Homologous Recombination Deficiency Among Patients with Germline or Somatic non-BRCA1/2 Homologous Recombination Repair Gene Variations

Homologous Recombination Deficiency Among Patients with Germline or Somatic non-BRCA1/2 Homologous Recombination Repair Gene Variations | 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 Homologous Recombination Deficiency Among Patients with Germline or Somatic non-BRCA1/2 Homologous Recombination Repair Gene Variations Yue Li, Xinhua Yang, Haoyang Cai, Fang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5538972/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jun, 2025 Read the published version in npj Precision Oncology → Version 1 posted 8 You are reading this latest preprint version Abstract This study examined the relationship between homologous recombination deficiency (HRD) and variations in non- BRCA1/2 homologous recombination repair (HRR) genes. 27.3% (132/483) of the patients with ovarian, breast, endometrial, prostate, and pancreatic cancers carrying non- BRCA1/2 HRR variations were HRD+. Germline mutations were associated with significantly higher HRD + rates than somatic mutations, while biallelic alterations did not show stronger associations with HRD compared to monoallelic alterations. High HRD + rates (66.7–100.0%) were associated with variations in PALB2, RAD51C/D , and RAD54L , while low HRD + rates (0–37.5%) corresponded with variations in PTEN, ATM, BRIP1, CDK12 , and NBN , which may be influenced by variation grade and tissue origin. HRD positivity was mutually exclusive with HER2 + status in breast cancer and with TMB-H/MSI-H in endometrial cancer. Overall, these findings highlight the different strengths of the correlation between non- BRCA1/2 HRR gene variations and HRD and guide HRD testing in cases of “ BRCA1/2 -wildtype” results. Biological sciences/Cancer Health sciences/Biomarkers Health sciences/Molecular medicine Health sciences/Oncology HRD non-BRCA1/2 HRR variations GIS TAI LOH LST Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Homologous recombination deficiency (HRD) arises as a phenotypic consequence of defects in the homologous recombination repair (HRR) pathway, characterized by the inability of cells to repair double-stranded DNA (dsDNA) breaks via high-fidelity HRR mechanisms 1 , 2 . HRD is commonly observed in several cancers, such as ovarian, breast, pancreatic, and prostate cancers, rendering these tumors highly susceptible to platinum-based drugs and poly (ADP-ribose) polymerase inhibitors (PARPi) 3 , 4 . While the deleterious variants of BRCA1/2 , the breast cancer susceptibility genes, remain the primary biomarkers for predicting response to PARPi 2 , HRD has recently been established as an officially recognized biomarker for PARPi use in combination with bevacizumab as a first-line maintenance therapy in ovarian cancer, as recommended by NCCN Guidelines. This advancement highlights the increasing clinical need for HRD status testing 5 , 6 . Cells with HRD rely more heavily on the error-prone non-homologous end-joining pathway to repair dsDNA breaks, leading to the accumulation of genomic scars 7 . These scars are measured by the genomic instability score (GIS), which combines three metrics: telomeric allelic imbalance (TAI) 8 , loss of heterozygosity (LOH) 9 , and large-scale state transitions (LST) 10 . Current diagnostic HRD tests define HRD-positive (HRD+) status as the presence of a deleterious mutation in BRCA1/2 or a GIS of ≥ 42 11 . Notably, a substantial portion of HRD + tumors with high GIS lacks deleterious BRCA1/2 mutations, referred to as non- BRCA1/2 HRD-associated tumors 12 . Variations in certain non- BRCA1/2 HRR genes are also important causes of HRD 13 , such as PALB2 and RAD51C/D 12,14 . However, the specific strength of the association between different non- BRCA1/2 HRR genes and HRD remains poorly characterized. In routine clinical practice, we observed that patients with variations in different non- BRCA1/2 HRR genes exhibited varying HRD + rates. If this trend is observed consistently, it could help clinicians determine when to recommend an additional, costly HRD test for their patients. Therefore, in this study, we aimed to comprehensively investigate the associations between HRD positivity and different non- BRCA1/2 HRR genes, considering factors such as variation origin, variation class, hit type, and cancer type. The findings of this study may help clinicians in developing a framework to categorize patients for appropriate molecular testing. 2. Results 2.1 Patient characteristics overall and by gene panels Among the 483 patients included in this study, 371 and 112 patients with non- BRCA1/2 HRR mutations were from gene panels 1 and 2, respectively (Fig. 1 ). Most patients were diagnosed with ovarian (218, 45.1%) or endometrial cancer (172, 35.6%), whereas smaller proportions of patients were diagnosed with breast (65, 13.5%), prostate (20, 4.1%), and pancreatic (8, 1.7%) cancers. No somatic class Ⅰ mutations were identified, and no significant differences were observed between the two gene panels regarding age at diagnosis (> 50 vs. ≤50, p = 0.1929) or the ratios of somatic variation classes (class Ⅱ vs. class Ⅲ, p = 0.4320) (Table 1 ). The proportion of ovarian cancer cases (90.1% vs. 31.5%, p < 0.0001), HRD + rate (58.0% vs. 18.3%, p < 0.0001) and the ratio of germline variations (80.4% vs. 7.0%, p < 0.0001) were significantly higher in gene panel 2 than in gene panel 1, whereas the proportions of endometrial cancer cases (5.4% vs. 44.7%, p < 0.0001), breast cancer cases (0.9% vs. 17.3%, p < 0.0001), and pathogenic/likely pathogenic (P/LP) germline variations (23.3% vs. 92.3%, p 50 307(63.6%) 230 (62.0%) 77 (68.8%) 0.1929 ≤ 50 176 (36.4%) 141 (38.0%) 35 (31.2%) Cancer type Ovarian cancer 218 (45.1%) 117 (31.5%) 101 (90.1%) < 0.0001 Endometrial cancer 172 (35.6%) 166 (44.7%) 6 (5.4%) Breast cancer 65 (13.5%) 64 (17.3%) 1 (0.9%) Prostate cancer 20 (4.1%) 16 (4.3%) 4 (3.6%) Pancreatic cancer 8 (1.7%) 8 (2.2%) 0 (0.0%) HRD status Positive 132 (27.3%) 68 (18.3%) 64 (57.1%) < 0.0001 Negative 351 (72.7%) 303 (81.7%) 48 (42.9%) Variation origin Germline 115 (23.8%) 26 (7.0%) 89 (79.5%) < 0.0001 Somatic 368 (76.2%) 345 (93.0%) 23 (20.5%) Germline class P/LP 45 (39.1%) 24 (92.3%) 21 (23.6%) < 0.0001 VUS 70 (60.9%) 2 (7.7%) 68 (76.4%) Somatic class II 236 (64.1%) 223 (64.6%) 13 (56.5%) 0.4320 III 132 (35.9%) 122 (35.4%) 10 (43.5%) Mutated HRR genes multiple 146 (30.2%) 132 (39.1%) 14 (12.5%) < 0.0001 isolated 337 (69.8%) 239 (70.9%) 98 (87.5%) HRD: homologous recombination deficiency; HRR: homologous recombination repair; P/LP: pathogenic/likely pathogenic; VUS: variants of uncertain significance. We previously detected 80 cases (75 with ovarian cancer and five with endometrial cancer) using gene panels 1 and 2. The consistency rate of HRD status was 97.5% (78/80, Supplementary Table 1), whereas the consistency rate of 15 cases with BRCA1/2 deleterious variations was 100%, supporting the integration of data from two gene panel groups to enrich the sample size and composition. 2.2 HRD prevalence based on non- BRCA1/2 HRR gene variation origin and hit type In clinical practice, both P and LP germline mutations in BRCA1/2 are approved indications for PARP inhibitors 15 . According to our data, we observed no significant differences in HRD + rates (50.0% vs. 48.0%, p = 0.8939) (Supplementary Fig. 1a), or GISs (38.5 vs. 35.4, p = 0.6619) between the P and LP groups, regardless of the HRD + status (58.4 vs. 54.4, p = 0.3901) or HRD- status (18.5 vs. 18.2, p = 0.9572) (Supplementary Fig. 1b). The data above support the integration of cases with P and LP germline mutations in the subsequent analysis. The HRD + rate for the overall cohort was 27.3% (132/483). Patients with germline variations in non- BRCA1/2 HRR genes exhibited a significantly higher HRD + rate than those with somatic mutations (49.6% vs. 20.4%, p < 0.0001; Fig. 2 a). Within the germline group, P/LP non- BRCA1/2 HRR variations were associated with a similar HRD + rate to those of variants of uncertain significance (VUS) (48.9% vs. 50.0%, p = 0.8548), regardless of whether they were biallelic alterations (BAs, 58.3% vs. 50.0%, p = 0.4807) or monoallelic alterations (MAs, 38.1% vs. 50.0%, p = 0.3379). The HRD + rate of patients with germline BAs was numerically but not statistically higher than that of patients with germline MAs (58.3% vs. 38.1%, p = 0.1754) (Fig. 2 b). Within the somatic group, patients with class III variations had a significantly higher HRD + rate than those with class II variations (33.6% vs. 13.3%, p < 0.0001), regardless of whether they were BAs (33.6% vs. 10.6%, p < 0.0001) or MAs (33.6% vs. 18.8%, p = 0.0202). The HRD + rate of patients with somatic BAs was similar to that of patients with somatic MAs (10.6% vs. 18.8%, p = 0.0809) (Fig. 2 c). Here, we observed close associations between HRD + rate and germline non- BRCA1/2 HRR mutations; however, the hit type (BAs or MAs) did not affect the HRD + rate. 2.3 GIS, TAI, LOH, and LST score distributions based on non- BRCA1/2 HRR gene variation origin, hit type, and HRD status In the HRD + group with germline variations, GIS (62.2 vs. 56.0, p = 0.0410) and TAI scores (20.7 vs. 17.7, p = 0.0231) for tumors with VUS were significantly higher than those for tumors with P/LP variations (Fig. 2 d). In the HRD-negative (HRD-) group with germline variations, GIS, TAI, LOH, and LST scores of the cases with BAs were all significantly higher than those of the cases with MAs. Similar with HRD + tumors with germline mutations, the TAI score for HRD- tumors with germline VUS were significantly higher than that for tumors with P/LP variations (10.6 vs. 6.6, p = 0.0177, Fig. 2 e), indicating that the TAI score is more sensitive than the LOH or LST scores in cases with germline non- BRCA1/2 HRR mutations. In contrast, no significant differences in these scores were observed between tumors with class III and II variations in the HRD + group (Fig. 2 f). In the HRD- group with somatic variations, GIS (17.3 vs. 8.4, p < 0.0001), TAI (5.1 vs. 2.5, p < 0.0001), LOH (4.9 vs. 2.1, p < 0.0001), and LST (7.3 vs. 3.8, p < 0.0001) scores for tumors with class III variations were significantly higher than those for tumors with class II variations, no matter they were BAs or MAs (all p -values < 0.0001, Fig. 2 g). Here, we found that somatic BAs were linked with higher HRD-related scores than MAs, though the hit type actually did not affect the HRD + rate. 2.4 Strengths of association between different non- BRCA1/2 HRR variants and HRD Variations in different HRR genes may exhibit different strengths of association with HRD + rates. To test this hypothesis, HRD + rates were independently analyzed for each non- BRCA1/2 HRR gene after excluding cases with mutations in more than one HRR gene (i.e., co-occurring HRR mutations). For germline variants (Fig. 3 a), high HRD + rates were associated with P/LP variations in PALB2 (4/4, 100.0%), RAD51C (5/7, 71.4%), and RAD51D (4/6, 66.7%), as well as those with VUS in RAD54L (6/6, 100.0%), FANCD2 (3/3, 100.0%), and ATR (3/4, 75.0%). Conversely, low HRD + rates were associated with P/LP variations in ATM (0/2, 0.0%) and BRIP1 (1/4, 25.0%), as well as those with VUS in EMSY (0/4, 0.0%), NBN (0/3, 0.0%), and CDK12 (0/2, 0.0%). For somatic variants (Fig. 3 b), high HRD + rates were associated with tumors with class II variations in RAD54L (2/2, 100.0%) and class III variations in FANCD2 (3/3, 100.0%). Conversely, low HRD + rates were associated with tumors with class II variations in PTEN (7/110, 6.4%), as well as those with class III variations in ATM (0/7, 0.0%), CDK12 (1/10, 10.0%), EMSY (1/8, 12.5%), NBN (2/11, 18.2%), and BRIP1 (3/8, 37.5%). When considering the hit type, germline BAs of RAD51C (5/6, 83.3%) and RAD51D (4/5, 80.0%), and MAs of PALB2 (3/3, 100.0%) were strongly associated with high HRD + rates (Fig. 3 c). Both somatic BAs (4/78, 5.1%) and MAs (3/32, 9.4%) of PTEN were related to low HRD + rates (Fig. 3 d). The low number of cases with other HRR mutations precluded meaningful comparisons. We downloaded TCGA somatic mutations and paired HRD scores to compensate for this limitation. Excluding cases with co-occurring HRR mutations, 245 and 234 cases with somatic non- BRCA1/2 HRR variations from the present study and TCGA data were included. Compared to the present study, the ratio of cases with pancreatic cancer was higher, while the ratio of cases with breast cancer was lower in the TCGA cohort (Fig. 4 a). When using a cut-off of 30 defined by a previous large-scale study 16 , the HRD + rate in the TCGA group was similar to that in the present study (28.6% vs. 23.3%, p = 0.1801, Fig. 4 b). Upon integration, class II variations in RAD54L (4/4, 100.0%) and ATR (2/3, 66.7%), and class III variations in BARD1 (4/5, 80.0%) were associated with high HRD + rates. Conversely, class II variations in PTEN (18/181, 9.9%) and ATM (4/19, 21.1%), and class III variations in NBN (2/12, 16.7%), PTEN (6/32, 18.8%), CDK12 (3/16, 18.8%), and ATM (8/28, 28.6%) were associated with low HRD + rates (Fig. 4 c). For germline variations, we integrated data from the present study and seven previous studies 12 , 14 , 17 – 21 focusing on germline P/LP variations of HRR genes to increase sample size and analysis reliability (Table 2 ). After integration, the number of cases with germline P/LP variations of ATM, BRIP1, CHEK2, PALB2 , and RAD51B/C/D increased significantly. Upon integration, germline P/LP variations of PALB2 (24/26, 92.3%), RAD51C (16/25, 64.0%), and RAD51D (12/18, 66.7%) were robustly associated with high HRD + rates, while germline P/LP variations of ATM (1/40, 2.5%), CHEK2 (1/17, 5.9%), BRIP1 (1/8, 12.5%), RAD51B (1/8, 12.5%) were steadily associated with low HRD + rates, particularly in prostate and pancreatic cancers (Table 2 ). Table 2 The HRD + rates of cases with germline non- BRCA1/2 HRR mutations in the present study and literature review. References and Cancer types The present study Kahn et al. 2024 12 Torres-Esquius et al. 2024 14 Lotan et al. 2021 17 Mandelker et al. 2023 18 Xie et al. 2022 19 Lee et al. 2018 20 Golan et al. 2021 21 Total BC, EC, OC, PAC OC BC, OC PRC PAC PAC BC PAC BC, EC, OC, PAC, PRC ATM 0/2 (0%) 1/3 (33.3%) 0/26 (0%) 0/2 (0%) 0/1 (0%) 0/6 (0%) 1/40 (2.5%) BLM 0/1 (0%) 0/1 (0%) BRIP1 1/4 (25.0%) 0/2 (0%) 0/2 (0%) 1/8 (12.5%) CHEK2 0/13 (0%) 1/1 (100%) 0/1 (0%) 0/2 (0%) 1/17 (5.9%) FANCA 0/1 (0%) 1/1 (100%) 1/2 (50.0%) FANCI 0/1 (0%) 0/1 (0%) FANCL 1/1 (100%) 1/1 (100%) MRE11 0/1 (0%) 0/1 (0%) NBN 0/1 (0%) 0/1 (0%) 0/2 (0%) PALB2 4/4 (100%) 1/1 (100%) 2/2 (100%) 14/15 (93.3%) 3/4 (75.0%) 24/26 (92.3%) PTEN 0/1 (0%) 0/1 (0%) RAD50 1/1 (100%) 1/1 (100%) RAD51B 1/2 (50.0%) 0/2 (0%) 0/4 (0%) 1/8 (12.5%) RAD51C 5/7 (71.4%) 11/18 (61.1%) 16/25 (64.0%) RAD51D 4/6 (66.7%) 1/2 (50.0%) 7/10 (70.0%) 12/18 (66.7%) RAD54B 1/1 (100%) 1/1 (100%) RAD54L 0/2 (0%) 0/2 (0%) Total 18/34 (52.9%) 4/12 (33.3%) 18/28 (64.3%) 0/39 (0%) 3/5 (60.0%) 0/10 (0%) 14/15 (93.3%) 3/12 (25.0%) 60/153 (39.2%) BC: breast cancer; EC: endometrial cancer; OC: ovarian cancer; PAC: pancreatic cancer; PRC: prostate cancer. 2.5 HRD prevalence based on individual non- BRCA1/2 HRR gene and cancer type The HRD + rates and corresponding average GIS, TAI, LOH, and LST scores were explored based on cancer type. As shown in the OncoPlots, the HRD + rates were 47.7%, 40.0%, 21.5%, 3.5%, and 0% in the ovarian, prostate, breast, endometrial, and pancreatic cancers, respectively (Fig. 5 a–e). PTEN was the most frequently mutated gene in endometrial (84.3%), breast (20.0%), and ovarian (12.4%) cancers, followed by ATM in endometrial (22.1%) and ovarian (11.0%) cancers, most of which were somatic variations. We observed a high frequency of germline BAs in RAD51D (60.0%) and RAD51C (50.0%) in ovarian cancer, and a high frequency of somatic BAs in PTEN in breast (76.9%), endometrial (75.2%), and ovarian (66.7%) cancers. Among the overall cohort, 25.8% of the cases had mutations of more than one non- BRCA1/2 HRR gene. Interestingly, the HRD + rate of the cases with co-occurring HRR mutations was not higher than that of the cases with isolated HRR gene mutation (20.8% vs. 29.6%, p = 0.0625), indicating that multiple HRR mutations may not produce significant synergistic effects on HRD. As we can see, germline mutations mainly existed in cases with ovarian cancer (87.0%, 80/92). Within ovarian cancer, germline mutations of RAD51C/D , RAD54B/L , and FANCD2 were associated with high HRD + rates (66.7–100.0%), while germline mutations of CDK12, EMSY, FANCI, NBN, RAD51B, MRE11, BRIP1 , and RAD50 were associated with low HRD + rates (0–33.3%) (Fig. 6 a). Although the small numbers of cases with the other four caner types limited the evaluation of the influence of tissue origin on HRD + rate corresponding to individual genes, published data suggest that germline P/LP variations of PALB2 was associated with high HRD + rate, while germline P/LP variations of ATM , and CHEK2 were associated with high HRD + rates in prostate and pancreatic cancers (Table 2 ). Considering the HRD + rates in cases with somatic Ⅱ and Ⅲ were significantly different (Fig. 2 c), we separated the two grades of somatic mutations when drawing the heatmap (integrated with somatic mutation data from TCGA). Notably, somatic mutations of PTEN were robustly related to low HRD + rates across all five cancer types, but the other gene mutations exhibited different association strengths in different cancers. For instance, somatic mutations of ATM were linked with high HRD + rate only in breast cancer but not the other four malignancies, whereas CDK12 somatic Ⅱ mutations were related with low HRD + rates in breast and prostate cancers but not in ovarian cancer. RAD51D somatic Ⅱ mutations were linked to high HRD + rate and GIS in ovarian cancer; RAD51D somatic Ⅲ mutations were related to low HRD + rates and GIS in ovarian cancer, but with high HRD + rates and GIS in breast and prostate cancer (Fig. 6 b). These observations confirm that certain mutations may exert different effects in different cancer types, thus the tissue origin, along with variation origin and grade, should be taken into account when judging its association with HRD. Triple-negative breast cancer (TNBC) has been previously reported to exhibit much higher signature scores than the ER/PR + and HER2 + subtypes 22 . However, our results showed only numerical, but not statistically significant, differences in the HRD + rate between TNBC and the other subtypes (34.8% vs. 14.6%, p = 0.0614, Table 3 ). Although no significant differences were observed in GIS (34.3 vs. 24.2, p = 0.0631) or LST scores (15.8 vs. 11.7, p = 0.1528), patients with TNBC had slightly higher TAI (8.9 vs. 6.1, p = 0.0404) and LOH (9.6 vs. 6.4, p = 0.0386) scores than those with non-TNBC (Supplementary Fig. 2). Notably, 14 cases of HRD + breast cancer were HER2-, and eight HER2 + cases were HRD- (Table 3 ), which demonstrated the mutual exclusivity of HER2 + status and HRD in breast cancer. Table 3 The HRD prevalence in different breast cancer subtypes. BC subtype HRD+ HRD- p TNBC 8 15 0.0614 non-TNBC 6 36 HER2+ 0 8 0.1848 HER2- 14 43 Total 14 65 HRD: homologous recombination deficiency; TNBC: triple-negative breast cancer. 2.6 Associations between HRD and TMB-H/MSI-H in non- BRCA1/2 HRR mutant cases As demonstrated previously, endometrial cancer is characterized by high tumor mutational burden and high microsatellite instability (TMB-H/MSI-H). Here, we found that the average GIS (7.6 vs. 35.1) and TAI (2.0 vs. 8.2), LOH (2.2 vs. 10.8), and LST (3.4 vs. 16.2) scores for patients with endometrial cancer were significantly lower than those of patients with other cancer types (all p -values < 0.0001), particularly in the HRD- group (GIS: 5.8 vs. 18.2, TAI: 1.5 vs. 4.4, LOH: 1.7 vs. 6.4, LST: 2.6 vs. 7.4; all p -values < 0.0001; Supplementary Table 2), suggesting that effect of TMB-H/MSI-H status on HRD + rate should be further analyzed. TMB status, TMB value, and MSI status were available from the gene panel 1 data (n = 371, Table 4 ), with 26.1% of cases classified as TMB-H and 13.5% as MSI-H. The results indicated that the HRD + rate of non- BRCA1/2 HRR mutant cases was significantly lower in the TMB-H (11.3% vs. 20.8%, p = 0.0384) and MSI-H (4.0% vs. 20.6%, p = 0.0049) subgroups than in their TMB-low (TMB-L) and microsatellite stable (MSS) counterparts. Consistently, the average TMB value in the HRD + group was markedly lower than that in the HRD- group (6.1 vs. 22.2 mutations/Mb, p = 0.0384). Table 4 Associations of HRD and TMB/MSI status in non- BRCA1/2 HRR mutant cases. Total HRD+ HRD- HRD + rate p n = 371 n = 68 n = 303 TMB status High 97(26.1%) 11 86 11.3% 0.0384 Low 274 (73.9%) 57 217 20.8% TMB value 6.1 22.2 - 0.0380 MSI status MSI-H 50 (13.5%) 2 48 4.0% 0.0049 MSS 321 (86.5%) 66 255 20.6% HRD: homologous recombination deficiency; TMB: tumor mutational burden; MSI: microsatellite instability; MSS: microsatellite stable. Given that endometrial cancer accounted for a high proportion of TMB-H and MSI-H cases (69.1% and 86.0%, respectively), we separately investigated the associations between TMB-H/MSI-H and HRD scores in patients with endometrial cancer and patients with the other cancers. The GIS, TAI, LOH, and LST scores were significantly lower in the TMB-H/MSI-H subsets than in the TMB-L and MSS subsets both in patients with all included cancers (Fig. 7 a, all p < 0.0001) and in patients with endometrial cancer (Fig. 7 b, all p < 0.05) but were similar in patients with non-endometrial cancers (Fig. 7 c). These observations support the previous hypothesis that defects affecting single strand repair (MSI-H) are mutually exclusive with defects affecting double strand repair (HRD) 23 , which is specifically evident in endometrial cancer. 2.7 Variations in the six most common non- BRCA1/2 HRR genes in HRD + cases To explore if any “hot spot” exists, we analyzed the locations of both germline and somatic variations on the non- BRCA1/2 HRR genes. The six most frequently observed non- BRCA1/2 HRR genes in HRD + cases were RAD54L (n = 12), ATR (n = 11), FANCD2 (n = 11), FANCA (n = 10), RAD51C (n = 8), and RAD51D (n = 6). The specific amino acid changes in these genes were displayed according to their domain locations. Although the number of HRD + cases was relatively small, several protein alterations appeared repeatedly, including RAD51D K91Ifs*13 (n = 4), RAD54L L621P (n = 2), ATR A1488V (n = 2), FANCA A1442T (n = 2), and RAD51C T132Nfs*23 (n = 2) (Fig. 8 a–f). The recurrent appearance of the above variations may increase their value on HRD prediction. 3. Discussion The identification of HRD plays a crucial role in guiding the selection of patients who would benefit from targeted therapies; however, previous research has primarily focused on BRCA1/2 mutations, rendering clinicians understandably inquisitory about the likelihood of HRD in patients with non- BRCA1/2 HRR mutations. In the present study, we found that patients with mutations in PALB2, RAD51C/D , and RAD54L exhibited a tendency toward high HRD + rates (66.7–100.0%), whereas those with variations in PTEN, ATM, BRIP1, CDK12 , and NBN had low HRD + rates (0–37.5%). Germline mutations were associated with significantly higher HRD + rates than somatic mutations, while the hit type, namely BAs or MAs, did not affect HRD + rates significantly. The HRD + rates associated with individual HRR genes might be influenced by variation grade and tissue origin, offering potentially valuable insights that may guide clinicians in cases of “ BRCA1/2 -wildtype” results 24 , 25 . Notably, patients with germline variations in non- BRCA1/2 HRR genes exhibited significantly higher HRD rates compared to those with somatic variations (49.6% vs 20.4%, p < 0.0001, Fig. 2 a). We found that the somatic II PTEN mutations were related to very low HRD + rates (6.4%), regardless of whether they were BAs (5.1%) or MAs (9.4%) (Fig. 3 b and 3 d). Therefore, the high proportion of cases with PTEN mutations (114/245, 46.5%, excluding cases with co-occurring HRR mutations) may partly explain the lower HRD + rate observed in the somatic group. When PTEN mutations were excluded from both the germline and somatic groups, the discrepancy narrowed but was still significant (37.4% vs. 51.6%, p = 0.0351). This result is logical, because germline alterations are static and long-lasting. In contrast, somatic alterations provide only a snapshot of the tumor at a specific stage and can be influenced by various factors, including sampling site, tumor purity, and chemotherapy treatment 26 . As previously reported, PTEN is a tumor suppressor gene critical for the maintenance of genome stability 27 . A PTEN inhibitor was reported to increase double-strand breaks through the modulation of the MRE11-RAD50-NBN complex and enhance the inhibitory effect of olaparib on breast cancer cells 28 . A pan-cancer analysis by E. Rempel et al. reported that PTEN was the most frequently affected gene in breast cancer (34%) and prostate cancer (59%), and HRD scores were significantly higher in PTEN -mutated ovarian cancer (fold change = 1.4) and prostate cancer (fold change = 1.3) 23 . Additionally, Pérez-Villatoro et al. found that tumors with somatic PTEN mutations exhibited higher levels of ovaHRDscar, a type of HRD score built on TCGA ovarian cancer multi-omics dataset, compared to the reference group 29 . According to our data, PTEN was the most frequently affected gene in ovarian, breast, and endometrial cancer (Fig. 5 ); however, somatic mutations of PTEN were associated with very low HRD + rates. Both the HRD + rate (0% vs. 25%, p < 0.0001) and average GIS (5.5 vs. 25.6, p < 0.0001) were significantly lower in PTEN -mutated endometrial cancer cases compared to PTEN -mutated non-endometrial cancer cases. Therefore, the discrepancy in PTEN -related HRD + rates observed in the current study and previous reports could be partly explained by the high ratio of endometrial cancer cases (77.1%) within the PTEN -mutated group. Regrettably, there were too few cases of germline PTEN mutations in the current study to draw any meaningful conclusions. It has been found that some HRD-related genes were specific to certain races. For instance, mutations of ATM, BRCA2, POLE , and TOP2B were more prevalent in ‘White’ and ‘Asian’ populations, while PTEN and EGFG mutations were more frequent in the ‘White’ and ‘African American/Black’ populations 30 . The data analyzed by E. Rempel et al. 23 were obtained from TCGA, while our data were obtained from a Chinese population; therefore, the race-specific factor may partly explain the observed difference in the PTEN mutation rate and HRD + rate in PTEN somatic variants, in addition to the limited sample size of the current study. Our results also indicated that GIS and TAI, LOH, and LST scores varied based on different non- BRCA1/2 HRR variation classes (germline P/LP vs. VUS, somatic Ⅱ vs. Ⅲ). It may seem counterintuitive that class Ⅲ variations were more strongly associated with HRD than class II variations. The exact reason for this discrepancy is unclear; however, it is possible that somatic class II/Ⅲ variations could be “passenger” mutations rather than “driver” mutations, given that the true mechanisms underlying the HRD phenotype are complex, involving a combination of genetic and epigenetic changes (underlying causes of HRD), genomic scars and mutational signatures (consequences of HRD), and HRR activity 7 , 31 . Therefore, the value of somatic variations should be interpreted cautiously, particularly for class Ⅲ variations with little pathogenic evidence. A former study conducted by Guillaume Beinse et al. 32 analyzed the association between a transcriptomic model and genomic scars in gynaecological cancers and did not focus on individual HRR genes but rather captured gene expression levels in HRD-related pathways (nuclear structure, chromatin remodeling, and so on), which also helps explain why some tumors exhibit HRD features even without BRCA1/2 mutations. We agreed with Guillaume Beinse et al. 32 in that a gradient of intermediate levels of deficiency exist beyond BRCA1/2 -altered tumors, just as we found the variations of several non- BRCA1/2 genes, such as ATR and FANCA/C , had intermediate strengths of association with HRD + rates (42.9–55.6%). Since somatic or germline mutations in HR genes are not found in all HRD tumors and VUS can be difficult to interpret, the transcriptional features, which might be the consequential reflect of specific HRR gene variations, may serve as a supplementary predictor of HRD status in cases without HRR variations. Several HRR genes, including RAD51C/D, PALB2, BRIP1, BARD1, CHEK2, ATM, H2AX, MRE11 , and those associated with Fanconi anemia, have been previously recognized as contributors to a “BRCAness” phenotype in 30–50% of high-grade serous ovarian cancer cases 33 – 35 . However, the strength of the correlation between mutations in these genes and the benefits of PARPi has been variable 33 . Our findings revealed that tumors with P/LP variations in PALB2 and RAD51C/D accounted for a high proportion of HRD + cases (72.2%, 13/18), whereas tumors with P/LP variations in BRIP1 constituted a lower proportion of HRD + cases (5.6%, 1/18), consistent with previous research results 12 , 14 . Notably, RAD51D c.270_271dup p.K91Ifs*13 was detected in four HRD + cases (both germline and somatic) in the present study. This specific frameshift mutation was detected in 1.7% (13/781) of Chinese patients with ovarian cancer, was the most common mutation in RAD51D 36 , and has been linked to steady platinum and PARPi benefits in ovarian cancer 37 , 38 . Based on these findings, we hypothesize that this mutation may be a founder mutation in the Chinese population. Furthermore, 9/10 patients with germline variations and 4/17 patients with somatic variations in RAD54L were classified as HRD+. The RAD51/RAD54 complex is essential for activating dsDNA break repair 39 . Rad54, an ATP-dependent motor protein, dissociates Rad51 from dsDNA, the product complex of DNA strand exchange 40 . The combined depletion of RAD54L and RAD54B or overexpression of RAD51 could impede replication and promote chromosome segregation defects 41 . RAD54L P/LP variations have been associated with outcomes in patients with metastatic castration-resistant prostate cancer in the “HRR non-BRCA” cohort, which showed better results than the BRCA cohort but poorer outcomes than the non-HRR cohort 42 . Additionally, a missense mutation in RAD54L (c.604C > T) was identified in one carrier with a family history of ovarian cancer, as well as in a 29-year-old patient with breast cancer with a family history of ten breast cancer cases 43 . Although the function of RAD54L in hereditary breast and ovarian cancer syndromes remains unclear, certain functional RAD54L mutations may play a role in the formation of HRD. GI is a well-established hallmark of cancer, with HRD and MSI representing two distinct forms of GI based on different repair mechanisms 44 . Previous studies have demonstrated that tumors with MSI-H exhibit low levels of genomic scars across colon, gastric, and endometrial cancers, with particularly low genomic scar levels in endometrial cancer, with median TAI, LOH, and LST scores of 3.0, 2.0, and 2.0, respectively 22 . Consistently, our analysis revealed a mutual exclusivity between HRD and TMB-H/MSI-H status, particularly in endometrial cancer, with median TAI, LOH, and LST scores of 2.0, 2.2, and 3.4, respectively (Supplementary Table 2). This evident tendency toward mutual exclusivity indicates that in MSI-H tumors, MSI may precede the GI that generate genomic scars. This observation suggests that HRD testing may be unnecessary for MSI-H cancers. TNBC has been reported to exhibit significantly higher signature scores than the ER/PR + and HER2 + subtypes, with median TAI, LOH, and LST values of 27.0, 21.5, and 21.0, respectively 22 . However, our data showed only numerically, but not statistically, significant higher HRD + rates in TNBCs than in non-TNBCs (34.8% vs. 14.6%). The median TAI, LOH, and LST values for TNBCs were 8.9, 9.6, and 15.8, respectively, which were lower than previously reported. This discrepancy may be caused by the relatively small number of included TNBC cases (14 cases) and the different gene panels used in the two studies. Notably, our analysis revealed a mutual exclusivity between HRD and HER2 + status in breast cancer, which seems reasonable because tumors are generally promoted by only one strong functional driver gene variation during a specific period 45 , 46 . This observation also suggests that HER2 + breast cancer may not require additional HRD testing in clinical practice. In the present study, only 20 and eight patients with prostate and pancreatic cancer, respectively, harboring non- BRCA1/2 HRR variations were included, which precluded meaningful statistical comparisons of associations with HRD of different non- BRCA1/2 HRR variants. After integration with TCGA data, the number of prostate cancer cases increased from 20 to 39, and the number of pancreatic cancer cases increased from 8 to 35. We observed that germline P/LP variations of ATM and CHEK2 were associated with low HRD + rates in prostate cancer and pancreatic cancer. Although the low frequency of HRR pathway gene alterations was also limited other HRD-related studies in these two malignancies, some useful clues can be obtained from large-scale studies 16 . In prostate cancer, BRCA2 (6.76%), ATM (4.50%), and CHEK2 (1.92%) were most frequently mutated HRR genes 47 , and tumors with germline BRCA2 mutations had higher HRD scores (median = 27). In contrast, tumors with germline ATM (median = 16.5, p = 0.029) or CHEK2 (median = 9.0, p < 0.001) mutations had significantly lower HRD scores 17 . A meta-analysis suggested that HRD occurs in 14.5–16.5% of pancreatic cancer cases 48 , and the HRD + rate was lower in tumors with deleterious non- BRCA1/2 HRR mutations than those with deleterious BRCA1/2 mutations 23 . This confirms that the cutoff for HRD in prostate and pancreatic cancers may need to be defined differently based on treatment effectiveness. This study has some limitations. First, the data on progression-free survival remains immature owing to the relatively short follow-up period. Therefore, further long-term studies are needed to determine whether the prognosis differs between HRD + patients with BRCA1/2 mutations and those with non- BRCA1/2 HRR mutations. Such studies should also assess the prognostic implications of these genetic alterations and their potential effect on treatment response. Additionally, the two gene panels used in this study do not cover all genes involved in the HRR pathway; therefore, the associations between HRD and other non- BRCA1/2 HRR genes not investigated in this study remain uncertain. BLM, FANCE, FANCF , and WRN are only covered by gene panel 1 but not gene panel 2, which might generate bias in the HRD + rate of cases carrying mutations of these four genes. Finally, the retrospective, single-institutional nature and limited sample size of this analysis may restrict the generalizability of the findings. These factors highlight the need for caution when interpreting the observed associations and emphasize the necessity for larger, multi-center studies to validate these findings and assess their clinical relevance. In conclusion, this study demonstrated the varying strength of association between different non- BRCA1/2 HRR mutations and HRD across several cancer types. These findings suggest that specific genetic alterations could guide molecular testing and treatment strategies in a cancer type-specific setting. Nevertheless, further research is needed to explore the underlying mechanisms and prognostic effects of non- BRCA1/2 HRR gene variations in larger patient populations. 4. Methods 4.1 Patient selection and data collection HRD and non- BRCA1/2 HRR gene sequencing results for patients diagnosed with ovarian, breast, endometrial, prostate, and pancreatic cancers through pathological examination at the Sun Yat-sen University Cancer Center (SYSUCC, Guangzhou, China) between June 1, 2023, and September 30, 2024, were retrospectively investigated. The HRD status was detected by gene panels 1 and 2. Cases with germline or somatic alterations in at least one of 30 non- BRCA1/2 HRR genes were included in the final analysis (Fig. 1 ). Among the 30 non- BRCA1/2 HRR genes, 26 genes are shared by both gene panel 1 (covering 520 genes) and gene panel 2 (covering 36 genes), including ABRAXAS1 (FAM175A), ATM, ATR, BAP1, BARD1, BRIP1, EMSY (C11ORF30), CDK12, CHEK1, CHEK2, FANCA, FANCC, FANCD2, FANCI, FANCL, MRE11, NBN, PALB2, PPP2R2A, PTEN, RAD50, RAD51B, RAD51C, RAD51D, RAD54B , and RAD54 L. The other four genes, BLM, FANCE, FANCF , and WRN , only covered by gene panel 1, were also included due to their involvement in a previous large-scale HRD-related study 23 . HRD status, GIS, TAI, LOH, and LST scores, variated genes, variation class, protein changes, patient age, and cancer type were systematically collected. Additionally, TMB value, TMB status, and MSI status, which can be obtained from gene panel 1 but not gene panel 2, were also evaluated. The use of clinical and NGS data was approved by the Ethics Committee of the Sun Yat-Sen University Cancer Center (approval number B2020-344-01). All patients provided written informed consent, and the study was performed in accordance with the guidelines outlined in the Declaration of Helsinki. 4.2 DNA isolation and capture-based targeted DNA sequencing Formalin-fixed paraffin-embedded tumor specimens were processed for DNA extraction using 8–10 slides of 5 µm thickness, and the final slide was stained with hematoxylin and eosin (H&E). Two independent pathologists reviewed the H&E-stained slide to confirm the pathological diagnosis and ensure tumor cellularity (at least 30%). Genomic DNA was isolated from the tumor specimens and matched peripheral white blood cells. DNA concentration was measured using the dsDNA HS assay kit (Thermo Fisher Scientific, Waltham, MA) with a Qubit Fluorometer. Sequencing was performed using gene panel 1 (OncoScreenPlus™, Burning Rock, Guangzhou, China) 49 and gene panel 2 (Precision Scientific, Beijing, China) 50 . Detailed experimental procedures and bioinformatic analysis have been described previously 51 . 4.3 Variation classification and hit type identification of non- BRCA1/2 HRR genes Germline variants were classified as P/LP or VUS according to the American College of Medical Genetics and Genomics recommendations for interpreting sequence variations 52 . Somatic variants were classified as class Ⅰ/Ⅱ (variants of strong/potential clinical significance) or class Ⅲ (variants of unknown clinical significance) according to the categories of clinical and/or experimental evidence 52 . It has been established that BAs, rather than ‘single-hits’ or MAs, of HRR genes beyond BRCA1/2 are strongly associated with phenotypic functional HR deficiency and LST score in breast cancer 53 , and with elevated genome-wide LOH in breast, ovarian, pancreatic, and prostate cancers 54 . In light of that, we further identified BAs based on the following criteria 50 : (i) deleterious mutation in one allele and LOH in the other, (ii) two deleterious mutations in the same gene. The deleterious variations in only one allele without LOH in the other allele were considered as MAs, where “deleterious” refers to germline P/LP or somatic Ⅱ variations. Cases with co-occurring HRR mutations were excluded from hit type identification and individual gene analyses as other researchers did previously 55 , 56 , otherwise, the gene-specific HRD + rate and zygosity on the patient level could not be assessed. 4.4 GIS calculation GIS is calculated as the sum of TAI, LOH, and LST. The TAI score measures the frequency of allelic imbalance that do not cross the centromere and extends to the ends of the chromosome telomeres. LOH occurs when a normal gene copy is lost due to the deletion of a large chromosome segment. The LOH score is calculated as the count of LOH regions that are greater than 15 Magabytes (Mb) but less than the entire length of the chromosome. The LST score quantifies the number of chromosomal breakpoints between two adjacent regions, each at least 10 Mb in length, with a distance of no more than 3 Mb, and not passing through the centromere. The GI algorithm of gene panel 1 utilized over 9,000 single-nucleotide polymorphisms (SNPs) from a 520-gene panel, estimating allele-specific copy numbers with a custom script based on logR and median coverage. Minor allele frequency and logR data were segmented using circular binary segmentation, and a probabilistic model was used to estimate tumor copy number, purity, and ploidy. LOH, TAI, and LST were calculated as described previously, with the GIS being the sum of these metrics 49 . The development of the HRD assay for gene panel 2 has been previously detailed 50 . In brief, the GI algorithm calculated the GIS in four steps. First, the genome was split into segments, and heterozygous SNPs were selected based on sequencing depth and allele frequency. Second, four parameters were estimated using maximum likelihood estimation: major allele count per segment, minor allele count per segment, tumor purity, and tumor ploidy. Third, LOH, TAI, and LST scores were calculated based on these parameters. Finally, the GIS was derived by summing the LOH, TAI, and LST scores. A positive GIS was defined by a cutoff value of ≥ 42 for both gene panels 1 and 2. The positive and negative predictive values of the HRD tests, as claimed by the manufacturers, are 98.4% and 96.2% for gene panel 1, and 100% and 100% for gene panel 2, respectively. 4.5 TMB and MSI evaluation TMB and MSI were evaluated using gene panel 1 data. TMB was calculated as the ratio of the total number of nonsynonymous mutations detected to the total coding region size (1.003 Mb). Only mutations with an allelic fraction ≥ 2% were included, and the mutation count did not include hot spot mutation events, copy number variations, structural variations, or germline SNPs 49 . MSI status was determined using a read-count-distribution-based method, as described previously 57 . 4.6 Online data acquisition We downloaded somatic mutation information from the somatic mutation profile published by the Multi-Center Mutation Calling in Multiple Cancers (MC3) project 58 (mc3.v0.2.8.PUBLIC.maf), and obtained HRD scores from the supplemental data of a previous paper, in which more than 10,000 tumors comprising 33 cancer types from TCGA were analyzed for immunogenomic characteristic 59 , and matched the somatic mutations to the HRD scores based on the TCGA_sample_ID. Thereafter, we eliminated low-quality variations (t_depth < 25, t_alt_count < 3, or t_alt_count/t_depth < 0.05) after filtering by five cancer types and 30 non- BRCA1/2 HRR genes (Fig. 1 ) focused in the present study. Excluding cases with co-occurring HRR mutations, we extracted 234 cases whose somatic non- BRCA1/2 HRR mutations and HRD scores were both available. A cutoff of ≥ 30 was defined for HRD + status based on another large-scale study where the usefulness of HRD analysis and an agnostic cutoff for HRD scores were explored in all cancer types of TCGA 16 . 4.7 Statistical analysis The clinical characteristics of cases were evaluated using the two gene panels, and the associations between TMB/MSI status and HRD status were compared using the chi-square test. Additionally, differences in GIS, TAI, LOH, and LST scores, and TMB values across different groups were assessed using a Mann–Whitney test. Statistical significance was determined based on two-tailed tests with a p- value < 0.05. All statistical analyses were performed using GraphPad Prism (version 9.5.0, GraphPad Software, San Diego, CA, USA). Declarations Data availability Raw sequencing data files of patients cannot be publicly shared under the obtained institutional review board approval, as patients did not consent to share raw sequencing data beyond the research and clinical terms. The datasets that support the conclusions of this article are available in the Research Data Deposit repository (No. RDDA2024644861, http://www.researchdata.org.cn/). Code availability The R code used for processing and analysis is available upon request from the authors. Acknowledgments This study was funded by the National Natural Science Foundation of China (Grant number 82002561), and the Guangdong Basic and Applied Basic Research Foundation (Grant numbers 2024A1515012191). The funder played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. The authors thank Ting Hou and Jing Zhao (Burning Rock Biotech, Guangzhou, China), Fancheng Kong and Kun Yang (Precision Scientific Co., Ltd., Beijing, China), for their support in the revision process. Author contributions LY analyzed and interpreted the data, and was a major contributor in writing the manuscript. YXH and CHY collected the data. WF and LY designed the study. All authors read and approved the final manuscript. Competing interests All authors declare no financial or non-financial competing interests. References Magadeeva, S. et al. Assessing the Phenotype of a Homologous Recombination Deficiency Using High Resolution Array-Based Comparative Genome Hybridization in Ovarian Cancer. Int J Mol Sci 24 , (2023). Yamamoto, H. & Hirasawa, A. Homologous recombination deficiencies and hereditary tumors. Int J Mol Sci 23 , (2022). Luo, Y. et al. 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Supplementary Files Supplementaryinformation.pdf Cite Share Download PDF Status: Published Journal Publication published 17 Jun, 2025 Read the published version in npj Precision Oncology → Version 1 posted Editorial decision: Revision requested 27 May, 2025 Reviews received at journal 27 May, 2025 Reviews received at journal 18 May, 2025 Reviewers agreed at journal 21 Apr, 2025 Reviewers agreed at journal 21 Apr, 2025 Reviewers invited by journal 21 Apr, 2025 Submission checks completed at journal 11 Apr, 2025 First submitted to journal 20 Mar, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5538972","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":445689134,"identity":"9318e310-1f78-468d-88ef-408b50609b52","order_by":0,"name":"Yue Li","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Li","suffix":""},{"id":445689135,"identity":"219ccf1e-7d30-45be-9eae-4215e6a1c58d","order_by":1,"name":"Xinhua Yang","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Xinhua","middleName":"","lastName":"Yang","suffix":""},{"id":445689136,"identity":"be118b15-f2b4-4ff9-8fd9-8384a4c49ed5","order_by":2,"name":"Haoyang Cai","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Haoyang","middleName":"","lastName":"Cai","suffix":""},{"id":445689137,"identity":"1180c838-f803-409b-a5f8-c132af441953","order_by":3,"name":"Fang Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYLCCCiDmJ03LGSCWbCBZi8EBYlUbHD97+MWBmjt2m8+fMf7MU2PHwC99/ALDzx14tJzJS7M4cOxZ8rYbOWbSPMeSGST7cgoYe8/g1mJ2IMfM+APb4WSzGzxmzLwNB4CG8CQwM7bh0XL+jZnBgX+Hk437gQ4jTsuNHOMHB9sO2xkw5BhIQ7SwH8Crxf7GGzOGg32HEyRupJVJzjmWzCPZw8NwsBePFsn+HOMPB74dtufvP7z5w5saOzl+HvaHD37i0QIEbBJAIrGBgcMAxOMBIoJxxPwB5EAGBvYHUAE4YxSMglEwCkYBGAAA9DhWiT7y4iUAAAAASUVORK5CYII=","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Fang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-11-28 03:23:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5538972/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5538972/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41698-025-00999-2","type":"published","date":"2025-06-17T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81509740,"identity":"42ebf169-35c7-4324-876f-52b72233fad7","added_by":"auto","created_at":"2025-04-28 06:02:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":819052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient selection and non-\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA1/2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e HRR genes included in this study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5538972/v1/ef6228a09a86f1aaa87e297d.png"},{"id":81507414,"identity":"c27fa528-83d2-453f-bd96-757758a4e0a9","added_by":"auto","created_at":"2025-04-28 05:35:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":787303,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence of HRD and HRD-related scores based on non-\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA1/2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e HRR gene variation origin, hit type, and HRD status.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003ePatients with germline non-\u003cem\u003eBRCA1/2\u003c/em\u003eHRR variations demonstrated a significantly higher HRD+ rate than those with somatic variations. \u003cstrong\u003eb\u003c/strong\u003e Patients with germline P/LP non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR variations had a similar HRD+ rate to those with VUS, regardless of whether they were BAs or MAs. \u003cstrong\u003ec\u003c/strong\u003e Patients with class III variations had a significantly higher HRD+ rate than those with class II variations, regardless of whether they were BAs or MAs. GIS, TAI, LOH, and LST scores in HRD+ groups with germline \u003cstrong\u003ed\u003c/strong\u003e and somatic \u003cstrong\u003ee\u003c/strong\u003evariations, and in HRD- groups with germline \u003cstrong\u003ef\u003c/strong\u003e and somatic \u003cstrong\u003eg\u003c/strong\u003evariations. BAs: biallelic alterations; GIS: genomic instability score; HRD: homologous recombination deficiency; LOH: loss of heterozygosity; LP: likely pathogenic; LST: large-scale state transitions; MAs: monoallelic alterations; P: pathogenic; TAI: telomeric allelic imbalance; VUS: variants of uncertain significance. *\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01; ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001; ns: not significant.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5538972/v1/48e3008d85fd3180c6d6aebd.png"},{"id":81507431,"identity":"1cf69752-c37c-4898-971b-dc517b9c0653","added_by":"auto","created_at":"2025-04-28 05:35:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2894924,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHRD+ rate associated with individual non-\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA1/2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e HRR gene by variant origin and hit type.\u003c/strong\u003e BAs: biallelic alterations; HRD: homologous recombination deficiency; LP: likely pathogenic; MAs: monoallelic alterations; P: pathogenic; VUS: variants of uncertain significance.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5538972/v1/531268db5a6bb3bd7a4236b1.png"},{"id":81507426,"identity":"a11c5df6-3a51-44be-ba96-fab8fc51e755","added_by":"auto","created_at":"2025-04-28 05:35:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1675746,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe HRD+ rates of cases with somatic non-\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA1/2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e HRR variations based on integrated data from the present study and TCGA. a\u003c/strong\u003e The proportions of cases with five cancer types in the present study and TCGA data. \u003cstrong\u003eb\u003c/strong\u003e The HRD+ rates were similar among cases with somatic mutations in the present study and TCGA data. \u003cstrong\u003ec\u003c/strong\u003e HRD+ rate associated with somatic individual non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR gene variations.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5538972/v1/d9d2070063de5efe6c9de86d.png"},{"id":81508986,"identity":"b906dcfd-1480-42e2-a295-852860205b3f","added_by":"auto","created_at":"2025-04-28 05:43:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":13731769,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOncoPlots of non-\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA1/2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e HRR gene mutation incidences by individual gene in ovarian a, endometrial b, breast c, prostate d, and pancreatic e cancers.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5538972/v1/d18c9de5d371f4b1331c06ef.png"},{"id":81508985,"identity":"87f548da-93dc-485e-9366-a70ab59d9b22","added_by":"auto","created_at":"2025-04-28 05:43:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2834274,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of per-cancer GIS, HRD+ rate, and case frequency based on germline a and somatic Ⅱ and Ⅲ b variations. \u003c/strong\u003eThe numbers inside represent the mean GIS, HRD+ rate, and percentage of cases per cancer type in the left, center, and right panels, respectively. The numbers in parentheses next to gene symbols indicate the number of cases.\u003cstrong\u003e \u003c/strong\u003eBC:\u003cstrong\u003e \u003c/strong\u003ebreast cancer; endometrial cancer; OC: ovarian cancer; PAC: pancreatic cancer; PRC: prostate cancer.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5538972/v1/fca20867e4cc4079c7a3a087.png"},{"id":81507419,"identity":"408c09cc-cb2e-4c9c-9b12-9f4c87a50607","added_by":"auto","created_at":"2025-04-28 05:35:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":728332,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between TMB-H/MSI-H status and GIS, TAI, LOH, and LST scores.\u003c/strong\u003e The GIS and TAI, LOH, and LST scores were significantly lower in the TMB-H/MSI-H subsets than in the TMB-L and MSS subsets, both in patients with all included cancers \u003cstrong\u003ea\u003c/strong\u003e and in patients with endometrial cancer \u003cstrong\u003eb\u003c/strong\u003e, but were similar in patients with non-endometrial cancers \u003cstrong\u003ec\u003c/strong\u003e. **\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01; ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001. ns: not significant.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5538972/v1/c665631b681ef6da7e5da1bb.png"},{"id":81507432,"identity":"6f74b4e0-8424-4fb0-9a95-7ce12e784523","added_by":"auto","created_at":"2025-04-28 05:35:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":532637,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariations in six most frequently observed non-\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBRCA1/2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eHRR genes in HRD+ cases.\u003c/strong\u003e Amino acid changes and frequencies were annotated using MutationMapper (http://www.cbioportal.org/mutation_mapper. Blue: germline variations, black: somatic variations, red: germline and somatic variations.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5538972/v1/9d57cce2fb198a290c3a0cb8.png"},{"id":85231391,"identity":"caf3e026-2894-4e0a-96c2-f448e49bb482","added_by":"auto","created_at":"2025-06-23 16:07:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16362781,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5538972/v1/c472749d-e1e5-4493-ac4c-7fed9279a044.pdf"},{"id":81507437,"identity":"8fd87f67-795d-4f13-95c5-f96bf79833dc","added_by":"auto","created_at":"2025-04-28 05:35:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":185197,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5538972/v1/d1a456def9d19213209502a2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Homologous Recombination Deficiency Among Patients with Germline or Somatic non-BRCA1/2 Homologous Recombination Repair Gene Variations","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHomologous recombination deficiency (HRD) arises as a phenotypic consequence of defects in the homologous recombination repair (HRR) pathway, characterized by the inability of cells to repair double-stranded DNA (dsDNA) breaks via high-fidelity HRR mechanisms\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. HRD is commonly observed in several cancers, such as ovarian, breast, pancreatic, and prostate cancers, rendering these tumors highly susceptible to platinum-based drugs and poly (ADP-ribose) polymerase inhibitors (PARPi)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. While the deleterious variants of \u003cem\u003eBRCA1/2\u003c/em\u003e, the breast cancer susceptibility genes, remain the primary biomarkers for predicting response to PARPi\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, HRD has recently been established as an officially recognized biomarker for PARPi use in combination with bevacizumab as a first-line maintenance therapy in ovarian cancer, as recommended by NCCN Guidelines. This advancement highlights the increasing clinical need for HRD status testing\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCells with HRD rely more heavily on the error-prone non-homologous end-joining pathway to repair dsDNA breaks, leading to the accumulation of genomic scars\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These scars are measured by the genomic instability score (GIS), which combines three metrics: telomeric allelic imbalance (TAI)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, loss of heterozygosity (LOH)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and large-scale state transitions (LST)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Current diagnostic HRD tests define HRD-positive (HRD+) status as the presence of a deleterious mutation in \u003cem\u003eBRCA1/2\u003c/em\u003e or a GIS of \u0026ge;\u0026thinsp;42\u003csup\u003e11\u003c/sup\u003e. Notably, a substantial portion of HRD\u0026thinsp;+\u0026thinsp;tumors with high GIS lacks deleterious \u003cem\u003eBRCA1/2\u003c/em\u003e mutations, referred to as non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRD-associated tumors\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Variations in certain non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR genes are also important causes of HRD\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, such as \u003cem\u003ePALB2\u003c/em\u003e and \u003cem\u003eRAD51C/D\u003c/em\u003e\u003csup\u003e12,14\u003c/sup\u003e. However, the specific strength of the association between different non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR genes and HRD remains poorly characterized.\u003c/p\u003e \u003cp\u003eIn routine clinical practice, we observed that patients with variations in different non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR genes exhibited varying HRD\u0026thinsp;+\u0026thinsp;rates. If this trend is observed consistently, it could help clinicians determine when to recommend an additional, costly HRD test for their patients. Therefore, in this study, we aimed to comprehensively investigate the associations between HRD positivity and different non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR genes, considering factors such as variation origin, variation class, hit type, and cancer type. The findings of this study may help clinicians in developing a framework to categorize patients for appropriate molecular testing.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Patient characteristics overall and by gene panels\u003c/h2\u003e\n \u003cp\u003eAmong the 483 patients included in this study, 371 and 112 patients with non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR mutations were from gene panels 1 and 2, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Most patients were diagnosed with ovarian (218, 45.1%) or endometrial cancer (172, 35.6%), whereas smaller proportions of patients were diagnosed with breast (65, 13.5%), prostate (20, 4.1%), and pancreatic (8, 1.7%) cancers. No somatic class Ⅰ mutations were identified, and no significant differences were observed between the two gene panels regarding age at diagnosis (\u0026gt;\u0026thinsp;50 vs. \u0026le;50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1929) or the ratios of somatic variation classes (class Ⅱ vs. class Ⅲ, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4320) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The proportion of ovarian cancer cases (90.1% vs. 31.5%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), HRD\u0026thinsp;+\u0026thinsp;rate (58.0% vs. 18.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and the ratio of germline variations (80.4% vs. 7.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) were significantly higher in gene panel 2 than in gene panel 1, whereas the proportions of endometrial cancer cases (5.4% vs. 44.7%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), breast cancer cases (0.9% vs. 17.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and pathogenic/likely pathogenic (P/LP) germline variations (23.3% vs. 92.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) were markedly higher in gene panel 1 compared to the gene panel 2 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatient characteristics.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene panel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene panel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;483\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;371\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;112\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge at diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e307(63.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230 (62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77 (68.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35 (31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOvarian cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e218 (45.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101 (90.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndometrial cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166 (44.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65 (13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProstate cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePancreatic cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHRD status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68 (18.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e351 (72.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e303 (81.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariation origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89 (79.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e368 (76.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e345 (93.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermline class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP/LP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45 (39.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24 (92.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70 (60.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68 (76.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSomatic class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e236 (64.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e223 (64.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13 (56.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132 (35.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122 (35.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (43.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMutated HRR genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146 (30.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132 (39.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eisolated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e337 (69.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e239 (70.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98 (87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eHRD: homologous recombination deficiency; HRR: homologous recombination repair; P/LP: pathogenic/likely pathogenic; VUS: variants of uncertain significance.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003eWe previously detected 80 cases (75 with ovarian cancer and five with endometrial cancer) using gene panels 1 and 2. The consistency rate of HRD status was 97.5% (78/80, Supplementary Table 1), whereas the consistency rate of 15 cases with \u003cem\u003eBRCA1/2\u003c/em\u003e deleterious variations was 100%, supporting the integration of data from two gene panel groups to enrich the sample size and composition.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 HRD prevalence based on non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR gene variation origin and hit type\u003c/h2\u003e\n \u003cp\u003eIn clinical practice, both P and LP germline mutations in \u003cem\u003eBRCA1/2\u003c/em\u003e are approved indications for PARP inhibitors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. According to our data, we observed no significant differences in HRD\u0026thinsp;+\u0026thinsp;rates (50.0% vs. 48.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8939) (Supplementary Fig. 1a), or GISs (38.5 vs. 35.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.6619) between the P and LP groups, regardless of the HRD\u0026thinsp;+\u0026thinsp;status (58.4 vs. 54.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3901) or HRD- status (18.5 vs. 18.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9572) (Supplementary Fig.\u0026nbsp;1b). The data above support the integration of cases with P and LP germline mutations in the subsequent analysis.\u003c/p\u003e\n \u003cp\u003eThe HRD\u0026thinsp;+\u0026thinsp;rate for the overall cohort was 27.3% (132/483). Patients with germline variations in non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR genes exhibited a significantly higher HRD\u0026thinsp;+\u0026thinsp;rate than those with somatic mutations (49.6% vs. 20.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). Within the germline group, P/LP non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR variations were associated with a similar HRD\u0026thinsp;+\u0026thinsp;rate to those of variants of uncertain significance (VUS) (48.9% vs. 50.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8548), regardless of whether they were biallelic alterations (BAs, 58.3% vs. 50.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4807) or monoallelic alterations (MAs, 38.1% vs. 50.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3379). The HRD\u0026thinsp;+\u0026thinsp;rate of patients with germline BAs was numerically but not statistically higher than that of patients with germline MAs (58.3% vs. 38.1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1754) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). Within the somatic group, patients with class III variations had a significantly higher HRD\u0026thinsp;+\u0026thinsp;rate than those with class II variations (33.6% vs. 13.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), regardless of whether they were BAs (33.6% vs. 10.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) or MAs (33.6% vs. 18.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0202). The HRD\u0026thinsp;+\u0026thinsp;rate of patients with somatic BAs was similar to that of patients with somatic MAs (10.6% vs. 18.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0809) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec). Here, we observed close associations between HRD\u0026thinsp;+\u0026thinsp;rate and germline non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR mutations; however, the hit type (BAs or MAs) did not affect the HRD\u0026thinsp;+\u0026thinsp;rate.\u003c/p\u003e\u003cspan\u003e\n \u003ch2\u003e2.3 GIS, TAI, LOH, and LST score distributions based on non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR gene variation origin, hit type, and HRD status\u003c/h2\u003e\n \u003c/span\u003e\n \u003cp\u003eIn the HRD\u0026thinsp;+\u0026thinsp;group with germline variations, GIS (62.2 vs. 56.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0410) and TAI scores (20.7 vs. 17.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0231) for tumors with VUS were significantly higher than those for tumors with P/LP variations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed). In the HRD-negative (HRD-) group with germline variations, GIS, TAI, LOH, and LST scores of the cases with BAs were all significantly higher than those of the cases with MAs. Similar with HRD\u0026thinsp;+\u0026thinsp;tumors with germline mutations, the TAI score for HRD- tumors with germline VUS were significantly higher than that for tumors with P/LP variations (10.6 vs. 6.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0177, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ee), indicating that the TAI score is more sensitive than the LOH or LST scores in cases with germline non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR mutations. In contrast, no significant differences in these scores were observed between tumors with class III and II variations in the HRD\u0026thinsp;+\u0026thinsp;group (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ef). In the HRD- group with somatic variations, GIS (17.3 vs. 8.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), TAI (5.1 vs. 2.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), LOH (4.9 vs. 2.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and LST (7.3 vs. 3.8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) scores for tumors with class III variations were significantly higher than those for tumors with class II variations, no matter they were BAs or MAs (all \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eg). Here, we found that somatic BAs were linked with higher HRD-related scores than MAs, though the hit type actually did not affect the HRD\u0026thinsp;+\u0026thinsp;rate.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Strengths of association between different non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR variants and HRD\u003c/h2\u003e\n \u003cp\u003eVariations in different HRR genes may exhibit different strengths of association with HRD\u0026thinsp;+\u0026thinsp;rates. To test this hypothesis, HRD\u0026thinsp;+\u0026thinsp;rates were independently analyzed for each non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR gene after excluding cases with mutations in more than one HRR gene (i.e., co-occurring HRR mutations). For germline variants (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea), high HRD\u0026thinsp;+\u0026thinsp;rates were associated with P/LP variations in \u003cem\u003ePALB2\u003c/em\u003e (4/4, 100.0%), \u003cem\u003eRAD51C\u003c/em\u003e (5/7, 71.4%), and \u003cem\u003eRAD51D\u003c/em\u003e (4/6, 66.7%), as well as those with VUS in \u003cem\u003eRAD54L\u003c/em\u003e (6/6, 100.0%), \u003cem\u003eFANCD2\u003c/em\u003e (3/3, 100.0%), and \u003cem\u003eATR\u003c/em\u003e (3/4, 75.0%). Conversely, low HRD\u0026thinsp;+\u0026thinsp;rates were associated with P/LP variations in \u003cem\u003eATM\u003c/em\u003e (0/2, 0.0%) and \u003cem\u003eBRIP1\u003c/em\u003e (1/4, 25.0%), as well as those with VUS in \u003cem\u003eEMSY\u003c/em\u003e (0/4, 0.0%), \u003cem\u003eNBN\u003c/em\u003e (0/3, 0.0%), and \u003cem\u003eCDK12\u003c/em\u003e (0/2, 0.0%).\u003c/p\u003e\n \u003cp\u003eFor somatic variants (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb), high HRD\u0026thinsp;+\u0026thinsp;rates were associated with tumors with class II variations in \u003cem\u003eRAD54L\u003c/em\u003e (2/2, 100.0%) and class III variations in \u003cem\u003eFANCD2\u003c/em\u003e (3/3, 100.0%). Conversely, low HRD\u0026thinsp;+\u0026thinsp;rates were associated with tumors with class II variations in \u003cem\u003ePTEN\u003c/em\u003e (7/110, 6.4%), as well as those with class III variations in \u003cem\u003eATM\u003c/em\u003e (0/7, 0.0%), \u003cem\u003eCDK12\u003c/em\u003e (1/10, 10.0%), \u003cem\u003eEMSY\u003c/em\u003e (1/8, 12.5%), \u003cem\u003eNBN\u003c/em\u003e (2/11, 18.2%), and \u003cem\u003eBRIP1\u003c/em\u003e (3/8, 37.5%). When considering the hit type, germline BAs of \u003cem\u003eRAD51C\u003c/em\u003e (5/6, 83.3%) and \u003cem\u003eRAD51D\u003c/em\u003e (4/5, 80.0%), and MAs of \u003cem\u003ePALB2\u003c/em\u003e (3/3, 100.0%) were strongly associated with high HRD\u0026thinsp;+\u0026thinsp;rates (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec). Both somatic BAs (4/78, 5.1%) and MAs (3/32, 9.4%) of \u003cem\u003ePTEN\u003c/em\u003e were related to low HRD\u0026thinsp;+\u0026thinsp;rates (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e\n \u003cp\u003eThe low number of cases with other HRR mutations precluded meaningful comparisons. We downloaded TCGA somatic mutations and paired HRD scores to compensate for this limitation. Excluding cases with co-occurring HRR mutations, 245 and 234 cases with somatic non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR variations from the present study and TCGA data were included. Compared to the present study, the ratio of cases with pancreatic cancer was higher, while the ratio of cases with breast cancer was lower in the TCGA cohort (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). When using a cut-off of 30 defined by a previous large-scale study\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, the HRD\u0026thinsp;+\u0026thinsp;rate in the TCGA group was similar to that in the present study (28.6% vs. 23.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1801, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). Upon integration, class II variations in \u003cem\u003eRAD54L\u003c/em\u003e (4/4, 100.0%) and \u003cem\u003eATR\u003c/em\u003e (2/3, 66.7%), and class III variations in \u003cem\u003eBARD1\u003c/em\u003e (4/5, 80.0%) were associated with high HRD\u0026thinsp;+\u0026thinsp;rates. Conversely, class II variations in \u003cem\u003ePTEN\u003c/em\u003e (18/181, 9.9%) and \u003cem\u003eATM\u003c/em\u003e (4/19, 21.1%), and class III variations in \u003cem\u003eNBN\u003c/em\u003e (2/12, 16.7%), \u003cem\u003ePTEN\u003c/em\u003e (6/32, 18.8%), \u003cem\u003eCDK12\u003c/em\u003e (3/16, 18.8%), and \u003cem\u003eATM\u003c/em\u003e (8/28, 28.6%) were associated with low HRD\u0026thinsp;+\u0026thinsp;rates (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e\n \u003cp\u003eFor germline variations, we integrated data from the present study and seven previous studies\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e focusing on germline P/LP variations of HRR genes to increase sample size and analysis reliability (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). After integration, the number of cases with germline P/LP variations of \u003cem\u003eATM, BRIP1, CHEK2, PALB2\u003c/em\u003e, and \u003cem\u003eRAD51B/C/D\u003c/em\u003e increased significantly. Upon integration, germline P/LP variations of \u003cem\u003ePALB2\u003c/em\u003e (24/26, 92.3%), \u003cem\u003eRAD51C\u003c/em\u003e (16/25, 64.0%), and \u003cem\u003eRAD51D\u003c/em\u003e (12/18, 66.7%) were robustly associated with high HRD\u0026thinsp;+\u0026thinsp;rates, while germline P/LP variations of \u003cem\u003eATM\u003c/em\u003e (1/40, 2.5%), \u003cem\u003eCHEK2\u003c/em\u003e (1/17, 5.9%), \u003cem\u003eBRIP1\u003c/em\u003e (1/8, 12.5%), \u003cem\u003eRAD51B\u003c/em\u003e (1/8, 12.5%) were steadily associated with low HRD\u0026thinsp;+\u0026thinsp;rates, particularly in prostate and pancreatic cancers (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe HRD\u0026thinsp;+\u0026thinsp;rates of cases with germline non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR mutations in the present study and literature review.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eReferences and Cancer types\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThe present study\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKahn et al. 2024\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTorres-Esquius et al. 2024\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLotan et al. 2021\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMandelker\u003c/p\u003e\n \u003cp\u003eet al. 2023\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eXie et al. 2022\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLee et al. 2018\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGolan et al. 2021\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBC, EC, OC, PAC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBC, OC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePRC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePAC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePAC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePAC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBC, EC, OC, PAC, PRC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/2 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/3 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/26 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/2 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/6 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/40 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRIP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/4 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/2 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/2 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/8 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHEK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/13 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/2 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/17 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFANCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/2 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFANCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFANCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRE11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNBN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/2 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePALB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4/4 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2/2 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14/15 (93.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/4 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24/26 (92.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePTEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAD50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAD51B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/2 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/2 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/4 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/8 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAD51C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5/7 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11/18 (61.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16/25 (64.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAD51D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4/6 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/2 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7/10 (70.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12/18 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAD54B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAD54L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/2 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/2 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18/34 (52.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4/12 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18/28 (64.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/39 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/5 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/10 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14/15 (93.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/12 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60/153 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eBC: breast cancer; EC: endometrial cancer; OC: ovarian cancer; PAC: pancreatic cancer; PRC: prostate cancer.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 HRD prevalence based on individual non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR gene and cancer type\u003c/h2\u003e\n \u003cp\u003eThe HRD\u0026thinsp;+\u0026thinsp;rates and corresponding average GIS, TAI, LOH, and LST scores were explored based on cancer type. As shown in the OncoPlots, the HRD\u0026thinsp;+\u0026thinsp;rates were 47.7%, 40.0%, 21.5%, 3.5%, and 0% in the ovarian, prostate, breast, endometrial, and pancreatic cancers, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea\u0026ndash;e). \u003cem\u003ePTEN\u003c/em\u003e was the most frequently mutated gene in endometrial (84.3%), breast (20.0%), and ovarian (12.4%) cancers, followed by \u003cem\u003eATM\u003c/em\u003e in endometrial (22.1%) and ovarian (11.0%) cancers, most of which were somatic variations. We observed a high frequency of germline BAs in \u003cem\u003eRAD51D\u003c/em\u003e (60.0%) and \u003cem\u003eRAD51C\u003c/em\u003e (50.0%) in ovarian cancer, and a high frequency of somatic BAs in \u003cem\u003ePTEN\u003c/em\u003e in breast (76.9%), endometrial (75.2%), and ovarian (66.7%) cancers. Among the overall cohort, 25.8% of the cases had mutations of more than one non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR gene. Interestingly, the HRD\u0026thinsp;+\u0026thinsp;rate of the cases with co-occurring HRR mutations was not higher than that of the cases with isolated HRR gene mutation (20.8% vs. 29.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0625), indicating that multiple HRR mutations may not produce significant synergistic effects on HRD.\u003c/p\u003e\n \u003cp\u003eAs we can see, germline mutations mainly existed in cases with ovarian cancer (87.0%, 80/92). Within ovarian cancer, germline mutations of \u003cem\u003eRAD51C/D\u003c/em\u003e, \u003cem\u003eRAD54B/L\u003c/em\u003e, and \u003cem\u003eFANCD2\u003c/em\u003e were associated with high HRD\u0026thinsp;+\u0026thinsp;rates (66.7\u0026ndash;100.0%), while germline mutations of \u003cem\u003eCDK12, EMSY, FANCI, NBN, RAD51B, MRE11, BRIP1\u003c/em\u003e, and \u003cem\u003eRAD50\u003c/em\u003e were associated with low HRD\u0026thinsp;+\u0026thinsp;rates (0\u0026ndash;33.3%) (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). Although the small numbers of cases with the other four caner types limited the evaluation of the influence of tissue origin on HRD\u0026thinsp;+\u0026thinsp;rate corresponding to individual genes, published data suggest that germline P/LP variations of \u003cem\u003ePALB2\u003c/em\u003e was associated with high HRD\u0026thinsp;+\u0026thinsp;rate, while germline P/LP variations of \u003cem\u003eATM\u003c/em\u003e, and \u003cem\u003eCHEK2\u003c/em\u003e were associated with high HRD\u0026thinsp;+\u0026thinsp;rates in prostate and pancreatic cancers (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eConsidering the HRD\u0026thinsp;+\u0026thinsp;rates in cases with somatic Ⅱ and Ⅲ were significantly different (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec), we separated the two grades of somatic mutations when drawing the heatmap (integrated with somatic mutation data from TCGA). Notably, somatic mutations of \u003cem\u003ePTEN\u003c/em\u003e were robustly related to low HRD\u0026thinsp;+\u0026thinsp;rates across all five cancer types, but the other gene mutations exhibited different association strengths in different cancers. For instance, somatic mutations of \u003cem\u003eATM\u003c/em\u003e were linked with high HRD\u0026thinsp;+\u0026thinsp;rate only in breast cancer but not the other four malignancies, whereas \u003cem\u003eCDK12\u003c/em\u003e somatic Ⅱ mutations were related with low HRD\u0026thinsp;+\u0026thinsp;rates in breast and prostate cancers but not in ovarian cancer. \u003cem\u003eRAD51D\u003c/em\u003e somatic Ⅱ mutations were linked to high HRD\u0026thinsp;+\u0026thinsp;rate and GIS in ovarian cancer; \u003cem\u003eRAD51D\u003c/em\u003e somatic Ⅲ mutations were related to low HRD\u0026thinsp;+\u0026thinsp;rates and GIS in ovarian cancer, but with high HRD\u0026thinsp;+\u0026thinsp;rates and GIS in breast and prostate cancer (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). These observations confirm that certain mutations may exert different effects in different cancer types, thus the tissue origin, along with variation origin and grade, should be taken into account when judging its association with HRD.\u003c/p\u003e\n \u003cp\u003eTriple-negative breast cancer (TNBC) has been previously reported to exhibit much higher signature scores than the ER/PR\u0026thinsp;+\u0026thinsp;and HER2\u0026thinsp;+\u0026thinsp;subtypes\u003csup\u003e22\u003c/sup\u003e. However, our results showed only numerical, but not statistically significant, differences in the HRD\u0026thinsp;+\u0026thinsp;rate between TNBC and the other subtypes (34.8% vs. 14.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0614, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Although no significant differences were observed in GIS (34.3 vs. 24.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0631) or LST scores (15.8 vs. 11.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1528), patients with TNBC had slightly higher TAI (8.9 vs. 6.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0404) and LOH (9.6 vs. 6.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0386) scores than those with non-TNBC (Supplementary Fig. 2). Notably, 14 cases of HRD\u0026thinsp;+\u0026thinsp;breast cancer were HER2-, and eight HER2\u0026thinsp;+\u0026thinsp;cases were HRD- (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), which demonstrated the mutual exclusivity of HER2\u0026thinsp;+\u0026thinsp;status and HRD in breast cancer.\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe HRD prevalence in different breast cancer subtypes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBC subtype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHRD+\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHRD-\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-TNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1848\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eHRD: homologous recombination deficiency; TNBC: triple-negative breast cancer.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Associations between HRD and TMB-H/MSI-H in non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR mutant cases\u003c/h2\u003e\n \u003cp\u003eAs demonstrated previously, endometrial cancer is characterized by high tumor mutational burden and high microsatellite instability (TMB-H/MSI-H). Here, we found that the average GIS (7.6 vs. 35.1) and TAI (2.0 vs. 8.2), LOH (2.2 vs. 10.8), and LST (3.4 vs. 16.2) scores for patients with endometrial cancer were significantly lower than those of patients with other cancer types (all \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), particularly in the HRD- group (GIS: 5.8 vs. 18.2, TAI: 1.5 vs. 4.4, LOH: 1.7 vs. 6.4, LST: 2.6 vs. 7.4; all \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Supplementary Table\u0026nbsp;2), suggesting that effect of TMB-H/MSI-H status on HRD\u0026thinsp;+\u0026thinsp;rate should be further analyzed.\u003c/p\u003e\n \u003cp\u003eTMB status, TMB value, and MSI status were available from the gene panel 1 data (n\u0026thinsp;=\u0026thinsp;371, Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), with 26.1% of cases classified as TMB-H and 13.5% as MSI-H. The results indicated that the HRD\u0026thinsp;+\u0026thinsp;rate of non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR mutant cases was significantly lower in the TMB-H (11.3% vs. 20.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0384) and MSI-H (4.0% vs. 20.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0049) subgroups than in their TMB-low (TMB-L) and microsatellite stable (MSS) counterparts. Consistently, the average TMB value in the HRD\u0026thinsp;+\u0026thinsp;group was markedly lower than that in the HRD- group (6.1 vs. 22.2 mutations/Mb, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0384).\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociations of HRD and TMB/MSI status in non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR mutant cases.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHRD+\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHRD-\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHRD\u0026thinsp;+\u0026thinsp;rate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;371\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;68\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;303\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTMB status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97(26.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274 (73.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTMB value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSI status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSI-H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321 (86.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eHRD: homologous recombination deficiency; TMB: tumor mutational burden; MSI: microsatellite instability; MSS: microsatellite stable.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003eGiven that endometrial cancer accounted for a high proportion of TMB-H and MSI-H cases (69.1% and 86.0%, respectively), we separately investigated the associations between TMB-H/MSI-H and HRD scores in patients with endometrial cancer and patients with the other cancers. The GIS, TAI, LOH, and LST scores were significantly lower in the TMB-H/MSI-H subsets than in the TMB-L and MSS subsets both in patients with all included cancers (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and in patients with endometrial cancer (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eb, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but were similar in patients with non-endometrial cancers (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ec). These observations support the previous hypothesis that defects affecting single strand repair (MSI-H) are mutually exclusive with defects affecting double strand repair (HRD)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, which is specifically evident in endometrial cancer.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7 Variations in the six most common non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR genes in HRD\u0026thinsp;+\u0026thinsp;cases\u003c/h2\u003e\n \u003cp\u003eTo explore if any \u0026ldquo;hot spot\u0026rdquo; exists, we analyzed the locations of both germline and somatic variations on the non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR genes. The six most frequently observed non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR genes in HRD\u0026thinsp;+\u0026thinsp;cases were \u003cem\u003eRAD54L\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;12), ATR (n\u0026thinsp;=\u0026thinsp;11), \u003cem\u003eFANCD2\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;11), \u003cem\u003eFANCA\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;10), \u003cem\u003eRAD51C\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;8), and \u003cem\u003eRAD51D\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;6). The specific amino acid changes in these genes were displayed according to their domain locations. Although the number of HRD\u0026thinsp;+\u0026thinsp;cases was relatively small, several protein alterations appeared repeatedly, including \u003cem\u003eRAD51D\u003c/em\u003e K91Ifs*13 (n\u0026thinsp;=\u0026thinsp;4), \u003cem\u003eRAD54L\u003c/em\u003e L621P (n\u0026thinsp;=\u0026thinsp;2), \u003cem\u003eATR\u003c/em\u003e A1488V (n\u0026thinsp;=\u0026thinsp;2), \u003cem\u003eFANCA\u003c/em\u003e A1442T (n\u0026thinsp;=\u0026thinsp;2), and \u003cem\u003eRAD51C\u003c/em\u003e T132Nfs*23 (n\u0026thinsp;=\u0026thinsp;2) (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ea\u0026ndash;f). The recurrent appearance of the above variations may increase their value on HRD prediction.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThe identification of HRD plays a crucial role in guiding the selection of patients who would benefit from targeted therapies; however, previous research has primarily focused on \u003cem\u003eBRCA1/2\u003c/em\u003e mutations, rendering clinicians understandably inquisitory about the likelihood of HRD in patients with non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR mutations. In the present study, we found that patients with mutations in \u003cem\u003ePALB2, RAD51C/D\u003c/em\u003e, and \u003cem\u003eRAD54L\u003c/em\u003e exhibited a tendency toward high HRD\u0026thinsp;+\u0026thinsp;rates (66.7\u0026ndash;100.0%), whereas those with variations in \u003cem\u003ePTEN, ATM, BRIP1, CDK12\u003c/em\u003e, and \u003cem\u003eNBN\u003c/em\u003e had low HRD\u0026thinsp;+\u0026thinsp;rates (0\u0026ndash;37.5%). Germline mutations were associated with significantly higher HRD\u0026thinsp;+\u0026thinsp;rates than somatic mutations, while the hit type, namely BAs or MAs, did not affect HRD\u0026thinsp;+\u0026thinsp;rates significantly. The HRD\u0026thinsp;+\u0026thinsp;rates associated with individual HRR genes might be influenced by variation grade and tissue origin, offering potentially valuable insights that may guide clinicians in cases of \u0026ldquo;\u003cem\u003eBRCA1/2\u003c/em\u003e-wildtype\u0026rdquo; results\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, patients with germline variations in non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR genes exhibited significantly higher HRD rates compared to those with somatic variations (49.6% vs 20.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). We found that the somatic II \u003cem\u003ePTEN\u003c/em\u003e mutations were related to very low HRD\u0026thinsp;+\u0026thinsp;rates (6.4%), regardless of whether they were BAs (5.1%) or MAs (9.4%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Therefore, the high proportion of cases with \u003cem\u003ePTEN\u003c/em\u003e mutations (114/245, 46.5%, excluding cases with co-occurring HRR mutations) may partly explain the lower HRD\u0026thinsp;+\u0026thinsp;rate observed in the somatic group. When \u003cem\u003ePTEN\u003c/em\u003e mutations were excluded from both the germline and somatic groups, the discrepancy narrowed but was still significant (37.4% vs. 51.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0351). This result is logical, because germline alterations are static and long-lasting. In contrast, somatic alterations provide only a snapshot of the tumor at a specific stage and can be influenced by various factors, including sampling site, tumor purity, and chemotherapy treatment\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. As previously reported, \u003cem\u003ePTEN\u003c/em\u003e is a tumor suppressor gene critical for the maintenance of genome stability\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. A \u003cem\u003ePTEN\u003c/em\u003e inhibitor was reported to increase double-strand breaks through the modulation of the MRE11-RAD50-NBN complex and enhance the inhibitory effect of olaparib on breast cancer cells\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. A pan-cancer analysis by E. Rempel et al. reported that \u003cem\u003ePTEN\u003c/em\u003e was the most frequently affected gene in breast cancer (34%) and prostate cancer (59%), and HRD scores were significantly higher in \u003cem\u003ePTEN\u003c/em\u003e-mutated ovarian cancer (fold change\u0026thinsp;=\u0026thinsp;1.4) and prostate cancer (fold change\u0026thinsp;=\u0026thinsp;1.3)\u003csup\u003e23\u003c/sup\u003e. Additionally, P\u0026eacute;rez-Villatoro et al. found that tumors with somatic \u003cem\u003ePTEN\u003c/em\u003e mutations exhibited higher levels of ovaHRDscar, a type of HRD score built on TCGA ovarian cancer multi-omics dataset, compared to the reference group\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. According to our data, \u003cem\u003ePTEN\u003c/em\u003e was the most frequently affected gene in ovarian, breast, and endometrial cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e); however, somatic mutations of \u003cem\u003ePTEN\u003c/em\u003e were associated with very low HRD\u0026thinsp;+\u0026thinsp;rates. Both the HRD\u0026thinsp;+\u0026thinsp;rate (0% vs. 25%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and average GIS (5.5 vs. 25.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) were significantly lower in \u003cem\u003ePTEN\u003c/em\u003e-mutated endometrial cancer cases compared to \u003cem\u003ePTEN\u003c/em\u003e-mutated non-endometrial cancer cases. Therefore, the discrepancy in \u003cem\u003ePTEN\u003c/em\u003e-related HRD\u0026thinsp;+\u0026thinsp;rates observed in the current study and previous reports could be partly explained by the high ratio of endometrial cancer cases (77.1%) within the \u003cem\u003ePTEN\u003c/em\u003e-mutated group. Regrettably, there were too few cases of germline \u003cem\u003ePTEN\u003c/em\u003e mutations in the current study to draw any meaningful conclusions. It has been found that some HRD-related genes were specific to certain races. For instance, mutations of \u003cem\u003eATM, BRCA2, POLE\u003c/em\u003e, and \u003cem\u003eTOP2B\u003c/em\u003e were more prevalent in \u0026lsquo;White\u0026rsquo; and \u0026lsquo;Asian\u0026rsquo; populations, while \u003cem\u003ePTEN\u003c/em\u003e and \u003cem\u003eEGFG\u003c/em\u003e mutations were more frequent in the \u0026lsquo;White\u0026rsquo; and \u0026lsquo;African American/Black\u0026rsquo; populations\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The data analyzed by E. Rempel et al.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e were obtained from TCGA, while our data were obtained from a Chinese population; therefore, the race-specific factor may partly explain the observed difference in the \u003cem\u003ePTEN\u003c/em\u003e mutation rate and HRD\u0026thinsp;+\u0026thinsp;rate in \u003cem\u003ePTEN\u003c/em\u003e somatic variants, in addition to the limited sample size of the current study.\u003c/p\u003e \u003cp\u003eOur results also indicated that GIS and TAI, LOH, and LST scores varied based on different non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR variation classes (germline P/LP vs. VUS, somatic Ⅱ vs. Ⅲ). It may seem counterintuitive that class Ⅲ variations were more strongly associated with HRD than class II variations. The exact reason for this discrepancy is unclear; however, it is possible that somatic class II/Ⅲ variations could be \u0026ldquo;passenger\u0026rdquo; mutations rather than \u0026ldquo;driver\u0026rdquo; mutations, given that the true mechanisms underlying the HRD phenotype are complex, involving a combination of genetic and epigenetic changes (underlying causes of HRD), genomic scars and mutational signatures (consequences of HRD), and HRR activity\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Therefore, the value of somatic variations should be interpreted cautiously, particularly for class Ⅲ variations with little pathogenic evidence. A former study conducted by Guillaume Beinse et al.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e analyzed the association between a transcriptomic model and genomic scars in gynaecological cancers and did not focus on individual HRR genes but rather captured gene expression levels in HRD-related pathways (nuclear structure, chromatin remodeling, and so on), which also helps explain why some tumors exhibit HRD features even without \u003cem\u003eBRCA1/2\u003c/em\u003e mutations. We agreed with Guillaume Beinse et al.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e in that a gradient of intermediate levels of deficiency exist beyond \u003cem\u003eBRCA1/2\u003c/em\u003e-altered tumors, just as we found the variations of several non-\u003cem\u003eBRCA1/2\u003c/em\u003e genes, such as \u003cem\u003eATR\u003c/em\u003e and \u003cem\u003eFANCA/C\u003c/em\u003e, had intermediate strengths of association with HRD\u0026thinsp;+\u0026thinsp;rates (42.9\u0026ndash;55.6%). Since somatic or germline mutations in HR genes are not found in all HRD tumors and VUS can be difficult to interpret, the transcriptional features, which might be the consequential reflect of specific HRR gene variations, may serve as a supplementary predictor of HRD status in cases without HRR variations.\u003c/p\u003e \u003cp\u003eSeveral HRR genes, including \u003cem\u003eRAD51C/D, PALB2, BRIP1, BARD1, CHEK2, ATM, H2AX, MRE11\u003c/em\u003e, and those associated with Fanconi anemia, have been previously recognized as contributors to a \u0026ldquo;BRCAness\u0026rdquo; phenotype in 30\u0026ndash;50% of high-grade serous ovarian cancer cases\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. However, the strength of the correlation between mutations in these genes and the benefits of PARPi has been variable\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Our findings revealed that tumors with P/LP variations in \u003cem\u003ePALB2\u003c/em\u003e and \u003cem\u003eRAD51C/D\u003c/em\u003e accounted for a high proportion of HRD\u0026thinsp;+\u0026thinsp;cases (72.2%, 13/18), whereas tumors with P/LP variations in \u003cem\u003eBRIP1\u003c/em\u003e constituted a lower proportion of HRD\u0026thinsp;+\u0026thinsp;cases (5.6%, 1/18), consistent with previous research results\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Notably, \u003cem\u003eRAD51D\u003c/em\u003e c.270_271dup p.K91Ifs*13 was detected in four HRD\u0026thinsp;+\u0026thinsp;cases (both germline and somatic) in the present study. This specific frameshift mutation was detected in 1.7% (13/781) of Chinese patients with ovarian cancer, was the most common mutation in \u003cem\u003eRAD51D\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, and has been linked to steady platinum and PARPi benefits in ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Based on these findings, we hypothesize that this mutation may be a founder mutation in the Chinese population.\u003c/p\u003e \u003cp\u003eFurthermore, 9/10 patients with germline variations and 4/17 patients with somatic variations in \u003cem\u003eRAD54L\u003c/em\u003e were classified as HRD+. The RAD51/RAD54 complex is essential for activating dsDNA break repair\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Rad54, an ATP-dependent motor protein, dissociates Rad51 from dsDNA, the product complex of DNA strand exchange\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The combined depletion of \u003cem\u003eRAD54L\u003c/em\u003e and \u003cem\u003eRAD54B\u003c/em\u003e or overexpression of \u003cem\u003eRAD51\u003c/em\u003e could impede replication and promote chromosome segregation defects\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eRAD54L\u003c/em\u003e P/LP variations have been associated with outcomes in patients with metastatic castration-resistant prostate cancer in the \u0026ldquo;HRR non-BRCA\u0026rdquo; cohort, which showed better results than the BRCA cohort but poorer outcomes than the non-HRR cohort\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Additionally, a missense mutation in \u003cem\u003eRAD54L\u003c/em\u003e (c.604C\u0026thinsp;\u0026gt;\u0026thinsp;T) was identified in one carrier with a family history of ovarian cancer, as well as in a 29-year-old patient with breast cancer with a family history of ten breast cancer cases\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Although the function of \u003cem\u003eRAD54L\u003c/em\u003e in hereditary breast and ovarian cancer syndromes remains unclear, certain functional \u003cem\u003eRAD54L\u003c/em\u003e mutations may play a role in the formation of HRD.\u003c/p\u003e \u003cp\u003eGI is a well-established hallmark of cancer, with HRD and MSI representing two distinct forms of GI based on different repair mechanisms\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Previous studies have demonstrated that tumors with MSI-H exhibit low levels of genomic scars across colon, gastric, and endometrial cancers, with particularly low genomic scar levels in endometrial cancer, with median TAI, LOH, and LST scores of 3.0, 2.0, and 2.0, respectively\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Consistently, our analysis revealed a mutual exclusivity between HRD and TMB-H/MSI-H status, particularly in endometrial cancer, with median TAI, LOH, and LST scores of 2.0, 2.2, and 3.4, respectively (Supplementary Table\u0026nbsp;2). This evident tendency toward mutual exclusivity indicates that in MSI-H tumors, MSI may precede the GI that generate genomic scars. This observation suggests that HRD testing may be unnecessary for MSI-H cancers.\u003c/p\u003e \u003cp\u003eTNBC has been reported to exhibit significantly higher signature scores than the ER/PR\u0026thinsp;+\u0026thinsp;and HER2\u0026thinsp;+\u0026thinsp;subtypes, with median TAI, LOH, and LST values of 27.0, 21.5, and 21.0, respectively\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, our data showed only numerically, but not statistically, significant higher HRD\u0026thinsp;+\u0026thinsp;rates in TNBCs than in non-TNBCs (34.8% vs. 14.6%). The median TAI, LOH, and LST values for TNBCs were 8.9, 9.6, and 15.8, respectively, which were lower than previously reported. This discrepancy may be caused by the relatively small number of included TNBC cases (14 cases) and the different gene panels used in the two studies. Notably, our analysis revealed a mutual exclusivity between HRD and HER2\u0026thinsp;+\u0026thinsp;status in breast cancer, which seems reasonable because tumors are generally promoted by only one strong functional driver gene variation during a specific period\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. This observation also suggests that HER2\u0026thinsp;+\u0026thinsp;breast cancer may not require additional HRD testing in clinical practice.\u003c/p\u003e \u003cp\u003eIn the present study, only 20 and eight patients with prostate and pancreatic cancer, respectively, harboring non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR variations were included, which precluded meaningful statistical comparisons of associations with HRD of different non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR variants. After integration with TCGA data, the number of prostate cancer cases increased from 20 to 39, and the number of pancreatic cancer cases increased from 8 to 35. We observed that germline P/LP variations of \u003cem\u003eATM\u003c/em\u003e and \u003cem\u003eCHEK2\u003c/em\u003e were associated with low HRD\u0026thinsp;+\u0026thinsp;rates in prostate cancer and pancreatic cancer. Although the low frequency of HRR pathway gene alterations was also limited other HRD-related studies in these two malignancies, some useful clues can be obtained from large-scale studies\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In prostate cancer, \u003cem\u003eBRCA2\u003c/em\u003e (6.76%), \u003cem\u003eATM\u003c/em\u003e (4.50%), and \u003cem\u003eCHEK2\u003c/em\u003e (1.92%) were most frequently mutated HRR genes\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, and tumors with germline \u003cem\u003eBRCA2\u003c/em\u003e mutations had higher HRD scores (median\u0026thinsp;=\u0026thinsp;27). In contrast, tumors with germline \u003cem\u003eATM\u003c/em\u003e (median\u0026thinsp;=\u0026thinsp;16.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) or \u003cem\u003eCHEK2\u003c/em\u003e (median\u0026thinsp;=\u0026thinsp;9.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) mutations had significantly lower HRD scores\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. A meta-analysis suggested that HRD occurs in 14.5\u0026ndash;16.5% of pancreatic cancer cases\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, and the HRD\u0026thinsp;+\u0026thinsp;rate was lower in tumors with deleterious non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR mutations than those with deleterious \u003cem\u003eBRCA1/2\u003c/em\u003e mutations\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This confirms that the cutoff for HRD in prostate and pancreatic cancers may need to be defined differently based on treatment effectiveness.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, the data on progression-free survival remains immature owing to the relatively short follow-up period. Therefore, further long-term studies are needed to determine whether the prognosis differs between HRD\u0026thinsp;+\u0026thinsp;patients with \u003cem\u003eBRCA1/2\u003c/em\u003e mutations and those with non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR mutations. Such studies should also assess the prognostic implications of these genetic alterations and their potential effect on treatment response. Additionally, the two gene panels used in this study do not cover all genes involved in the HRR pathway; therefore, the associations between HRD and other non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR genes not investigated in this study remain uncertain. \u003cem\u003eBLM, FANCE, FANCF\u003c/em\u003e, and \u003cem\u003eWRN\u003c/em\u003e are only covered by gene panel 1 but not gene panel 2, which might generate bias in the HRD\u0026thinsp;+\u0026thinsp;rate of cases carrying mutations of these four genes. Finally, the retrospective, single-institutional nature and limited sample size of this analysis may restrict the generalizability of the findings. These factors highlight the need for caution when interpreting the observed associations and emphasize the necessity for larger, multi-center studies to validate these findings and assess their clinical relevance.\u003c/p\u003e \u003cp\u003eIn conclusion, this study demonstrated the varying strength of association between different non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR mutations and HRD across several cancer types. These findings suggest that specific genetic alterations could guide molecular testing and treatment strategies in a cancer type-specific setting. Nevertheless, further research is needed to explore the underlying mechanisms and prognostic effects of non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR gene variations in larger patient populations.\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Patient selection and data collection\u003c/h2\u003e \u003cp\u003eHRD and non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR gene sequencing results for patients diagnosed with ovarian, breast, endometrial, prostate, and pancreatic cancers through pathological examination at the Sun Yat-sen University Cancer Center (SYSUCC, Guangzhou, China) between June 1, 2023, and September 30, 2024, were retrospectively investigated. The HRD status was detected by gene panels 1 and 2. Cases with germline or somatic alterations in at least one of 30 non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR genes were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the 30 non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR genes, 26 genes are shared by both gene panel 1 (covering 520 genes) and gene panel 2 (covering 36 genes), including \u003cem\u003eABRAXAS1 (FAM175A), ATM, ATR, BAP1, BARD1, BRIP1, EMSY (C11ORF30), CDK12, CHEK1, CHEK2, FANCA, FANCC, FANCD2, FANCI, FANCL, MRE11, NBN, PALB2, PPP2R2A, PTEN, RAD50, RAD51B, RAD51C, RAD51D, RAD54B\u003c/em\u003e, and \u003cem\u003eRAD54\u003c/em\u003eL. The other four genes, \u003cem\u003eBLM, FANCE, FANCF\u003c/em\u003e, and \u003cem\u003eWRN\u003c/em\u003e, only covered by gene panel 1, were also included due to their involvement in a previous large-scale HRD-related study\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHRD status, GIS, TAI, LOH, and LST scores, variated genes, variation class, protein changes, patient age, and cancer type were systematically collected. Additionally, TMB value, TMB status, and MSI status, which can be obtained from gene panel 1 but not gene panel 2, were also evaluated. The use of clinical and NGS data was approved by the Ethics Committee of the Sun Yat-Sen University Cancer Center (approval number B2020-344-01). All patients provided written informed consent, and the study was performed in accordance with the guidelines outlined in the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 DNA isolation and capture-based targeted DNA sequencing\u003c/h2\u003e \u003cp\u003eFormalin-fixed paraffin-embedded tumor specimens were processed for DNA extraction using 8\u0026ndash;10 slides of 5 \u0026micro;m thickness, and the final slide was stained with hematoxylin and eosin (H\u0026amp;E). Two independent pathologists reviewed the H\u0026amp;E-stained slide to confirm the pathological diagnosis and ensure tumor cellularity (at least 30%). Genomic DNA was isolated from the tumor specimens and matched peripheral white blood cells. DNA concentration was measured using the dsDNA HS assay kit (Thermo Fisher Scientific, Waltham, MA) with a Qubit Fluorometer. Sequencing was performed using gene panel 1 (OncoScreenPlus\u0026trade;, Burning Rock, Guangzhou, China)\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and gene panel 2 (Precision Scientific, Beijing, China)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Detailed experimental procedures and bioinformatic analysis have been described previously\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Variation classification and hit type identification of non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR genes\u003c/h2\u003e \u003cp\u003eGermline variants were classified as P/LP or VUS according to the American College of Medical Genetics and Genomics recommendations for interpreting sequence variations\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Somatic variants were classified as class Ⅰ/Ⅱ (variants of strong/potential clinical significance) or class Ⅲ (variants of unknown clinical significance) according to the categories of clinical and/or experimental evidence\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. It has been established that BAs, rather than \u0026lsquo;single-hits\u0026rsquo; or MAs, of HRR genes beyond \u003cem\u003eBRCA1/2\u003c/em\u003e are strongly associated with phenotypic functional HR deficiency and LST score in breast cancer\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, and with elevated genome-wide LOH in breast, ovarian, pancreatic, and prostate cancers\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. In light of that, we further identified BAs based on the following criteria\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e: (i) deleterious mutation in one allele and LOH in the other, (ii) two deleterious mutations in the same gene. The deleterious variations in only one allele without LOH in the other allele were considered as MAs, where \u0026ldquo;deleterious\u0026rdquo; refers to germline P/LP or somatic Ⅱ variations. Cases with co-occurring HRR mutations were excluded from hit type identification and individual gene analyses as other researchers did previously\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, otherwise, the gene-specific HRD\u0026thinsp;+\u0026thinsp;rate and zygosity on the patient level could not be assessed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 GIS calculation\u003c/h2\u003e \u003cp\u003eGIS is calculated as the sum of TAI, LOH, and LST. The TAI score measures the frequency of allelic imbalance that do not cross the centromere and extends to the ends of the chromosome telomeres. LOH occurs when a normal gene copy is lost due to the deletion of a large chromosome segment. The LOH score is calculated as the count of LOH regions that are greater than 15 Magabytes (Mb) but less than the entire length of the chromosome. The LST score quantifies the number of chromosomal breakpoints between two adjacent regions, each at least 10 Mb in length, with a distance of no more than 3 Mb, and not passing through the centromere.\u003c/p\u003e \u003cp\u003eThe GI algorithm of gene panel 1 utilized over 9,000 single-nucleotide polymorphisms (SNPs) from a 520-gene panel, estimating allele-specific copy numbers with a custom script based on logR and median coverage. Minor allele frequency and logR data were segmented using circular binary segmentation, and a probabilistic model was used to estimate tumor copy number, purity, and ploidy. LOH, TAI, and LST were calculated as described previously, with the GIS being the sum of these metrics\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe development of the HRD assay for gene panel 2 has been previously detailed\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. In brief, the GI algorithm calculated the GIS in four steps. First, the genome was split into segments, and heterozygous SNPs were selected based on sequencing depth and allele frequency. Second, four parameters were estimated using maximum likelihood estimation: major allele count per segment, minor allele count per segment, tumor purity, and tumor ploidy. Third, LOH, TAI, and LST scores were calculated based on these parameters. Finally, the GIS was derived by summing the LOH, TAI, and LST scores.\u003c/p\u003e \u003cp\u003eA positive GIS was defined by a cutoff value of \u0026ge;\u0026thinsp;42 for both gene panels 1 and 2. The positive and negative predictive values of the HRD tests, as claimed by the manufacturers, are 98.4% and 96.2% for gene panel 1, and 100% and 100% for gene panel 2, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.5 TMB and MSI evaluation\u003c/h2\u003e \u003cp\u003eTMB and MSI were evaluated using gene panel 1 data. TMB was calculated as the ratio of the total number of nonsynonymous mutations detected to the total coding region size (1.003 Mb). Only mutations with an allelic fraction\u0026thinsp;\u0026ge;\u0026thinsp;2% were included, and the mutation count did not include hot spot mutation events, copy number variations, structural variations, or germline SNPs\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. MSI status was determined using a read-count-distribution-based method, as described previously\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Online data acquisition\u003c/h2\u003e \u003cp\u003eWe downloaded somatic mutation information from the somatic mutation profile published by the Multi-Center Mutation Calling in Multiple Cancers (MC3) project\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e (mc3.v0.2.8.PUBLIC.maf), and obtained HRD scores from the supplemental data of a previous paper, in which more than 10,000 tumors comprising 33 cancer types from TCGA were analyzed for immunogenomic characteristic\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, and matched the somatic mutations to the HRD scores based on the TCGA_sample_ID. Thereafter, we eliminated low-quality variations (t_depth\u0026thinsp;\u0026lt;\u0026thinsp;25, t_alt_count\u0026thinsp;\u0026lt;\u0026thinsp;3, or t_alt_count/t_depth\u0026thinsp;\u0026lt;\u0026thinsp;0.05) after filtering by five cancer types and 30 non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) focused in the present study. Excluding cases with co-occurring HRR mutations, we extracted 234 cases whose somatic non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR mutations and HRD scores were both available. A cutoff of \u0026ge;\u0026thinsp;30 was defined for HRD\u0026thinsp;+\u0026thinsp;status based on another large-scale study where the usefulness of HRD analysis and an agnostic cutoff for HRD scores were explored in all cancer types of TCGA\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe clinical characteristics of cases were evaluated using the two gene panels, and the associations between TMB/MSI status and HRD status were compared using the chi-square test. Additionally, differences in GIS, TAI, LOH, and LST scores, and TMB values across different groups were assessed using a Mann\u0026ndash;Whitney test. Statistical significance was determined based on two-tailed tests with a \u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All statistical analyses were performed using GraphPad Prism (version 9.5.0, GraphPad Software, San Diego, CA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw sequencing data files of patients cannot be publicly shared under the obtained institutional review board approval, as patients did not consent to share raw sequencing data beyond the research and clinical terms. The datasets that support the conclusions of this article are available in the Research Data Deposit repository (No. RDDA2024644861, http://www.researchdata.org.cn/).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe R code used for processing and analysis is available upon request from the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Natural Science Foundation of China (Grant number 82002561), and the Guangdong Basic and Applied Basic Research Foundation (Grant numbers 2024A1515012191). The funder played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. The authors thank Ting Hou and Jing Zhao (Burning Rock Biotech, Guangzhou, China), Fancheng Kong and Kun Yang (Precision Scientific Co., Ltd., Beijing, China), for their support in the revision process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLY analyzed and interpreted the data, and was a major contributor in writing the manuscript. YXH and CHY collected the data. WF and LY designed the study. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no financial or non-financial competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMagadeeva, S. \u003cem\u003eet al.\u003c/em\u003e Assessing the Phenotype of a Homologous Recombination Deficiency Using High Resolution Array-Based Comparative Genome Hybridization in Ovarian Cancer. \u003cem\u003eInt J Mol Sci\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, (2023).\u003c/li\u003e\n\u003cli\u003eYamamoto, H. \u0026amp; Hirasawa, A. 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L. \u003cem\u003eet al.\u003c/em\u003e Prevalence of Homologous Recombination\u0026ndash;Related Gene Mutations Across Multiple Cancer Types. \u003cem\u003eJCO Precis Oncol\u003c/em\u003e \u003cstrong\u003e2018\u003c/strong\u003e, PO.17.00286 (2018).\u003c/li\u003e\n\u003cli\u003eCasolino, R. \u003cem\u003eet al.\u003c/em\u003e Homologous Recombination Deficiency in Pancreatic Cancer: A Systematic Review and Prevalence Meta-Analysis. \u003cem\u003eJ Clin Oncol\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 2617\u0026ndash;2631 (2021).\u003c/li\u003e\n\u003cli\u003eFeng, J. \u003cem\u003eet al.\u003c/em\u003e Combination of genomic instability score and TP53 status for prognosis prediction in lung adenocarcinoma. \u003cem\u003eNPJ Precis Oncol\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, (2023).\u003c/li\u003e\n\u003cli\u003eLi, L. \u003cem\u003eet al.\u003c/em\u003e HRD effects on first-line adjuvant chemotherapy and PARPi maintenance therapy in Chinese ovarian cancer patients. \u003cem\u003eNPJ Precis Oncol\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, (2023).\u003c/li\u003e\n\u003cli\u003eLi, Y. \u003cem\u003eet al.\u003c/em\u003e SWI/SNF complex gene variations are associated with a higher tumor mutational burden and a better response to immune checkpoint inhibitor treatment: a pan-cancer analysis of next-generation sequencing data corresponding to 4591 cases. \u003cem\u003eCancer Cell Int\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, (2022).\u003c/li\u003e\n\u003cli\u003eRichards, S. \u003cem\u003eet al.\u003c/em\u003e Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. \u003cem\u003eGenet Med\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 405\u0026ndash;24 (2015).\u003c/li\u003e\n\u003cli\u003eMutter, R. 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Germline mutations were associated with significantly higher HRD\u0026thinsp;+\u0026thinsp;rates than somatic mutations, while biallelic alterations did not show stronger associations with HRD compared to monoallelic alterations. High HRD\u0026thinsp;+\u0026thinsp;rates (66.7\u0026ndash;100.0%) were associated with variations in \u003cem\u003ePALB2, RAD51C/D\u003c/em\u003e, and \u003cem\u003eRAD54L\u003c/em\u003e, while low HRD\u0026thinsp;+\u0026thinsp;rates (0\u0026ndash;37.5%) corresponded with variations in \u003cem\u003ePTEN, ATM, BRIP1, CDK12\u003c/em\u003e, and \u003cem\u003eNBN\u003c/em\u003e, which may be influenced by variation grade and tissue origin. HRD positivity was mutually exclusive with HER2\u0026thinsp;+\u0026thinsp;status in breast cancer and with TMB-H/MSI-H in endometrial cancer. Overall, these findings highlight the different strengths of the correlation between non-\u003cem\u003eBRCA1/2\u003c/em\u003e HRR gene variations and HRD and guide HRD testing in cases of \u0026ldquo;\u003cem\u003eBRCA1/2\u003c/em\u003e-wildtype\u0026rdquo; results.\u003c/p\u003e","manuscriptTitle":"Homologous Recombination Deficiency Among Patients with Germline or Somatic non-BRCA1/2 Homologous Recombination Repair Gene Variations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 05:35:00","doi":"10.21203/rs.3.rs-5538972/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-27T14:49:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-27T11:44:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-19T00:13:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62024236881311966168373598611354469","date":"2025-04-22T02:03:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63373035121725671797446918239124184763","date":"2025-04-21T10:05:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-21T09:51:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-11T12:40:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Precision Oncology","date":"2025-03-20T16:49:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-precision-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjprecisiononcology","sideBox":"Learn more about [npj Precision Oncology](http://www.nature.com/npjprecisiononcology/)","snPcode":"41698","submissionUrl":"https://submission.springernature.com/new-submission/41698/3","title":"npj Precision Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4a2cba7f-4634-480a-b2b7-e267cff9ca13","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47442766,"name":"Biological sciences/Cancer"},{"id":47442767,"name":"Health sciences/Biomarkers"},{"id":47442768,"name":"Health sciences/Molecular medicine"},{"id":47442769,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-06-23T16:02:09+00:00","versionOfRecord":{"articleIdentity":"rs-5538972","link":"https://doi.org/10.1038/s41698-025-00999-2","journal":{"identity":"npj-precision-oncology","isVorOnly":false,"title":"npj Precision Oncology"},"publishedOn":"2025-06-17 15:57:43","publishedOnDateReadable":"June 17th, 2025"},"versionCreatedAt":"2025-04-28 05:35:00","video":"","vorDoi":"10.1038/s41698-025-00999-2","vorDoiUrl":"https://doi.org/10.1038/s41698-025-00999-2","workflowStages":[]},"version":"v1","identity":"rs-5538972","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5538972","identity":"rs-5538972","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}