival in g BRCA2 pv-carriers (median OS 8.05 years NF1 loss vs. 5.72 years NF1 retained) but not in g BRCA1 pv-carriers (median OS 4.74 years NF1 loss vs. 4.69 years NF1 retained; Extended Data Fig. 7b). NF1 loss also was associated with a longer survival among non-carriers (median OS 5.01 years NF1 loss vs. 3.36 years NF1 retained; Extended Data Fig. 7b). In the independent OTTA cohort with NF1 mRNA expression and survival data available (n = 5666), low NF1 expression (lowest quantile) was associated with improved survival compared to high expression (2nd to 5th quantiles) (median OS 4.19 vs. 3.56 years, P < 0.0001; Extended Data Figs. 1 and 7c). Consistent with the other cohorts, g BRCA2 pv-carriers with low NF1 expression (n = 36) showed an improved survival (median OS 6.42 years NF1 low vs. 5.66 years NF1 high), while there was no effect in g BRCA1 pv-carriers (median OS 5.41 years NF1 low vs. 5.65 years NF1 high, Extended Data Fig. 7d). PIK3CA and RAD21 amplifications are associated with short survival in BRCA2 -deficient HGSC We found an enrichment of PIK3CA and RAD21 gene amplifications in BRCA2 -deficient tumors in patients with short compared to long survival ( PIK3CA : 5/10, 50% vs 4/24, 16.7%, P adj =0.232 and RAD21 : 5/10, 50% vs 4/24, 16.7%, P adj =0.105, respectively; Fig. 3 d,e). Co-occurrence of RAD21 and PIK3CA amplification was observed in 8.8% (3/34) patients with BRCA2 -deficiency (Supplementary Tables S15 and S16 ) . PIK3CA and RAD21 mRNA expression was highly correlated with copy number ( P < 0.0001), and tumors with gene amplification (≥ 7 copies) had a significantly higher expression ( P < 0.001 and P = 0.02, respectively) (Extended Data Fig. 8a,b and Supplementary Tables S17 and S18). Patients with combined BRCA2 -deficiency and PIK3CA amplification ( n = 9, median OS 2.89 years) or RAD21 amplification ( n = 9, median OS 2.89 years) had a significantly worse prognosis compared to patients with BRCA2 -deficient tumors without PIK3CA amplification (n = 25, median OS 11.92 years) or RAD21 amplification ( n = 25, median OS 11.53 years; Extended Data Fig. 8c,d and Supplementary Table S9). PI-3 kinase pathway activity is thought to contribute to tolerance to genome doubling and PIK3CA amplification in whole-genome duplicated tumors is a frequent event in HRD end-stage HGSC 49 , 62 . The STS BRCA2 -deficient group was characterized by high ploidy ( P adj =0.0073) and whole-genome duplication ( P adj =0.0404), in contrast to BRCA1 -deficient and BRCA -proficient tumors where the LTS groups tended to have higher ploidy (Extended Data Fig. 5 a). The association between PIK3CA and survival by BRCA status was further corroborated in the OTTA cohort, where g BRCA2 pv carriers with high PIK3CA RNA expression (highest quantile) had shorter survival relative to their counterparts with low expression (median OS 4.09 vs 7.43 years, P < 0.0001; Extended Data Fig. 8e). By contrast, g BRCA1 pv carriers with high PIK3CA RNA expression showed improved survival (median OS 7.67 vs 5.23 years). Elevated HRD scarring is prognostic for survival in BRCA -deficient HGSC High tumor mutation burden has been shown to be associated with long-term survival in ovarian cancer 22 . However, we found that tumor mutation burden and predicted neoantigen counts were equivalent in BRCA1 -deficient and BRCA2 -deficient tumors between STS and LTS groups (Fig. 4 a-c, Extended Data Fig. 5 a, and Supplementary Table S19). Among various genomic features that were compared between these groups (Extended Data Fig. 5 a), the HRD score 27 was elevated in BRCA1 -deficient tumors with long survival times compared to those with short survival times ( P = 0.017; Fig. 4 d). HRD score is a measure of genomic scarring associated with impaired HR repair, suggesting a more profound inactivation of the HR pathway in patients with good outcome. Retention of the wildtype allele with absence of locus specific LOH has been reported to influence outcomes in g BRCA pv-carriers in ovarian and breast cancer 63 – 66 . However, in our cohort there was only one g BRCA2 pv carrier without loss of the wildtype allele (patient BRCA_9; Supplementary Table S11 and Supplementary Information). Concordantly this tumor was HR-proficient with an HRD score of 27 (HRP ≤ 42 HRD sum score) and CHORD score of 0 (HRP ≤ 0.5 CHORD score), and the patient had short OS (< 3 years). We observed a dynamic range in HRD scores, even among tumors with pathogenic BRCA mutations, suggesting a non-equivalence of alterations. The cutoff of the HRD score has been debated, with 42 mainly used in recent clinical trials 67 – 71 , and a more stringent threshold of 63 has been proposed for ovarian cancer 72 . Indeed, patients whose tumors had a high HRD score (≥ 63) had longer OS (median OS 10 years) compared to those with HRD scores of 42–62 (median OS 2.66 years) and ≤ 41 (median OS 2.5 years), regardless of BRCA -status ( P = 0.039; Fig. 4 e and Supplementary Table S9). Applying a threshold of 63 to divide samples into high and low HRD, all BRCA -proficient tumors had a low HRD score. Furthermore, patients with BRCA1 - and BRCA2 -deficient tumors and HRD scores ≥ 63 had longer OS compared to patients with lower HRD scores (median OS 6.76 vs. 2.01 years and 11.88 vs. 6.73 years, respectively; Fig. 4 f and Supplementary Table S9). Notably, patients with BRCA1 -deficient tumors with HRD scores < 63 had similar OS to patients with BRCA -proficient tumors (median OS 2.01 years vs 2.21 years). Gene set enrichment analysis 73 (GSEA; Methods) revealed distinct patterns of pathway regulation based on HRD scores and BRCA status in patients with HGSC. Specifically, pathway activation in BRCA1 - and BRCA2 -deficient patients with low HRD (< 63) closely resembled those of BRCA -proficient patients (Fig. 4 g). In contrast, BRCA1 -deficient patients with high (≥ 63) HRD scores showed an upregulation of several pathways, including interferon-gamma and inflammatory response. These pathways are primarily involved in host defense and immune surveillance 74 , underscoring their potential role in modulating the tumor microenvironment and influencing immune response in patients with BRCA1 -deficient tumors. CD8 + PD-1 + T cells are prognostic for survival in g BRCA pv-carriers We considered whether BRCA -deficient cases with shorter survival would have fewer mutation-associated neoantigens to drive anti-tumor responses, but there was no difference in neoantigen counts between the STS and LTS groups for both BRCA1 and BRCA2 ( P = 0.51 and P = 0.39, respectively; Fig. 4 c). Tumor samples from 143 HGSC g BRCA pv-carriers were analyzed by multi-color immunofluorescence to determine the epithelial and stromal immune cell densities and their associations with survival groups (Extended Data Fig. 1 ). Aside from intraepithelial B cells and CD4 + T cells (OR = 1.0), all other immune cell subsets had a positive association with survival (OR < 1.0; Supplementary Table S20). Only intrastromal and intraepithelial CD8 + PD-1 + T cells were significantly more abundant in g BRCA pv-carriers with LTS compared to those with STS ( P = 0.043 and P = 0.029, respectively; Supplementary Table S20). The mesenchymal features c-KIT and mast cells are associated with poor outcome in HGSC Immune cell abundance was estimated in 154 HGSC tumor samples using CIBERSORTx 75 . Unsupervised clustering of the inferred immune cell densities identified six groups of patients (Fig. 5 a, and Supplementary Table S21) associated with differential survival outcomes ( P = 0.0053; Fig. 5 b). The IMMB.1 ( n = 30) and IMMB.6 ( n = 25) clusters had exceptionally long survival (median OS 14.87 and 10.45 years, respectively; Supplementary Table S9). The group with the shortest survival (cluster IMMB.5, n = 24, median OS 2.03 years) was enriched with activated dendritic cells and resting mast cells, a feature associated with the C1.MES subtype ( P = 0.0021; Fig. 5 c). Multivariable Cox regression analysis showed that resting mast cells (HR: 1.26, 95% CI 1.06–1.5, P = 0.009) were the immune cell type most strongly associated with short survival (Extended Data Fig. 9a). BRCA1 -deficient tumors in patients with STS had increased expression of the mast cell growth factor receptor c-KIT (CD117) compared to those with LTS ( P = 0.003, P adj =0.101; Extended Data Fig. 9b). Patients with high c-KIT tumor expression had significantly shorter OS than those with low c-KIT tumor expression, regardless of BRCA and HRD status (HR: 1.71, 95% CI 1.16–2.53, P = 0.0071; Extended Data Fig. 9c). The C1.MES subtype showed higher expression of c-KIT , together with an upregulation of the epithelial mesenchymal transition (EMT) pathway, compared to the C2.IMM subtype ( P adj <0.001) (Extended Data Fig. 9d,e). DISCUSSION Our study highlights the complexity of survival determinants in patients with HGSC, demonstrating that it is the intersection of multiple factors, including surgical residual disease, immune response, and somatic gene alterations, which may influence outcome rather than BRCA mutation status alone. This interplay was particularly apparent in the diminished adverse impact of surgical residual disease in g BRCA pv-carriers compared to non-carriers. Previous reports have suggested that surgery in a BRCA -deficient setting may have a lesser impact on survival in both first-line and platinum-sensitive setting 33 , 47 , 76 , indicating that it may be particularly important to achieve complete resection of BRCA -proficient tumors. In addition, an exploratory analysis of the PAOLA-1/ENGOT-ov25 trial 77 showed that patients with BRCA -proficient tumors classified as higher risk (FIGO stage III with primary cytoreductive surgery and residual disease, or NACT; FIGO stage IV) had notably worse PFS compared to lower-risk patients, while this difference was less pronounced in patients with BRCA -deficient tumors. These results emphasize the importance of primary cytoreductive surgery with complete resection for non-carriers, who may also benefit more from secondary cytoreductive surgery in contrast to g BRCA pv-carriers 78 . Equally, it may be that the positive effect of optimal cytoreduction is not as apparent in BRCA carriers, due to the chemotherapy (platinum) sensitivity associated with BRCA -deficiency. In the current study, the association between NACT and survival appeared to differ by g BRCA pv status, with a potential attenuation of survival benefit among g BRCA pv-carriers who received NACT. However, the subgroup analyses by g BRCA pv status and treatment type were likely underpowered, limiting definitive conclusions regarding potential interactions. Given the rapid increase in the uptake of NACT in recent years 79 , it will be important to determine if patients with BRCA -deficient tumors may be negatively impacted by NACT 80 . The acquisition of BRCA reversion mutations is frequent 48 – 50 , and it is plausible that reversion events may be more common where chemotherapy commences with a large tumor volume from which resistant clones could emerge under selection 58 . This is especially important in the PARPi era, where the early development of platinum resistance could negatively impact on the potential benefit gained from PARPi treatment. While the impact of NACT on outcomes according to BRCA status is not yet known, it is becoming increasingly important to more rapidly determine the BRCA and broader HR status of a patient’s tumor at diagnosis to make the most informed decisions at primary treatment. Our study highlights the spectrum of HRD scores seen in patients with BRCA -deficient tumors. While all but two exceeded a threshold (> 42) required for classification as HRD, the improved OS and PFS seen with a more stringent threshold (≥ 63) shows that HRD should not be considered a binary classification but rather appears to be a continuous variable. This finding is consistent with a previous analysis of 537 HGSC cases from The Cancer Genome Atlas which showed that patients with HRD scores ≥ 63 were associated with better survival outcomes, while those with intermediate (42–62) and low (≤ 42) HRD scores had overlapping survival curves 72 . It is important to mention that in our study, samples were collected over nearly 20 years, a timeframe that encompasses changes in treatment practices, making it challenging to determine how evolving therapies, particularly the introduction of PARPi, may have influenced outcomes. It is notable that the HRD score threshold of 42 was originally established to predict response to neoadjuvant platinum-containing chemotherapy in patients with breast cancer 81 , which tends to have less genomic scarring compared to ovarian cancer 27 , 72 . As HRD scores ≥ 63 strongly predicted better outcomes in BRCA -deficient HGSC, our findings support the prognostic value of HRD score thresholds. However, it is premature to conclude that a higher threshold should alter therapy selection. To establish this, a comprehensive analysis of maintenance PARPi trials, incorporating HRD scores, would be necessary to confirm their predictive role in guiding treatment decisions. Furthermore, it would be ideal to extend this investigation to include other relevant genomic alterations identified in trial samples to refine patient stratification further. This refinement would help identify patients for whom no maintenance therapy or additional targeted therapy may be more appropriate, while avoiding potentially ineffective treatments for those with lower HRD scores, thereby personalizing therapy to maximize efficacy and minimize unnecessary side effects. Our analyses corroborated Labidi-Galy et al.'s findings that pathogenic variants in the RAD51-BD of BRCA2 44 and the DBD of BRCA1 43 are associated with improved outcomes in HGSC. By contrast, alterations outside BRCA1 exon 10, particularly in the BRCT and RING regions, are not associated with a significantly improved survival compared to non-carriers and in some cases may confer platinum and PARPi resistance 45 . While BRCA1 exon 10 mutations have been associated with improved outcomes in multiple studies, including ours, there is evidence that tumors may express the BRCA1 -Δ11q splice isoform, which bypasses exon 10 mutations and results in a shorter but partially functional protein that is permissive of treatment resistance 43 , 46 . In a relatively small sample size for which we had RNA-seq data (n = 19 BRCA1 exon 10 mutated tumors), we found that patients with a pathogenic BRCA1 variant in exon 10 and high Δ11q expression had a shorter survival. We were unable to measure Δ11q expression during or following treatment. This is important because Δ11q expression may increase or fluctuate under the selective pressure of treatment, which would influence treatment response and survival outcomes. CD8 + PD1 + T cells are associated with improved outcomes in ovarian cancer 82 , contributing to enhanced anti-tumor immunity. In our analysis, the presence of these cells in tumors were prognostic for survival in g BRCA pv-carriers, although to a lesser extent. This suggests that while cytotoxic T-cell activity remains important in BRCA -deficient tumors, additional factors may influence survival. Given the established association between BRCA and HR status and increased TMB 22 , it is possible that immune exhaustion, suppressive signaling or tumor-intrinsic immune resistance pathways may counteract the expected immunogenicity. Intriguingly, BRCA1 -deficient tumors with high HRD scores had evidence of enhanced immune-related gene transcription. In addition, while our study did not include cigarette smoking in the survival models, smoking has been identified as a potential factor influencing survival in g BRCA pv-carriers 83 , which may also influence the immune response. Further research into markers of T-cell exhaustion and other immune regulators is needed to better understand the differential immune responses in these patients. NF1 gene loss-of-function emerged as a good prognostic factor in BRCA2 -deficient HGSC. Loss-of-function of NF1 is common in epithelial ovarian cancer with a prevalence of 12–31% 13,20,22,58,84,85 . NF1 inactivation by gene breakage or mutations may contribute to initial good prognosis but later chemoresistance in patients with HGSC and BRCA -deficiency 84 . This is consistent with recent findings that deleterious NF1 mutations are associated with improved PFS in ovarian cancer 20 and low mRNA expression of NF1 predicts longer overall survival 22 . In contrast, PIK3CA amplification and high mRNA expression were associated with shorter survival in patients with BRCA2 -deficient HGSC. As a major regulator of the phosphoinositide 3-kinase (PI3K) pathway, PIK3CA activation promotes cell proliferation and survival, especially in genomically unstable cancers 49 , 62 . Its amplification may enhance tolerance to genome doubling and contribute to the aggressive nature of BRCA2 -deficient tumors. The contrasting survival outcomes between PIK3CA amplification and NF1 loss-of-function underscore the heterogeneity of HGSC tumors, highlighting the need for personalized therapeutic strategies, even within the BRCA2 -deficient subgroup. METHODS Ethics statement Written informed consent or an approved waiver of consent was obtained at each participating study site for patient recruitment and the use of samples and linked clinical information (Supplementary Table S22). Investigations were performed after approval by local human research ethics/institutional review board committees at each site. This study was conducted in accordance with the principles of Good Clinical Practice, the Declaration of Helsinki and local regulations. Study population This retrospective, multi-center study included patients diagnosed with HGSC between 2002 and 2019. The Australian Ovarian Cancer Study (AOCS) cohort (n = 1389) included all stages (FIGO I-IV), and the Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) cohort (n = 154) was restricted to advanced stage disease (FIGO III and IV; Table 1 , Extended Data Fig. 1 , and Supplementary Table S22). Patients were categorized based on OS into short (< 3 years) and long (≥ 3 years) OS groups (Supplementary Information). For multi-omics analysis, 154 patients had fresh-frozen tumor obtained during primary cytoreductive surgery and matched blood samples, or were previously analyzed 22 , 58 . Findings were validated in an independent HGSC cohort (n = 5875) from the Ovarian Tumor Tissue Analysis Consortium (OTTA) for which g BRCA pv status was available. Molecular data Single-nucleotide polymorphism (SNP) arrays Tumor and matched normal DNA was analyzed with the Infinium OmniExpress-24 BeadChip arrays as described previously 22 . The concordance of normal and tumor DNA was assessed using HYSYS 86 . Tumor DNA samples with estimated tumor cellularity > 40% (determined by qPure 87 and ASCAT 88 ) were considered appropriate for whole genome sequencing and methylation arrays. Whole genome sequencing (WGS) For WGS, libraries were generated from tumor and matched normal genomic DNA from peripheral blood mononuclear cells with a minimum base coverage of 60x and 30x, respectively. FASTQ files were assessed for sequencing quality using FASTQC (v0.11.8) and, for contaminants using FastQ Screen 89 (v0.11.4). Adapters, N-content and low-quality bases were trimmed using fastq-mcf (v1.05). Sequenced data was mapped to the human genome reference GRCh37 b37 using the aligner BWA mem 90 (v.0.7.17-r1188). Aligned BAM files per lane were then sorted, merged and duplicates marked using Picard Tools (v.2.17.3). Further processing of the aligned files included base recalibration using GATK BaseRecalibrator (v4.0.10.1). Coverage calculation was performed using GATK DepthOfCoverage (v3.8-1-0-gf15c1c3ef). GATK HaplotypeCaller (v.4.0.10.1) was used on germline BAMs to generate Genomic Variant Call Format (GVCF) files which were used as the Panel of Normals (PoN) in the Mutect2 somatic variant calling workflow. Tumor purity and ploidy were estimated using FACETS 91 . RNA-sequencing (RNA-seq) Extracted RNA from tumor tissue samples underwent RNA-seq, with initial quality control checks on raw FASTQ files performed using FastQC 89 (v0.11.8). Adapter, poly (A) tails, N content and low quality base trimming was done using fastq-mcf (v1.05), and contamination was assessed using FastQ Screen 89 (v(0.11.4). Reads were then mapped to the human reference GRCh37.92 using the STAR 92 (v2.6.0b) two-pass method. The mapped reads were then sorted using Picard Tools (v2.17.3). Counts were generated using HTSeq 93 (v0.10.0) on the GRCh37.92 Ensembl release gene annotation. Raw count data was then subsetted to protein coding genes and lowly expressed genes were removed using the following strategy. First, raw counts were converted to CPM (counts per million) and only protein coding genes with a CPM of greater than 0.5 in at least 10 samples were retained. The resulting raw count matrix was then normalized using the trimmed mean of M values (TMM) method using edgeR 94 (v3.28.1). Batch effects were removed using limma's 95 (v3.48.2) removeBatchEffect function. Batch effect removal was done by applying batch correction on the library type (stranded/unstranded) while preserving the survival group (long/short). Methylation arrays The generation and processing of methylation array data was performed as previously described by Garsed et al. 22 . Briefly, initial quality control was performed by QuantiFluor (Promega). Subsequently, 500 ng tumor DNA was converted using the EZ DNA Methylation kit (Zymo Research) and analyzed using the Infinium MethylationEPIC BeadChip arrays. The R package minfi 96 (v1.32.0) was then used for quality control assessment and processing of the methylation data as previously described 22 . Immunofluorescence (IF) data Tissue microarrays (TMAs) were constructed from formalin-fixed paraffin-embedded (FFPE) blocks of tumor tissue and stained by IF with two panels of antibodies against immune markers of interest. Panel 1 detected CD3, CD8, CD20, FOXP3 and CD79; panel 2 detected CD3, CD8, PD-1, PD-L1 and CD68. Both panels also detected pan-cytokeratin to identify tumor epithelium. Automated cell scoring, including separation of epithelial and stromal regions, was performed using QuPath (v0.2m2), with extensive manual training and validation. CD4 + T cells were defined as CD3 + CD8- cells, as previously 97 . Immunohistochemistry (IHC) : Sections of 4 µm thickness were cut from previously constructed TMAs of FFPE tumor samples. Deparaffinized sections were stained with the C-terminal NF1 antibody (clone NFC, SIGMA #MABE1820; St. Louis, MO, USA) using our previously described protocol on a DAKO Omnis platform: 30 min of pre-treatment heat-induced antigen retrieval in Tris-EDTA buffer, pH = 9.0; primary antibody incubation for 1h at dilution 1/50, 10 min of a mouse linker, and 30 min for the peroxidase labelled Dako EnVision + polymer-based detection system (Dako protocol 1 h-10M-30, Agilent, Santa Clara, CA, USA) 85 . Samples were scored as follows: inactivated (loss of expression with retained internal control), normal retained expression, subclonal loss, uninterpretable (loss of tumor expression but no internal control present), and exclude (no tumor in core) ( Supplementary Information ). mRNA expression data by NanoString Tumor mRNA expression data for genes of interest ( NF1, PIK3CA, c-KIT , and RB1 ) and transcriptional molecular subtypes in the OTTA cohort were determined using NanoString, as previously described 98 , 99 . Measurements Variant detection and annotation Variant calling was performed for: 1. germline base substitution and INDEL variants by VarDictJava 100 (v1.5.7 with –r = 2 –Q = 10 –f = 0.1). 2. somatic base substitution and INDEL variants using four separate variant callers as follows: by Mutect2 101 (v4.0.11.0 with defaults), VarDictJava 100 (v1.5.7 with –r = 2 –Q = 10 –V = 0.05 –f = 0.01), Strelka2 102 (v2.9.9 with defaults), and VarScan2 103 (SAMtools 104 ) v1.9 for mpileup and VarScan2 v2.4.3 with -min-coverage 7 -min-var-freq = 0.05 -min-freq-for-hom = 0.75 -p-value = 0.99 -somatic-p-value = 0.05 -strand-filter = 0). Variant calls were decomposed and normalized using vt 105 GATKs ReadBackPhasing tool (v3.8-1-0-gf15c1c3ef with -phaseQualityThresh = 10 – enableMergePhasedSegregatingPolymorphismsToMNP -min_base_quality_score = 10 -min_mapping_quality_score = 10 -maxGenomicDistanceForMNP = 2) was applied on the passing variants per tool to combine contiguous SNVs to MNVs (multi-nucleotide variants). GATK’s CombineVariants (v3.8-1-0-gf15c1c3ef with -genotypeMergeOptions UNIQUIFY -priority Strelka2, Mutect2, VarScan2, VarDictJava) was used to merge the variant calls from all four callers into a consensus variant call set. The resulting variant call format (VCF) file was once again decomposed and normalized using vt. Forward and reverse strand counts for the reference and alternate alleles were calculated using bam-readcount (v0.8.0). Finally, all variants were annotated for Duke and DAC blacklisted regions. Any variants that were passed in at least two callers, had at least one variant read in each strand, and were not in the database of FrequentLy mutAted GeneS (FLAGS) 106 or the Duke and DAC blacklist regions were deemed high-confidence. 3. structural variants (SV) using four separate callers Manta 107 + BreakPointInspector (v1.5.0), GRIDSS 108 (v2.0.1), Smoove (v0.2.2) and SvABA 109 (v134). The SV calls were split into germline and somatic VCFs per caller. The findBreakpointsOverlaps method of the R library StructuralVariantAnnotation (v1.3.1) with a value of 10 for the ‘maxgap’ parameter was used to intersect common breakpoints between the callers. SVs were annotated to constituent types (duplication, deletion, inversion or translocation) using a simple annotation script provided by the GRIDSS tool. High-confidence SVs were categorized as those called by two or more callers. 4. copy number variations (CNV) detection by FACETS 91 and cnv_facets (v0.13.0) as described previously 22 . The detected variants were filtered for variants with a high probability of pathogenicity as described in detail before 22 . Mutation burden and downsampling We downsampled the higher coverage tumor BAM files using Picard DownsampleSam (v2.17.3) to achieve balanced median coverage sequencing batches, to compare mutation burden across samples with inconsistent coverage 22 . The median coverage of the International Cancer Genome Consortium (ICGC) tumors was 52.15x, the MOCOG tumors was 77.81x and the short survival BRCA dataset tumors was 64.98x. So, to get the same median coverage across the three batches we downsampled the MOCOG and short survival BRCA dataset tumors to the ICGC median by specifying downsampling fractions of 0.67 and 0.8 respectively. See Supplementary Table S19 for details on the tumor sample coverage before and after downsampling and the number of SNVs, MNVs, indels and SVs called after downsampling. Neoantigen prediction Neoantigen prediction was performed as previously reported by Garsed et al. 22 . Briefly, HLA-VBSeq 110 (v11_22_2018) was used to generate HLA types which were then used to identify and construct neoantigen using pVACtools 111 pVACseq (v1.3.5). Homologous recombination deficiency (HRD) HRD status was determined using 1) scarHRD 112 , which uses loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large scale state transition (LST) in tumor genomes to generate a HRD sum score, and 2) CHORD (Classifier of Homologous Recombination Deficiency) 60 , which uses specific base substitution, indel and structural rearrangement signatures detected in tumor genomes to generate BRCA1 -type and BRCA2 -type HRD scores. RNA-seq data analysis Raw count data was subsetted to protein coding genes and lowly expressed genes were removed using the following strategy. First, raw counts were converted to CPM (counts per million) and only protein coding genes with a CPM of greater than 0.5 in at least 10 samples were retained. The resulting raw count matrix was then normalized using the trimmed mean of M values (TMM) method using edgeR 94 (v3.28.1). Batch effects were removed using limma's 95 (v3.48.2) removeBatchEffect function. Batch effect removal was done by applying batch correction on the library type (stranded/unstranded) while preserving the survival group (long-term/short-term). RNA differential expression and pathway analysis by grouping Groupings For differential expression and pathway analysis, various groupings were used alone or in combination, namely 1) BRCA -deficiency status, 2) HRD groups, survival groups, and 3) molecular subtypes ( Supplementary Information ). Differential expression analysis To identify differentially expressed protein-coding genes between the comparison groups of interest, DESeq2 (v1.26.0) 113 was applied. Raw counts were filtered to remove low expressed genes prior to analysis and batch effects were accounted for in the model 22 . Gene Set Enrichment Analysis (GSEA) FGSEA v1.15.1 was used to calculate gene set enrichment across the comparison groups. P -values obtained from DESeq2 were transformed to signed P -values and then sorted and fed into FGSEA to generate enrichment scores and FDR-adjusted P -values across the Hallmark gene sets in the MSigDB database49 (v7.4) via its function fgseaMultilevel (minSize = 15, maxSize = 500, gseaParam = 0, eps = 0) 22 . CIBERSORTx CIBERSORTx analysis was performed as previously described 22 . Briefly, CIBERSORTx 75 with the LM22 matrix was used on RNA-seq data for immune cell deconvolution. Immune clusters were then generated with k-means clustering of the generated absolute cell abundances using ConsensusClusterPlus 114 (Supplementary Information). Immunofluorescence Data were categorized based on epithelial content, measured directly by pan-cytokeratin positivity and cell morphology (assessed by automated image analysis). Epithelium-negative, cellular (i.e., non-necrotic) tumor regions were defined as stroma. Immunomarker density (D; cells/mm 2 ) for a given marker was calculated separately for epithelial and stromal compartments. For cases with multiple cores, the epithelial area was taken as the sum of all their individual TMA epithelial areas and similarly for the stromal area. We categorized marker D values into quartiles (separately for epithelial and stromal markers) to provide categorical comparisons for ease of interpretation of the odds ratios (ORs). Conditional logistic regression models were fitted for the long survival group vs short survival group. Logistic regression analyses were performed with the quartile values (scored as 1, 2, 3, 4). Immune clusters were then generated by k-means clustering of the immune cell type densities using ConsensusClusterPlus 114 . Statistical analyses Continuous variables were compared between groups using the Kruskal-Wallis test and the difference between proportions of categorical data were assessed using the Chi-squared or Fisher's exact test. Correlations between continuous variables were assessed using Spearman correlation. Benjamini-Hochberg adjusted P -values are reported as P adj to account for multiple testing. Median PFS and OS were estimated using the Kaplan-Meier method and survival distribution were compared using the log-rank (Mantel-Cox) test. For the AOCS cohort, univariable and multivariable survival analyses were performed using Accelerated Failure Time (AFT) models 54 with a log-logistic distribution to evaluate associations between clinical and molecular variables and time-to-event outcomes. Results were reported as Time Ratios (TR) with 95% confidence intervals (CI), where a TR > 1 indicates longer time to progression or death, and a TR < 1 indicates shorter survival. Wald tests were used to compute P -values for individual covariates and interaction terms. Age at diagnosis was modelled using restricted cubic splines with three knots to allow for potential non-linear effects. Model assumptions were assessed using quantile-quantile plots of deviance residuals and Cox-Snell residuals to evaluate overall model fit. The Akaike Information Criterion (AIC) was used to compare alternative parametric distributions and confirm the suitability of the log-logistic model 115 . For survival analyses of the OTTA cohort, Cox proportional hazards models were applied. Left truncation was used to account for delayed study enrolment at some sites, and follow-up time was right-censored at 10 years from diagnosis to minimize the influence of non-ovarian cancer-related deaths. P -values from Cox models correspond to Wald and log-rank tests. The proportional hazards assumption was assessed using the Grambsch-Therneau test based on scaled Schoenfeld residuals and further evaluated through graphical inspection of Schoenfeld residual plots 115 , 116 . All statistical tests were two sided and considered significant when P < 0.05 or P adj <0.1. All analyses were performed using the statistical software R version 4.1.3 117 . Declarations Data availability Short survival BRCA dataset: WGS, RNA-seq and SNP array data from short-term survivors generated as part of the current study have been deposited in the European Genome-phenome Archive (EGA) repository (https://ega-archive.org) under accession code EGAS00001008059. WGS and RNA-seq data are available as raw FASTQ files for each sample type (tumor/normal) and SNP array data are available as raw signal intensity files in text format for each sample type (tumor/normal). Access to patient sequence data can be gained for academic use through application to the independent Data Access Committee ( [email protected] ). Responses to data requests will be provided within two weeks. Information on how to apply for access is available at the EGA under accession code EGAS00001008059. The raw methylation data sets have been submitted to the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession code GSE292140 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE292140) with no access restrictions. no access restrictions. ICGC dataset: Previously published WGS and RNA-seq data generated as part of the ICGC Ovarian Cancer project 58 are available from the EGA repository as a single bam file for each sample type (tumor/normal), under the accession code EGAD00001000877 ("https://ega-archive.org/datasets/EGAD00001000877"https://ega-archive.org/datasets/EGAD00001000877). Due to the sensitive nature of these patient datasets, access is subject to approval from the ICGC Data Access Compliance Office (https://docs.icgc.org/download/data-access/), an independent body who authorizes controlled access to ICGC sequencing data. ICGC SNP array and methylation data sets have been deposited into the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession code GSE65821 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65821), without access restrictions. ICGC gene count level transcriptomic data has been deposited into the GEO under accession code GSE209964 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE209964).https://docs.icgc.org/download/data-access/), an independent body who authorizes controlled access to ICGC sequencing data. ICGC SNP array and methylation data sets have been deposited into GEOhttps://www.ncbi.nlm.nih.gov/geo/ under accession code GSE65821 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65821), without access restrictions. ICGC gene count level transcriptomic data has been deposited into the GEO under accession code GSE209964 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE209964). MOCOG dataset: WGS, RNA-seq and SNP array data from long-term survivors generated as part of the MOCOG study 22 have been deposited in the EGA repository under accession code EGAS00001005984. WGS and RNA-seq data are available as raw FASTQ files for each sample type (tumor/normal) and SNP array data are available as raw signal intensity files in text format for each sample type (tumor/normal). Access to patient sequence data can be gained for academic use through application to the independent Data Access Committee ( [email protected] ). Responses to data requests will be provided within two weeks. Information on how to apply for access is available at the EGA under accession code EGAS00001005984. The MOCOG cohort raw methylation data sets have been submitted to the GEO under accession code GSE211687 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE211687), with no access restrictions. OTTA dataset: Participants of this study did not agree to their data being shared publicly; accordingly, the data used in this research will not be made available. Uniformly processed somatic variant data from the ICGC, MOCOG, and short survival BRCA cohorts is deposited in Synapse under accession code syn65463502 and processed expression and methylation data from all cohorts has been submitted into the GEO under accession code GSE292140 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE292140) and GSE292142 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE292142https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE292142, without access restrictions. All other data are available within the article (and its Supplementary Information files) or from the corresponding authors on request. Population frequencies of genetic variants can be accessed via the Genome Aggregation Database (gnomAD) at https://gnomad.broadinstitute.org/. Supporting evidence for pathogenicity of genomic alterations can be accessed via ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/), BRCA Exchange (https://brcaexchange.org/) and the TP53 Database (https://tp53.isb-cgc.org/). The Ensembl ranked order of severity of variant consequences is available at: https://m.ensembl.org/info/genome/variation/prediction/predicted_data.html. Mutational signature reference databases can be accessed via COSMIC (https://cancer.sanger.ac.uk/signatures/) and Signal (https://signal.mutationalsignatures.com/). The LM22 signature matrix used for immune cell deconvolution can be downloaded here: https://cibersortx.stanford.edu/. MSigDB hallmark gene sets can be accessed here: https://www.gsea-msigdb.org/gsea/msigdb/. Illumina methylation probes that were filtered out due to poor performance (e.g. cross reactive or non-specific probes) can be found here: https://github.com/sirselim/illumina450k_filtering. Germline polymorphic sites for reference and variant allele read counts used in FACETS analysis can be found at ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b151_GRCh37p13/VCF/common_all_20180423.vcf.gz. The GTF used for annotation and RNA-seq counts is available here: ftp://ftp.ensembl.org/pub/grch37/release-92/. Code availability No custom code or software was used in the data analyses and for the figures. All results can be replicated using publicly available tools and software. The tools and versions used are described in the Methods and Supplementary Information. Acknowledgments We thank A. Freimund, R. Lupat, J. Ellul, and the Peter MacCallum Cancer Centre Research Computing Facility for their contributions to the study. This work was supported by the National Health and Medical Research Council (NHMRC) of Australia (GNT1186505 and GNT2029088), the US Army Medical Research and Materiel Command Ovarian Cancer Research Program (Award No. W81XWH-16-2-0010 and W81XWH-21-1-0401), the National Institutes of Health (NIH) (R21-CA267050, K07-CA080668, R01-CA95023, R01-CA248288, P50-CA136393, P30-CA015083, MO1-RR000056), the Swiss National Foundation (P500PM_20726); Bangerter-Rhyner Stiftung (0297); Margarete and Walter Lichtenstein-Stiftung; and Freie Gesellschaft Basel. The Gynaecological Oncology Biobank at Westmead was funded by the NHMRC (ID310670, ID628903); the Cancer Institute NSW (12/RIG/1-17, 15/RIG/1-16); the Department of Gynaecological Oncology, Westmead Hospital; and acknowledges financial support from the Sydney West Translational Cancer Research Centre, funded by the Cancer Institute NSW (15/TRC/1-01). Direct funding for the generation of the NanoString data for OTTA was provided by the NIH (R01-CA172404, and R01-CA168758), the Canadian Institutes for Health Research (Proof-of-Principle I program) and the United States Department of Defense Ovarian Cancer Research Program (OC110433). T.A.Z. is supported by the Swiss National Foundation Return CH Postdoc.Mobility (P5R5PM_222151).D.W.G. is supported by a Victorian Cancer Agency/Ovarian Cancer Australia Low-Survival Cancer Philanthropic Mid-Career Research Fellowship (MCRF22018) and the Ovarian Cancer Research Foundation (2025/OCRF0071). S.J.R. is supported by the NHMRC (2009840). M.J.G is supported by the Ministerio de Ciencia, Innovación y Universidades (MICIU)/AEI/10.13039/501100011033 and ERDF, EU (Project PID2023-151298OB-I00). A.O. is partially funded by Ministerio de Ciencia e Innovación, Instituto de Salud Carlos III (PI23/01235) supported by FEDER funds and the Spanish Network on Rare Diseases (CIBERER). K.M.D., T.P.C., and G.L.M. were supported by awards from the Uniformed Services University of the Health Sciences and the Defense Health Program to the Henry M Jackson Foundation (HJF) for the Advancement of Military Medicine Inc. to the Gynecologic Cancer Center of Excellence Program including HU0001-16-2-0006 (PIs: Chad A. Hamilton and G. Larry Maxwell), HU0001-19-2-0031, HU0001-20-2-0033, and HU0001-21-2-0027 (PIs: Yovanni Casablanca and G. Larry Maxwell), HU0001-22-2-0016 and HU0001-23-2-0038 (PIs: Neil T. Phippen and G. Larry Maxwell), as well as HU0001-23-2-0038 and HU0001-24-2-0047 (PIs Christopher M Tarney and G. Larry Maxwell). T.V.G. is a Senior Clinical Investigator of the Fund for Scientific Research-Flanders (FWO Vlaanderen 18B2921N). A.DeF. is supported by the NHMRC (2033042). The AOV study was funded by the Canadian Institutes for Health Research (MOP-86727). The Generations Study was funded by Breast Cancer Now and the United Kingdom National Health Service funding to the Royal Marsden/Institute of Cancer Research. The UK Ovarian Cancer Population study (UKOPS) was funded by The Eve Appeal (The Oak Foundation) with contribution to authors’ salary through MRC core funding MC_UU_00004/01 and the NIH Research University College London Hospitals Biomedical Research Centre. The contents of the published material are solely the responsibility of the authors and do not reflect the views of the NHMRC, NIH, and other funders. Authors contributions T.A.Z.: Conceptualization, data curation, formal analysis, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. S.F.: Conceptualization, data curation, validation, methodology, writing–original draft, writing–review and editing. A.P.: Conceptualization, data curation, formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. D.A.: Data curation, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. M.W.J.: Formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. L.T.: Formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. A.F.: Conceptualization, data curation, investigation, writing–review and editing. C.M.L.: Formal analysis, validation, investigation, methodology, writing–review and editing. C.J.K.: Resources, data curation, methodology, writing–original draft, writing–review and editing. A.B.: Resources, writing–review and editing. N.S.M.: Resources, writing–review and editing. K.M.: Resources, writing–review and editing. P.H.: Data curation, formal analysis, validation, investigation, methodology, writing–review and editing. J.A.: Resources, writing–review and editing. A.C.A.: Resources, writing–review and editing. G.A-Y.: Resources, writing–review and editing. M.W.B.: Resources, writing–review and editing. A.B.: Resources, writing–review and editing. C.B.: Resources, writing–review and editing. F.B.: Resources, writing–review and editing. C.B.: Resources, writing–review and editing. J.B.: Resources, writing–review and editing. A.H.B.: Resources, writing–review and editing. M.E.C.: Resources, writing–review and editing. A.C-J.: Resources, writing–review and editing. D.S.C.: Resources, writing–review and editing. E.L.C.: Resources, writing–review and editing. A.C-G.: Resources, writing–review and editing. P.C.: Resources, writing–review and editing. K.L.C-H.: Resources, writing–review and editing. C.C.: Resources, writing–review and editing. K.M.D.: Resources, writing–review and editing. C.D.: Resources, writing–review and editing. T.D.: Resources, writing–review and editing. A.B.E.: Resources, writing–review and editing. E.E.: Resources, writing–review and editing. J.E.: Resources, writing–review and editing. T.E.: Resources, writing–review and editing. R.F.: Resources, writing–review and editing. A.F.: Resources, writing–review and editing. M.G-C.: Resources, writing–review and editing. A.G-M.: Resources, writing–review and editing. P.G.: Resources, writing–review and editing. R.G.: Resources, writing–review and editing. P.H.: Resources, writing–review and editing. A.D.H.: Resources, writing–review and editing. A.H.: Resources, writing–review and editing. S.H.: Resources, writing–review and editing. B.Y.H.: Resources, writing–review and editing. A.H.: Resources, writing–review and editing. S.H.: Resources, writing–review and editing. D.G.H.: Resources, writing–review and editing. M.J-L.: Resources, writing–review and editing. M.E.J.: Resources, writing–review and editing. E.K.: Resources, writing–review and editing. E.K.: Resources, writing–review and editing. T.K.: Resources, writing–review and editing. F.K.F.K.: Resources, writing–review and editing. G.K.: Resources, writing–review and editing. R.F.P.M.K.: Resources, writing–review and editing. J.K.: Resources, writing–review and editing. D.L.: Resources, writing–review and editing. C-H.L.: Resources, writing–review and editing. J.L.: Resources, writing–review and editing. S.C.Y.L.: Resources, writing–review and editing. Y.L.: Resources, writing–review and editing. A.L.: Resources, writing–review and editing. J.L.: Resources, writing–review and editing. L.L.: Resources, writing–review and editing. J.L.: Resources, writing–review and editing. C.M.: Resources, writing–review and editing. I.A.M.: Resources, writing–review and editing. M.M.: Resources, writing–review and editing. G.S.N.: Resources, writing–review and editing. N.N.: Resources, writing–review and editing. A.O.: Resources, writing–review and editing. S.O.: Resources, writing–review and editing. A.O.: Resources, writing–review and editing. C.M.Q.: Resources, writing–review and editing. G.RM.: Resources, writing–review and editing. I.R-C.: Resources, writing–review and editing. C.R-A.: Resources, writing–review and editing. P.R.: Resources, writing–review and editing. M.R.: Resources, writing–review and editing. S.G.S.: Resources, writing–review and editing. S.S.: Resources, writing–review and editing. M.J.S.: Resources, writing–review and editing. H-P.S.: Resources, writing–review and editing. G.S.S.: Resources, writing–review and editing. L.S.: Resources, writing–review and editing. C.J.R.S.: Resources, writing–review and editing. A.T.: Resources, writing–review and editing. A.T.: Resources, writing–review and editing. C.M.T.: Resources, writing–review and editing. S.E.T.: Resources, writing–review and editing. K.K.V.: Resources, writing–review and editing. M.A.A.: Resources, writing–review and editing. T.G.: Resources, writing–review and editing. E.N.: Resources, writing–review and editing. L.W.: Resources, writing–review and editing. A.E.W-H.: Resources, writing–review and editing. C.W.: Resources, writing–review and editing. C.W.: Resources, writing–review and editing. J.W.: Resources, writing–review and editing. N.W.: Resources, writing–review and editing. L.R.W.: Resources, writing–review and editing. S.J.W.: Resources, writing–review and editing. B.W.: Resources, writing–review and editing. M.S.A.: Resources, writing–review and editing. A.B.: Resources, writing–review and editing. F.J.C-R.: Resources, writing–review and editing. P.A.C.: Resources, writing–review and editing. T.P.C.: Resources, writing–review and editing. P.C.: Resources, writing–review and editing. J.A.D.: Resources, writing–review and editing. P.A.F.: Resources, writing–review and editing. R.T.F.: Resources, writing–review and editing. M.J.G.: Resources, writing–review and editing. S.A.G.: Resources, writing–review and editing. M.T.G.: Resources, writing–review and editing. J.G.: Resources, writing–review and editing. H.R.H.: Resources, writing–review and editing. F.H.: Resources, writing–review and editing. H.M.H.: Resources, writing–review and editing. B.Y.K.: Resources, writing–review and editing. L.E.K.: Resources, writing–review and editing. G.L.M.: Resources, writing–review and editing. U.M.: Resources, writing–review and editing. F.M.: Resources, writing–review and editing. S.L.N.: Resources, writing–review and editing. J.M.S.: Resources, writing–review and editing. A.S.: Resources, writing–review and editing. A.J.S.: Resources, writing–review and editing. I.V.: Resources, writing–review and editing. A.H.W.: Resources, writing–review and editing. J.D.B.: Resources, writing–review and editing. P.D.P.P.: Resources, writing–review and editing. C.L.P.: Resources, writing–review and editing. M.C.P.: Resources, writing–review and editing. E.L.G.: Resources, writing–review and editing. S.J.R.: Conceptualization, resources, data curation, supervision, funding acquisition, validation, writing–original draft, project administration, writing–review and editing. M.K.: Conceptualization, resources, data curation, investigation, formal analysis, validation, visualization, supervision, methodology, writing–original draft, writing–review and editing. B.N.: Resources, data curation, investigation, formal analysis, validation, visualization, methodology, writing–review and editing. A.DF.: Conceptualization, Resources, writing–review and editing. M.L.F.: Conceptualization, Resources, writing–review and editing. D.D.L.B.: Conceptualization, resources, supervision, funding acquisition, writing–original draft, writing–review and editing. D.W.G.: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. Competing interests T.A.Z. reports personal consulting fees from AbbVie that are outside the submitted work. D.D.L.B. reports research support grants from AstraZeneca, Roche-Genentech and BeiGene paid to institution outside the submitted work; also, personal consulting fees from Exo Therapeutics that are outside the submitted work. G.A.-Y. reports research support grants from AstraZeneca and Roche-Genentech paid to institution outside the submitted work; also, personal consulting fees from Incyclix Bio that are outside the submitted work. A.DeF. reports research support from AstraZeneca and Illumina. N.N. reports research support from Illumina. P.A.C. reports speakers’ honoraria from AstraZeneca, Merck Sharpe and Dohme, and GlaxoSmithKline, and personal consulting fees from Astra Zeneca outside the remit of the submitted work. U.M. and A.G.M. report personal consulting fees from Mercy BioAnalytics Ltd and research support grants from Intelligent Lab on Fiber, RNA Guardian, and MercyBio Analytics that are all outside the remit of the submitted work. E.L.C. reports research support from AstraZeneca paid to institution outside the submitted work and speakers’ honoraria from AstraZeneca and GSK. S.E.T reports consulting fees from AstraZeneca and IntegraConnect outside the submitted work. P.H. reports honoraria and consulting fees from Amgen, Astra Zeneca, GSK, Roche, Immunogen, Sotio, Stryker, ZaiLab, MSD, Clovis, Miltenyi, Eisai, Mersana, Exscientia, Daiichi Sankyo, Karyopharm, Abbvie, Novartis, Corcept, BionTech, Zymeworks and Research funding (Institutional) from Astra Zeneca, Roche, GSK, Genmab, Immunogen, Seagen, Clovis, Novartis, Immatics, Abbvie, MSD. I.V. has participated in consulting advisory boards for Akesobio, Bristol Myers Squibb, Eisai, F. Hoffmann-La Roche, Genmab, GSK, ITM, Karyopharm, MSD, Novocure, Oncoinvent, Sanofi, Regeneron, and Seagen, and has participated in consulting data monitoring committees for Abbvie, Agenus, AstraZeneca, Corcept, Daiichi, F. Hoffmann-La Roche, Immunogen, Kronos Bio, Mersana, Novartis, OncXerna, Verastem Oncology, and Zentalis. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 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Regression modeling strategies (2nd ed.). Springer International Publishing (2016). https://doi.org/https://doi.org/10.1007/978-3-319-19425-7 Grambsch, P. M. & Therneau, T. M. Proportional Hazards Tests and Diagnostics Based on Weighted Residuals. Biometrika 81 , 515-526 (1994). Team, R. C. R: A language and environment for statistical computing. (2023). Additional Declarations Yes there is potential Competing Interest. T.A.Z. reports personal consulting fees from AbbVie that are outside the submitted work. D.D.L.B. reports research support grants from AstraZeneca, Roche-Genentech and BeiGene paid to institution outside the submitted work; also, personal consulting fees from Exo Therapeutics that are outside the submitted work. G.A.-Y. reports research support grants from AstraZeneca and Roche-Genentech paid to institution outside the submitted work; also, personal consulting fees from Incyclix Bio that are outside the submitted work. A.DeF. reports research support from AstraZeneca and Illumina. N.N. reports research support from Illumina. P.A.C. reports speakers’ honoraria from AstraZeneca, Merck Sharpe and Dohme, and GlaxoSmithKline, and personal consulting fees from Astra Zeneca outside the remit of the submitted work. U.M. and A.G.M. report personal consulting fees from Mercy BioAnalytics Ltd and research support grants from Intelligent Lab on Fiber, RNA Guardian, and MercyBio Analytics that are all outside the remit of the submitted work. E.L.C. reports research support from AstraZeneca paid to institution outside the submitted work and speakers’ honoraria from AstraZeneca and GSK. S.E.T reports consulting fees from AstraZeneca and IntegraConnect outside the submitted work. P.H. reports honoraria and consulting fees from Amgen, Astra Zeneca, GSK, Roche, Immunogen, Sotio, Stryker, ZaiLab, MSD, Clovis, Miltenyi, Eisai, Mersana, Exscientia, Daiichi Sankyo, Karyopharm, Abbvie, Novartis, Corcept, BionTech, Zymeworks and Research funding (Institutional) from Astra Zeneca, Roche, GSK, Genmab, Immunogen, Seagen, Clovis, Novartis, Immatics, Abbvie, MSD. I.V. has participated in consulting advisory boards for Akesobio, Bristol Myers Squibb, Eisai, F. Hoffmann-La Roche, Genmab, GSK, ITM, Karyopharm, MSD, Novocure, Oncoinvent, Sanofi, Regeneron, and Seagen, and has participated in consulting data monitoring committees for Abbvie, Agenus, AstraZeneca, Corcept, Daiichi, F. Hoffmann-La Roche, Immunogen, Kronos Bio, Mersana, Novartis, OncXerna, Verastem Oncology, and Zentalis. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Supplementary Files ExtendedDataFigures.pdf Extended Data Fig. 1 | Patient cohorts and case selection: Overview for clinical, molecular, and validation analysis. Overview of patient cohorts and case selection for the clinical, molecular and validation analysis. HGSC=Tubo-ovarian high-grade serous carcinoma, AOCS =Australian Ovarian Cancer Study, MOCOG=Multidisciplinary Ovarian Cancer Outcome Group, gBRCApv=pathogenic germline BRCA variant, mcIF=multicolor immunofluorescence, OTTA = Ovarian Tumor Tissue Analysis, mRNA=messenger ribonucleic acid, OS=overall survival Extended Data Fig. 2 | Association of BRCA status and residual disease with survival and distribution of molecular features in HGSC: Insights from the OTTA cohort. a, Multivariable Cox proportional hazards model of the interaction term BRCA and residual disease status and clinicopathological and molecular predictive features on overall survival with patients from the OTTA cohort. P values were derived using the Wald test; values < 0.05 are colored red (*, P < 0.05; **, P < 0.01; ***, P < 0.001; **** P < 0.0001). b, Distribution of molecular features (CD8+ TIL density, molecular subtypes, and RB1 loss) within the BRCA and residual groups by odds ratios. P -values were calculated based on odds ratios (OR). R=Residual disease, R0=No residual disease, gBRCApv=pathogenic germline BRCA variant, HR=Hazard ratio, CI=confidence interval, TIL= tumor-infiltrating lymphocyte Extended Data Fig. 3 | Association of BRCA status and neoadjuvant chemotherapy on survival in HGSC. Kaplan-Meier analysis of overall survival stratified by the interaction term BRCA and neoadjuvant chemotherapy status from patients of the Australian Ovarian Cancer Study (AOCS) cohort. P value calculated by log-rank test. gBRCApv=pathogenic germline BRCA variant, NACT=neoadjuvant chemotherapy, n=Number of patients, OS=Overall survival Extended Data Fig. 4 | Prognostic significance of pathogenic germline BRCA1 and BRCA2 variant by domain location and mutation type on outcome in HGSC. a, and b, shows the distribution of pathogenic germline mutations on the BRCA1 and BRCA2 gene, respectively. c, and d, show the distribution of mutation types within the different BRCA1 and BRCA2 functional domains. Fisher’s exact test P value is reported. gBRCApv=pathogenic germline BRCA variant, DBD=DNA binding domain, RING=Really Interesting New Gene domain, RAD51-BD=RAD51-binding domain, BRCT=BRCA c-terminal domain, n=Number of patients Extended Data Fig. 5 | Distribution analysis of clinical and molecular features by BRCA and survival groups in HGSC. a, Box plots summarizing numerical, clinical and genomic characteristics by BRCA and survival groups; dots represent each sample, boxes show the interquartile range (25-75th percentiles), central lines show the median, whiskers show the smallest/largest values within 1.5 times the interquartile range. Kruskal-Wallis test Benjamini-Hochberg adjusted P -values and pairwise Mann-Whitney-Wilcoxon test P values (two sided) are reported (ns, P >0.1; . , P <0.1; *, P < 0.05; **, P < 0.01; ***, P < 0.001). Features are ranked according to their significance. b, Proportion of patients with categorical characteristics per BRCA and survival group. Features are ordered by significance using Fisher's exact test (two-tailed) and clusters are ordered by proportion of long-term survivors. Fisher's test P -values shown are Benjamini-Hochberg adjusted P -values. Features are ordered by significance. LST=Large scale transitions, LOH= Loss of heterozygosity, SV= Structural variants, DEL=Deletion, DUP=Duplication, AI=Allelic imbalance, INV=Inversion; Long term survivor (LTS)= OS >3 years, Short term survivor (STS)= OS ≤3 years, BRCA-P=BRCA-proficient; HR status= Homologous recombination status, P=Progression, PF= Progression-free; R=residual disease, R0=no residual disease; High ≥63, Moderate= 42-62, Low= ≤41, IMMB=Immune clusters (by CIBERSORTx), Molecular subtypes: C1.MES=C1 mesenchymal subtype, C2.IMM=C2 immunoreactive subtype, C4.DIF=C4 differentiated subtype, C5.PRO=C5 proliferative subtype Extended Data Fig. 6 | NF1 gene alterations and expression. a, Scatter graphs (right) show NF1 expression (y-axis) plotted against copy number (x-axis) in primary tumors (n=153, Spearman correlation analysis). Boxplots (left) summarize NF1 expression by NF1 alterations with and without locus specific loss of heterozygosity (LOH); lines indicate median, and whiskers show range. Kruskal–Wallis test P value is reported as well as pairwise Wilcoxon rank-sum test P values comparing altered groups to wildtype (non-significant (ns), P >0.05; ****, P <0.0001). b, Kaplan-Meier analysis of overall survival in patients stratified by NF1 alterations exhibiting locus specific LOH and c, in patients with BRCA1 -and BRCA2 -deficient tumors stratified by NF1 alteration status. P values calculated by log-rank test. TMM=Trimmed Mean of M-values Extended Data Fig. 7 | Survival analysis by NF1 expression HGSC, with stratification by BRCA status: Findings from MOCOG and OTTA cohorts. Kaplan-Meier curves for overall survival (OS) comparing a patients with HGSC from the MOCOG cohort by NF1 protein expression status (NF1 retained vs loss) and in b additionally stratified by germline BRCA mutation status. c, Kaplan-Meier curve comparing the overall survival of patients with HGSC from the OTTA cohort by NF1 RNA expression status (low=lowest quantile, high=2 nd to 5 th quantiles) and in d additionally stratified by germline BRCA mutation status. P values calculated by log-rank test Extended Data Fig. 8 | PIK3CA and RAD21 gene alterations in HGSC. a, b, Scatter graphs (right) of the expression (y-axis) of PIK3CA (a) and RAD21 (b) plotted against copy number (x-axis) in primary tumors (n=153, Spearman correlation analysis). Boxplots (left) summarize expression by mutation type; lines indicate median, and whiskers show range. Kruskal–Wallis test P value is reported as well as pairwise Wilcoxon rank-sum test P values comparing altered groups to wildtype (non-significant (ns), P >0.05; *** , P <0.0001; ** , P <0.001; **, P <0.01). c, Kaplan-Meier analysis of overall survival in patients with HGSC stratified by BRCA -status and PIK3CA amplification vs no amplification . P value calculated by log-rank test. d, Kaplan-Meier analysis of overall survival in patients with HGSC stratified by BRCA -status and RAD21 amplification vs no amplification. P value calculated by log-rank test. e, Kaplan-Meier analysis of overall survival in patients with HGSC from the OTTA cohort stratified by PIK3CA RNA expression status (high=highest quantile, low=1 st to 4 th quantiles) and stratified by germline BRCA mutation status. P value calculated by log-rank test. SV=Structural variants, amp=amplification, WG=Whole gene, BRCA-P=BRCA-proficient, TMM=Trimmed Mean of M-values Extended Data Fig. 9 | c-KIT gene expression in HGSC: Association with survival, molecular subtypes, and BRCA status. a, Forest plot (left) indicates the hazard ratio (HR, squares) and 95% confidence interval (CI; whiskers) for overall survival (OS) calculated using a multivariable Cox proportional hazard regression model based on the LM22 immune cell types detected by CIBERSORTx analysis (n = 153 patients). Cell types are arranged by HR. P values were derived by Wald test; values < 0.05 are colored red ( P < 0.05, ** P < 0.01). b, Differential expression analysis was performed using DESeq2 to determine fold change (right) of gene expression between the BRCA survival groups ( BRCA1=BRCA1 -deficient; BRCA2=BRCA2 -deficient; BRCA-P = BRCA -proficient; Long term survivor (LTS) = OS >3 years; Short term survivor (STS) = OS ≤3 years) (two-tailed Wald test, both unadjusted P values and Benjamini-Hochberg adjusted P values ( P adj ) are shown). c, Multivariable Cox proportional hazards model for OS comparing c-KIT with high vs low RNA expression levels by median and adjusted for HRD sum score and BRCA HRD status. P values were derived by Wald test; values < 0.05 are colored red ( P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001). d, Boxplots summarize RNA expression of the c-KIT gene marker across the molecular subtypes (C1.MES=C1 mesenchymal subtype, C2.IMM=C2 immunoreactive subtype, C4.DIF=C4 differentiated subtype, C5.PRO=C5 proliferative subtype); points represent each sample, boxes show the interquartile range (25–75th percentiles), central lines indicate the median, and whiskers show the smallest/largest values within 1.5 times the interquartile range. Differential expression analysis was performed using DESeq2 to determine fold change (right) of gene expression between the molecular subtypes (two-tailed Wald test, both unadjusted P values and Benjamini-Hochberg adjusted P values ( P adj ) are shown). e, Clustered heatmap summarizing gene set enrichment analysis (GSEA) using the hallmark Molecular Signatures Database (MSigDB) gene sets. Direction and color of triangles relate to the normalized enrichment score (NES) as generated by FGSEA. P values (two-sided) were calculated using the FGSEA default Monte Carlo method; the size of the triangles corresponds to the negative log 10 Benjamini-Hochberg adjusted P value ( P adj ). Columns are separated by molecular subtypes with the direction of enrichment indicated by the first group mentioned in the x-axis label. BRCA-P= BRCA-proficient, Survival group: Long-term survivor (LTS)= OS >3 years, Short-term survivor (STS)= OS ≤3 years, TMM=Trimmed Mean of M-values SupplementaryInformation.docx Supplementary Information SupplementaryTables.xlsx Supplementary Tables Table S1 Clinical data of the AOCS cohort Table S2 Mutation type and location g BRCA pv-carriers AOCS cohort Table S3 Univariable and multivariable Accelerated Failure Time (AFT) model results of clinical features, including interaction analyses with gBRCApv status, on overall survival in patients with HGSC from the AOCS cohort. Table S4 Univariable and multivariable Accelerated Failure Time (AFT) model results of clinical features, including interaction analyses with gBRCApv status, on progression-free survival in patients with HGSC from the AOCS cohort. Table S5 Distribution of molecular subtypes, tumor-infiltrating lymphocytes, RB1 protein expression status, and CCNE1 amplification status stratified by residual disease or no residual disease, and by g BRCA pv carrier or non-carrier status. Percentages are given in parentheses and calculated within each subgroup. Table S6 Univariable and multivariable Accelerated Failure Time (AFT) model of gBRCApv and neoadjuvant chemotherapy status and clinicopathological predictive features on overall survival in patients with HGSC from the AOCS cohort. Table S7 Clinical data of the multi-omics cohort Table S8 Mutation location with Δ11q proportion and Δ11q BRCA1 expression Table S9 Results of the Kaplan–Meier analysis of overall survival in 154 patients with HGSC from the multi-omics cohort stratified by the main features of interest. Table S10 BRCA groups, HRD score, CHORD scores, whole genome duplication, and molecular signatures multi-omics cohort Table S11 Germline alterations in genes of interest with loss of wildtype allele and clonality Table S12 Somatic alterations in genes of interest Table S13 Frequency of genes identified as significant in both differential methylation and differential expression analyses. Table S14 NF1 alteration type, segment copy number, clonality, loss of heterozygosity and, and RNA expression. Table S15 Mutual exclusivity and co-occurrence analysis whole multi-omics cohort Table S16 Mutual exclusivity and co-occurrence analysis short survival BRCA group Table S17 PIK3CA segment copy number, RNA expression, and alteration type Table S18 RAD21 segment copy number, RNA expression, and alteration type Table S19 Mutation and neoantigen burden Table S20 Quartile odds ratios of immune cell subsets comparing short-term (n=36) vs long-term (n=106) survival group Table S21 Relative CIBERSORTx abundance of LM22 cell types of the multi-omics cohort with cluster Table S22 Details of participating study sites and ethics approvals from the AOCS, MOCOG and OTTA cohort. Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Francesmary","middleName":"","lastName":"Modugno","suffix":""},{"id":518537272,"identity":"272233c1-338a-4013-af64-b35a193bf660","order_by":130,"name":"Susan Neuhausen","email":"","orcid":"https://orcid.org/0000-0001-5053-0390","institution":"Beckman Research Institute of City of Hope","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"","lastName":"Neuhausen","suffix":""},{"id":518537273,"identity":"3eed8687-1c2e-40bf-a0af-1487bc2506fd","order_by":131,"name":"Joellen Schildkraut","email":"","orcid":"","institution":"Department of Epidemiology, Rollins School of Public Health, Emory University","correspondingAuthor":false,"prefix":"","firstName":"Joellen","middleName":"","lastName":"Schildkraut","suffix":""},{"id":518537274,"identity":"40e21389-03d2-4a6c-873f-604519892a03","order_by":132,"name":"Annette Staebler","email":"","orcid":"","institution":"Institute of Pathology and Neuropathology, Tuebingen University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Annette","middleName":"","lastName":"Staebler","suffix":""},{"id":518537275,"identity":"73cdb582-a573-4a0b-ad03-8682216b1bae","order_by":133,"name":"Karin Sundfeldt","email":"","orcid":"","institution":"Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Center for Cancer Research, University of Gothenburg","correspondingAuthor":false,"prefix":"","firstName":"Karin","middleName":"","lastName":"Sundfeldt","suffix":""},{"id":518537276,"identity":"1b64acc8-513b-44c5-b952-8ec46ff817a1","order_by":134,"name":"Anthony Swedlow","email":"","orcid":"","institution":"Division of Genetics and Epidemiology, The Institute of Cancer Research, London","correspondingAuthor":false,"prefix":"","firstName":"Anthony","middleName":"","lastName":"Swedlow","suffix":""},{"id":518537277,"identity":"cad9af89-432e-405b-aeca-9ee9fdadb1f5","order_by":135,"name":"Ignace Vergote","email":"","orcid":"","institution":"Division of Gynecologic Oncology, Department of Gynecology and Obstetrics. Leuven Cancer Institute","correspondingAuthor":false,"prefix":"","firstName":"Ignace","middleName":"","lastName":"Vergote","suffix":""},{"id":518537278,"identity":"bcde2b9e-e0b1-43ec-af57-6ceb792a67e8","order_by":136,"name":"Anna Wu","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Wu","suffix":""},{"id":518537279,"identity":"37a432c8-0628-4c6f-af18-915b5ab9ad56","order_by":137,"name":"James Brenton","email":"","orcid":"https://orcid.org/0000-0002-5738-6683","institution":"Cancer Research UK Cambridge Institute, University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Brenton","suffix":""},{"id":518537280,"identity":"b2d3bd39-da7d-4561-a481-66b66e9845ed","order_by":138,"name":"Paul Pharoah","email":"","orcid":"https://orcid.org/0000-0001-8494-732X","institution":"Cedars-Sinai Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Pharoah","suffix":""},{"id":518537281,"identity":"449a200b-4ad2-454e-b87f-e60ad6737e78","order_by":139,"name":"Celeste Pearce","email":"","orcid":"","institution":"University of Michigan–Ann Arbor","correspondingAuthor":false,"prefix":"","firstName":"Celeste","middleName":"","lastName":"Pearce","suffix":""},{"id":518537282,"identity":"a6fb3700-0866-4dd7-add0-b0c1f099c029","order_by":140,"name":"Malcolm Pike","email":"","orcid":"","institution":"mskcc","correspondingAuthor":false,"prefix":"","firstName":"Malcolm","middleName":"","lastName":"Pike","suffix":""},{"id":518537283,"identity":"7fe33ece-5fc8-4fed-9a42-5ca62dae4952","order_by":141,"name":"Ellen Goode","email":"","orcid":"https://orcid.org/0000-0002-9094-8326","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Ellen","middleName":"","lastName":"Goode","suffix":""},{"id":518537284,"identity":"cd7e3164-ce1c-4f35-9e5b-2c81c9623c94","order_by":142,"name":"Susan Ramus","email":"","orcid":"https://orcid.org/0000-0003-0005-7798","institution":"University of New South Wales Sydney","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"","lastName":"Ramus","suffix":""},{"id":518537285,"identity":"958c596b-4ec5-4802-bd9b-fe67a772a365","order_by":143,"name":"Martin Köbel","email":"","orcid":"https://orcid.org/0000-0002-6615-2037","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Köbel","suffix":""},{"id":518537286,"identity":"c2580e74-02e1-4fde-857d-61d0d7e08e5f","order_by":144,"name":"Brad Nelson","email":"","orcid":"https://orcid.org/0000-0002-4445-5539","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Brad","middleName":"","lastName":"Nelson","suffix":""},{"id":518537287,"identity":"69cfe9fe-912f-4d66-98b8-4f2ce8d9e46d","order_by":145,"name":"Anna DeFazio","email":"","orcid":"https://orcid.org/0000-0003-0057-4744","institution":"University of Sydney, Westmead Institute for Medical Research","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"DeFazio","suffix":""},{"id":518537288,"identity":"df368823-7233-44a1-b12e-86df59f6f0f8","order_by":146,"name":"Michael Friedlander","email":"","orcid":"https://orcid.org/0000-0003-3090-795X","institution":"University of South Wales","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Friedlander","suffix":""},{"id":518537289,"identity":"a5c6f34a-dd89-46f4-85d1-494819acaec8","order_by":147,"name":"David Bowtell","email":"","orcid":"","institution":"Peter Mac Callum Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Bowtell","suffix":""}],"badges":[],"createdAt":"2025-09-09 09:32:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7572112/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7572112/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92738065,"identity":"48eff1ed-105c-4465-bf45-2933fe24e5be","added_by":"auto","created_at":"2025-10-03 16:48:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53678,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBRCA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e status and residual disease as predictors of overall survival in HGSC. \u003c/strong\u003eKaplan-Meier survival curve for the interaction term \u003cem\u003eBRCA\u003c/em\u003e and Residual status from patients of \u003cstrong\u003ea\u003c/strong\u003e, the Australian Ovarian Cancer Study (AOCS) cohort and \u003cstrong\u003eb\u003c/strong\u003e, the Ovarian Tumor Tissue Consortium (OTTA) cohort. \u003cem\u003eP\u003c/em\u003e values calculated by log-rank test.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eR=Residual disease, R0=No residual disease, gBRCApv=pathogenic germline BRCA variant, n=Number of patients, OS=Overall survival\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-7572112/v1/e804ba635c2726fd4c9e89d9.png"},{"id":92738066,"identity":"85921d97-a86f-423c-85d3-9deac181d30d","added_by":"auto","created_at":"2025-10-03 16:48:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37624,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of pathogenic germline \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA2 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003evariants and isoform expression on survival in HGSC. a\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eIllustrates the RNA-seq coverage and splice junction reads across the \u003cem\u003eBRCA1\u003c/em\u003e gene for two samples (BRCA-7 and BRCA-14). The top and middle panels show the expression levels, with BRCA-7 and BRCA-14 indicating overall expression coverage. The bottom panel depicts the structure of the \u003cem\u003eBRCA1\u003c/em\u003e isoforms, where the canonical isoform includes exon 10, while the Δ11q isoform excludes it. Grey arcs in the top and middle panels represent splice junction reads supporting the canonical isoform, while red arcs indicate reads supporting the Δ11q isoform. The higher expression of the Δ11q isoform in BRCA-14 compared to BRCA-7 highlights differential splicing events between these samples. \u003cstrong\u003eb\u003c/strong\u003e, Illustrates a comparison of \u003cem\u003eBRCA1\u003c/em\u003e Δ11q expression among patients with mutations in \u003cem\u003eBRCA1\u003c/em\u003eexon 10 and outside exon 10, \u003cem\u003eBRCA2\u003c/em\u003e exon 11 and outside exon 11, and patients with \u003cem\u003eBRCA\u003c/em\u003e wildtype. Kruskal–Wallis test \u003cem\u003eP\u003c/em\u003e value is reported as well as pairwise Wilcoxon rank-sum test \u003cem\u003eP\u003c/em\u003e values. \u003cstrong\u003ec\u003c/strong\u003e, Shows HRD sum score distribution among patients with mutations in \u003cem\u003eBRCA1\u003c/em\u003e exon 10 (high and low Δ11q expression) and outside exon 10, \u003cem\u003eBRCA2\u003c/em\u003e exon 11 and outside exon 11 and \u003cem\u003eBRCA\u003c/em\u003e wildtype tumors. Kruskal–Wallis test \u003cem\u003eP\u003c/em\u003e value is reported as well as pairwise Wilcoxon rank-sum test \u003cem\u003eP\u003c/em\u003e values. \u003cstrong\u003ed\u003c/strong\u003e, Kaplan-Meier analysis of overall survival comparing high vs low Δ11q expression (divided by median) in patients with a \u003cem\u003eBRCA1 \u003c/em\u003emutation on Exon 10. \u003cem\u003eP\u003c/em\u003e value calculated by log-rank test. The distribution of mutation types within \u003cem\u003eBRCA1\u003c/em\u003e outside exon 10 vs. on exon 10 and for \u003cem\u003eBRCA2\u003c/em\u003e outside exon 11 vs. on exon 11 is presented in \u003cstrong\u003ee\u003c/strong\u003e and \u003cstrong\u003ef\u003c/strong\u003e, respectively. Fisher’s exact test \u003cem\u003eP\u003c/em\u003e values are reported.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBRCAwt=BRCA wildtype, HR=Hazard ratio, n=Number of patients, SV= Structural variants, n= number of patients, LST=Large scale transitions, LOH= Loss of heterozygosity, AI= Allelic imbalance\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-7572112/v1/aee502d798364e804878e510.png"},{"id":92738070,"identity":"10f558c1-4ad3-43f7-8a9f-cc218d7c498e","added_by":"auto","created_at":"2025-10-03 16:48:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic landscape of HGSC stratified by \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e status and survival.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e, Oncoprint showing germline and somatic alterations of homologous recombination (HR) genes and other genes of interest stratified by \u003cem\u003eBRCA\u003c/em\u003e-status and survival group. The distribution of the mutation type within the \u003cem\u003eBRCA\u003c/em\u003e survival group is shown for \u003cstrong\u003eb \u003c/strong\u003e\u003cem\u003eCCNE1\u003c/em\u003e, \u003cstrong\u003ec\u003c/strong\u003e \u003cem\u003eNF1\u003c/em\u003e, \u003cstrong\u003ed \u003c/strong\u003e\u003cem\u003ePIK3CA\u003c/em\u003e, and \u003cstrong\u003ee\u003c/strong\u003e \u003cem\u003eRAD21.\u003c/em\u003e \u0026nbsp;\u003cem\u003eP\u003c/em\u003e-values were calculated by the Fisher’s exact test and Benjamini-Hochberg (BH) adjusted (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBRCA status group: Long-term survivor (LTS) = OS \u0026gt;3 years, Short-term survivor (STS) = OS ≤3 years, BRCA-P=BRCA-proficient, HRD score: High= ≥ 63 HRD Sum, Moderate=42-62, Low= ≤41 HRD Sum, HRD= Homologous recombination deficiency, CHORD= Classifier of HOmologous Recombination Deficiency, SV=Structural variant, WG=Whole gene, BH=Benjamini-Hochberg\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-7572112/v1/4949848ba36e7b395d3624e6.png"},{"id":92738067,"identity":"48bf2f3b-a614-4512-a315-a22244ac3ed1","added_by":"auto","created_at":"2025-10-03 16:48:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":65497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInfluence of homologous recombination deficiency in HGSC independent of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e status. \u003c/strong\u003eComparison of \u003cstrong\u003ea\u003c/strong\u003e SV total counts, \u003cstrong\u003eb\u003c/strong\u003e SNV counts per megabase, \u003cstrong\u003ec\u003c/strong\u003e neoantigen counts, and \u003cstrong\u003ed\u003c/strong\u003e HRD sum score between \u003cem\u003eBRCA\u003c/em\u003e survival groups (\u003cem\u003eBRCA1=BRCA1\u003c/em\u003e-deficient; \u003cem\u003eBRCA2=BRCA2\u003c/em\u003e-deficient; \u003cem\u003eBRCA-P\u003c/em\u003e=\u003cem\u003eBRCA\u003c/em\u003e-proficient; Long term survivor (LTS) = OS \u0026gt;3 years; Short term survivor (STS) = OS ≤3 years). \u003cem\u003eP\u003c/em\u003e-values were calculated by the Kruskal Wallis test and Benjamini-Hochberg (BH) adjusted (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e). \u003cstrong\u003ee\u003c/strong\u003e, Kaplan-Meier analysis of overall survival stratified by different thresholds of the HRD sum score (High ≥63, Moderate 42-62, Low ≤42) in 154 patients with \u003cem\u003eBRCA\u003c/em\u003e-deficient and \u003cem\u003eBRCA\u003c/em\u003e-proficient HGSC. \u003cem\u003eP\u003c/em\u003e value calculated by log-rank test. \u003cstrong\u003ef\u003c/strong\u003e, Kaplan-Meier analysis of overall survival in patients with HGSC stratified by \u003cem\u003eBRCA\u003c/em\u003e-status and high (High ≥63) or low (Low \u0026lt; 63) HRD sum score. \u003cem\u003eP\u003c/em\u003e value calculated by log-rank test. \u003cstrong\u003eg\u003c/strong\u003e, Clustered heatmap summarizing gene set enrichment analysis (GSEA) using the hallmark Molecular Signatures Database (MSigDB) gene sets. Direction and color of triangles relate to the normalized enrichment score (NES) as generated by FGSEA. P values (two-sided) were calculated using the FGSEA default Monte Carlo method; the size of the triangles corresponds to the negative log10 Benjamini-Hochberg (BH) adjusted P value (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e). Columns are separated by \u003cem\u003eBRCA-\u003c/em\u003estatus and HRD score groups (\u003cem\u003eBRCA1\u003c/em\u003e; \u003cem\u003eBRCA2\u003c/em\u003e; \u003cem\u003eBRCA\u003c/em\u003e-P, \u003cem\u003eBRCA\u003c/em\u003e-proficient, High ≥ 63; Low \u0026lt;63) with the direction of enrichment indicated by the first group mentioned in the x-axis label.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSV=Structural variants, SNV=Single nucleotide variant, MB=Megabase, HRD=Homologous recombination deficiency, HRP=Homologous recombination proficiency, BRCA-P=BRCA-proficient, LST=Large scale transitions, LOH= Loss of heterozygosity, AI= Allelic imbalance\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Binder14.png","url":"https://assets-eu.researchsquare.com/files/rs-7572112/v1/7bf355aca9a01c5c883c7f8e.png"},{"id":92738069,"identity":"719aa8bd-c197-46da-9b09-529abe146764","added_by":"auto","created_at":"2025-10-03 16:48:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":60288,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegration of immune cell profiling by CIBERSORTx and survival analysis in HGSC. a\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eSummary of the immune cell types arising from the CIBERSORTxanalysis from \u003cem\u003eBRCA-\u003c/em\u003edeficient and\u003cem\u003e BRCA\u003c/em\u003e-proficient samples (n = 153 patients). Tumors fell into 6 major clusters (IMM.1-IMM.6) of immune cell types associated with survival. Each patient is annotated with survival group, status at last follow-up, CIBERSORTx absolute immune scores, molecular subtype, HRD score, \u003cem\u003eBRCA\u003c/em\u003e status and CHORD score. \u003cstrong\u003eb\u003c/strong\u003e, Kaplan-Meier analysis of overall survival stratified by immune clusters. \u003cem\u003eP\u003c/em\u003evalue calculated by log-rank test. \u003cstrong\u003ec\u003c/strong\u003e, Boxplots summarize the absolute cell enrichment score of mast cells resting markers across the molecular subtype (C1.MES; C2.IMM; C4.DIF; C5.PRO); points represent each sample, boxes show the interquartile range (25–75th percentiles), central lines indicate the median, and whiskers show the smallest/largest values within 1.5 times the interquartile range. Kruskal–Wallis test \u003cem\u003eP\u003c/em\u003e value is reported as well as pairwise Wilcoxon rank-sum test \u003cem\u003eP\u003c/em\u003evalues comparing molecular subtypes (C2.IMM; C4.DIF; C5.PRO) to C1.MES (**, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSurvival group: Long term survivor (LTS)= OS \u0026gt;3 years, Short term survivor (STS)= OS ≤3 years, HRD=Homologous recombination deficiency, HRD score: High= ≥ 63 HRD Sum, Moderate=42-62, Low= ≤41 HRD Sum, Molecular subtypes: C1.MES=C1 mesenchymal subtype, C2.IMM=C2 immunoreactive subtype, C4.DIF=C4 differentiated subtype, C5.PRO=C5 proliferative subtype, Status: D=Dead, PF=Progression-free, P=Progression, IMMB=Immune cluster BadBRCA (IMMB.1-IMMB.6),\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Binder15.png","url":"https://assets-eu.researchsquare.com/files/rs-7572112/v1/fedcb30816d223364f3697f9.png"},{"id":92739960,"identity":"43f2c639-731f-47d3-8ae8-9807264f841d","added_by":"auto","created_at":"2025-10-03 17:12:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3701377,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7572112/v1/2af44cb4-45cb-43b9-aade-917f2a6efe8a.pdf"},{"id":92739301,"identity":"4c341718-a5a5-478b-b7dd-285e1b390fef","added_by":"auto","created_at":"2025-10-03 17:04:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1249864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 1 | Patient cohorts and case selection: Overview for clinical, molecular, and validation analysis. \u003c/strong\u003eOverview of patient cohorts and case selection for the clinical, molecular and validation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHGSC=Tubo-ovarian high-grade serous carcinoma, AOCS =Australian Ovarian Cancer Study, MOCOG=Multidisciplinary Ovarian Cancer Outcome Group, gBRCApv=pathogenic germline BRCA variant, mcIF=multicolor immunofluorescence, OTTA = Ovarian Tumor Tissue Analysis, mRNA=messenger ribonucleic acid, OS=overall survival\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 2 | Association of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e status and residual disease with survival and distribution of molecular features in HGSC: Insights from the OTTA cohort. a\u003c/strong\u003e, Multivariable Cox proportional hazards model of the interaction term \u003cem\u003eBRCA\u003c/em\u003e and residual disease status and clinicopathological and molecular predictive features on overall survival with patients from the OTTA cohort. \u003cem\u003eP\u003c/em\u003e values were derived using the Wald test; values \u0026lt; 0.05 are colored red (*, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). \u003cstrong\u003eb\u003c/strong\u003e, Distribution of molecular features (CD8+ TIL density, molecular subtypes,\u003cstrong\u003e \u003c/strong\u003eand \u003cem\u003eRB1 \u003c/em\u003eloss) within the \u003cem\u003eBRCA\u003c/em\u003e and residual groups by odds ratios. \u003cem\u003eP\u003c/em\u003e-values were calculated based on odds ratios (OR).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eR=Residual disease, R0=No residual disease, gBRCApv=pathogenic germline BRCA variant, HR=Hazard ratio, CI=confidence interval, TIL= tumor-infiltrating lymphocyte\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 3 | Association of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e status and neoadjuvant chemotherapy on survival in HGSC. \u003c/strong\u003eKaplan-Meier analysis of overall survival stratified by the interaction term \u003cem\u003eBRCA\u003c/em\u003e and neoadjuvant chemotherapy status from patients of the Australian Ovarian Cancer Study (AOCS) cohort. \u003cem\u003eP\u003c/em\u003e value calculated by log-rank test.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003egBRCApv=pathogenic germline BRCA variant, NACT=neoadjuvant chemotherapy, n=Number of patients, OS=Overall survival\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 4 |\u003c/strong\u003e \u003cstrong\u003ePrognostic significance of pathogenic germline \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA2 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003evariant by domain location and mutation type on outcome in HGSC.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eand \u003cstrong\u003eb\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eshows the distribution of pathogenic germline\u003cem\u003e \u003c/em\u003emutations on the\u003cem\u003e BRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e gene, respectively. \u003cstrong\u003ec\u003c/strong\u003e, and \u003cstrong\u003ed\u003c/strong\u003e, show the distribution of mutation types within the different \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e functional domains. Fisher’s exact test \u003cem\u003eP\u003c/em\u003e value is reported. \u003cem\u003egBRCApv=pathogenic germline BRCA variant, DBD=DNA binding domain, RING=Really Interesting New Gene domain, RAD51-BD=RAD51-binding domain, BRCT=BRCA c-terminal domain, n=Number of patients\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 5 | Distribution analysis of clinical and molecular features by \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and survival groups in HGSC. a\u003c/strong\u003e, Box plots summarizing numerical, clinical and genomic characteristics by \u003cem\u003eBRCA\u003c/em\u003e and survival groups; dots represent each sample, boxes show the interquartile range (25-75th percentiles), central lines show the median, whiskers show the smallest/largest values within 1.5 times the interquartile range. Kruskal-Wallis test Benjamini-Hochberg adjusted \u003cem\u003eP\u003c/em\u003e-values and pairwise Mann-Whitney-Wilcoxon test \u003cem\u003eP\u003c/em\u003e values (two sided) are reported (ns, \u003cem\u003eP\u003c/em\u003e\u0026gt;0.1; \u003csup\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.1; *, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Features are ranked according to their significance. \u003cstrong\u003eb\u003c/strong\u003e, Proportion of patients with categorical characteristics per\u003cem\u003e BRCA\u003c/em\u003e and survival group. Features are ordered by significance using Fisher's exact test (two-tailed) and clusters are ordered by proportion of long-term survivors. Fisher's test \u003cem\u003eP\u003c/em\u003e-values shown are Benjamini-Hochberg adjusted \u003cem\u003eP\u003c/em\u003e-values. Features are ordered by significance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLST=Large scale transitions, LOH= Loss of heterozygosity, SV= Structural variants, DEL=Deletion, DUP=Duplication, AI=Allelic imbalance, INV=Inversion; Long term survivor (LTS)= OS \u0026gt;3 years, Short term survivor (STS)= OS ≤3 years, BRCA-P=BRCA-proficient; HR status= Homologous recombination status, P=Progression, PF= Progression-free; R=residual disease, R0=no residual disease; High ≥63, Moderate= 42-62, Low= ≤41, IMMB=Immune clusters (by CIBERSORTx), Molecular subtypes: C1.MES=C1 mesenchymal subtype, C2.IMM=C2 immunoreactive subtype, C4.DIF=C4 differentiated subtype, C5.PRO=C5 proliferative subtype\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 6 | \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eNF1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e gene alterations and expression. a\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eScatter graphs (right) show \u003cem\u003eNF1\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eexpression (y-axis) plotted against copy number (x-axis) in primary tumors (n=153, Spearman correlation analysis). Boxplots (left) summarize \u003cem\u003eNF1\u003c/em\u003e expression by\u003cem\u003e NF1\u003c/em\u003e alterations with and without locus specific loss of heterozygosity (LOH); lines indicate median, and whiskers show range. Kruskal–Wallis test \u003cem\u003eP\u003c/em\u003e value is reported as well as pairwise Wilcoxon rank-sum test \u003cem\u003eP\u003c/em\u003e values comparing altered groups to wildtype (non-significant (ns), \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05; ****, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.0001). \u003cstrong\u003eb\u003c/strong\u003e, Kaplan-Meier analysis of overall survival in patients stratified by \u003cem\u003eNF1\u003c/em\u003e alterations exhibiting locus specific LOH and \u003cstrong\u003ec\u003c/strong\u003e, in patients with \u003cem\u003eBRCA1\u003c/em\u003e-and \u003cem\u003eBRCA2\u003c/em\u003e-deficient tumors stratified by \u003cem\u003eNF1\u003c/em\u003e alteration status. \u003cem\u003eP\u003c/em\u003e values calculated by log-rank test.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTMM=Trimmed Mean of M-values\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 7\u003c/strong\u003e \u003cstrong\u003e|\u003c/strong\u003e \u003cstrong\u003eSurvival analysis by \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eNF1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e expression HGSC, with stratification by \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e status: Findings from MOCOG and OTTA cohorts.\u003c/strong\u003e Kaplan-Meier curves for overall survival (OS) comparing \u003cstrong\u003ea\u003c/strong\u003e patients with HGSC from the MOCOG cohort by NF1 protein expression status (NF1 retained vs loss) and in \u003cstrong\u003eb \u003c/strong\u003eadditionally stratified by germline \u003cem\u003eBRCA \u003c/em\u003emutation status. \u003cstrong\u003ec\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eKaplan-Meier curve comparing the overall survival of patients with HGSC from the OTTA cohort by \u003cem\u003eNF1\u003c/em\u003e RNA expression status (low=lowest quantile, high=2\u003csup\u003end\u003c/sup\u003e to 5\u003csup\u003eth\u003c/sup\u003e quantiles) and in \u003cstrong\u003ed \u003c/strong\u003eadditionally stratified by germline \u003cem\u003eBRCA \u003c/em\u003emutation status. \u003cem\u003eP\u003c/em\u003e values calculated by log-rank test\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 8 | \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePIK3CA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eRAD21\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e gene alterations in HGSC. a\u003c/strong\u003e, \u003cstrong\u003eb\u003c/strong\u003e, Scatter graphs (right) of the expression (y-axis) of \u003cem\u003ePIK3CA\u003c/em\u003e (a) and\u003cem\u003e RAD21\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003e(b) plotted against copy number (x-axis) in primary tumors (n=153, Spearman correlation analysis). Boxplots (left) summarize expression by mutation type; lines indicate median, and whiskers show range. Kruskal–Wallis test \u003cem\u003eP\u003c/em\u003e value is reported as well as pairwise Wilcoxon rank-sum test \u003cem\u003eP\u003c/em\u003e values comparing altered groups to wildtype (non-significant (ns), \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05; ****, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.0001; ***, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001; **, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01). \u003cstrong\u003ec\u003c/strong\u003e,\u003cem\u003e \u003c/em\u003eKaplan-Meier analysis of overall survival in patients with HGSC stratified by \u003cem\u003eBRCA\u003c/em\u003e-status and \u003cem\u003ePIK3CA \u003c/em\u003eamplification vs no amplification\u003cem\u003e. P\u003c/em\u003e value calculated by log-rank test.\u003cstrong\u003e d\u003c/strong\u003e,\u003cem\u003e \u003c/em\u003eKaplan-Meier analysis of overall survival in patients with HGSC stratified by \u003cem\u003eBRCA\u003c/em\u003e-status and \u003cem\u003eRAD21 \u003c/em\u003eamplification vs no amplification. \u003cem\u003eP\u003c/em\u003e value calculated by log-rank test. \u003cstrong\u003ee\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eKaplan-Meier analysis of overall survival in patients with HGSC from the OTTA cohort stratified by \u003cem\u003ePIK3CA\u003c/em\u003e RNA expression status (high=highest quantile, low=1\u003csup\u003est\u003c/sup\u003e to 4\u003csup\u003eth\u003c/sup\u003e quantiles) and stratified by germline \u003cem\u003eBRCA \u003c/em\u003emutation status. \u003cem\u003eP\u003c/em\u003e value calculated by log-rank test.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSV=Structural variants, amp=amplification, WG=Whole gene, BRCA-P=BRCA-proficient, TMM=Trimmed Mean of M-values\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 9 | \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ec-KIT\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e gene expression in HGSC: Association with survival, molecular subtypes, and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e status. a\u003c/strong\u003e, Forest plot (left) indicates the hazard ratio (HR, squares) and 95% confidence interval (CI; whiskers) for overall survival (OS) calculated using a multivariable Cox proportional hazard regression model based on the LM22 immune cell types detected by CIBERSORTx analysis (n = 153 patients). Cell types are arranged by HR. \u003cem\u003eP\u003c/em\u003e values were derived by Wald test; values \u0026lt; 0.05 are colored red (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). \u003cstrong\u003eb\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eDifferential expression analysis was performed using DESeq2 to determine fold change (right) of gene expression between the \u003cem\u003eBRCA\u003c/em\u003e survival groups (\u003cem\u003eBRCA1=BRCA1\u003c/em\u003e-deficient; \u003cem\u003eBRCA2=BRCA2\u003c/em\u003e-deficient; \u003cem\u003eBRCA-P\u003c/em\u003e=\u003cem\u003eBRCA\u003c/em\u003e-proficient; Long term survivor (LTS) = OS \u0026gt;3 years; Short term survivor (STS) = OS ≤3 years) (two-tailed Wald test, both unadjusted \u003cem\u003eP\u003c/em\u003e values and Benjamini-Hochberg adjusted \u003cem\u003eP\u003c/em\u003e values (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e) are shown). \u003cstrong\u003ec\u003c/strong\u003e, Multivariable Cox proportional hazards model for OS comparing \u003cem\u003ec-KIT\u003c/em\u003e with high vs low RNA expression levels by median and adjusted for HRD sum score and BRCA HRD status. \u003cem\u003eP\u003c/em\u003e values were derived by Wald test; values \u0026lt; 0.05 are colored red (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). \u003cstrong\u003ed\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eBoxplots summarize RNA expression of the \u003cem\u003ec-KIT\u003c/em\u003e gene marker across the molecular subtypes (C1.MES=C1 mesenchymal subtype, C2.IMM=C2 immunoreactive subtype, C4.DIF=C4 differentiated subtype, C5.PRO=C5 proliferative subtype); points represent each sample, boxes show the interquartile range (25–75th percentiles), central lines indicate the median, and whiskers show the smallest/largest values within 1.5 times the interquartile range. Differential expression analysis was performed using DESeq2 to determine fold change (right) of gene expression between the molecular subtypes (two-tailed Wald test, both unadjusted \u003cem\u003eP\u003c/em\u003e values and Benjamini-Hochberg adjusted \u003cem\u003eP\u003c/em\u003e values (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e) are shown). \u003cstrong\u003ee\u003c/strong\u003e, Clustered heatmap summarizing gene set enrichment analysis (GSEA) using the hallmark Molecular Signatures Database (MSigDB) gene sets. Direction and color of triangles relate to the normalized enrichment score (NES) as generated by FGSEA. \u003cem\u003eP\u003c/em\u003e values (two-sided) were calculated using the FGSEA default Monte Carlo method; the size of the triangles corresponds to the negative log\u003csub\u003e10\u003c/sub\u003e Benjamini-Hochberg adjusted \u003cem\u003eP\u003c/em\u003e value (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e). Columns are separated by molecular subtypes with the direction of enrichment indicated by the first group mentioned in the x-axis label.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBRCA-P= BRCA-proficient,\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eSurvival group: Long-term survivor (LTS)= OS \u0026gt;3 years, Short-term survivor (STS)= OS ≤3 years, TMM=Trimmed Mean of M-values\u003c/em\u003e\u003c/p\u003e","description":"","filename":"ExtendedDataFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7572112/v1/5b8070b178ccb6d881872b5c.pdf"},{"id":92738068,"identity":"a956bf6d-c132-4c4b-a918-a44a33162cba","added_by":"auto","created_at":"2025-10-03 16:48:29","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2121295,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7572112/v1/ede086fe843ebeb0f1ca6e6f.docx"},{"id":92739004,"identity":"870ab6d5-eee0-47a0-8393-5006a6e09800","added_by":"auto","created_at":"2025-10-03 16:56:30","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":444398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Tables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1 \u003c/strong\u003eClinical data of the AOCS cohort\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2 \u003c/strong\u003eMutation type and location g\u003cem\u003eBRCA\u003c/em\u003epv-carriers AOCS cohort\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S3\u003c/strong\u003e Univariable and multivariable Accelerated Failure Time (AFT) model results of clinical features, including interaction analyses with gBRCApv status, on overall survival in patients with HGSC from the AOCS cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S4\u003c/strong\u003e Univariable and multivariable Accelerated Failure Time (AFT) model results of clinical features, including interaction analyses with gBRCApv status, on progression-free survival in patients with HGSC from the AOCS cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S5\u003c/strong\u003e Distribution of molecular subtypes, tumor-infiltrating lymphocytes, RB1 protein expression status, and \u003cem\u003eCCNE1\u003c/em\u003e amplification status stratified by residual disease or no residual disease, and by g\u003cem\u003eBRCA\u003c/em\u003epv carrier or non-carrier status. Percentages are given in parentheses and calculated within each subgroup.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S6 \u003c/strong\u003eUnivariable and multivariable Accelerated Failure Time (AFT) model of gBRCApv and neoadjuvant chemotherapy status and clinicopathological predictive features on overall survival in patients with HGSC from the AOCS cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S7 \u003c/strong\u003eClinical data of the multi-omics cohort\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S8 \u003c/strong\u003eMutation location with Δ11q proportion and Δ11q \u003cem\u003eBRCA1\u003c/em\u003e expression\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S9 \u003c/strong\u003eResults of the Kaplan–Meier analysis of overall survival in 154 patients with HGSC from the multi-omics cohort stratified by the main features of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S10 \u003c/strong\u003e\u003cem\u003eBRCA\u003c/em\u003e groups, HRD score, CHORD scores, whole genome duplication, and molecular signatures multi-omics cohort\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S11 \u003c/strong\u003eGermline alterations in genes of interest with loss of wildtype allele and clonality\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S12 \u003c/strong\u003eSomatic alterations in genes of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S13\u003c/strong\u003e Frequency of genes identified as significant in both differential methylation and differential expression analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S14 \u003c/strong\u003e\u003cem\u003eNF1 \u003c/em\u003ealteration type, segment copy number, clonality, loss of heterozygosity and, and RNA expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S15 \u003c/strong\u003eMutual exclusivity and co-occurrence analysis whole multi-omics cohort\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S16 \u003c/strong\u003eMutual exclusivity and co-occurrence analysis short survival \u003cem\u003eBRCA\u003c/em\u003e group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S17 \u003c/strong\u003e\u003cem\u003ePIK3CA \u003c/em\u003esegment copy number, RNA expression, and alteration type\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S18 \u003c/strong\u003e\u003cem\u003eRAD21\u003c/em\u003e segment copy number, RNA expression, and alteration type\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S19 \u003c/strong\u003eMutation and neoantigen burden\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S20 \u003c/strong\u003eQuartile odds ratios of immune cell subsets comparing short-term (n=36) vs long-term (n=106) survival group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S21 \u003c/strong\u003eRelative CIBERSORTx abundance of LM22 cell types of the multi-omics cohort with cluster\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S22 \u003c/strong\u003eDetails of participating study sites and ethics approvals from the AOCS, MOCOG and OTTA cohort.\u003c/p\u003e","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7572112/v1/bb59635195c145cc69fc8aa4.xlsx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nT.A.Z. reports personal consulting fees from AbbVie that are outside the submitted work. D.D.L.B. reports research support grants from AstraZeneca, Roche-Genentech and BeiGene paid to institution outside the submitted work; also, personal consulting fees from Exo Therapeutics that are outside the submitted work. G.A.-Y. reports research support grants from AstraZeneca and Roche-Genentech paid to institution outside the submitted work; also, personal consulting fees from Incyclix Bio that are outside the submitted work. A.DeF. reports research support from AstraZeneca and Illumina. N.N. reports research support from Illumina. P.A.C. reports speakers’ honoraria from AstraZeneca, Merck Sharpe and Dohme, and GlaxoSmithKline, and personal consulting fees from Astra Zeneca outside the remit of the submitted work. U.M. and A.G.M. report personal consulting fees from Mercy BioAnalytics Ltd and research support grants from Intelligent Lab on Fiber, RNA Guardian, and MercyBio Analytics that are all outside the remit of the submitted work. E.L.C. reports research support from AstraZeneca paid to institution outside the submitted work and speakers’ honoraria from AstraZeneca and GSK. S.E.T reports consulting fees from AstraZeneca and IntegraConnect outside the submitted work. P.H. reports honoraria and consulting fees from Amgen, Astra Zeneca, GSK, Roche, Immunogen, Sotio, Stryker, ZaiLab, MSD, Clovis, Miltenyi, Eisai, Mersana, Exscientia, Daiichi Sankyo, Karyopharm, Abbvie, Novartis, Corcept, BionTech, Zymeworks and Research funding (Institutional) from Astra Zeneca, Roche, GSK, Genmab, Immunogen, Seagen, Clovis, Novartis, Immatics, Abbvie, MSD. I.V. has participated in consulting advisory boards for Akesobio, Bristol Myers Squibb, Eisai, F. Hoffmann-La Roche, Genmab, GSK, ITM, Karyopharm, MSD, Novocure, Oncoinvent, Sanofi, Regeneron, and Seagen, and has participated in consulting data monitoring committees for Abbvie, Agenus, AstraZeneca, Corcept, Daiichi, F. Hoffmann-La Roche, Immunogen, Kronos Bio, Mersana, Novartis, OncXerna, Verastem Oncology, and Zentalis. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","formattedTitle":"Beyond BRCA deficiency: Clinical and molecular predictors of survival in patients with BRCA-deficient tubo-ovarian high-grade serous carcinoma","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe identification of clinical and molecular determinants of survival in patients with cancer has the dual benefits of finding biomarkers that may guide patient management or provide novel therapeutic opportunities. Until relatively recently, the identification of prognostic biomarkers in ovarian cancer has been confounded by a lack of appreciation of the distinctly different molecular characteristics of the various histologic subtypes that make up epithelial ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Evaluating histologically homogenous sets of ovarian tumors has been critical in deciphering the prognostic importance of proteins such as p53\u003csup\u003e2,3\u003c/sup\u003e and WT1\u003csup\u003e4\u003c/sup\u003e, and identifying genetic risk loci\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHigh-grade serous carcinoma (HGSC) is the most common histotype, accounting for approximately 70% of ovarian cancer deaths in Western countries\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Homologous recombination-mediated DNA repair deficiency (HRD) is frequent in HGSC and is most often associated with mutations in \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e\u003csup\u003e17\u0026ndash;19\u003c/sup\u003e. Approximately fifty percent of HGSC are regarded to have HRD, a feature that can be inferred through specific patterns of genomic scarring in tumor cells\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. HRD leads to genomic instability and tumorigenesis, providing a vulnerability in tumor cells with increased sensitivity to double-strand DNA breaks that can be exploited therapeutically\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. As a result, platinum-based chemotherapy and poly (ADP-ribose) polymerase inhibitor (PARPi) maintenance therapy are generally more effective in patients with HRD tumors\u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile HRD status is informative, accurate prediction of treatment response and survival in HGSC cannot be simply determined by the presence or absence of mutations in genes associated with HR DNA repair. The initial survival advantage for carriers of pathogenic germline \u003cem\u003eBRCA1\u003c/em\u003e variants (g\u003cem\u003eBRCA1\u003c/em\u003epv) diminishes over time, with fewer g\u003cem\u003eBRCA1\u003c/em\u003epv-carriers surviving 10 years after diagnosis than either g\u003cem\u003eBRCA2\u003c/em\u003epv-carriers or non-carriers\u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Factors associated with survival outcome in HGSC include residual disease following cytoreductive surgery\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, the molecular subtype of the tumor\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, age at diagnosis\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, and the extent of T- and B-cell infiltration into tumors\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In germline pathogenic variant carriers, the location of mutations within \u003cem\u003eBRCA1\u003c/em\u003e or \u003cem\u003eBRCA2\u003c/em\u003e or the retention of the wildtype allele in the tumor can result in a hypomorphic phenotype associated with resistance to platinum-based therapy\u003csup\u003e\u003cspan additionalcitationids=\"CR44 CR45 CR46\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Furthermore, revertant mutations restoring \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e function contribute to acquired resistance to platinum-based therapy and PARPis, impacting treatment response and patient outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eComparing patients who represent the extremes of survival outcomes may provide increased sensitivity to identify prognostic biomarkers that are relevant to a wider patient population\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Using this approach, we have recently shown that plasma cell infiltration and other molecular changes, including co-loss of \u003cem\u003eBRCA\u003c/em\u003e and the tumor suppressor \u003cem\u003eRB1\u003c/em\u003e, are associated with especially long-term survival in HGSC\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. The current study evaluates \u003cem\u003eBRCA\u003c/em\u003e-deficient HGSC by first focusing on g\u003cem\u003eBRCA\u003c/em\u003epv-carriers and then expanding to include somatic mutations and promoter methylation in \u003cem\u003eBRCA1/2\u003c/em\u003e, and other key HR genes, as well as evaluating tumor HRD status. We focus on patients with either poor or favorable survival outcomes, harnessing the value of analyzing patients with exceptional survival outcomes while comparing cohorts that are as similar as possible in other respects.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eAssociation of residual disease with prognosis is attenuated in g\u003c/b\u003e\u003cb\u003eBRCA\u003c/b\u003e\u003cb\u003epv-carriers\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePathogenic germline \u003cem\u003eBRCA\u003c/em\u003e variants (g\u003cem\u003eBRCA\u003c/em\u003epv) were identified in 20% of patients in the Australian Ovarian Cancer Study (AOCS) cohort (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;282/1389) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Tables S1 and S2). In applying a survival model, there was evidence that the proportional hazards assumption did not hold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), thus an Accelerated Failure Time (AFT) model\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e was used with results reported as Time Ratios (TR; see Methods), where TR\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates longer time to progression or death, and a TR\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates shorter survival or time to progression. Patients with g\u003cem\u003eBRCA\u003c/em\u003epvs exhibited improved overall survival (OS; TR: 1.53, 95% CI: 1.33\u0026ndash;1.76, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and progression-free survival (PFS; TR: 1.34 95% CI: 1.28\u0026ndash;1.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared with non-carriers (Supplementary Tables S3 and S4).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the clinicopathological features from patients with high-grade serous ovarian cancer (HGSC) of the Australian Ovarian Cancer Study (AOCS) cohort.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;1,389\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at diagnosis (years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u0026ndash;87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (0.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGermline\u003c/b\u003e \u003cb\u003eBRCA\u003c/b\u003e \u003cb\u003estatus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWildtype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,107 (79.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg\u003cem\u003eBRCA1\u003c/em\u003epv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e175 (12.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg\u003cem\u003eBRCA2\u003c/em\u003epv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107 (7.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGrade\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,100 (79.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e237 (17.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (3.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFIGO stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII-IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,193 (85.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI-II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134 (9.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (4.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrimary site\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOvary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,008 (72.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeritoneum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e215 (15.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFallopian tube\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140 (10.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (1.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary cytoreductive surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e991 (71.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterval cytoreductive surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e299 (21.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (2.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidual disease status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e829 (59.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo residual disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e467 (33.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (6.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeoadjuvant chemotherapy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,060 (76.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e322 (23.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (0.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePARP inhibitor 1st line\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,350 (97.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (2.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProgression-free survival (months)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0-285\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (0.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOverall survival (months)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1-290\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (0.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStatus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e984 (70.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e393 (28.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (0.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe considered whether clinical characteristics differed by germline \u003cem\u003eBRCA\u003c/em\u003e status and found a statistically significant interaction with residual disease status (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.011; Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Using this interaction term, we found that the negative effect of residual disease after cytoreductive surgery on OS was less pronounced in g\u003cem\u003eBRCA\u003c/em\u003epv-carriers (TR: 0.87, 95% CI: 0.72\u0026ndash;1.06, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.162) than in non-carriers (TR: 0.51, 95% CI: 0.44\u0026ndash;0.59, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The importance of residual disease for survival in non-carriers was confirmed in the independent OTTA cohort (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1004, g\u003cem\u003eBRCA\u003c/em\u003epv-carriers\u0026thinsp;=\u0026thinsp;221, 22%; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Extended Data Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable Accelerated Failure Time (AFT) model of BRCA and residual disease status and clinicopathological predictive features on overall survival in patients from the Australian Ovarian Cancer Study (AOCS) cohort. The model was fitted using a log-logistic distribution. Results are expressed as Time Ratios (TR) with corresponding 95% confidence intervals (CI) and p-values derived from Wald tests. A TR\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates a longer survival time, whereas a TR\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates a shorter survival time. Age at diagnosis was modeled using restricted cubic splines with 3 knots and is presented as two spline terms.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eUnivariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eMultivariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003elower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eupper\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003elower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eupper\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg\u003cem\u003eBRCA\u003c/em\u003epv \u0026amp; Residual status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon carriers \u0026amp; R0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon carriers \u0026amp; R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg\u003cem\u003eBRCA\u003c/em\u003epv carriers \u0026amp; R0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg\u003cem\u003eBRCA\u003c/em\u003epv carriers \u0026amp; R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIGO stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI\u0026thinsp;+\u0026thinsp;II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIII\u0026thinsp;+\u0026thinsp;IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary site\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOvary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeritoneum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYears Spline 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.669\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYears Spline 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary CS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInterval CS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.703\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeoadjuvant CHT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePARP inhibitor 1st line\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eR\u0026thinsp;=\u0026thinsp;Residual disease, R0\u0026thinsp;=\u0026thinsp;No residual disease, G2\u0026thinsp;=\u0026thinsp;Grade 2, G3\u0026thinsp;=\u0026thinsp;Grade 3, OS\u0026thinsp;=\u0026thinsp;Overall survival, gBRCApv\u0026thinsp;=\u0026thinsp;pathogenic germline BRCA variant, TR\u0026thinsp;=\u0026thinsp;Time ratio, CI\u0026thinsp;=\u0026thinsp;confidence interval, CHT\u0026thinsp;=\u0026thinsp;chemotherapy, CS\u0026thinsp;=\u0026thinsp;cytoreductive surgery, FT\u0026thinsp;=\u0026thinsp;fallopian tube\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe examined the relationship of residual disease and \u003cem\u003eBRCA\u003c/em\u003e status to known immune and molecular features associated with survival, including tumor-infiltrating lymphocytes (TIL)\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eRB1\u003c/em\u003e loss\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, and transcriptional molecular subtypes\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Non-carriers with residual disease had an inverse association with high CD8\u0026thinsp;+\u0026thinsp;TIL density (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), with 38.3% of tumors classified as having low or no TIL (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Supplementary Table S5). This group also showed an inverse association with the C4/differentiated (C4.DIF) molecular subtype (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010; Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). We observed an association between the C1/mesenchymal (C1.MES) molecular subtype and residual disease as previously reported\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, but this was only statistically significant among non-carriers (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). \u003cem\u003eRB1\u003c/em\u003e loss was associated with g\u003cem\u003eBRCA\u003c/em\u003epv-carriers without residual disease (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eAlthough no statistically significant interaction between neoadjuvant chemotherapy (NACT) and \u003cem\u003eBRCA\u003c/em\u003e status was observed (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.12; Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), there was evidence of heterogeneity of effect in these subgroups. Among participants who did not receive NACT, g\u003cem\u003eBRCA\u003c/em\u003epv-carriers showed a survival benefit compared to non-carriers (TR: 1.60, 95% CI: 1.37\u0026ndash;1.87, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplementary Table S6, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, the overall survival benefit in g\u003cem\u003eBRCA\u003c/em\u003epv-carriers versus non-carriers was not statistically significant in the NACT group (TR: 1.39 and 1.17, 95% CI: 0.75\u0026ndash;2.60 and 0.62\u0026ndash;2.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.298 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.634 respectively, compared to non-carriers who did not receive NACT).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eg\u003c/b\u003e\u003cb\u003eBRCA\u003c/b\u003e\u003cb\u003epv location and type are associated with survival and therapy response\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMutations located in various functional domains of \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e have been associated with differences in survival and responses to PARPi in ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The mutation type and location of g\u003cem\u003eBRCA\u003c/em\u003epvs was ascertained for 240 of the patients in the AOCS cohort from their clinical records and/or previous sequencing analyses\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e (Extended Data Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea,b and Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Following adjustment for FIGO stage, residual disease status, primary site, age, and first-line treatment, patients with g\u003cem\u003eBRCA1\u003c/em\u003epvs in exon 10 had a statistically significant improved OS and PFS (TR: 1.54 and 1.49, 95% CI: 1.19-2.00 and 1.16\u0026ndash;1.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, respectively ), but the association was attenuated for those with variants outside exon 10 (TR: 1.21 and 1.18, 95% CI: 0.97\u0026ndash;1.51 and 0.96\u0026ndash;1.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12, respectively) compared to non-carriers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). More specifically, pathogenic variants in the DNA binding domain (DBD) of \u003cem\u003eBRCA1\u003c/em\u003e, located in exon 10, were associated with an OS and PFS benefit compared to non-carriers (TR: 1.60 and 1.58, 95% CI: 1.14\u0026ndash;2.25 and 1.15\u0026ndash;2.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, respectively; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, the OS and PFS benefit was not statistically significant for patients with pathogenic variants in the Really Interesting New Gene (RING) (TR: 1.28 and 1.15, 95% CI: 0.87\u0026ndash;1.90 and 0.82\u0026ndash;1.61, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.216 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.419, respectively) and C-terminal domains of \u003cem\u003eBRCA1\u003c/em\u003e (BRCT) (TR: 1.35 and 1.43, 95% CI: 0.83\u0026ndash;2.20 and 0.90\u0026ndash;2.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.222 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.126, respectively), located outside of exon 10.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAdjusted Accelerated Failure Time (AFT) model analysis of germline \u003cem\u003eBRCA\u003c/em\u003e pathogenic variant (g\u003cem\u003eBRCA\u003c/em\u003epv) location and progression-free survival and overall survival in patients from the Australian Ovarian Cancer Study (AOCS) cohort. Models were adjusted for FIGO stage, residual disease status, primary tumor site, type of surgery, age at diagnosis (modelled with restricted cubic splines, 3 knots), use of neoadjuvant chemotherapy, tumor grade, and PARP inhibitor use in first-line treatment. AFT models were fitted using a log-logistic distribution. Results are presented as Time Ratios (TR) with 95% confidence intervals (CI) and \u003cem\u003eP\u003c/em\u003e-values derived from Wald tests. A TR\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates an association with longer time to progression or death, while a TR\u0026thinsp;\u0026lt;\u0026thinsp;1 reflects shorter survival. The reference group for all comparisons is non-carriers of g\u003cem\u003eBRCA\u003c/em\u003epv.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eProgression-free survival\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u003cp\u003eOverall survival\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003elower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eupper\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003elower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eupper\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg\u003cem\u003eBRCA\u003c/em\u003epv exon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon carriers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg\u003cem\u003eBRCA1\u003c/em\u003epv Exon 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg\u003cem\u003eBRCA1\u003c/em\u003epv outside Exon 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg\u003cem\u003eBRCA2\u003c/em\u003epv Exon 11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg\u003cem\u003eBRCA2\u003c/em\u003epv outside Exon 11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eg\u003cem\u003eBRCA\u003c/em\u003epv domain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon carriers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg\u003cem\u003eBRCA1\u003c/em\u003epv BRCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg\u003cem\u003eBRCA1\u003c/em\u003epv DBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg\u003cem\u003eBRCA1\u003c/em\u003epv outside domain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg\u003cem\u003eBRCA1\u003c/em\u003epv RING\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg\u003cem\u003eBRCA2\u003c/em\u003epv DBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.528\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg\u003cem\u003eBRCA2\u003c/em\u003epv outside domain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg\u003cem\u003eBRCA2\u003c/em\u003epv RAD51-BD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eDBD\u0026thinsp;=\u0026thinsp;DNA Binding Domain, RING\u0026thinsp;=\u0026thinsp;Really Interesting New Gene, RAD51-BD\u0026thinsp;=\u0026thinsp;RAD51 Binding Domain, BRCT\u0026thinsp;=\u0026thinsp;BRCA1 C-Terminal\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePatients with \u003cem\u003eBRCA1\u003c/em\u003e variants in exon 10 have been reported to have poorer outcomes\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e due to expression of an alternative splice isoform called \u003cem\u003eBRCA1\u003c/em\u003e-delta11q (Δ11q) that bypasses almost all of exon 10 of \u003cem\u003eBRCA1\u003c/em\u003e (historically referred to as exon 11). To explore this further, we assessed \u003cem\u003eBRCA1\u003c/em\u003e isoform expression in our multi-omics cohort (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;154) using the bulk RNA sequencing reads spanning the exon 10 to exon 11 junction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Supplementary Tables S7 and S8, Supplementary Information). The Δ11q isoform was widely expressed regardless of \u003cem\u003eBRCA-\u003c/em\u003estatus, but patients with \u003cem\u003eBRCA1\u003c/em\u003e variants in exon 10 had significantly higher proportions of Δ11q transcripts relative to canonical transcripts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Patients with \u003cem\u003eBRCA1\u003c/em\u003e variants in exon 10 were classified as having high (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10) or low (n\u0026thinsp;=\u0026thinsp;9) \u003cem\u003eBRCA1\u003c/em\u003e Δ11q expression, according to the median. Patients with high Δ11q expression had a shorter survival (median OS 2.74 years) compared to those with low Δ11q expression (median OS not reached), although this was not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.083) and was not associated with differences in the HRD sum score (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec,d and Supplementary Table S9).\u003c/p\u003e\u003cp\u003eOverall, patients with g\u003cem\u003eBRCA2\u003c/em\u003epv had an improved OS compared to non-carriers, regardless of mutation location (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The only exception was the small group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13) with pathogenic variants in the DNA binding domain (DBD) of \u003cem\u003eBRCA2\u003c/em\u003e, located outside of exon 11, who did not show a statistically significant OS or PFS benefit compared to non-carriers (TR: 0.79 and 0.81, 95% CI: 0.39\u0026ndash;1.63 and 0.43\u0026ndash;1.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.528 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.506, respectively).\u003c/p\u003e\u003cp\u003eThe type of mutation in \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e also plays a predictive role in response to PARPi therapy in ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In our analysis, pathogenic variants in \u003cem\u003eBRCA1\u003c/em\u003e exon 10 and \u003cem\u003eBRCA2\u003c/em\u003e exon 11 were more likely to be truncating (98.6% and 92.3%) than those outside these exons (60% and 76.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032 respectively; Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee,f). \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e domains associated with prolonged survival were more likely to have truncating variants than missense or splice site variants (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.067, respectively; Extended Data Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec,d).\u003c/p\u003e\u003cp\u003e\u003cb\u003eNF1\u003c/b\u003e \u003cb\u003egene alterations are associated with improved survival in\u003c/b\u003e \u003cb\u003eBRCA2\u003c/b\u003e\u003cb\u003e-deficient HGSC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo identify genomic features associated with short survival in HRD tumors, we compared tumor genomes and transcriptomes between short (OS\u0026thinsp;\u0026le;\u0026thinsp;3 years, STS) and long-term (OS\u0026thinsp;\u0026gt;\u0026thinsp;3 years, LTS) survival groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Tumor genomes were classified as either \u003cem\u003eBRCA1\u003c/em\u003e-deficient, \u003cem\u003eBRCA2\u003c/em\u003e-deficient or \u003cem\u003eBRCA\u003c/em\u003e-proficient, which incorporated germline and somatic alterations in \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e, as well as other well-defined HR genes, and tumor HRD status as determined by a mutational signature-based classifier (CHORD, Classifier of HOmologous Recombination Deficiency)\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e (Supplementary Information and Supplementary Tables S10-S12). \u003cem\u003eCCNE1\u003c/em\u003e amplifications (gene level copy number\u0026thinsp;\u0026ge;\u0026thinsp;7) were associated with \u003cem\u003eBRCA\u003c/em\u003e-proficiency, and particularly the short-survival \u003cem\u003eBRCA-\u003c/em\u003eproficient group (50%, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e\u0026lt;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). \u003cem\u003eBRCA\u003c/em\u003e-proficient tumors had less genomic scarring and were associated with an older age at diagnosis compared to \u003cem\u003eBRCA1\u003c/em\u003e-deficient and \u003cem\u003eBRCA2\u003c/em\u003e-deficient tumors (Extended Data Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea,b). Gene methylation has been identified as a prognostic factor in HGSC\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e, but no significantly differentially methylated genes with corresponding up- or down-regulated gene expression were observed between STS and LTS groups in \u003cem\u003eBRCA1\u003c/em\u003e- and \u003cem\u003eBRCA2\u003c/em\u003e-deficient tumors (Supplementary Table S13 and Supplementary Information).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAlterations in \u003cem\u003eNF1\u003c/em\u003e were most common in \u003cem\u003eBRCA\u003c/em\u003e-deficient tumors, regardless of survival group (\u003cem\u003eBRCA1\u003c/em\u003e STS 43.8%, \u003cem\u003eBRCA1\u003c/em\u003e LTS 33.3%, \u003cem\u003eBRCA2\u003c/em\u003e STS 30%, \u003cem\u003eBRCA2\u003c/em\u003e LTS 37.5%, \u003cem\u003eBRCA\u003c/em\u003e-P STS 21.4%, \u003cem\u003eBRCA\u003c/em\u003e-P LTS 14.3%, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e=0.061; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and Supplementary Table S14). Notably, gene breakage caused by large-scale deletions was enriched in \u003cem\u003eBRCA2\u003c/em\u003e-deficient tumors in the LTS group. We hypothesized that not all alteration types equivalently disrupt gene function. Indeed, only 54.2% (26/48) of \u003cem\u003eNF1\u003c/em\u003e alterations showed a locus-specific loss of heterozygosity (LOH) suggesting a loss-of-function (Supplementary Table S14 and Supplementary Information). Concordantly, \u003cem\u003eNF1\u003c/em\u003e mRNA expression varied in tumors according to the type of \u003cem\u003eNF1\u003c/em\u003e alteration and was particularly depleted in those with locus-specific LOH (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Extended Data Fig.\u0026nbsp;6a). Patients with tumors that harbored loss-of-function \u003cem\u003eNF1\u003c/em\u003e alterations showed an improved survival compared to non-loss-of-function \u003cem\u003eNF1\u003c/em\u003e alterations (median OS 11.92 years vs 5.17 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032; Extended Data Fig.\u0026nbsp;6b). In particular, the combination of both \u003cem\u003eBRCA2\u003c/em\u003e-deficiency and loss-of-function \u003cem\u003eNF1\u003c/em\u003e alteration (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11) was associated with the best survival outcome (median OS 16.96 years), almost twice as long as those with \u003cem\u003eBRCA2\u003c/em\u003e-deficient tumors with no loss-of-function \u003cem\u003eNF1\u003c/em\u003e alteration (median OS 8.84 years; Extended Data Fig.\u0026nbsp;6c and Supplementary Table S9).\u003c/p\u003e\u003cp\u003eNF1 protein expression was assessed by IHC in a larger cohort enriched for long-term survivors (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;658; Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). NF1 protein loss was observed in 13.37% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;88/658) of patients and was associated with improved survival compared to retained NF1 expression (median OS 4.70 vs. 3.58 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028; Extended Data Fig.\u0026nbsp;7a). Although there were few patients with NF1 protein loss and germline \u003cem\u003eBRCA1\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21) or \u003cem\u003eBRCA2\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6) pathogenic variants, NF1 loss was associated with better survival in g\u003cem\u003eBRCA2\u003c/em\u003epv-carriers (median OS 8.05 years NF1 loss vs. 5.72 years NF1 retained) but not in g\u003cem\u003eBRCA1\u003c/em\u003epv-carriers (median OS 4.74 years NF1 loss vs. 4.69 years NF1 retained; Extended Data Fig.\u0026nbsp;7b). NF1 loss also was associated with a longer survival among non-carriers (median OS 5.01 years NF1 loss vs. 3.36 years NF1 retained; Extended Data Fig.\u0026nbsp;7b).\u003c/p\u003e\u003cp\u003eIn the independent OTTA cohort with \u003cem\u003eNF1\u003c/em\u003e mRNA expression and survival data available (n\u0026thinsp;=\u0026thinsp;5666), low \u003cem\u003eNF1\u003c/em\u003e expression (lowest quantile) was associated with improved survival compared to high expression (2nd to 5th quantiles) (median OS 4.19 vs. 3.56 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Extended Data Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and 7c). Consistent with the other cohorts, g\u003cem\u003eBRCA2\u003c/em\u003epv-carriers with low \u003cem\u003eNF1\u003c/em\u003e expression (n\u0026thinsp;=\u0026thinsp;36) showed an improved survival (median OS 6.42 years \u003cem\u003eNF1\u003c/em\u003e low vs. 5.66 years \u003cem\u003eNF1\u003c/em\u003e high), while there was no effect in g\u003cem\u003eBRCA1\u003c/em\u003epv-carriers (median OS 5.41 years \u003cem\u003eNF1\u003c/em\u003e low vs. 5.65 years \u003cem\u003eNF1\u003c/em\u003e high, Extended Data Fig.\u0026nbsp;7d).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePIK3CA\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eRAD21\u003c/b\u003e \u003cb\u003eamplifications are associated with short survival in\u003c/b\u003e \u003cb\u003eBRCA2\u003c/b\u003e\u003cb\u003e-deficient HGSC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe found an enrichment of \u003cem\u003ePIK3CA\u003c/em\u003e and \u003cem\u003eRAD21\u003c/em\u003e gene amplifications in \u003cem\u003eBRCA2\u003c/em\u003e-deficient tumors in patients with short compared to long survival (\u003cem\u003ePIK3CA\u003c/em\u003e: 5/10, 50% vs 4/24, 16.7%, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e=0.232 and \u003cem\u003eRAD21\u003c/em\u003e: 5/10, 50% vs 4/24, 16.7%, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e=0.105, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed,e). Co-occurrence of \u003cem\u003eRAD21\u003c/em\u003e and \u003cem\u003ePIK3CA\u003c/em\u003e amplification was observed in 8.8% (3/34) patients with \u003cem\u003eBRCA2\u003c/em\u003e-deficiency (Supplementary Tables S15 and S16\u003cb\u003e)\u003c/b\u003e. \u003cem\u003ePIK3CA\u003c/em\u003e and \u003cem\u003eRAD21\u003c/em\u003e mRNA expression was highly correlated with copy number (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and tumors with gene amplification (\u0026ge;\u0026thinsp;7 copies) had a significantly higher expression (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, respectively) (Extended Data Fig.\u0026nbsp;8a,b and Supplementary Tables S17 and S18). Patients with combined \u003cem\u003eBRCA2\u003c/em\u003e-deficiency and \u003cem\u003ePIK3CA\u003c/em\u003e amplification (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9, median OS 2.89 years) or \u003cem\u003eRAD21\u003c/em\u003e amplification (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9, median OS 2.89 years) had a significantly worse prognosis compared to patients with \u003cem\u003eBRCA2\u003c/em\u003e-deficient tumors without \u003cem\u003ePIK3CA\u003c/em\u003e amplification (n\u0026thinsp;=\u0026thinsp;25, median OS 11.92 years) or \u003cem\u003eRAD21\u003c/em\u003e amplification (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25, median OS 11.53 years; Extended Data Fig.\u0026nbsp;8c,d and Supplementary Table S9).\u003c/p\u003e\u003cp\u003ePI-3 kinase pathway activity is thought to contribute to tolerance to genome doubling and \u003cem\u003ePIK3CA\u003c/em\u003e amplification in whole-genome duplicated tumors is a frequent event in HRD end-stage HGSC\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. The STS \u003cem\u003eBRCA2\u003c/em\u003e-deficient group was characterized by high ploidy (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e=0.0073) and whole-genome duplication (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e=0.0404), in contrast to \u003cem\u003eBRCA1\u003c/em\u003e-deficient and \u003cem\u003eBRCA\u003c/em\u003e-proficient tumors where the LTS groups tended to have higher ploidy (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The association between \u003cem\u003ePIK3CA\u003c/em\u003e and survival by \u003cem\u003eBRCA\u003c/em\u003e status was further corroborated in the OTTA cohort, where g\u003cem\u003eBRCA2\u003c/em\u003epv carriers with high \u003cem\u003ePIK3CA\u003c/em\u003e RNA expression (highest quantile) had shorter survival relative to their counterparts with low expression (median OS 4.09 vs 7.43 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Extended Data Fig.\u0026nbsp;8e). By contrast, g\u003cem\u003eBRCA1\u003c/em\u003epv carriers with high \u003cem\u003ePIK3CA\u003c/em\u003e RNA expression showed improved survival (median OS 7.67 vs 5.23 years).\u003c/p\u003e\u003cp\u003e\u003cb\u003eElevated HRD scarring is prognostic for survival in\u003c/b\u003e \u003cb\u003eBRCA\u003c/b\u003e\u003cb\u003e-deficient HGSC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHigh tumor mutation burden has been shown to be associated with long-term survival in ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, we found that tumor mutation burden and predicted neoantigen counts were equivalent in \u003cem\u003eBRCA1\u003c/em\u003e-deficient and \u003cem\u003eBRCA2\u003c/em\u003e-deficient tumors between STS and LTS groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-c, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, and Supplementary Table S19). Among various genomic features that were compared between these groups (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), the HRD score\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e was elevated in \u003cem\u003eBRCA1\u003c/em\u003e-deficient tumors with long survival times compared to those with short survival times (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). HRD score is a measure of genomic scarring associated with impaired HR repair, suggesting a more profound inactivation of the HR pathway in patients with good outcome. Retention of the wildtype allele with absence of locus specific LOH has been reported to influence outcomes in g\u003cem\u003eBRCA\u003c/em\u003epv-carriers in ovarian and breast cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR64 CR65\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. However, in our cohort there was only one g\u003cem\u003eBRCA2\u003c/em\u003epv carrier without loss of the wildtype allele (patient BRCA_9; Supplementary Table S11 and Supplementary Information). Concordantly this tumor was HR-proficient with an HRD score of 27 (HRP\u0026thinsp;\u0026le;\u0026thinsp;42 HRD sum score) and CHORD score of 0 (HRP\u0026thinsp;\u0026le;\u0026thinsp;0.5 CHORD score), and the patient had short OS (\u0026lt;\u0026thinsp;3 years).\u003c/p\u003e\u003cp\u003eWe observed a dynamic range in HRD scores, even among tumors with pathogenic \u003cem\u003eBRCA\u003c/em\u003e mutations, suggesting a non-equivalence of alterations. The cutoff of the HRD score has been debated, with 42 mainly used in recent clinical trials\u003csup\u003e\u003cspan additionalcitationids=\"CR68 CR69 CR70\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, and a more stringent threshold of 63 has been proposed for ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Indeed, patients whose tumors had a high HRD score (\u0026ge;\u0026thinsp;63) had longer OS (median OS 10 years) compared to those with HRD scores of 42\u0026ndash;62 (median OS 2.66 years) and \u0026le;\u0026thinsp;41 (median OS 2.5 years), regardless of \u003cem\u003eBRCA\u003c/em\u003e-status (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee and Supplementary Table S9). Applying a threshold of 63 to divide samples into high and low HRD, all \u003cem\u003eBRCA\u003c/em\u003e-proficient tumors had a low HRD score. Furthermore, patients with \u003cem\u003eBRCA1\u003c/em\u003e- and \u003cem\u003eBRCA2\u003c/em\u003e-deficient tumors and HRD scores\u0026thinsp;\u0026ge;\u0026thinsp;63 had longer OS compared to patients with lower HRD scores (median OS 6.76 vs. 2.01 years and 11.88 vs. 6.73 years, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef and Supplementary Table S9). Notably, patients with \u003cem\u003eBRCA1\u003c/em\u003e-deficient tumors with HRD scores\u0026thinsp;\u0026lt;\u0026thinsp;63 had similar OS to patients with \u003cem\u003eBRCA\u003c/em\u003e-proficient tumors (median OS 2.01 years vs 2.21 years).\u003c/p\u003e\u003cp\u003eGene set enrichment analysis\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e (GSEA; Methods) revealed distinct patterns of pathway regulation based on HRD scores and \u003cem\u003eBRCA\u003c/em\u003e status in patients with HGSC. Specifically, pathway activation in \u003cem\u003eBRCA1\u003c/em\u003e- and \u003cem\u003eBRCA2\u003c/em\u003e-deficient patients with low HRD (\u0026lt;\u0026thinsp;63) closely resembled those of \u003cem\u003eBRCA\u003c/em\u003e-proficient patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg). In contrast, \u003cem\u003eBRCA1\u003c/em\u003e-deficient patients with high (\u0026ge;\u0026thinsp;63) HRD scores showed an upregulation of several pathways, including interferon-gamma and inflammatory response. These pathways are primarily involved in host defense and immune surveillance\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e, underscoring their potential role in modulating the tumor microenvironment and influencing immune response in patients with \u003cem\u003eBRCA1\u003c/em\u003e-deficient tumors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCD8\u0026thinsp;+\u0026thinsp;PD-1\u0026thinsp;+\u0026thinsp;T cells are prognostic for survival in g\u003c/b\u003e\u003cb\u003eBRCA\u003c/b\u003e\u003cb\u003epv-carriers\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe considered whether \u003cem\u003eBRCA\u003c/em\u003e-deficient cases with shorter survival would have fewer mutation-associated neoantigens to drive anti-tumor responses, but there was no difference in neoantigen counts between the STS and LTS groups for both \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.51 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.39, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Tumor samples from 143 HGSC g\u003cem\u003eBRCA\u003c/em\u003epv-carriers were analyzed by multi-color immunofluorescence to determine the epithelial and stromal immune cell densities and their associations with survival groups (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Aside from intraepithelial B cells and CD4\u0026thinsp;+\u0026thinsp;T cells (OR\u0026thinsp;=\u0026thinsp;1.0), all other immune cell subsets had a positive association with survival (OR\u0026thinsp;\u0026lt;\u0026thinsp;1.0; Supplementary Table S20). Only intrastromal and intraepithelial CD8\u0026thinsp;+\u0026thinsp;PD-1\u0026thinsp;+\u0026thinsp;T cells were significantly more abundant in g\u003cem\u003eBRCA\u003c/em\u003epv-carriers with LTS compared to those with STS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029, respectively; Supplementary Table S20).\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe mesenchymal features\u003c/b\u003e \u003cb\u003ec-KIT\u003c/b\u003e \u003cb\u003eand mast cells are associated with poor outcome in HGSC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eImmune cell abundance was estimated in 154 HGSC tumor samples using CIBERSORTx\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Unsupervised clustering of the inferred immune cell densities identified six groups of patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, and Supplementary Table S21) associated with differential survival outcomes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0053; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The IMMB.1 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30) and IMMB.6 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25) clusters had exceptionally long survival (median OS 14.87 and 10.45 years, respectively; Supplementary Table S9). The group with the shortest survival (cluster IMMB.5, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24, median OS 2.03 years) was enriched with activated dendritic cells and resting mast cells, a feature associated with the C1.MES subtype (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0021; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Multivariable Cox regression analysis showed that resting mast cells (HR: 1.26, 95% CI 1.06\u0026ndash;1.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) were the immune cell type most strongly associated with short survival (Extended Data Fig.\u0026nbsp;9a). \u003cem\u003eBRCA1\u003c/em\u003e-deficient tumors in patients with STS had increased expression of the mast cell growth factor receptor \u003cem\u003ec-KIT\u003c/em\u003e (CD117) compared to those with LTS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e=0.101; Extended Data Fig.\u0026nbsp;9b). Patients with high \u003cem\u003ec-KIT\u003c/em\u003e tumor expression had significantly shorter OS than those with low \u003cem\u003ec-KIT\u003c/em\u003e tumor expression, regardless of \u003cem\u003eBRCA\u003c/em\u003e and HRD status (HR: 1.71, 95% CI 1.16\u0026ndash;2.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0071; Extended Data Fig.\u0026nbsp;9c). The C1.MES subtype showed higher expression of \u003cem\u003ec-KIT\u003c/em\u003e, together with an upregulation of the epithelial mesenchymal transition (EMT) pathway, compared to the C2.IMM subtype (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e\u0026lt;0.001) (Extended Data Fig.\u0026nbsp;9d,e).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study highlights the complexity of survival determinants in patients with HGSC, demonstrating that it is the intersection of multiple factors, including surgical residual disease, immune response, and somatic gene alterations, which may influence outcome rather than \u003cem\u003eBRCA\u003c/em\u003e mutation status alone. This interplay was particularly apparent in the diminished adverse impact of surgical residual disease in g\u003cem\u003eBRCA\u003c/em\u003epv-carriers compared to non-carriers. Previous reports have suggested that surgery in a \u003cem\u003eBRCA\u003c/em\u003e-deficient setting may have a lesser impact on survival in both first-line and platinum-sensitive setting\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e, indicating that it may be particularly important to achieve complete resection of \u003cem\u003eBRCA\u003c/em\u003e-proficient tumors. In addition, an exploratory analysis of the PAOLA-1/ENGOT-ov25 trial\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e showed that patients with \u003cem\u003eBRCA\u003c/em\u003e-proficient tumors classified as higher risk (FIGO stage III with primary cytoreductive surgery and residual disease, or NACT; FIGO stage IV) had notably worse PFS compared to lower-risk patients, while this difference was less pronounced in patients with \u003cem\u003eBRCA\u003c/em\u003e-deficient tumors. These results emphasize the importance of primary cytoreductive surgery with complete resection for non-carriers, who may also benefit more from secondary cytoreductive surgery in contrast to g\u003cem\u003eBRCA\u003c/em\u003epv-carriers\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Equally, it may be that the positive effect of optimal cytoreduction is not as apparent in \u003cem\u003eBRCA\u003c/em\u003e carriers, due to the chemotherapy (platinum) sensitivity associated with \u003cem\u003eBRCA\u003c/em\u003e-deficiency.\u003c/p\u003e\u003cp\u003eIn the current study, the association between NACT and survival appeared to differ by g\u003cem\u003eBRCA\u003c/em\u003epv status, with a potential attenuation of survival benefit among g\u003cem\u003eBRCA\u003c/em\u003epv-carriers who received NACT. However, the subgroup analyses by g\u003cem\u003eBRCA\u003c/em\u003epv status and treatment type were likely underpowered, limiting definitive conclusions regarding potential interactions. Given the rapid increase in the uptake of NACT in recent years\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e, it will be important to determine if patients with \u003cem\u003eBRCA\u003c/em\u003e-deficient tumors may be negatively impacted by NACT\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. The acquisition of \u003cem\u003eBRCA\u003c/em\u003e reversion mutations is frequent\u003csup\u003e\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, and it is plausible that reversion events may be more common where chemotherapy commences with a large tumor volume from which resistant clones could emerge under selection\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. This is especially important in the PARPi era, where the early development of platinum resistance could negatively impact on the potential benefit gained from PARPi treatment. While the impact of NACT on outcomes according to \u003cem\u003eBRCA\u003c/em\u003e status is not yet known, it is becoming increasingly important to more rapidly determine the \u003cem\u003eBRCA\u003c/em\u003e and broader HR status of a patient\u0026rsquo;s tumor at diagnosis to make the most informed decisions at primary treatment.\u003c/p\u003e\u003cp\u003eOur study highlights the spectrum of HRD scores seen in patients with \u003cem\u003eBRCA\u003c/em\u003e-deficient tumors. While all but two exceeded a threshold (\u0026gt;\u0026thinsp;42) required for classification as HRD, the improved OS and PFS seen with a more stringent threshold (\u0026ge;\u0026thinsp;63) shows that HRD should not be considered a binary classification but rather appears to be a continuous variable. This finding is consistent with a previous analysis of 537 HGSC cases from The Cancer Genome Atlas which showed that patients with HRD scores\u0026thinsp;\u0026ge;\u0026thinsp;63 were associated with better survival outcomes, while those with intermediate (42\u0026ndash;62) and low (\u0026le;\u0026thinsp;42) HRD scores had overlapping survival curves\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. It is important to mention that in our study, samples were collected over nearly 20 years, a timeframe that encompasses changes in treatment practices, making it challenging to determine how evolving therapies, particularly the introduction of PARPi, may have influenced outcomes. It is notable that the HRD score threshold of 42 was originally established to predict response to neoadjuvant platinum-containing chemotherapy in patients with breast cancer\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e, which tends to have less genomic scarring compared to ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. As HRD scores\u0026thinsp;\u0026ge;\u0026thinsp;63 strongly predicted better outcomes in \u003cem\u003eBRCA\u003c/em\u003e-deficient HGSC, our findings support the prognostic value of HRD score thresholds. However, it is premature to conclude that a higher threshold should alter therapy selection. To establish this, a comprehensive analysis of maintenance PARPi trials, incorporating HRD scores, would be necessary to confirm their predictive role in guiding treatment decisions. Furthermore, it would be ideal to extend this investigation to include other relevant genomic alterations identified in trial samples to refine patient stratification further. This refinement would help identify patients for whom no maintenance therapy or additional targeted therapy may be more appropriate, while avoiding potentially ineffective treatments for those with lower HRD scores, thereby personalizing therapy to maximize efficacy and minimize unnecessary side effects.\u003c/p\u003e\u003cp\u003eOur analyses corroborated Labidi-Galy et al.'s findings that pathogenic variants in the RAD51-BD of \u003cem\u003eBRCA2\u003c/em\u003e\u003csup\u003e44\u003c/sup\u003e and the DBD of \u003cem\u003eBRCA1\u003c/em\u003e\u003csup\u003e43\u003c/sup\u003e are associated with improved outcomes in HGSC. By contrast, alterations outside \u003cem\u003eBRCA1\u003c/em\u003e exon 10, particularly in the BRCT and RING regions, are not associated with a significantly improved survival compared to non-carriers and in some cases may confer platinum and PARPi resistance\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. While \u003cem\u003eBRCA1\u003c/em\u003e exon 10 mutations have been associated with improved outcomes in multiple studies, including ours, there is evidence that tumors may express the \u003cem\u003eBRCA1\u003c/em\u003e-Δ11q splice isoform, which bypasses exon 10 mutations and results in a shorter but partially functional protein that is permissive of treatment resistance\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In a relatively small sample size for which we had RNA-seq data (n\u0026thinsp;=\u0026thinsp;19 \u003cem\u003eBRCA1\u003c/em\u003e exon 10 mutated tumors), we found that patients with a pathogenic \u003cem\u003eBRCA1\u003c/em\u003e variant in exon 10 and high Δ11q expression had a shorter survival. We were unable to measure Δ11q expression during or following treatment. This is important because Δ11q expression may increase or fluctuate under the selective pressure of treatment, which would influence treatment response and survival outcomes.\u003c/p\u003e\u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;PD1\u0026thinsp;+\u0026thinsp;T cells are associated with improved outcomes in ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e, contributing to enhanced anti-tumor immunity. In our analysis, the presence of these cells in tumors were prognostic for survival in g\u003cem\u003eBRCA\u003c/em\u003epv-carriers, although to a lesser extent. This suggests that while cytotoxic T-cell activity remains important in \u003cem\u003eBRCA\u003c/em\u003e-deficient tumors, additional factors may influence survival. Given the established association between \u003cem\u003eBRCA\u003c/em\u003e and HR status and increased TMB\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, it is possible that immune exhaustion, suppressive signaling or tumor-intrinsic immune resistance pathways may counteract the expected immunogenicity. Intriguingly, \u003cem\u003eBRCA1\u003c/em\u003e-deficient tumors with high HRD scores had evidence of enhanced immune-related gene transcription. In addition, while our study did not include cigarette smoking in the survival models, smoking has been identified as a potential factor influencing survival in g\u003cem\u003eBRCA\u003c/em\u003epv-carriers\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e, which may also influence the immune response. Further research into markers of T-cell exhaustion and other immune regulators is needed to better understand the differential immune responses in these patients.\u003c/p\u003e\u003cp\u003eNF1 gene loss-of-function emerged as a good prognostic factor in \u003cem\u003eBRCA2\u003c/em\u003e-deficient HGSC. Loss-of-function of \u003cem\u003eNF1\u003c/em\u003e is common in epithelial ovarian cancer with a prevalence of 12\u0026ndash;31%\u003csup\u003e13,20,22,58,84,85\u003c/sup\u003e. NF1 inactivation by gene breakage or mutations may contribute to initial good prognosis but later chemoresistance in patients with HGSC and \u003cem\u003eBRCA\u003c/em\u003e-deficiency\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. This is consistent with recent findings that deleterious \u003cem\u003eNF1\u003c/em\u003e mutations are associated with improved PFS in ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and low mRNA expression of \u003cem\u003eNF1\u003c/em\u003e predicts longer overall survival\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In contrast, \u003cem\u003ePIK3CA\u003c/em\u003e amplification and high mRNA expression were associated with shorter survival in patients with \u003cem\u003eBRCA2\u003c/em\u003e-deficient HGSC. As a major regulator of the phosphoinositide 3-kinase (PI3K) pathway, \u003cem\u003ePIK3CA\u003c/em\u003e activation promotes cell proliferation and survival, especially in genomically unstable cancers\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Its amplification may enhance tolerance to genome doubling and contribute to the aggressive nature of \u003cem\u003eBRCA2\u003c/em\u003e-deficient tumors. The contrasting survival outcomes between \u003cem\u003ePIK3CA\u003c/em\u003e amplification and \u003cem\u003eNF1\u003c/em\u003e loss-of-function underscore the heterogeneity of HGSC tumors, highlighting the need for personalized therapeutic strategies, even within the \u003cem\u003eBRCA2\u003c/em\u003e-deficient subgroup.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eEthics statement\u003c/h2\u003e\n \u003cp\u003eWritten informed consent or an approved waiver of consent was obtained at each participating study site for patient recruitment and the use of samples and linked clinical information (Supplementary Table S22). Investigations were performed after approval by local human research ethics/institutional review board committees at each site. This study was conducted in accordance with the principles of Good Clinical Practice, the Declaration of Helsinki and local regulations.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThis retrospective, multi-center study included patients diagnosed with HGSC between 2002 and 2019. The Australian Ovarian Cancer Study (AOCS) cohort (n\u0026thinsp;=\u0026thinsp;1389) included all stages (FIGO I-IV), and the Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) cohort (n\u0026thinsp;=\u0026thinsp;154) was restricted to advanced stage disease (FIGO III and IV; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, and Supplementary Table S22). Patients were categorized based on OS into short (\u0026lt;\u0026thinsp;3 years) and long (\u0026ge;\u0026thinsp;3 years) OS groups (Supplementary Information). For multi-omics analysis, 154 patients had fresh-frozen tumor obtained during primary cytoreductive surgery and matched blood samples, or were previously analyzed\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Findings were validated in an independent HGSC cohort (n\u0026thinsp;=\u0026thinsp;5875) from the Ovarian Tumor Tissue Analysis Consortium (OTTA) for which g\u003cem\u003eBRCA\u003c/em\u003epv status was available.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eMolecular data\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-nucleotide polymorphism (SNP) arrays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor and matched normal DNA was analyzed with the Infinium OmniExpress-24 BeadChip arrays as described previously\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The concordance of normal and tumor DNA was assessed using HYSYS\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. Tumor DNA samples with estimated tumor cellularity\u0026thinsp;\u0026gt;\u0026thinsp;40% (determined by qPure\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e and ASCAT\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e) were considered appropriate for whole genome sequencing and methylation arrays.\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cem\u003eWhole genome sequencing (WGS)\u003c/em\u003e For WGS, libraries were generated from tumor and matched normal genomic DNA from peripheral blood mononuclear cells with a minimum base coverage of 60x and 30x, respectively. FASTQ files were assessed for sequencing quality using FASTQC (v0.11.8) and, for contaminants using FastQ Screen\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e (v0.11.4). Adapters, N-content and low-quality bases were trimmed using fastq-mcf (v1.05). Sequenced data was mapped to the human genome reference GRCh37 b37 using the aligner BWA mem\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e (v.0.7.17-r1188). Aligned BAM files per lane were then sorted, merged and duplicates marked using Picard Tools (v.2.17.3). Further processing of the aligned files included base recalibration using GATK BaseRecalibrator (v4.0.10.1). Coverage calculation was performed using GATK DepthOfCoverage (v3.8-1-0-gf15c1c3ef). GATK HaplotypeCaller (v.4.0.10.1) was used on germline BAMs to generate Genomic Variant Call Format (GVCF) files which were used as the Panel of Normals (PoN) in the Mutect2 somatic variant calling workflow. Tumor purity and ploidy were estimated using FACETS\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eRNA-sequencing (RNA-seq)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExtracted RNA from tumor tissue samples underwent RNA-seq, with initial quality control checks on raw FASTQ files performed using FastQC\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e (v0.11.8). Adapter, poly (A) tails, N content and low quality base trimming was done using fastq-mcf (v1.05), and contamination was assessed using FastQ Screen\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e (v(0.11.4). Reads were then mapped to the human reference GRCh37.92 using the STAR\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e (v2.6.0b) two-pass method. The mapped reads were then sorted using Picard Tools (v2.17.3). Counts were generated using HTSeq\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e (v0.10.0) on the GRCh37.92 Ensembl release gene annotation. Raw count data was then subsetted to protein coding genes and lowly expressed genes were removed using the following strategy. First, raw counts were converted to CPM (counts per million) and only protein coding genes with a CPM of greater than 0.5 in at least 10 samples were retained. The resulting raw count matrix was then normalized using the trimmed mean of M values (TMM) method using edgeR\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e (v3.28.1). Batch effects were removed using limma\u0026apos;s\u003csup\u003e95\u003c/sup\u003e (v3.48.2) removeBatchEffect function. Batch effect removal was done by applying batch correction on the library type (stranded/unstranded) while preserving the survival group (long/short).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethylation arrays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe generation and processing of methylation array data was performed as previously described by Garsed et al.\u003csup\u003e22\u003c/sup\u003e. Briefly, initial quality control was performed by QuantiFluor (Promega). Subsequently, 500 ng tumor DNA was converted using the EZ DNA Methylation kit (Zymo Research) and analyzed using the Infinium MethylationEPIC BeadChip arrays. The R package minfi\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e (v1.32.0) was then used for quality control assessment and processing of the methylation data as previously described\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunofluorescence (IF) data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTissue microarrays (TMAs) were constructed from formalin-fixed paraffin-embedded (FFPE) blocks of tumor tissue and stained by IF with two panels of antibodies against immune markers of interest. Panel 1 detected CD3, CD8, CD20, FOXP3 and CD79; panel 2 detected CD3, CD8, PD-1, PD-L1 and CD68. Both panels also detected pan-cytokeratin to identify tumor epithelium. Automated cell scoring, including separation of epithelial and stromal regions, was performed using QuPath (v0.2m2), with extensive manual training and validation. CD4\u0026thinsp;+\u0026thinsp;T cells were defined as CD3\u0026thinsp;+\u0026thinsp;CD8- cells, as previously\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cem\u003eImmunohistochemistry (IHC)\u003c/em\u003e: Sections of 4 \u0026micro;m thickness were cut from previously constructed TMAs of FFPE tumor samples. Deparaffinized sections were stained with the C-terminal NF1 antibody (clone NFC, SIGMA #MABE1820; St. Louis, MO, USA) using our previously described protocol on a DAKO Omnis platform: 30 min of pre-treatment heat-induced antigen retrieval in Tris-EDTA buffer, pH\u0026thinsp;=\u0026thinsp;9.0; primary antibody incubation for 1h at dilution 1/50, 10 min of a mouse linker, and 30 min for the peroxidase labelled Dako EnVision\u0026thinsp;+\u0026thinsp;polymer-based detection system (Dako protocol 1 h-10M-30, Agilent, Santa Clara, CA, USA)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. Samples were scored as follows: inactivated (loss of expression with retained internal control), normal retained expression, subclonal loss, uninterpretable (loss of tumor expression but no internal control present), and exclude (no tumor in core) (\u003cstrong\u003eSupplementary Information\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003emRNA expression data by NanoString\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor mRNA expression data for genes of interest (\u003cem\u003eNF1, PIK3CA, c-KIT\u003c/em\u003e, and \u003cem\u003eRB1\u003c/em\u003e) and transcriptional molecular subtypes in the OTTA cohort were determined using NanoString, as previously described\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e98\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eMeasurements\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003eVariant detection and annotation\u003c/h2\u003e\n \u003cp\u003eVariant calling was performed for:\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1. germline base substitution and INDEL variants by VarDictJava\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e (v1.5.7 with \u0026ndash;r\u0026thinsp;=\u0026thinsp;2 \u0026ndash;Q\u0026thinsp;=\u0026thinsp;10 \u0026ndash;f\u0026thinsp;=\u0026thinsp;0.1).\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e2. somatic base substitution and INDEL variants using four separate variant callers as follows: by Mutect2\u003csup\u003e101\u003c/sup\u003e (v4.0.11.0 with defaults), VarDictJava\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e (v1.5.7 with \u0026ndash;r\u0026thinsp;=\u0026thinsp;2 \u0026ndash;Q\u0026thinsp;=\u0026thinsp;10 \u0026ndash;V\u0026thinsp;=\u0026thinsp;0.05 \u0026ndash;f\u0026thinsp;=\u0026thinsp;0.01), Strelka2\u003csup\u003e102\u003c/sup\u003e (v2.9.9 with defaults), and VarScan2\u003csup\u003e103\u003c/sup\u003e (SAMtools\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e) v1.9 for mpileup and VarScan2 v2.4.3 with -min-coverage 7 -min-var-freq\u0026thinsp;=\u0026thinsp;0.05 -min-freq-for-hom\u0026thinsp;=\u0026thinsp;0.75 -p-value\u0026thinsp;=\u0026thinsp;0.99 -somatic-p-value\u0026thinsp;=\u0026thinsp;0.05 -strand-filter\u0026thinsp;=\u0026thinsp;0). Variant calls were decomposed and normalized using vt\u003csup\u003e105\u003c/sup\u003e GATKs ReadBackPhasing tool (v3.8-1-0-gf15c1c3ef with -phaseQualityThresh\u0026thinsp;=\u0026thinsp;10 \u0026ndash; enableMergePhasedSegregatingPolymorphismsToMNP -min_base_quality_score\u0026thinsp;=\u0026thinsp;10 -min_mapping_quality_score\u0026thinsp;=\u0026thinsp;10 -maxGenomicDistanceForMNP\u0026thinsp;=\u0026thinsp;2) was applied on the passing variants per tool to combine contiguous SNVs to MNVs (multi-nucleotide variants). GATK\u0026rsquo;s CombineVariants (v3.8-1-0-gf15c1c3ef with -genotypeMergeOptions UNIQUIFY -priority Strelka2, Mutect2, VarScan2, VarDictJava) was used to merge the variant calls from all four callers into a consensus variant call set. The resulting variant call format (VCF) file was once again decomposed and normalized using vt. Forward and reverse strand counts for the reference and alternate alleles were calculated using bam-readcount (v0.8.0). Finally, all variants were annotated for Duke and DAC blacklisted regions. Any variants that were passed in at least two callers, had at least one variant read in each strand, and were not in the database of FrequentLy mutAted GeneS (FLAGS)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e or the Duke and DAC blacklist regions were deemed high-confidence.\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e3. structural variants (SV) using four separate callers Manta\u003csup\u003e107\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;BreakPointInspector (v1.5.0), GRIDSS\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e (v2.0.1), Smoove (v0.2.2) and SvABA\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e (v134). The SV calls were split into germline and somatic VCFs per caller. The findBreakpointsOverlaps method of the R library StructuralVariantAnnotation (v1.3.1) with a value of 10 for the \u0026lsquo;maxgap\u0026rsquo; parameter was used to intersect common breakpoints between the callers. SVs were annotated to constituent types (duplication, deletion, inversion or translocation) using a simple annotation script provided by the GRIDSS tool. High-confidence SVs were categorized as those called by two or more callers.\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e4. copy number variations (CNV) detection by FACETS\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e and cnv_facets (v0.13.0) as described previously\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eThe detected variants were filtered for variants with a high probability of pathogenicity as described in detail before\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eMutation burden and downsampling\u003c/h3\u003e\n\u003cp\u003eWe downsampled the higher coverage tumor BAM files using Picard DownsampleSam (v2.17.3) to achieve balanced median coverage sequencing batches, to compare mutation burden across samples with inconsistent coverage\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The median coverage of the International Cancer Genome Consortium (ICGC) tumors was 52.15x, the MOCOG tumors was 77.81x and the short survival \u003cem\u003eBRCA\u003c/em\u003e dataset tumors was 64.98x. So, to get the same median coverage across the three batches we downsampled the MOCOG and short survival \u003cem\u003eBRCA\u003c/em\u003e dataset tumors to the ICGC median by specifying downsampling fractions of 0.67 and 0.8 respectively. See \u003cstrong\u003eSupplementary Table S19\u003c/strong\u003e for details on the tumor sample coverage before and after downsampling and the number of SNVs, MNVs, indels and SVs called after downsampling.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eNeoantigen prediction\u003c/h2\u003e\n \u003cp\u003eNeoantigen prediction was performed as previously reported by Garsed et al.\u003csup\u003e22\u003c/sup\u003e. Briefly, HLA-VBSeq\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e110\u003c/span\u003e\u003c/sup\u003e (v11_22_2018) was used to generate HLA types which were then used to identify and construct neoantigen using pVACtools\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e pVACseq (v1.3.5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eHomologous recombination deficiency (HRD)\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eHRD status was determined using 1) scarHRD\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e, which uses loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large scale state transition (LST) in tumor genomes to generate a HRD sum score, and 2) CHORD (Classifier of Homologous Recombination Deficiency)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, which uses specific base substitution, indel and structural rearrangement signatures detected in tumor genomes to generate \u003cem\u003eBRCA1\u003c/em\u003e-type and \u003cem\u003eBRCA2\u003c/em\u003e-type HRD scores.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eRNA-seq data analysis\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eRaw count data was subsetted to protein coding genes and lowly expressed genes were removed using the following strategy. First, raw counts were converted to CPM (counts per million) and only protein coding genes with a CPM of greater than 0.5 in at least 10 samples were retained. The resulting raw count matrix was then normalized using the trimmed mean of M values (TMM) method using edgeR\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e (v3.28.1). Batch effects were removed using limma\u0026apos;s\u003csup\u003e95\u003c/sup\u003e (v3.48.2) removeBatchEffect function. Batch effect removal was done by applying batch correction on the library type (stranded/unstranded) while preserving the survival group (long-term/short-term).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eRNA differential expression and pathway analysis by grouping\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eGroupings\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFor differential expression and pathway analysis, various groupings were used alone or in combination, namely 1) \u003cem\u003eBRCA\u003c/em\u003e-deficiency status, 2) HRD groups, survival groups, and 3) molecular subtypes (\u003cstrong\u003eSupplementary Information\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDifferential expression analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo identify differentially expressed protein-coding genes between the comparison groups of interest, DESeq2 (v1.26.0)\u003csup\u003e113\u003c/sup\u003e was applied. Raw counts were filtered to remove low expressed genes prior to analysis and batch effects were accounted for in the model\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGene Set Enrichment Analysis (GSEA)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFGSEA v1.15.1 was used to calculate gene set enrichment across the comparison groups. \u003cem\u003eP\u003c/em\u003e-values obtained from DESeq2 were transformed to signed \u003cem\u003eP\u003c/em\u003e-values and then sorted and fed into FGSEA to generate enrichment scores and FDR-adjusted \u003cem\u003eP\u003c/em\u003e-values across the Hallmark gene sets in the MSigDB database49 (v7.4) via its function fgseaMultilevel (minSize\u0026thinsp;=\u0026thinsp;15, maxSize\u0026thinsp;=\u0026thinsp;500, gseaParam\u0026thinsp;=\u0026thinsp;0, eps\u0026thinsp;=\u0026thinsp;0)\u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eCIBERSORTx\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eCIBERSORTx analysis was performed as previously described\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Briefly, CIBERSORTx\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e with the LM22 matrix was used on RNA-seq data for immune cell deconvolution. Immune clusters were then generated with k-means clustering of the generated absolute cell abundances using ConsensusClusterPlus\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e (Supplementary Information).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eImmunofluorescence\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eData were categorized based on epithelial content, measured directly by pan-cytokeratin positivity and cell morphology (assessed by automated image analysis). Epithelium-negative, cellular (i.e., non-necrotic) tumor regions were defined as stroma. Immunomarker density (D; cells/mm\u003csup\u003e2\u003c/sup\u003e) for a given marker was calculated separately for epithelial and stromal compartments. For cases with multiple cores, the epithelial area was taken as the sum of all their individual TMA epithelial areas and similarly for the stromal area. We categorized marker D values into quartiles (separately for epithelial and stromal markers) to provide categorical comparisons for ease of interpretation of the odds ratios (ORs). Conditional logistic regression models were fitted for the long survival group vs short survival group. Logistic regression analyses were performed with the quartile values (scored as 1, 2, 3, 4). Immune clusters were then generated by k-means clustering of the immune cell type densities using ConsensusClusterPlus\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analyses\u003c/h2\u003e\n \u003cp\u003eContinuous variables were compared between groups using the Kruskal-Wallis test and the difference between proportions of categorical data were assessed using the Chi-squared or Fisher\u0026apos;s exact test. Correlations between continuous variables were assessed using Spearman correlation. Benjamini-Hochberg adjusted \u003cem\u003eP\u003c/em\u003e-values are reported as \u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e to account for multiple testing. Median PFS and OS were estimated using the Kaplan-Meier method and survival distribution were compared using the log-rank (Mantel-Cox) test.\u003c/p\u003e\n \u003cp\u003eFor the AOCS cohort, univariable and multivariable survival analyses were performed using Accelerated Failure Time (AFT) models\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e with a log-logistic distribution to evaluate associations between clinical and molecular variables and time-to-event outcomes. Results were reported as Time Ratios (TR) with 95% confidence intervals (CI), where a TR\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates longer time to progression or death, and a TR\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates shorter survival. Wald tests were used to compute \u003cem\u003eP\u003c/em\u003e-values for individual covariates and interaction terms. Age at diagnosis was modelled using restricted cubic splines with three knots to allow for potential non-linear effects. Model assumptions were assessed using quantile-quantile plots of deviance residuals and Cox-Snell residuals to evaluate overall model fit. The Akaike Information Criterion (AIC) was used to compare alternative parametric distributions and confirm the suitability of the log-logistic model\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eFor survival analyses of the OTTA cohort, Cox proportional hazards models were applied. Left truncation was used to account for delayed study enrolment at some sites, and follow-up time was right-censored at 10 years from diagnosis to minimize the influence of non-ovarian cancer-related deaths. \u003cem\u003eP\u003c/em\u003e-values from Cox models correspond to Wald and log-rank tests. The proportional hazards assumption was assessed using the Grambsch-Therneau test based on scaled Schoenfeld residuals and further evaluated through graphical inspection of Schoenfeld residual plots\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e115\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e116\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eAll statistical tests were two sided and considered significant when \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 or \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e \u0026lt;0.1. All analyses were performed using the statistical software R version 4.1.3\u003csup\u003e117\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003cbr\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eShort survival BRCA dataset:\u003c/em\u003e WGS, RNA-seq and SNP array data from short-term survivors generated as part of the current study have been deposited in the European Genome-phenome Archive (EGA) repository (https://ega-archive.org) under accession code EGAS00001008059. WGS and RNA-seq data are available as raw FASTQ files for each sample type (tumor/normal) and SNP array data are available as raw signal intensity files in text format for each sample type (tumor/normal). Access to patient sequence data can be gained for academic use through application to the independent Data Access Committee ([email protected]). Responses to data requests will be provided within two weeks. Information on how to apply for access is available at the EGA under accession code EGAS00001008059. The raw methylation data sets have been submitted to the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession code GSE292140 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE292140) with no access restrictions. no access restrictions. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eICGC dataset:\u003c/em\u003e Previously published WGS and RNA-seq data generated as part of the ICGC Ovarian Cancer project\u003csup\u003e58\u003c/sup\u003e are available from the EGA repository as a single bam file for each sample type (tumor/normal), under the accession code EGAD00001000877 (\"https://ega-archive.org/datasets/EGAD00001000877\"https://ega-archive.org/datasets/EGAD00001000877). Due to the sensitive nature of these patient datasets, access is subject to approval from the ICGC Data Access Compliance Office (https://docs.icgc.org/download/data-access/), an independent body who authorizes controlled access to ICGC sequencing data. ICGC SNP array and methylation data sets have been deposited into the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession code GSE65821 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65821), without access restrictions. ICGC gene count level transcriptomic data has been deposited into the GEO under accession code GSE209964 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE209964).https://docs.icgc.org/download/data-access/), an independent body who authorizes controlled access to ICGC sequencing data. ICGC SNP array and methylation data sets have been deposited into GEOhttps://www.ncbi.nlm.nih.gov/geo/ under accession code GSE65821 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65821), without access restrictions. ICGC gene count level transcriptomic data has been deposited into the GEO under accession code GSE209964 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE209964).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMOCOG dataset:\u003c/em\u003e WGS, RNA-seq and SNP array data from long-term survivors generated as part of the MOCOG study\u003csup\u003e22\u003c/sup\u003e have been deposited in the EGA repository under accession code EGAS00001005984. WGS and RNA-seq data are available as raw FASTQ files for each sample type (tumor/normal) and SNP array data are available as raw signal intensity files in text format for each sample type (tumor/normal). Access to patient sequence data can be gained for academic use through application to the independent Data Access Committee ([email protected]). Responses to data requests will be provided within two weeks. Information on how to apply for access is available at the EGA under accession code EGAS00001005984. The MOCOG cohort raw methylation data sets have been submitted to the GEO under accession code GSE211687 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE211687), with no access restrictions. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOTTA dataset: \u003c/em\u003eParticipants of this study did not agree to their data being shared publicly; accordingly, the data used in this research will not be made available.\u003c/p\u003e\n\u003cp\u003eUniformly processed somatic variant data from the ICGC, MOCOG, and short survival\u003cem\u003e BRCA \u003c/em\u003ecohorts is deposited in Synapse under accession code syn65463502 and processed expression and methylation data from all cohorts has been submitted into the GEO under accession code GSE292140 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE292140) and GSE292142 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE292142https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE292142, without access restrictions. All other data are available within the article (and its Supplementary Information files) or from the corresponding authors on request.\u003c/p\u003e\n\u003cp\u003ePopulation frequencies of genetic variants can be accessed via the Genome Aggregation Database (gnomAD) at https://gnomad.broadinstitute.org/. Supporting evidence for pathogenicity of genomic alterations can be accessed via ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/), BRCA Exchange (https://brcaexchange.org/) and the \u003cem\u003eTP53\u003c/em\u003e Database (https://tp53.isb-cgc.org/). The Ensembl ranked order of severity of variant consequences is available at: https://m.ensembl.org/info/genome/variation/prediction/predicted_data.html. Mutational signature reference databases can be accessed via COSMIC (https://cancer.sanger.ac.uk/signatures/) and Signal (https://signal.mutationalsignatures.com/). The LM22 signature matrix used for immune cell deconvolution can be downloaded here: https://cibersortx.stanford.edu/. MSigDB hallmark gene sets can be accessed here: https://www.gsea-msigdb.org/gsea/msigdb/. Illumina methylation probes that were filtered out due to poor performance (e.g. cross reactive or non-specific probes) can be found here: https://github.com/sirselim/illumina450k_filtering. Germline polymorphic sites for reference and variant allele read counts used in FACETS analysis can be found at ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b151_GRCh37p13/VCF/common_all_20180423.vcf.gz. The GTF used for annotation and RNA-seq counts is available here: ftp://ftp.ensembl.org/pub/grch37/release-92/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo custom code or software was used in the data analyses and for the figures. All results can be replicated using publicly available tools and software. The tools and versions used are described in the Methods and Supplementary Information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank A. Freimund, R. Lupat, J. Ellul, and the Peter MacCallum Cancer Centre Research Computing Facility for their contributions to the study. This work was supported by the National Health and Medical Research Council (NHMRC) of Australia (GNT1186505 and GNT2029088), the US Army Medical Research and Materiel Command Ovarian Cancer Research Program (Award No. W81XWH-16-2-0010 and W81XWH-21-1-0401), the National Institutes of Health (NIH) (R21-CA267050, K07-CA080668, R01-CA95023, R01-CA248288, P50-CA136393, P30-CA015083, MO1-RR000056), the Swiss National Foundation (P500PM_20726); Bangerter-Rhyner Stiftung (0297); Margarete and Walter Lichtenstein-Stiftung; and Freie Gesellschaft Basel. The Gynaecological Oncology Biobank at Westmead was funded by the NHMRC (ID310670, ID628903); the Cancer Institute NSW (12/RIG/1-17, 15/RIG/1-16); the Department of Gynaecological Oncology, Westmead Hospital; and acknowledges financial support from the Sydney West Translational Cancer Research Centre, funded by the Cancer Institute NSW (15/TRC/1-01). Direct funding for the generation of the NanoString data for OTTA was provided by the NIH (R01-CA172404, and R01-CA168758), the Canadian Institutes for Health Research (Proof-of-Principle I program) and the United States Department of Defense Ovarian Cancer Research Program (OC110433). T.A.Z. is supported by the Swiss National Foundation Return CH Postdoc.Mobility (P5R5PM_222151).D.W.G. is supported by a Victorian Cancer Agency/Ovarian Cancer Australia Low-Survival Cancer Philanthropic Mid-Career Research Fellowship (MCRF22018) and the Ovarian Cancer Research Foundation (2025/OCRF0071). S.J.R. is supported by the NHMRC (2009840). M.J.G is supported by the Ministerio de Ciencia, Innovación y Universidades (MICIU)/AEI/10.13039/501100011033 and ERDF, EU (Project PID2023-151298OB-I00). A.O. is partially funded by Ministerio de Ciencia e Innovación, Instituto de Salud Carlos III (PI23/01235) supported by FEDER funds and the Spanish Network on Rare Diseases (CIBERER). K.M.D., T.P.C., and G.L.M. were supported by awards from the Uniformed Services University of the Health Sciences and the Defense Health Program to the Henry M Jackson Foundation (HJF) for the Advancement of Military Medicine Inc. to the Gynecologic Cancer Center of Excellence Program including HU0001-16-2-0006 (PIs: Chad A. Hamilton and G. Larry Maxwell), HU0001-19-2-0031, HU0001-20-2-0033, and HU0001-21-2-0027 (PIs: Yovanni Casablanca and G. Larry Maxwell), HU0001-22-2-0016 and HU0001-23-2-0038 (PIs: Neil T. Phippen and G. Larry Maxwell), as well as HU0001-23-2-0038 and HU0001-24-2-0047 (PIs Christopher M Tarney and G. Larry Maxwell). T.V.G. is a Senior Clinical Investigator of the Fund for Scientific Research-Flanders (FWO Vlaanderen 18B2921N). A.DeF. is supported by the NHMRC (2033042). The AOV study was funded by the Canadian Institutes for Health Research (MOP-86727). The Generations Study was funded by Breast Cancer Now and the United Kingdom National Health Service funding to the Royal Marsden/Institute of Cancer Research. The UK Ovarian Cancer Population study (UKOPS) was funded by The Eve Appeal (The Oak Foundation) with contribution to authors’ salary through MRC core funding MC_UU_00004/01 and the NIH Research University College London Hospitals Biomedical Research Centre. The contents of the published material are solely the responsibility of the authors and do not reflect the views of the NHMRC, NIH, and other funders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.A.Z.: Conceptualization, data curation, formal analysis, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. S.F.: Conceptualization, data curation, validation, methodology, writing–original draft, writing–review and editing. A.P.: Conceptualization, data curation, formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. D.A.: Data curation, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. M.W.J.: Formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. L.T.: Formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. A.F.: Conceptualization, data curation, investigation, writing–review and editing. C.M.L.: Formal analysis, validation, investigation, methodology, writing–review and editing. C.J.K.: Resources, data curation, methodology, writing–original draft, writing–review and editing. A.B.: Resources, writing–review and editing. N.S.M.: Resources, writing–review and editing. K.M.: Resources, writing–review and editing. P.H.: Data curation, formal analysis, validation, investigation, methodology, writing–review and editing. J.A.: Resources, writing–review and editing. A.C.A.: Resources, writing–review and editing. G.A-Y.: Resources, writing–review and editing. M.W.B.: Resources, writing–review and editing. A.B.: Resources, writing–review and editing. C.B.: Resources, writing–review and editing. F.B.: Resources, writing–review and editing. C.B.: Resources, writing–review and editing. J.B.: Resources, writing–review and editing. A.H.B.: Resources, writing–review and editing. M.E.C.: Resources, writing–review and editing. A.C-J.: Resources, writing–review and editing. D.S.C.: Resources, writing–review and editing. E.L.C.: Resources, writing–review and editing. A.C-G.: Resources, writing–review and editing. P.C.: Resources, writing–review and editing. K.L.C-H.: Resources, writing–review and editing. C.C.: Resources, writing–review and editing. K.M.D.: Resources, writing–review and editing. C.D.: Resources, writing–review and editing. T.D.: Resources, writing–review and editing. A.B.E.: Resources, writing–review and editing. E.E.: Resources, writing–review and editing. J.E.: Resources, writing–review and editing. T.E.: Resources, writing–review and editing. R.F.: Resources, writing–review and editing. A.F.: Resources, writing–review and editing. M.G-C.: Resources, writing–review and editing. A.G-M.: Resources, writing–review and editing. P.G.: Resources, writing–review and editing. R.G.: Resources, writing–review and editing. P.H.: Resources, writing–review and editing. A.D.H.: Resources, writing–review and editing. A.H.: Resources, writing–review and editing. S.H.: Resources, writing–review and editing. B.Y.H.: Resources, writing–review and editing. A.H.: Resources, writing–review and editing. S.H.: Resources, writing–review and editing. D.G.H.: Resources, writing–review and editing. M.J-L.: Resources, writing–review and editing. M.E.J.: Resources, writing–review and editing. E.K.: Resources, writing–review and editing. E.K.: Resources, writing–review and editing. T.K.: Resources, writing–review and editing. F.K.F.K.: Resources, writing–review and editing. G.K.: Resources, writing–review and editing. R.F.P.M.K.: Resources, writing–review and editing. J.K.: Resources, writing–review and editing. D.L.: Resources, writing–review and editing. C-H.L.: Resources, writing–review and editing. J.L.: Resources, writing–review and editing. S.C.Y.L.: Resources, writing–review and editing. Y.L.: Resources, writing–review and editing. A.L.: Resources, writing–review and editing. J.L.: Resources, writing–review and editing. L.L.: Resources, writing–review and editing. J.L.: Resources, writing–review and editing. C.M.: Resources, writing–review and editing. I.A.M.: Resources, writing–review and editing. M.M.: Resources, writing–review and editing. G.S.N.: Resources, writing–review and editing. N.N.: Resources, writing–review and editing. A.O.: Resources, writing–review and editing. S.O.: Resources, writing–review and editing. A.O.: Resources, writing–review and editing. C.M.Q.: Resources, writing–review and editing. G.RM.: Resources, writing–review and editing. I.R-C.: Resources, writing–review and editing. C.R-A.: Resources, writing–review and editing. P.R.: Resources, writing–review and editing. M.R.: Resources, writing–review and editing. S.G.S.: Resources, writing–review and editing. S.S.: Resources, writing–review and editing. M.J.S.: Resources, writing–review and editing. H-P.S.: Resources, writing–review and editing. G.S.S.: Resources, writing–review and editing. L.S.: Resources, writing–review and editing. C.J.R.S.: Resources, writing–review and editing. A.T.: Resources, writing–review and editing. A.T.: Resources, writing–review and editing. C.M.T.: Resources, writing–review and editing. S.E.T.: Resources, writing–review and editing. K.K.V.: Resources, writing–review and editing. M.A.A.: Resources, writing–review and editing. T.G.: Resources, writing–review and editing. E.N.: Resources, writing–review and editing. L.W.: Resources, writing–review and editing. A.E.W-H.: Resources, writing–review and editing. C.W.: Resources, writing–review and editing. C.W.: Resources, writing–review and editing. J.W.: Resources, writing–review and editing. N.W.: Resources, writing–review and editing. L.R.W.: Resources, writing–review and editing. S.J.W.: Resources, writing–review and editing. B.W.: Resources, writing–review and editing. M.S.A.: Resources, writing–review and editing. A.B.: Resources, writing–review and editing. F.J.C-R.: Resources, writing–review and editing. P.A.C.: Resources, writing–review and editing. T.P.C.: Resources, writing–review and editing. P.C.: Resources, writing–review and editing. J.A.D.: Resources, writing–review and editing. P.A.F.: Resources, writing–review and editing. R.T.F.: Resources, writing–review and editing. M.J.G.: Resources, writing–review and editing. S.A.G.: Resources, writing–review and editing. M.T.G.: Resources, writing–review and editing. J.G.: Resources, writing–review and editing. H.R.H.: Resources, writing–review and editing. F.H.: Resources, writing–review and editing. H.M.H.: Resources, writing–review and editing. B.Y.K.: Resources, writing–review and editing. L.E.K.: Resources, writing–review and editing. G.L.M.: Resources, writing–review and editing. U.M.: Resources, writing–review and editing. F.M.: Resources, writing–review and editing. S.L.N.: Resources, writing–review and editing. J.M.S.: Resources, writing–review and editing. A.S.: Resources, writing–review and editing. A.J.S.: Resources, writing–review and editing. I.V.: Resources, writing–review and editing. A.H.W.: Resources, writing–review and editing. J.D.B.: Resources, writing–review and editing. P.D.P.P.: Resources, writing–review and editing. C.L.P.: Resources, writing–review and editing. M.C.P.: Resources, writing–review and editing. E.L.G.: Resources, writing–review and editing. S.J.R.: Conceptualization, resources, data curation, supervision, funding acquisition, validation, writing–original draft, project administration, writing–review and editing. M.K.: Conceptualization, resources, data curation, investigation, formal analysis, validation, visualization, supervision, methodology, writing–original draft, writing–review and editing. B.N.: Resources, data curation, investigation, formal analysis, validation, visualization, methodology, writing–review and editing. A.DF.: Conceptualization, Resources, writing–review and editing. M.L.F.: Conceptualization, Resources, writing–review and editing. D.D.L.B.: Conceptualization, resources, supervision, funding acquisition, writing–original draft, writing–review and editing. D.W.G.: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.A.Z. reports personal consulting fees from AbbVie that are outside the submitted work. D.D.L.B. reports research support grants from AstraZeneca, Roche-Genentech and BeiGene paid to institution outside the submitted work; also, personal consulting fees from Exo Therapeutics that are outside the submitted work. G.A.-Y. reports research support grants from AstraZeneca and Roche-Genentech paid to institution outside the submitted work; also, personal consulting fees from Incyclix Bio that are outside the submitted work. A.DeF. reports research support from AstraZeneca and Illumina. N.N. reports research support from Illumina. P.A.C. reports speakers’ honoraria from AstraZeneca, Merck Sharpe and Dohme, and GlaxoSmithKline, and personal consulting fees from Astra Zeneca outside the remit of the submitted work. U.M. and A.G.M. report personal consulting fees from Mercy BioAnalytics Ltd and research support grants from Intelligent Lab on Fiber, RNA Guardian, and MercyBio Analytics that are all outside the remit of the submitted work. E.L.C. reports research support from AstraZeneca paid to institution outside the submitted work and speakers’ honoraria from AstraZeneca and GSK. S.E.T reports consulting fees from AstraZeneca and IntegraConnect outside the submitted work. P.H. reports honoraria and consulting fees from Amgen, Astra Zeneca, GSK, Roche, Immunogen, Sotio, Stryker, ZaiLab, MSD, Clovis, Miltenyi, Eisai, Mersana, Exscientia, Daiichi Sankyo, Karyopharm, Abbvie, Novartis, Corcept, BionTech, Zymeworks and Research funding (Institutional) from Astra Zeneca, Roche, GSK, Genmab, Immunogen, Seagen, Clovis, Novartis, Immatics, Abbvie, MSD. I.V. has participated in consulting advisory boards for Akesobio, Bristol Myers Squibb, Eisai, F. Hoffmann-La Roche, Genmab, GSK, ITM, Karyopharm, MSD, Novocure, Oncoinvent, Sanofi, Regeneron, and Seagen, and has participated in consulting data monitoring committees for Abbvie, Agenus, AstraZeneca, Corcept, Daiichi, F. Hoffmann-La Roche, Immunogen, Kronos Bio, Mersana, Novartis, OncXerna, Verastem Oncology, and Zentalis. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHollis, R. L. Molecular characteristics and clinical behaviour of epithelial ovarian cancers. \u003cem\u003eCancer Lett\u003c/em\u003e \u003cstrong\u003e555\u003c/strong\u003e, 216057 (2023). https://doi.org/10.1016/j.canlet.2023.216057\u003c/li\u003e\n\u003cli\u003eAhmed, A. 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(2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7572112/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7572112/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBRCA\u003c/em\u003e-associated homologous recombination deficiency (HRD) is present in ~\u0026thinsp;50% of high-grade serous carcinomas (HGSC) and predicts sensitivity to platinum-based therapy. However, there is little understanding of why some patients with \u003cem\u003eBRCA\u003c/em\u003e-deficient tumors experience unexpectedly poor outcomes. We profiled 154 tumors, enriched for patients with \u003cem\u003eBRCA\u003c/em\u003e-deficient tumors that experienced short overall survival (\u0026le;\u0026thinsp;3 years, n\u0026thinsp;=\u0026thinsp;42), using whole-genome, transcriptome, and methylation analyses. All but one \u003cem\u003eBRCA\u003c/em\u003e-deficient tumor exceeded an accepted HRD genomic scarring threshold. However, patients with \u003cem\u003eBRCA1\u003c/em\u003e-deficient HGSC with a more elevated HRD score survived significantly longer. Patients with \u003cem\u003eBRCA2\u003c/em\u003e-deficient HGSC and loss of \u003cem\u003eNF1\u003c/em\u003e survived twice as long as those without \u003cem\u003eNF1\u003c/em\u003e loss, whereas \u003cem\u003ePIK3CA\u003c/em\u003e or \u003cem\u003eRAD21\u003c/em\u003e amplification defined \u003cem\u003eBRCA2\u003c/em\u003e-deficient HGSC with exceptionally short survival. \u003cem\u003eBRCA1\u003c/em\u003e-deficient tumors in short survivors had evidence of immunosuppressive c-kit signaling and EMT. In a large HGSC cohort (n\u0026thinsp;=\u0026thinsp;1,389) including 282 individuals with pathogenic germline \u003cem\u003eBRCA\u003c/em\u003e variants (g\u003cem\u003eBRCA\u003c/em\u003epv), the location of the mutation within functional domains stratified clinical outcomes. Notably, residual disease after primary surgery had limited prognostic effect in g\u003cem\u003eBRCA\u003c/em\u003epv-carriers compared to non-carriers. Our findings indicate that tumor HR proficiency in the context of therapy response and survival is not a binary property, and highlight genomic and immune modifiers of outcomes in \u003cem\u003eBRCA\u003c/em\u003e-deficient HGSC.\u003c/p\u003e","manuscriptTitle":"Beyond BRCA deficiency: Clinical and molecular predictors of survival in patients with BRCA-deficient tubo-ovarian high-grade serous carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 16:48:24","doi":"10.21203/rs.3.rs-7572112/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"38242350-2303-41b0-bcb0-06bad5ae5c23","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55095244,"name":"Biological sciences/Cancer/Gynaecological cancer/Ovarian cancer"},{"id":55095245,"name":"Health sciences/Medical research/Genetics research"},{"id":55095246,"name":"Biological sciences/Biological techniques/Genomic analysis"},{"id":55095247,"name":"Biological sciences/Molecular biology/Transcriptomics"},{"id":55095248,"name":"Biological sciences/Genetics/Genetic association study/Genome-wide association studies"}],"tags":[],"updatedAt":"2025-10-03T16:48:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 16:48:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7572112","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7572112","identity":"rs-7572112","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}