RB1 expression and HR proficiency define a poor-outcome molecular subtype of high grade serous ovarian carcinoma

RB1 expression and HR proficiency define a poor-outcome molecular subtype of high grade serous ovarian carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article RB1 expression and HR proficiency define a poor-outcome molecular subtype of high grade serous ovarian carcinoma Kyle C. Strickland, Zachary D. Wallen, Heidi C. Ko, Michelle F. Green, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5523163/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 6 You are reading this latest preprint version Abstract High-grade serous ovarian carcinoma (HGSOC) is a molecularly heterogeneous and lethal malignancy, with late-stage diagnosis contributing to high risk of recurrence and poor clinical outcomes. While homologous recombination (HR) deficiency and retinoblastoma gene ( RB1 ) expression have been implicated in prognosis, their combined role in shaping tumor biology and survival outcomes is not well defined. To investigate the relationship between HR status and RB1 expression and explore their potential as a combined prognostic marker, we analyzed data from two cohorts: (1) 272 HGSOC cases from The Cancer Genome Atlas (TCGA) with RB1 mRNA expression data and HR status previously annotated by Takaya et al. (HR-deficient, HRD; HR-proficient, HRP), and (2) 226 clinical HGSOC cases profiled by comprehensive genomic and immune profiling (CGIP) at OmniSeq, categorized as either HR-intact (HRi) or harboring BRCA1/2 alterations (BRCAa). Cases were additionally stratified according to RB1 mRNA expression level as RB1-high (> 25th percentile; RBH) or or RB1-low (≤ 25th percentile; RBL). HRP-RBH tumors (n = 120, 44.1%) were associated with significantly worse overall survival (OS) and progression free survival (PFS) compared to all other subgroups. Median OS for HRP-RBH was 35.9 mo, shorter than HRP-RBL (52.0 mo), HRD-RBL (57.1 mo), and HRD-RBH subgroups (53.3 mo; all p < 0.0001), and PFS demonstrated a similar trend (15.1 mo vs. 20.6, 20.2 and 20.4 mo, respectively, p = 0.0021). HRP-RBH tumors also showed higher aneuploidy scores (median 18 vs ≤ 10.5 in other subgroups, all p < 0.01). HRi-RBH tumors exhibited a distinct immune gene signature, including elevated mRNA expression of 213 differentially expressed genes and enrichment of pathways such as EMT, PI3K/AKT signaling, and interleukin signaling. Overall, this study suggests that molecular subtyping of HGSOC based on HR status and RB1 expression may provide valuable prognostic insight. HRP tumors with high RB1 expression represent a high-risk subgroup with a distinct molecular profiles and poor clinical outcomes, underscoring the need for novel therapeutic strategies targeting this aggressive subset. These findings provide a foundation for future studies aimed at developing biomarkers and treatments tailored to this challenging subset of HGSOC patients. Health sciences/Oncology/Cancer/Gynaecological cancer/Ovarian cancer Biological sciences/Genetics/Cancer genomics high grade serous ovarian carcinoma papillary serous adenocarcinoma ovarian cancer gynecologic oncology homologous recombination deficiency RB1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction High-grade serous ovarian carcinoma (HGSOC) represents the most lethal subtype of ovarian cancer, characterized by its late detection and poor prognosis. 1 , 2 Despite advancements in treatment, the five-year survival rate remains extremely low, 3 emphasizing the critical need for improved molecular insights and therapeutic strategies. HGSOC is marked by significant molecular heterogeneity, with diverse genetic and epigenetic landscapes influencing tumor behavior and response to treatment. 4 Approximately 50% of HGSOC have defects in homologous recombination (HR), a key cellular mechanism for repairing double-stranded DNA breaks. 5 Tumors deficient in HR (HRD) are particularly susceptible to specific chemotherapies, such as those inducing DNA damage (such as platinum-based chemotherapies) and those that inhibit the alternative non-homologous end joining pathway of repairing double-stranded DNA breaks (i.e., PARP inhibitors). 6 – 8 Approximately 40% of HGSOCs have alterations of genes regulating the G1 to S cell cycle transition, including participants in the retinoblastoma ( RB1 ) pathway such as CDKN2A , CCND1 , CDK4 , and CDK6 . 9 RB1 encodes a fundamental tumor suppressing protein that regulates the cell cycle by directly binding to and suppressing activity of transcription factors E2F1/2/3 and thus controlling the transition from the G1 to the S phase. 10 , 11 Alterations in RB1 and its cellular partners disrupt this control and are implicated in the pathogenesis of various cancers, including HGSOC. 12 – 15 Studies of HGSOC have demonstrated that loss of RB1 protein expression is associated with improved prognosis, 16 particularly when seen in conjunction with HRD, 17 – 19 but RB1 expression status is not incorporated into any clinical diagnostic, treatment, or prognostic paradigms at present. The molecular mechanisms underlying these associations remain uncertain. Increased understanding of the clinical course and molecular characteristics of HGSOC with respect to HR and RB1 status may enable prognostic tools and potential therapies. The goal of the current study is to explore the relationship between HR status and RB1 mRNA expression in HGSOC, focusing on their combined impact on clinical outcomes, genomic features, and gene expression changes. We hypothesize that RB1 mRNA expression, as a surrogate marker of RB1 protein expression, is associated with survival outcomes and immune-related gene expression changes depending on the HR status of HGSOC tumors. The current study validates the prognostic value of RB1 within the context of HR proficient and deficient HGSOC and provides insight into the biological consequences of these combined alterations. Materials and Methods Statistical analysis of cases from the HGSOC TCGA database Mutation, mRNA expression, copy number alteration (CNA), and protein quantification data were obtained from the Ovarian Serous Cystadenocarcinoma TCGA PanCancer database found on cBioportal. 4 , 20 – 22 Mutation data was processed to filter out silent mutations and create a binary mutation matrix. RB1 CNA status was evaluated by creating contingency tables with separate data frames for cases with RB1 deep deletion, hemi-deletion, and no change/gain. Aneuploidy score was defined as the total number of altered chromosome arms, ranging from 0 (no altered arms) to 39 (alterations in all chromosome arms). As an absolute inclusion criterion, our study was restricted to HGSOC cases from TCGA that were evaluated by Takaya et al. 23 , from which both HRD status and HRD scores were abstracted; inclusion in our study required availability of both metrics. Survival analyses were performed by grouping according to the scoring system used by Takaya et al.: 1) HR status score, with ≥ 63 considered HR-deficient (HRD) and 25th percentile considered RB1-high (RBH). Protein quantification data was acquired from the cBioPortal Firehose generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) via mass spectrometry, and protein values represent normalized, relative abundance levels on a log2 scale. Statistical analyses were performed using R statistical software (version 4.2.0). 24 Kaplan-Meier curves were fitted using the survival R package and plotted using ggplot2 and survminer. 24 – 26 Continuous variables were compared using the Mann-Whitney test. Statistical comparisons of RB1 protein levels were performed using the Wilcoxon rank-sum test for non-parametric pairwise analysis. Comprehensive genomic and immune gene expression profiling This study was approved by the Western Copernicus Group Institutional Review Board (WCG IRB, protocol #1340120). All methods were carried out in accordance with relevant guidelines and regulations, including those governing the ethical use of human-derived data. The requirement for informed consent was waived by the WCG IRB under 45 CFR 164.512, as the study involved retrospective analysis of de-identified clinical data. We queried our institutional database for cases of ovarian serous tumors that underwent comprehensive genomic and immune profiling (CGIP) at our laboratory (OmniSeq®, Labcorp Oncology) from June 2016 to October 2023. Cases were included in the cohort if they met the following criteria: (1) pathology reports from referring institutions contained a diagnosis of ovarian serous carcinoma, (2) histologic quality control performed during routine clinical confirmed concordance with serous morphology, and (3) tumors harbored at least one pathogenic somatic alteration in the TP53 gene, to enrich for high grade cases. Cases were subjected to testing using one of three assays: OmniSeq Comprehensive (OC), OmniSeq Advance (OA), or OmniSeq INSIGHT (OI) (Labcorp Oncology, Durham, NC). Each of these tests uses next-generation sequencing of DNA and/or RNA to profile solid tumors. OC (now retired) was an amplicon-based molecular panel that tested for mutations in 144 genes, fusions in 23 genes, and copy number changes. OC was later combined with a 54-gene mRNA expression profiling test, OmniSeq Immune Report Card (IRC), to define the OA test, which assessed both genomic alterations and mRNA gene expression. 27 Of note, OC/OA interrogated only a select number of HR genes including ATM, BAP1, BRCA1, BRCA2 , and PTEN . The third test, OI, utilized extracted DNA and RNA for genomic and immune profiling and includes DNA sequencing performed to evaluate 523 genes for SNVs, insertions, deletions, 59 genes for copy number amplifications, and 4 genes for copy number loss. 28 Tumor mutational burden was determined from small variant output, and microsatellite instability was assessed using up to 130 specific microsatellite sites. RNA sequencing was performed to evaluate 55 genes for fusions and 2 genes for splice variants, and immune gene expression profiling was achieved through RNA-sequencing of 395 immune-related transcripts. OI testing also includes PD-L1 immunohistochemistry, which utilizes 22C3 antibody for ovarian tumors and was scored using the tumor proportion score (TPS) method. We evaluated the following gene expression signatures as previously described: tumor immunogenicity score (TIGS), cellular proliferation (CP), and cancer testis antigen burden (CTAB). 29 , 30 Cases were included in the study provided the tumor harbored at least one pathogenic mutation in TP53 (as defined by ClinVar) and were stratified based on the presence or absence of genomic alterations in HR pathway genes. Cases with any pathogenic alteration in either BRCA1 or BRCA2 were classified as BRCA -altered (BRCAa). Cases with normal BRCA1 and BRCA2 sequences but harboring pathogenic alterations in other HR genes ( BRCA1, BRCA2, ATM, ATR, BARD1, BLM, BRIP1, CDK12, CHEK1, CHEK2, FANCA, FANCC, FANCD2, FANCE, FANCF, FANCI, FANCL, FANCM, MRE11, NBN, PALB2, RAD50, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L , and RPA1 ) were classified as non-BRCA-HR-altered (NBHRa), per the ARIEL3 study and other reports. 23 , 31 , 32 Cases with no pathogenic alterations in any HR genes were classified as HR-intact (HRi). Statistical analysis of the OmniSeq HGSOC cohort We compared TMB, PD-L1 IHC, nRPM (normalized reads per million mapped reads) values, and gene expression signature scores between cohorts. Differential gene expression between cohorts was assessed for all 395 immune-related transcripts and selected transcripts of interest, including RB1 , BRCA1 , BRCA2 , CD3 (calculated as the average of CD3D , CD3E , and CD3G ), CD68 , CD20 (calculated as the average of CD20A and CD20B ), PTEN , and MK67 . Continuous and categorical variables were evaluated using linear or Firth’s penalized logistic regression adjusting for the type of assay used to perform genomic and immune profiling (OC, OA, or OI). Continuous variables with positive skewness (TMB, nRPM values, CTAB signature scores) were given a pseudo-count of 1 to avoid 0 values and log transformed before testing. Estimated fold changes for gene expression changes between cohorts were calculated by taking the exponent of the regression coefficients from linear and Firth’s penalized regression. Multiple testing correction of P-values resulting from individual immune gene expression analysis was performed using the Benjamini-Hochberg false discovery rate (FDR) method. A significance threshold of < 0.05 and < 0.25 was used for uncorrected P-values and multiple testing corrected P-values, respectively. Genes in which expression was found to be significantly different between groups (FDR < 0.25) are referred to as differentially expressed genes (DEGs) between those groups. Of note, the < 0.25 threshold is a commonly used threshold for P -values that have been multiple testing corrected using the FDR procedure and when testing large numbers of features such as in pathway enrichment. 33 , 34 Pathway analysis of differentially expressed genes using the PANTHER classification system Lists of DEGs between BRCAa, HRi, and NBHRa groups of the OmniSeq cohort were submitted for pathway analysis utilizing the PANTHER (Protein ANalysis THrough Evolutionary Relationships) Classification System ( https://pantherdb.org ) to identify Reactome pathways related to DEGs based on a reference list of all genes from the Homo sapiens whole genome. 35 , 36 Reactome (Reactome version 86, released on 09-07-2023) is an open-source relational database of signaling and metabolic molecules, organized into biological pathways and processes, with reactions serving as its core unit. 37 Fisher's Exact test was applied to assess statistical significance of pathway enrichment, and FDR was calculated to correct for multiple testing. Pathways with a corrected p-value (FDR) < 0.05 were identified as DEG-related. Results Demographics, molecular characteristics, and survival analysis of the ovarian cancer cases from the TCGA PanCancer Atlas stratified by HR and RB1 expression status The Ovarian Serous Cystadenocarcinoma TCGA PanCancer Atlas dataset contained mutation-level data for 409 cases, which included 373 (91.2%) tumors harboring pathogenic alterations in TP53 . For subgroup and survival analyses, we abstracted data for TCGA cases with HR scores reported by Takaya et al. that also had mRNA expression data available, 23 producing an experimental cohort of 272 cases consisting of 150 (55.1%) HRP and 122 (44.9%) HRD tumors ( Supplemental Table 1 ). Patients in the HRP cohort were older (median age 60 vs 55.5 years, p=0.0002). The median HRD score was 48 for HRP and 77 for the HRD tumors, with median loss of heterozygosity (LOH) scores of 8 and 18, median telomeric-allelic imbalance (TAI) scores of 21 and 29, and median large-scale state transition (LST) scores of 17.5 and 30, respectively. HRP tumors harbored lower TMB (median 1.5 vs 2.2 mutations/Megabase, p=0.0019) but demonstrated higher aneuploidy scores (median 17 vs 10, p < 0.0001). HRP tumors demonstrated significantly increased RB1 mRNA expression levels, with a median of 983 normalized transcripts per million (nTPM) compared to 884 nTPM in HRD tumors (p0.05). We further stratified the HRP and HRD cohorts by RB1 mRNA expression level, with ≥701 nTPM (the 25 th percentile) as the cut-off for classification into either RBH or RBL groups ( Supplemental Table 1 ). Of HRP tumors (n=150), 120 (80%) were RBH and 30 (20%) were RBL. Of HRD tumors (n=122), 84 (68.9%) were RBH and 38 (31.1%) were RBL. Based on this simple classification, we observed an increased proportion of RBH tumors among the HRP tumors (p=0.048, Fisher’s exact test). Patients in the HRP-RBH group were older than those in either the HRD-RBH (median age 60 vs 55 years, p=0.0006) or HRD-RBL (median age 60 vs 56 years, p=0.039). HRP-RBH and HRP-RBL groups were comprised of patients of similar age (60 vs 59.5 years, respectively, p=0.78). We did not observe a statistically significant difference in ages of patients in the HRP-RBL group versus either the HRD-RBH (59.5 vs 55 years, p >0.05) or the HRD-RBL (59.5 vs 56 years, p >0.05) groups. To confirm that low RB1 mRNA was associated with low protein expression, we evaluated TCGA cases that had both RB1 protein quantification and mRNA expression data. We identified 73 cases from the TCGA cohort with these values, which included 12 (16%) HRP-RBL, 29 (40%) HRP-RBH, 5 (7%) HRD-RBL, and 27 (37%) HRD-RBH samples. Relative RB1 protein expression levels were significantly lower in RBL tumors compared to RBH tumors (median −0.59 vs 0.20, p = 3.7×10⁻⁶). When stratified by HRD status, RB1 protein was significantly reduced in RBL vs RBH tumors among HRP cases (median −0.40 vs 0.23, p = 7.4×10⁻⁶). A similar trend was observed in HRD tumors (median −0.85 vs 0.088), though it did not reach statistical significance (p = 0.062). No significant difference in RB1 protein levels was found when comparing all HRP vs HRD tumors irrespective of RB1 expression (median 0.072 vs 0.026, p = 0.99). Cross-comparisons of RB1 expression and HRD status showed that HRD/RBL tumors had significantly lower RB1 protein than HRP/RBH tumors (median −0.85 vs 0.23, p = 0.020). Conversely, HRD/RBH tumors had significantly higher RB1 protein than HRP/RBL tumors (median 0.088 vs −0.40, p = 1.2×10⁻⁴). HRD, LST, LOH, and TAI scores reported by Takaya et al. 2020 were compared within HRP and HRD groups to evaluate potential associations with low or high RB1 expression. Within the HRP cohort, RBH tumors demonstrated lower HRD scores than RBL tumors (47 vs 54.5, p=0.0016), with significantly lower LOH scores (7 vs 10, p=0.0300) ( Figure 1A ), significantly lower LST scores (17 vs 22, p=0.0023), and no difference in TAI scores (21 vs 21, p >0.05). Comparing HRD-RBH and HRD-RBL tumors, we did not observe statistically significant differences in HRD (75.5 vs 78, p >0.05), LOH (18 vs 19; p=0.0624), LST (30 vs 29.5, p >0.05) or TAI (29 vs 28, p >0.05) scores, respectively.Median aneuploidy scores were markedly higher in the HRP-RBH group compared to the other cohorts: HRP-RBL (18 vs 10.5, p=0.0013), HRD-RBL (18 vs 9, p<0.0001), and HRD-RBH (18 vs 10.5, p<0.0001) ( Figure 1B ). TMB varied across groups, with the lowest observed in the HRP-RBL group (1.3 mut/Mb) and the highest in the HRD-RBH group (2.3 mut/Mb). The differences in TMB reached statistical significance between HRP-RBH vs HRD-RBH groups (1.5 vs 2.3 mut/Mb, respectively; p=0.0003) and HRP-RBL vs HRD-RBH groups (1.3 vs 2.3 mut/Mb, respectively; p=0.0161) ( Figure 1C ). There was no difference in RB1 mRNA expression scores within HRP and HRD groups after stratifying samples as RBL or RBH: HRP-RBH vs HRD-RBH (1100 vs 1070 nTPM, p>0.05) and HRP-RBL vs HRD-RBL (523 vs 479 nTPM, p>0.05). Ki-67 mRNA expression levels were highest in the HRP-RBL group, which was statistically higher than that of the HRP-RBH (3135 vs 2082 nTPM, p=0.0078) and the HRD-RBH (3135 vs 2210, p=0.028) ( Figure 1D ). There was no statistically significant difference in Ki-67 mRNA expression between HRP-RBL and HRD-RBL groups (3135 vs 2277, p>0.05). Patients in the HRP cohort demonstrated significantly shorter OS (36.3 vs 53.3 months, p < 0.0001) and PFS (16.0 vs 20.2 months, p=0.0020) than those with HRD tumors ( Figure 2A-B ). After stratifying patients according to RB1 mRNA expression levels, survival outcomes for HRP-RBH patients were shorter compared to all other groups. Among HRP tumors, those with RBH had lower median OS (35.9 vs 52.0 months, p=0.015) and PFS (15.1 vs 20.6 months, p=0.031) ( Figure 2C-D ). Among RBH expressing tumors, HRP patients had markedly lower survival metrics compared to HRD-RBH tumors, including lower median OS (35.9 vs 57.1 months, p<0.0001), and PFS (15.1 vs 20.2 months, p=0.0007). Similarly, compared to the HRD-RBL group, HRP-RBH tumors were associated with decreased OS (35.9 vs 57.1 months, p=0.0017) and PFS (15.1 vs 20.2 months, p=0.013). In contrast to HRP-RBH tumors, we did not observe statistically significant differences in survival metrics between HRP-RBL tumors and either HRD-RBH and HRD-RBL groups. Among the RBL groups, HRP-RBL patients demonstrated a shorter median OS (52.0 vs 57.1 months, p >0.05) and longer PFS (20.6 vs 20.2 months, p >0.05) than HRD-RBL patients. Similar results were obtained when compared to HRD-RBH patients, with HRP-RBH patients having a shorter median OS (52.0 vs 53.3 months, p >0.05) and longer PFS (20.6 vs 20.4 months, p >0.05). Additionally, we did not observe a statistically significant difference in survival between the two groups of HRD patients; HRD-RBH patients demonstrated lower median OS (53.3 vs 57.1 months, p >0.05) and comparable PFS (20.4 vs 20.2 months, p >0.05). We identified 233 cases from the TCGA dataset that contained gene-level copy number information, in addition to mRNA expression data and HR evaluation by Takaya et al. 23 The majority of HGSOC contained hemizygous deletion of the RB1 gene (56.7%), 25.3% of cases were copy number neutral, 8.2% of cases had homozygous deletion of RB1 , and 9.9% had amplification of the RB1 gene ( Supplemental Table 2 ). RB1 mRNA expression was tightly correlated with RB1 copy number status: cases with homozygous deletion of RB1 demonstrated the lowest gene expression (median 486 nTPM), cases with hemizygous deletion of RB1 had lower expression than RB1 copy number neutral cases (median 865 vs 1135 nTPM), and cases with amplification demonstrated the highest gene expression (median 1525 nTPM). Only 2 of 19 (10.5%) cases with homozygous deletion demonstrated high levels of RB1 gene expression (TCGA-24-2262 and TCGA-61-2109). Among cases with hemizygous deletion of RB1 , 95 of 132 (72.0%) were RBH, and two cases with hemizygous deletion of RB1 harbored single nucleotide variants in RB1 : (1) TCGA-24-1469 harbored R763T and had an RB1 gene expression of 1644 nTPM and (2) TCGA-29-1703 harbored a hotspot splice site mutation (X405) and had an RB1 gene expression of 717 nTPM. Just 4 of 59 (6.8%) of RB1 copy number neutral cases were RBL, and 3 of these 4 (75%) had detectable, pathogenic SNVs in RB1 : (1) TCGA-61-1741 harbored a splice site alteration (X474) and had an RB1 gene expression of 319 nTPM , (2) TCGA-24-1849 harbored a frameshift mutation (F526Lfs*) and had an RB1 gene expression of 612 nTPM, and (3) TCGA-24-1463 harbored a frameshift mutation (C553Wfs*) and had an RB1 gene expression of 480 nTPM). None of the cases with amplification of RB1 were RBL or harbored SNVs in RB1 . HR gene status is associated with divergent immune gene expression profiles We next investigated associations between RB1 and HRD status in a separate cohort of HGOSC that were processed for clinical CGIP by our laboratory. We identified 406 ovarian high grade serous tumors from 249 provider facilities across the United States that had CGIP performed by OmniSeq and met inclusion criteria, including the presence of a pathogenic mutation in TP53 to exclude low grade serous tumors. Our final dataset included samples subjected to the following tests: OmniSeq Comprehensive (OC, n=64), OmniSeq Immune Report Card (IRC, n=26), OminSeq Advance (OA, n=97), and OmniSeq INSIGHT (OI, n=219). The most common pathogenic alterations were observed in the following genes ( Figure 3 ): BRCA1 (21.93%), PIK3R1 (18.23%), NF1 (17.98%), APC (15.76%), ZFHX3 (15.02%), and BRCA2 (14.53%). To evaluate the effect of HR gene alterations on molecular characteristics, we segregated our cases into three groups: (1) BRCAa, which included cases with alterations in BRCA1 and/or BRCA2 (n=128, 31.5%); (2) NBHRa, which included cases with alterations in any non- BRCA HR-related genes (n=114, 28.1%), and (3) HRi, defined as cases without alterations in HR-related genes (n=164, 40.4%). Patients had a median age at test ordering of 65 years (range: 25-90). The BRCAa group contained younger patients than the NBHRa (median 65 vs 71 years, P0.05). A comparison of CGIP results from the three HR-related groups revealed significant differences across several variables ( Supplemental Table 3 ). The mean TMB for all patients was 6.4 mut/Mb, with BRCAa patients showing a significantly higher mean TMB (12.1±72.5) compared to either NBHRa (4.4±2.48, P=0.0052) or HRi (3.9±2.3, P=0.0059) groups, and the percentage of patients with high TMB (≥10 mutations/Mb) was notably higher in the BRCAa group (3.5%) compared to the HRi group (0.7%, P=0.0043). Although PD-L1 IHC expression is not a predictive biomarker associated with sensitivity to FDA approved therapies for HGSOC patients, PD-L1 expression levels using TPS scoring criteria were available for all patients in this cohort. We observed no differences in PD-L1 TPS scores among the three groups (P>0.05), and the proportion of PD-L1 positive patients (≥1% TPS) was similar across all groups, with 48.0% in BRCAa, 37.4% in NBHRa, and 44.1% in HRi, with no significant differences seen in pairwise comparisons. Tumor Immunogenicity Score (TIGS), a composite immune gene expression score, was significantly lower in the BRCAa group (40.8±17.7) compared to the HRi group (46.5±21.8, P=0.033). BRCAa patients also had a lower frequency of strong TIGS (14.0%) compared to HRi (30.8%, P=0.0022). CP expression score was significantly higher in the BRCAa group (51.8±23.3) compared to the HRi group (44.4±24, P=0.036); however, no significant differences were observed across semi-quantitative CP score distributions (P>0.05). CTAB expression score was higher for the HRi group (317.4±236.5) compared to BRCAa tumors (285.2±225, P=0.014). Differential gene expression analysis revealed marked differences in gene expression between the BRCAa and HRi cohorts, with 14 DEGs elevated in BRCAa vs 97 elevated in the HRi group ( Figure 4, Supplemental Table 4 ). The NBHRa group showed fewer gene expression changes, with 5 DEGs elevated in BRCAa vs 6 in NBHRa and with no elevated DEGs with FDR <0.25 when comparing the NBHRa and HRi groups. RB1 expression status is associated with distinct immune gene expression profiles To evaluate the effect of RB1 mRNA expression on genomic features and immune profiles in the cohort, we stratified each HR-related groups based on RB1 mRNA expression levels, designating the lower quartile as RB1 “low” and combining the upper three quartiles as RB1 “high”. We identified 332 cases from the HGOSC cohort that completed gene expression profiling. The cohort was comprised of 114 (34.3%) RBL and 218 (65.7%) RBH malignancies, which included 44 (47.3%) RBL and 49 (52.7%) RBH BRCAa tumors, 28 (26.4%) RBL and 78% (73.6%) RBH NBHRa tumors, and 42 (31.6%) RBL and 91 (68.4%) RBH HRi tumors ( Supplemental Table 5 ). Chi-Square analysis demonstrated that HRi (P=0.0184) and NBHRa (P=0.0030) cohorts were both enriched for RBH tumors vs. the BRCAa cohort. In general, RBL status was associated with lower TIGS (37.7 vs 49.0, P<0.0001), lower CP (44.3 vs 50.9, P=0.033), and reduced expression of BRCA2 (P<0.0001), CD3 (P=0.0002), CD20 (P<0.0001), CD68 (P=0.0012), and PTEN (P0.05). Within the BRCAa subgroup, RBL vs. RBH tumors showed no statistically significant differences in TIGS (38.9 vs 42.6, P>0.05); in contrast, RBH status was associated with higher TIGS in HRi tumors (36.7 vs 51.0, P=0.0009). Regarding individual immune-related gene differences, RB1-status in BRCAa tumors was not associated with differences in CD3, CD20, and CD68 expression (P>0.05); in contrast, RBL tumors in the HRi cohort were associated with lower CD3 (P=0.0078), CD20 (P=0.0130), and CD68 (P=0.045) mRNA expression. Gene expression analysis revealed a smaller number of elevated DEGs in RBL vs RBH tumors (9 vs 276) in the OmniSeq cohort ( Figure 5, Supplemental Table 6 ). Among BRCAa tumors, RBL status was associated with 10 elevated DEGs in the BRCAa-RBL vs. 96 in the BRCAa-RBH samples. Among HRi tumors, RBL status revealed 5 DEGs with elevated fold change vs. 213 in the HRi-RBH group. Chi-square analysis demonstrated that the distribution of DEGs was different for BRCAa and HRi tumors when considering RB1 status, with a relatively greater proportion of DEGs occurring in the HRi-RBH vs. the BRCAa-RBH subgroup (P=0.0024). To further explore the differences in immune expression profiles, we compared molecular results between the HR-related subgroups restricted to either the RBL ( Supplemental Table 7 ) or RBH subsets ( Supplemental Table 8 ). Among RBL tumors, we observed a higher TMB (mut/Mb) in BRCAa vs HRi tumors (5.2 vs 3.9, P=0.0058) and in BRCAa vs NBHRa tumors (5.2 vs 5, P=0.039). In contrast, examination of the RBH tumors revealed that BRCAa tumors had lower TIGS than HRi tumors (42.6 vs 51.0, P=0.025) and lower CTAB than either HRi (232.6 vs 327.6, P=0.011) or NBHRa (232.6 vs 362.8, P=0.026) tumors. Among individual immune-related genes, CD68 mRNA expression was greater in HRi-RBH than BRCAa-RBH tumors (3266.4 vs 2022.6 nRPM, P=0.02). Next, we evaluated fold changes in gene expression to compare the HR-related groups stratified by RB1 status (i.e., BRCAa-RBL vs HRi-RBL and BRCAa-RBH vs. HRi-RBH). Among RBL tumors ( Supplemental Table 9 ) BRCAa-RBL tumors exhibited 7 DEGs with elevated fold change compared to zero elevated DEGs in the HRi-RBL group. Among RBH tumors ( Supplemental Table 10 ), BRCAa-RBH tumors had 8 DEGs with elevated fold change compared to 80 DEGs elevated in HRi-RBH tumors. Chi-square analysis showed that the proportion of elevated DEGs was significantly greater for HRi-RBH tumors (P>0.0001). Pathway analysis of differentially-expressed immune-related genes We performed pathway analysis of DEGs from the OmniSeq cohort using the PANTHER classification system to identify overrepresented Reactome pathways and calculate fold enrichment (FE) over expected values based on the reference gene list. DEG lists and pathways analysis results are detailed in Supplemental Table 11 . Stratification by HR status Considering DEGs related to HR gene status, BRCAa tumors showed enrichment of pathways related to interferon signaling, including interferon α/β signaling (FE>100, FDR=7.72x10 -22 ) and ISG15 antiviral mechanisms (FE=62.1, FDR=6.08x10 -3 ). Conversely, HRi tumors exhibited overrepresentation of epithelial-mesenchymal transition (EMT; FE>100, FDR=0.0143), adaptive immune regulation (IL-4/IL-13 signaling; FE=24.9, FDR=2.21x10 -12 ), and anti-inflammatory pathways including CD163 (FE=70.7, FDR=1.46x10 -3 ) and IL-10 signaling (FE=33, FDR=5.32x10 -7 ). Additionally, HRi tumors demonstrated DEGs of kinase pathways including PI3K/AKT signaling in cancer (FE=11.5, FDR=8.88x10 -3 ), ALK (FE=22.7, P=0.0276), and NTRK1 (FE=8.1, P=0.0330). Stratification by RB1 mRNA expression Next, we stratified tumors according to RB1 mRNA expression status. We found no significantly overrepresented pathways in the RBL group, whereas the RBH group demonstrated enrichment of multiple signaling pathways: EMT (FE=36.8, FDR=0.0191), interferon α/β (FE=14.5, FDR=1.30x10 -11 ), interferon γ (FE=15.3, FDR=4.12x10 -16 ), chemokine receptor (FE=33.5, FDR=3.38x10 -31 ), PD-1 (FE=33.0, FDR=3.79x10 -15 ), and interleukin pathways (FE=10.1, FDV=5.82x10 -42 ), including IL-4/13 (FE=20.5, FDR=1.11x10 -29 ), IL-6 (FE=12.3, FDR=6.22x10 -3 ), IL-10 (FE=35.9, FDR=3.87x10 -27 ), IL-12 (FE=11.8, FDR=3.71x10 -6 ), and IL-21 (FE=44.1, FDR=9.03x10 -8 ). Additionally, kinase signaling pathways were represented in RBH tumors, such as PIK3K cascade (FE=8.2, FDR=0.0240), MET signaling of PI3K/AKT (FE=29.4, FDR=0.0299), and STAT3 nuclear events downstream of ALK signaling (FE=33.4, FDR=1.11x10 -5 ). Stratification of BRCAa tumors by RB1 status Among BRCAa tumors, we identified no pathways enriched in RBL tumors compared to RB1 high tumors. However, BRCAa-RBH tumors showed enrichment in interferon α/β (FE=19.8, FDR=1.41x10 -5 ), trafficking and processing of endosomal toll-like receptor (FE=49.5, FDR=1.93x10 -3 ), chemokine receptor (FE=30.1,FDR=9.96x10 -8 ), and interleukin signaling pathways, including IL-2 (FE=35.7, FDR=0.0481), IL-4/IL-13 (FE=21.2, FDR=2.13x10 -9 ), IL-10 (FE=33.4, FDR=5.65x10 -7 ), and IL-12 (FE=15.3, FDR=0.0074). Additionally, BRCAa-RBH tumors demonstrated DEGs of kinase pathways including PI3K/AKT (FE=16.3, FDR=4.92x10 -5 ) and ALK (FE=45.9, FDR=9.98x10 -7 ). Stratification of HRi tumors by RB1 status In an analysis restricted to HRi tumors alone, we identified no pathways enriched in HRi-RBL tumors over HRi-RBH tumors. However, HRi-RBH tumors showed enrichment of EMT (FE=48.5, FDR=0.0139) and interleukin-related signaling pathways, including IL-6 (FE=35.3, FDR=1.52x10 -4 ), IL-9 (FE=43.1, FDR=6.80x10 -5 ), IL-10 (FE=41.0, FDR=7.81x10 -24 ), IL-12 (FE=12.1, FDR=8.30x10 -5 ), and IL-21 (FE=38.8, FDR=1.02x10 -4 ). Additionally, HRi-RBH tumors showed activation of the following kinase pathways over HRi-RBL tumors: PI3K/AKT (FE=10.6, FDR=2.70x10 -6 ), ALK (FE=15.3, FDR=5.90x10 -11 ), and MET (FE=6.4, FDR=0.0221). Stratification of RBL and RBH tumors by HR status Finally, we stratified tumors by RB1 expression status and compared DEGs elevated in BRCAa and HRi groups. Among RBL tumors, BRCAa was associated with interferon α/β signaling (FE>100, FDR=3.29x10 -8 ), cytokine signaling (FE=23.0, FDR=1.57x10 -5 ), and immune system activation (FE=8.4, FDR=4.61x10 -3 ); HRi-RBL tumors showed no elevated differentially expressed genes with an FDR 100, FDR=7.00x10 -3 ). Compared to BRCAa-RBH tumors, HRi-RBH tumors were associated with a variety of overrepresented pathways including EMT (FE>100, FDR=0.0179), IL-4/IL-13 (FE=25.5, FDR=2.27x1- -10 ), IL-10 (FE=4.6, FDR=2.61x10 -9 ), and general receptor tyrosine kinase signaling (FE=44.9, FDR=0.0229). Discussion The aim of this study was to explore the relationship between HR status and RB1 mRNA expression in HGSOC. As summarized in Fig. 6 , our results show that HRP-RBH tumors are a distinct molecular subgroup within HGSOC, characterized by aneuploidy, unique gene expression patterns, and poorer survival outcomes compared to other subgroups. These observations highlight the potential of HR and RB1 stratification to improve prognostic assessments and potentially tailor future therapeutic strategies. The loss of RB1, commonly observed in HGSOC, 4 , 9 has long been implicated as a core driver of malignant transformation. 12 – 15 , 38 We confirmed that RB1 expression levels closely correlate with RB1 copy number, where homozygous deletions lead to the lowest RB1 mRNA expression, and amplifications result in the highest levels. This gene dosage effect aligns with prior studies, 39 , 40 highlighting RB1 deletion as a major mechanism for its loss of function. However, in tumors with hemizygous deletions or low RB1 expression, epigenetic mechanisms, such as promoter hypermethylation, may silence the remaining allele, contributing to RB1 loss as seen in other tumor types. 41 – 45 Moreover, our analysis identified several inactivating single nucleotide variants (SNVs) in RB1, which, although infrequent, may substantially impair its function. These variants typically involved frameshift or splice site mutations, aligning with the role of RB1 as a critical protein-binding cell cycle checkpoint protein. 40 Notably, we also observed concordance between RB1 mRNA and protein levels within the TCGA cohort, supporting the use of mRNA as a surrogate marker for protein expression. Future studies incorporating immunohistochemistry or proteomic profiling may help validate the mRNA cutoffs used and potentially enable RB1 stratification for clinical purposes. RBH tumors, particularly within the HRi group, showed elevated DEGs of immune signaling pathways (e.g., IL-10, IL-4/13) and oncogenic kinase signaling pathways (e.g., PI3K/AKT, MET). These pathways suggest that RBH tumors are not only driven by tumor-protective immune regulation but also by pro-survival signaling. In contrast, RBL tumors lacked DEGs of specific pathways, possibly reflecting a more quiescent signaling environment driven by the absence of cell cycle regulation. Of note is that patients with HRP-RBH tumors in the TCGA cohort demonstrated the poorest survival metrics across all subgroups, but the mechanisms driving poor survival in this context are uncertain and should be investigated further. Future studies should investigate the relationship between RB1 expression and platinum resistance. RB1-status in HGSOC may have therapeutic implications because inhibition of CDK4/6 has been proposed as a therapeutic target. 9 , 46 , 47 In normal cell cycle regulation, phosphorylation of RB1 by CDK4/6 results in its disassociation from E2F1/2/3, resulting in transcriptional activation. Thus, inhibition of these RB1 regulators prevents inhibitory phosphorylation allowing RB1 to inhibit cellular proliferation and delay disease progression. 48 – 50 However, there is strong evidence that loss of RB1 removes the evolutionary advantage provided by CDK4/6 deregulation; 51 in other words, CDK4/6 inhibitors may not have any effect in tumors with loss of RB1 expression. Therefore, RB1 loss should be considered in exclusion criteria for trials that target CDK4/6 activity. Our study is orthogonal and complementary to existing molecular classifications of HGSOC, 4 , 52 , 53 notably the four major transcriptomic subtypes (immunoreactive, differentiated, proliferative, mesenchymal), by evaluating just two variables: HR status and RB1 expression. BRCAa tumors, resembling the immunoreactive subtype, exhibited a favorable prognosis and enhanced activation of innate immune responses, particularly through interferon signaling. This immune activation is likely driven by genomic instability, leading to the accumulation of neoantigens and cytosolic DNA. 54 – 56 In contrast, HRi-RBH tumors shared similarities with the mesenchymal subtype, demonstrating upregulation of immune evasion mechanisms, including anti-inflammatory pathways (e.g., IL-10, CD163) and EMT, suggesting these tumors rely on these mechanisms to promote tumor progression and evade immune detection. Of note, we observed a lack of significant gene expression changes in the NBHRa subgroup, suggesting that NBHRa included both HR-deficient and HR-proficient tumors, supporting its exclusion from our downstream analyses. The current study has several important limitations. As a retrospective study, it is inherently limited by the quality and completeness of the available data. While HR status was available for the TCGA cohort, we had to rely on gene alterations to assess HR status in the OmniSeq cohort, which may not be as reliable. The potential misclassification of HR status based on gene alterations alone may not capture the full complexity of gene expression changes associated with HR proficiency, leading to possible inaccuracies. Furthermore, our study's findings are based on a cohort of advanced HGSOC undergoing CGIP, and external validation is needed to ensure generalizability. Future studies should aim to replicate our findings in larger, more diverse datasets with well-defined HR status for each case. In conclusion, our study provides evidence for the molecular subtyping of HGSOC based on HR status and RB1 expression. We identified HRP-RBH tumors as the largest subgroup, characterized by the poorest prognosis and a distinct genomic instability profile marked by high aneuploidy and particularly low levels of HR deficiency. Molecular stratification of HGSOC based on HR status and RB1 expression may have implications for prognosis and therapeutic decision-making, with the potential to identify patients with platinum-resistant tumors. Our findings highlight the potential of RB1 mRNA expression as a prognostic biomarker, particularly for patients with HRP HGSOC. Given the distinct molecular, immune and prognostic profiles of HRP-RBH tumors, these patients may benefit from alternative follow-up strategies. CDK4/6 inhibitors, which target cell cycle regulation, could be promising for RB1-intact HGSOC, although further studies are needed to confirm their efficacy, particularly considering resistance mechanisms associated with RB1 loss. Declarations Competing Interests KCS, ZDW, HK, MFG, AD, SH, TJJ, ME, BJC, SR, EAS, and RAP are all employees of Labcorp and declare stock ownership from Labcorp. AR is an employee of Thermo Fisher Scientific and declares stock ownership from Thermo Fisher Scientific. Any remaining authors declare no conflicts of interest. Ethics Declaration Approval for this study was obtained from an independent institutional review board (IRB), the Western Copernicus Group (WCG) (www.wcgclinical.com), under protocol # 1340120. The WCG IRB determined this study met requirements for a waiver of informed consent under 45 CFR 164.512. Author Contribution KCS, ZDW, SP, EAS, and RAP conceptualized the study. 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STING agonism reprograms tumor-associated macrophages and overcomes resistance to PARP inhibition in BRCA1-deficient models of breast cancer. Nat. Commun. 13 10.1038/s41467-022-30568-1 (2022). Additional Declarations Competing interest reported. KCS, ZDW, HK, MFG, AD, SH, TJJ, ME, BJC, SR, EAS, and RAP are all employees of Labcorp and declare stock ownership from Labcorp. AR is an employee of Thermo Fisher Scientific and declares stock ownership from Thermo Fisher Scientific. Any remaining authors declare no conflicts of interest. Supplementary Files SupplementalTable1.xlsx SupplementalTable7.xlsx SupplementalTable2.xlsx SupplementalTable11.xlsx SupplementalTable5.xlsx SupplementalTable4.xlsx SupplementalTable8.xlsx SupplementalTable6.xlsx SupplementalTable9.xlsx SupplementalTable10.xlsx SupplementalTable3.xlsx Cite Share Download PDF Status: Published Journal Publication published 12 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Jul, 2025 Reviews received at journal 11 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Submission checks completed at journal 04 Jul, 2025 First submitted to journal 02 Jul, 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. 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Jensen","email":"","orcid":"","institution":"Labcorp (1)","correspondingAuthor":false,"prefix":"","firstName":"Taylor","middleName":"J.","lastName":"Jensen","suffix":""},{"id":483092535,"identity":"f7ff7e97-2f45-4555-b5ea-8b4525e03d7b","order_by":9,"name":"Marcia Eisenberg","email":"","orcid":"","institution":"Labcorp (2)","correspondingAuthor":false,"prefix":"","firstName":"Marcia","middleName":"","lastName":"Eisenberg","suffix":""},{"id":483092536,"identity":"db368d71-cc40-4372-8488-1368d0ce489b","order_by":10,"name":"Brian J. Caveney","email":"","orcid":"","institution":"Labcorp (2)","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"J.","lastName":"Caveney","suffix":""},{"id":483092537,"identity":"9f46b32c-49da-42c5-a5a7-e17e5d38ef4f","order_by":11,"name":"Shakti Ramkissoon","email":"","orcid":"","institution":"Labcorp (1)","correspondingAuthor":false,"prefix":"","firstName":"Shakti","middleName":"","lastName":"Ramkissoon","suffix":""},{"id":483092538,"identity":"edcf111c-88ec-485a-859f-f8b4c91aecf3","order_by":12,"name":"Eric A. Severson","email":"","orcid":"","institution":"Labcorp (1)","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"A.","lastName":"Severson","suffix":""},{"id":483092540,"identity":"79fdae3f-a734-4a5a-bac2-263495a172c9","order_by":13,"name":"Rebecca A. Previs","email":"","orcid":"","institution":"Labcorp (1)","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"A.","lastName":"Previs","suffix":""}],"badges":[],"createdAt":"2024-11-25 22:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5523163/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5523163/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-15156-9","type":"published","date":"2025-08-12T15:57:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86625506,"identity":"e754adce-95f6-4fb5-b57b-001b637f9294","added_by":"auto","created_at":"2025-07-14 05:11:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":868709,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular characteristics of cases from the Ovarian Serous Cystadenocarcinoma TCGA dataset.\u003c/strong\u003e We compared (A) HRD scores, (B) aneuploidy scores, (C) tumor mutational burden, and (D) Ki-67 gene expression for each molecular subgroup we identified from the ovarian TCGA data. ns P \u0026gt; 0.05, * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/d194e06cee9af8674d0b084d.png"},{"id":86625507,"identity":"ea533b8e-7ca2-4d8a-9f8c-059ee8ffb86a","added_by":"auto","created_at":"2025-07-14 05:11:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":835121,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival of HGSOC TCGA patients stratified by HR status and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eRB1 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eexpression status.\u003c/strong\u003e TCGA patients were stratified according to the HR status of their tumor as published by Takaya et al. 2020, and Kaplain Meier curves were generated comparing (A) overall survival and (B) progression-free survival between HRP and HRD cohorts. Patients were further stratified based on the level of RB1 mRNA expression, and Kaplan Meier curves were generated comparing (C) overall survival and (D) progression-free survival for the various subgroups.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/7e4e49044cba9a8becffc8c7.png"},{"id":86625508,"identity":"0f54a566-bc8c-4373-8223-2d551d9cd08c","added_by":"auto","created_at":"2025-07-14 05:11:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":282622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenomic landscape of cases from the OmniSeq HGSOC cohort.\u003c/strong\u003e The histogram below shows the prevalence and type of alterations for the most commonly altered genes in the OmniSeq HGSOC cohort. Of note, 100% of the tumors harbored pathogenic alterations in \u003cem\u003eTP53\u003c/em\u003e (not shown).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/b89ea500eb8f8fed0c2e5602.png"},{"id":86625505,"identity":"09d7f9b0-2b35-4331-9261-4a086fe8e469","added_by":"auto","created_at":"2025-07-14 05:11:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1411076,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene expression differences observed between BRCAa, NBHRa, and HRi HGSOC.\u003c/strong\u003e Volcano plots of statistical significance vs. fold change for HR-related groups from the OmniSeq HGSOC cohort. Diamond dots represent genes with FDR\u0026lt;0.25 threshold, whereas circular dots represent genes with an uncorrected P-value \u0026lt; 0.05. (A) We observed 111 DEGs when comparing the BRCAa and HRi cohorts (blue = increased in BRCAa; red = increased in HRi). (B) We observed 11 DEGs when comparing gene expression of BRCAa to NBHRa HGSOC (blue = increased in BRCAa; red = increased in NBHRa). (C) We observed no statistically significant DEGs (i.e. FDR\u0026lt;0.25) when comparing NBHRa to HRi tumors (blue = increased in NBHRa; red = increased in HRi).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/c4b2d681d8f30a7c5fcf6d99.png"},{"id":86627436,"identity":"5e319806-3cab-4402-9412-c221561c530e","added_by":"auto","created_at":"2025-07-14 05:35:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2310583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene expression changes associated with RB1 expression status and HR-related genes in HGSOC.\u003c/strong\u003e Volcano plots of statistical significance vs. fold change for HR-related groups from the OmniSeq HGSOC cohort. Diamond dots represent statistically significant differentially expressed genes (DEGs, i.e. FDR\u0026lt;0.25). Circular dots represent expression differences of potential interest with an uncorrected P-value \u0026lt; 0.05 and FDR\u0026gt;0.25. (A) We identified 285 DEGs when comparing RBL to RBH tumors (blue = increased in RBL; red = increased in RBH). (B) There were 106 DEGs identified when comparing BRCAa-RBL (increased in blue) and BRCAa-RBH tumors (increased in red). (C) We identified 218 DEGs when comparing HRi-RBL (increased in blue dots) and HRi-RBH tumors (increased in red dots). (D) Examining RBL groups, we found 7 DEGs increased in BRCAa-RBL (blue diamonds) and no DEGs elevated in HRi-RBL tumors (red diamonds). (E) Among RBH groups, 8 DEGs were elevated in BRCAa-RBH tumors (blue) compared to 80 DEGs elevated in HRi-RBH tumors (red). (F) Chi-square analysis demonstrated that the proportion of DEGs was increased in RBH tumors (upper panel) and specifically in HRi-RBH tumors (lower panel).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/986b7ec9f650d2a0c81178a6.png"},{"id":86627447,"identity":"f71c80e4-1986-45ef-88f6-9b92544a93e6","added_by":"auto","created_at":"2025-07-14 05:35:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2478548,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary of study cohorts and results. \u0026nbsp;\u003c/strong\u003eWe examined two cohorts enriched for HGSOC, which were categorized based on HR scores (TCGA) or HR gene alterations (OmniSeq) and further subclassified based on RB1 mRNA expression status. As expected, HRD \u0026amp; BRCAa cohorts were associated with a lower age than HRP \u0026amp; HRi. OS and PFS was diminished in patients with HRP-RBH tumors, which were also associated with increased aneuploidy. Evaluation of immune-related gene expression was performed on tumors with intact HR genes, demonstrating that HRi-RBH tumors showed gene expression changes related to epithelial-mesenchymal transformation (EMT), potentially oncogenic kinase activation, and interleukin signaling. In contrast, BRCAa tumors showed relative activation of interferon α/β and cytokine activation relative to HRi tumors.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/5c02085e37f8c2025628ccd9.png"},{"id":89310597,"identity":"cdf6dfc4-4ba6-4cf0-9324-7523dfcfdf84","added_by":"auto","created_at":"2025-08-18 16:08:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9233683,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/89b24056-d4eb-4149-8f97-f952b12df35f.pdf"},{"id":86625512,"identity":"01b08408-25b0-4f7b-9bcb-7b65536bf2bd","added_by":"auto","created_at":"2025-07-14 05:11:22","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14101,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/7caa735132283a11083ebbe6.xlsx"},{"id":86625521,"identity":"b15e065a-ebcc-4b0a-bef5-04189009e88c","added_by":"auto","created_at":"2025-07-14 05:11:22","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13407,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/36da4bb055a75f16e8e697e0.xlsx"},{"id":86625511,"identity":"772f4b3a-baea-48b8-a16a-54422374fb45","added_by":"auto","created_at":"2025-07-14 05:11:22","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11195,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/25922ace68118ad5ca751719.xlsx"},{"id":86627449,"identity":"cd1db492-f2c9-48e6-bf7f-d43ab6758bb1","added_by":"auto","created_at":"2025-07-14 05:36:05","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":49906,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable11.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/dd68a49ab888eb9619092eb3.xlsx"},{"id":86625520,"identity":"b2ebf3d4-597c-4243-891e-953f6fd2e1b3","added_by":"auto","created_at":"2025-07-14 05:11:22","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15918,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/04d3a766d53a6e0c5953b863.xlsx"},{"id":86627456,"identity":"37edbbc2-06b9-4ab6-aefd-69132f13d4f0","added_by":"auto","created_at":"2025-07-14 05:36:14","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":486327,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/960110c2be412bf51d2aa1b8.xlsx"},{"id":86625504,"identity":"ce9fe7ae-834b-4d66-9902-f7ed1d653b44","added_by":"auto","created_at":"2025-07-14 05:11:21","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":13506,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/aee97c4daba2bbed84031e09.xlsx"},{"id":86625556,"identity":"24b7ec89-f09b-49bd-8afa-17d645c04ba5","added_by":"auto","created_at":"2025-07-14 05:11:23","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":947297,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/42d6d9cac8d9ea0c6bfadc01.xlsx"},{"id":86625532,"identity":"601a1758-7c39-47c6-bfbb-11f72cf88bfe","added_by":"auto","created_at":"2025-07-14 05:11:23","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":661876,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/ef3f15cd692924d9f12f030b.xlsx"},{"id":86625548,"identity":"ab7a4b80-c50d-4998-967e-e6fc3ac45035","added_by":"auto","created_at":"2025-07-14 05:11:23","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":757111,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable10.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/952c9a91fd2e41d9c052d6b4.xlsx"},{"id":86625550,"identity":"2a0a8b92-6a2b-4357-88ad-3a4e6ff5b3b0","added_by":"auto","created_at":"2025-07-14 05:11:23","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":19478,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5523163/v1/596421392ea42cd1d47e8e1f.xlsx"}],"financialInterests":"Competing interest reported. KCS, ZDW, HK, MFG, AD, SH, TJJ, ME, BJC, SR, EAS, and RAP are all employees of Labcorp and declare stock ownership from Labcorp. AR is an employee of Thermo Fisher Scientific and declares stock ownership from Thermo Fisher Scientific. Any remaining authors declare no conflicts of interest.","formattedTitle":"RB1 expression and HR proficiency define a poor-outcome molecular subtype of high grade serous ovarian carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHigh-grade serous ovarian carcinoma (HGSOC) represents the most lethal subtype of ovarian cancer, characterized by its late detection and poor prognosis.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Despite advancements in treatment, the five-year survival rate remains extremely low,\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e emphasizing the critical need for improved molecular insights and therapeutic strategies. HGSOC is marked by significant molecular heterogeneity, with diverse genetic and epigenetic landscapes influencing tumor behavior and response to treatment.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Approximately 50% of HGSOC have defects in homologous recombination (HR), a key cellular mechanism for repairing double-stranded DNA breaks.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Tumors deficient in HR (HRD) are particularly susceptible to specific chemotherapies, such as those inducing DNA damage (such as platinum-based chemotherapies) and those that inhibit the alternative non-homologous end joining pathway of repairing double-stranded DNA breaks (i.e., PARP inhibitors).\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eApproximately 40% of HGSOCs have alterations of genes regulating the G1 to S cell cycle transition, including participants in the retinoblastoma (\u003cem\u003eRB1\u003c/em\u003e) pathway such as \u003cem\u003eCDKN2A\u003c/em\u003e, \u003cem\u003eCCND1\u003c/em\u003e, \u003cem\u003eCDK4\u003c/em\u003e, and \u003cem\u003eCDK6\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eRB1\u003c/em\u003e encodes a fundamental tumor suppressing protein that regulates the cell cycle by directly binding to and suppressing activity of transcription factors E2F1/2/3 and thus controlling the transition from the G1 to the S phase.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Alterations in \u003cem\u003eRB1\u003c/em\u003e and its cellular partners disrupt this control and are implicated in the pathogenesis of various cancers, including HGSOC.\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Studies of HGSOC have demonstrated that loss of RB1 protein expression is associated with improved prognosis,\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e particularly when seen in conjunction with HRD,\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e but RB1 expression status is not incorporated into any clinical diagnostic, treatment, or prognostic paradigms at present.\u003c/p\u003e\u003cp\u003eThe molecular mechanisms underlying these associations remain uncertain. Increased understanding of the clinical course and molecular characteristics of HGSOC with respect to HR and RB1 status may enable prognostic tools and potential therapies. The goal of the current study is to explore the relationship between HR status and RB1 mRNA expression in HGSOC, focusing on their combined impact on clinical outcomes, genomic features, and gene expression changes. We hypothesize that RB1 mRNA expression, as a surrogate marker of RB1 protein expression, is associated with survival outcomes and immune-related gene expression changes depending on the HR status of HGSOC tumors. The current study validates the prognostic value of RB1 within the context of HR proficient and deficient HGSOC and provides insight into the biological consequences of these combined alterations.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003eStatistical analysis of cases from the HGSOC TCGA database\u003c/em\u003e\u003c/p\u003e\u003cp\u003eMutation, mRNA expression, copy number alteration (CNA), and protein quantification data were obtained from the Ovarian Serous Cystadenocarcinoma TCGA PanCancer database found on cBioportal.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Mutation data was processed to filter out silent mutations and create a binary mutation matrix. RB1 CNA status was evaluated by creating contingency tables with separate data frames for cases with RB1 deep deletion, hemi-deletion, and no change/gain. Aneuploidy score was defined as the total number of altered chromosome arms, ranging from 0 (no altered arms) to 39 (alterations in all chromosome arms). As an absolute inclusion criterion, our study was restricted to HGSOC cases from TCGA that were evaluated by Takaya et al.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, from which both HRD status and HRD scores were abstracted; inclusion in our study required availability of both metrics. Survival analyses were performed by grouping according to the scoring system used by Takaya et al.: 1) HR status score, with \u0026ge;\u0026thinsp;63 considered HR-deficient (HRD) and \u0026lt;\u0026thinsp;63 considered HR-proficient (HRP), and 2) RNA-seq RB1 mRNA expression quartile, with \u0026le;\u0026thinsp;25th percentile for all TCGA cases considered RB-low (RBL) and \u0026gt;\u0026thinsp;25th percentile considered RB1-high (RBH). Protein quantification data was acquired from the cBioPortal Firehose generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) via mass spectrometry, and protein values represent normalized, relative abundance levels on a log2 scale. Statistical analyses were performed using R statistical software (version 4.2.0).\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Kaplan-Meier curves were fitted using the survival R package and plotted using ggplot2 and survminer.\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Continuous variables were compared using the Mann-Whitney test. Statistical comparisons of RB1 protein levels were performed using the Wilcoxon rank-sum test for non-parametric pairwise analysis.\u003c/p\u003e\u003cp\u003e\u003cem\u003eComprehensive genomic and immune gene expression profiling\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThis study was approved by the Western Copernicus Group Institutional Review Board (WCG IRB, protocol #1340120). All methods were carried out in accordance with relevant guidelines and regulations, including those governing the ethical use of human-derived data. The requirement for informed consent was waived by the WCG IRB under 45 CFR 164.512, as the study involved retrospective analysis of de-identified clinical data. We queried our institutional database for cases of ovarian serous tumors that underwent comprehensive genomic and immune profiling (CGIP) at our laboratory (OmniSeq\u0026reg;, Labcorp Oncology) from June 2016 to October 2023. Cases were included in the cohort if they met the following criteria: (1) pathology reports from referring institutions contained a diagnosis of ovarian serous carcinoma, (2) histologic quality control performed during routine clinical confirmed concordance with serous morphology, and (3) tumors harbored at least one pathogenic somatic alteration in the \u003cem\u003eTP53\u003c/em\u003e gene, to enrich for high grade cases. Cases were subjected to testing using one of three assays: OmniSeq Comprehensive (OC), OmniSeq Advance (OA), or OmniSeq INSIGHT (OI) (Labcorp Oncology, Durham, NC). Each of these tests uses next-generation sequencing of DNA and/or RNA to profile solid tumors. OC (now retired) was an amplicon-based molecular panel that tested for mutations in 144 genes, fusions in 23 genes, and copy number changes. OC was later combined with a 54-gene mRNA expression profiling test, OmniSeq Immune Report Card (IRC), to define the OA test, which assessed both genomic alterations and mRNA gene expression.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Of note, OC/OA interrogated only a select number of HR genes including \u003cem\u003eATM, BAP1, BRCA1, BRCA2\u003c/em\u003e, and \u003cem\u003ePTEN\u003c/em\u003e. The third test, OI, utilized extracted DNA and RNA for genomic and immune profiling and includes DNA sequencing performed to evaluate 523 genes for SNVs, insertions, deletions, 59 genes for copy number amplifications, and 4 genes for copy number loss.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Tumor mutational burden was determined from small variant output, and microsatellite instability was assessed using up to 130 specific microsatellite sites. RNA sequencing was performed to evaluate 55 genes for fusions and 2 genes for splice variants, and immune gene expression profiling was achieved through RNA-sequencing of 395 immune-related transcripts. OI testing also includes PD-L1 immunohistochemistry, which utilizes 22C3 antibody for ovarian tumors and was scored using the tumor proportion score (TPS) method. We evaluated the following gene expression signatures as previously described: tumor immunogenicity score (TIGS), cellular proliferation (CP), and cancer testis antigen burden (CTAB).\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eCases were included in the study provided the tumor harbored at least one pathogenic mutation in \u003cem\u003eTP53\u003c/em\u003e (as defined by ClinVar) and were stratified based on the presence or absence of genomic alterations in HR pathway genes. Cases with any pathogenic alteration in either \u003cem\u003eBRCA1\u003c/em\u003e or \u003cem\u003eBRCA2\u003c/em\u003e were classified as \u003cem\u003eBRCA\u003c/em\u003e-altered (BRCAa). Cases with normal \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e sequences but harboring pathogenic alterations in other HR genes (\u003cem\u003eBRCA1, BRCA2, ATM, ATR, BARD1, BLM, BRIP1, CDK12, CHEK1, CHEK2, FANCA, FANCC, FANCD2, FANCE, FANCF, FANCI, FANCL, FANCM, MRE11, NBN, PALB2, RAD50, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54L\u003c/em\u003e, and \u003cem\u003eRPA1\u003c/em\u003e) were classified as non-BRCA-HR-altered (NBHRa), per the ARIEL3 study and other reports.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Cases with no pathogenic alterations in any HR genes were classified as HR-intact (HRi).\u003c/p\u003e\u003cp\u003e\u003cem\u003eStatistical analysis of the OmniSeq HGSOC cohort\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe compared TMB, PD-L1 IHC, nRPM (normalized reads per million mapped reads) values, and gene expression signature scores between cohorts. Differential gene expression between cohorts was assessed for all 395 immune-related transcripts and selected transcripts of interest, including \u003cem\u003eRB1\u003c/em\u003e, \u003cem\u003eBRCA1\u003c/em\u003e, \u003cem\u003eBRCA2\u003c/em\u003e, \u003cem\u003eCD3\u003c/em\u003e (calculated as the average of \u003cem\u003eCD3D\u003c/em\u003e, \u003cem\u003eCD3E\u003c/em\u003e, and \u003cem\u003eCD3G\u003c/em\u003e), \u003cem\u003eCD68\u003c/em\u003e, \u003cem\u003eCD20\u003c/em\u003e (calculated as the average of \u003cem\u003eCD20A\u003c/em\u003e and \u003cem\u003eCD20B\u003c/em\u003e), \u003cem\u003ePTEN\u003c/em\u003e, and \u003cem\u003eMK67\u003c/em\u003e. Continuous and categorical variables were evaluated using linear or Firth\u0026rsquo;s penalized logistic regression adjusting for the type of assay used to perform genomic and immune profiling (OC, OA, or OI). Continuous variables with positive skewness (TMB, nRPM values, CTAB signature scores) were given a pseudo-count of 1 to avoid 0 values and log transformed before testing. Estimated fold changes for gene expression changes between cohorts were calculated by taking the exponent of the regression coefficients from linear and Firth\u0026rsquo;s penalized regression. Multiple testing correction of P-values resulting from individual immune gene expression analysis was performed using the Benjamini-Hochberg false discovery rate (FDR) method. A significance threshold of \u0026lt;\u0026thinsp;0.05 and \u0026lt;\u0026thinsp;0.25 was used for uncorrected P-values and multiple testing corrected P-values, respectively. Genes in which expression was found to be significantly different between groups (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25) are referred to as differentially expressed genes (DEGs) between those groups. Of note, the \u0026lt;\u0026thinsp;0.25 threshold is a commonly used threshold for \u003cem\u003eP\u003c/em\u003e-values that have been multiple testing corrected using the FDR procedure and when testing large numbers of features such as in pathway enrichment.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePathway analysis of differentially expressed genes using the PANTHER classification system\u003c/em\u003e\u003c/p\u003e\u003cp\u003eLists of DEGs between BRCAa, HRi, and NBHRa groups of the OmniSeq cohort were submitted for pathway analysis utilizing the PANTHER (Protein ANalysis THrough Evolutionary Relationships) Classification System (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pantherdb.org\u003c/span\u003e\u003cspan address=\"https://pantherdb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify Reactome pathways related to DEGs based on a reference list of all genes from the \u003cem\u003eHomo sapiens\u003c/em\u003e whole genome.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Reactome (Reactome version 86, released on 09-07-2023) is an open-source relational database of signaling and metabolic molecules, organized into biological pathways and processes, with reactions serving as its core unit.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Fisher's Exact test was applied to assess statistical significance of pathway enrichment, and FDR was calculated to correct for multiple testing. Pathways with a corrected p-value (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were identified as DEG-related.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eDemographics, molecular characteristics, and survival analysis of the ovarian cancer cases from the TCGA PanCancer Atlas stratified by HR and RB1 expression status\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Ovarian Serous Cystadenocarcinoma TCGA PanCancer Atlas dataset contained mutation-level data for 409 cases, which included 373 (91.2%) tumors harboring pathogenic alterations in \u003cem\u003eTP53\u003c/em\u003e. For subgroup and survival analyses, we abstracted data for TCGA cases with HR scores reported by Takaya et al. that also had mRNA expression data available,\u003csup\u003e23\u003c/sup\u003e producing an experimental cohort of 272 cases consisting of 150 (55.1%) HRP and 122 (44.9%) HRD tumors (\u003cstrong\u003eSupplemental Table 1\u003c/strong\u003e). Patients in the HRP cohort were older (median age 60 vs 55.5 years, p=0.0002). The median HRD score was 48 for HRP and 77 for the HRD tumors, with median loss of heterozygosity (LOH) scores of 8 and 18, median telomeric-allelic imbalance (TAI) scores of 21 and 29, and median large-scale state transition (LST) scores of 17.5 and 30, respectively. HRP tumors harbored lower TMB (median 1.5 vs 2.2 mutations/Megabase, p=0.0019) but demonstrated higher aneuploidy scores (median 17 vs 10, p \u0026lt; 0.0001). HRP tumors demonstrated significantly increased RB1 mRNA expression levels, with a median of 983 normalized transcripts per million (nTPM) compared to 884 nTPM in HRD tumors (p\u0026lt;0.0001). Ki-67 mRNA expression was similar for both HRP and HRD groups (median 2191 vs 2253, respectively, p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eWe further stratified the HRP and HRD cohorts by RB1 mRNA expression level, with ≥701 nTPM (the 25\u003csup\u003eth\u003c/sup\u003e percentile) as the cut-off for classification into either RBH or RBL groups (\u003cstrong\u003eSupplemental Table 1\u003c/strong\u003e). Of HRP tumors (n=150), 120 (80%) were RBH and 30 (20%) were RBL. Of HRD tumors (n=122), 84 (68.9%) were RBH and 38 (31.1%) were RBL. Based on this simple classification, we observed an increased proportion of RBH tumors among the HRP tumors (p=0.048, Fisher’s exact test). Patients in the HRP-RBH group were older than those in either the HRD-RBH (median age 60 vs 55 years, p=0.0006) or HRD-RBL (median age 60 vs 56 years, p=0.039). HRP-RBH and HRP-RBL groups were comprised of patients of similar age (60 vs 59.5 years, respectively, p=0.78). We did not observe a statistically significant difference in ages of patients in the HRP-RBL group versus either the HRD-RBH (59.5 vs 55 years, p \u0026gt;0.05) or the HRD-RBL (59.5 vs 56 years, p \u0026gt;0.05) groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo confirm that low RB1 mRNA was associated with low protein expression, we evaluated TCGA cases that had both RB1 protein quantification and mRNA expression data. We identified 73 cases from the TCGA cohort with these values, which included 12 (16%) HRP-RBL, 29 (40%) HRP-RBH, 5 (7%) HRD-RBL, and 27 (37%) HRD-RBH samples. Relative RB1 protein expression levels were significantly lower in RBL tumors compared to RBH tumors (median −0.59 vs 0.20, p = 3.7×10⁻⁶). When stratified by HRD status, RB1 protein was significantly reduced in RBL vs RBH tumors among HRP cases (median −0.40 vs 0.23, p = 7.4×10⁻⁶). A similar trend was observed in HRD tumors (median −0.85 vs 0.088), though it did not reach statistical significance (p = 0.062). No significant difference in RB1 protein levels was found when comparing all HRP vs HRD tumors irrespective of RB1 expression (median 0.072 vs 0.026, p = 0.99). Cross-comparisons of RB1 expression and HRD status showed that HRD/RBL tumors had significantly lower RB1 protein than HRP/RBH tumors (median −0.85 vs 0.23, p = 0.020). Conversely, HRD/RBH tumors had significantly higher RB1 protein than HRP/RBL tumors (median 0.088 vs −0.40, p = 1.2×10⁻⁴).\u003c/p\u003e\n\u003cp\u003eHRD, LST, LOH, and TAI scores reported by Takaya et al. 2020 were compared within HRP and HRD groups to evaluate potential associations with low or high RB1 expression. Within the HRP cohort, RBH tumors demonstrated lower HRD scores than RBL tumors (47 vs 54.5, p=0.0016), with significantly lower LOH scores (7 vs 10, p=0.0300) (\u003cstrong\u003eFigure 1A\u003c/strong\u003e), significantly lower LST scores (17 vs 22, p=0.0023), and no difference in TAI scores (21 vs 21, p \u0026gt;0.05). Comparing HRD-RBH and HRD-RBL tumors, we did not observe statistically significant differences in HRD (75.5 vs 78, p \u0026gt;0.05), LOH (18 vs 19; p=0.0624), LST (30 vs 29.5, p \u0026gt;0.05) or TAI (29 vs 28, p \u0026gt;0.05) scores, respectively.Median aneuploidy scores were markedly higher in the HRP-RBH group compared to the other cohorts: \u0026nbsp;HRP-RBL (18 vs 10.5, p=0.0013), HRD-RBL (18 vs 9, p\u0026lt;0.0001), and HRD-RBH (18 vs 10.5, p\u0026lt;0.0001) (\u003cstrong\u003eFigure 1B\u003c/strong\u003e). TMB varied across groups, with the lowest observed in the HRP-RBL group (1.3 mut/Mb) and the highest in the HRD-RBH group (2.3 mut/Mb). The differences in TMB reached statistical significance between HRP-RBH vs HRD-RBH groups (1.5 vs 2.3 mut/Mb, respectively; p=0.0003) and HRP-RBL vs HRD-RBH groups (1.3 vs 2.3 mut/Mb, respectively; p=0.0161) (\u003cstrong\u003eFigure 1C\u003c/strong\u003e). There was no difference in RB1 mRNA expression scores within HRP and HRD groups after stratifying samples as RBL or RBH: \u0026nbsp; HRP-RBH vs HRD-RBH (1100 vs 1070 nTPM, p\u0026gt;0.05) and HRP-RBL vs HRD-RBL (523 vs 479 nTPM, p\u0026gt;0.05). Ki-67 mRNA expression levels were highest in the HRP-RBL group, which was statistically higher than that of the HRP-RBH (3135 vs 2082 nTPM, p=0.0078) and the HRD-RBH (3135 vs 2210, p=0.028) (\u003cstrong\u003eFigure 1D\u003c/strong\u003e). There was no statistically significant difference in Ki-67 mRNA expression between HRP-RBL and HRD-RBL groups (3135 vs 2277, p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003ePatients in the HRP cohort demonstrated significantly shorter OS (36.3 vs 53.3 months, p \u0026lt; 0.0001) and PFS (16.0 vs 20.2 months, p=0.0020) than those with HRD tumors (\u003cstrong\u003eFigure 2A-B\u003c/strong\u003e). After stratifying patients according to RB1 mRNA expression levels, survival outcomes for HRP-RBH patients were shorter compared to all other groups. Among HRP tumors, those with RBH had lower median OS (35.9 vs 52.0 months, p=0.015) and PFS (15.1 vs 20.6 months, p=0.031) (\u003cstrong\u003eFigure 2C-D\u003c/strong\u003e). Among RBH expressing tumors, HRP patients had markedly lower survival metrics compared to HRD-RBH tumors, including lower median OS (35.9 vs 57.1 months, p\u0026lt;0.0001), and PFS (15.1 vs 20.2 months, p=0.0007). Similarly, compared to the HRD-RBL group, HRP-RBH tumors were associated with decreased OS (35.9 vs 57.1 months, p=0.0017) and PFS (15.1 vs 20.2 months, p=0.013). In contrast to HRP-RBH tumors, we did not observe statistically significant differences in survival metrics between HRP-RBL tumors and either HRD-RBH and HRD-RBL groups. Among the RBL groups, HRP-RBL patients demonstrated a shorter median OS (52.0 vs 57.1 months, p \u0026gt;0.05) and longer PFS (20.6 vs 20.2 months, p \u0026gt;0.05) than HRD-RBL patients. Similar results were obtained when compared to HRD-RBH patients, with HRP-RBH patients having a shorter median OS (52.0 vs 53.3 months, p \u0026gt;0.05) and longer PFS (20.6 vs 20.4 months, p \u0026gt;0.05). Additionally, we did not observe a statistically significant difference in survival between the two groups of HRD patients; HRD-RBH patients demonstrated lower median OS (53.3 vs 57.1 months, p \u0026gt;0.05) and comparable PFS (20.4 vs 20.2 months, p \u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eWe identified 233 cases from the TCGA dataset that contained gene-level copy number information, in addition to mRNA expression data and HR evaluation by Takaya et al.\u003csup\u003e23\u003c/sup\u003e The majority of HGSOC contained hemizygous deletion of the \u003cem\u003eRB1\u003c/em\u003e gene (56.7%), 25.3% of cases were copy number neutral, 8.2% of cases had homozygous deletion of \u003cem\u003eRB1\u003c/em\u003e, and 9.9% had amplification of the \u003cem\u003eRB1\u003c/em\u003e gene (\u003cstrong\u003eSupplemental Table 2\u003c/strong\u003e). RB1 mRNA expression was tightly correlated with \u003cem\u003eRB1\u003c/em\u003e copy number status: cases with homozygous deletion of \u003cem\u003eRB1\u0026nbsp;\u003c/em\u003edemonstrated the lowest gene expression (median 486 nTPM), cases with hemizygous deletion of \u003cem\u003eRB1\u003c/em\u003e had lower expression than \u003cem\u003eRB1\u003c/em\u003e copy number neutral cases (median 865 vs 1135 nTPM), and cases with amplification demonstrated the highest gene expression (median 1525 nTPM). Only 2 of 19 (10.5%) cases with homozygous deletion demonstrated high levels of RB1 gene expression (TCGA-24-2262 and TCGA-61-2109). Among cases with hemizygous deletion of \u003cem\u003eRB1\u003c/em\u003e, 95 of 132 (72.0%) were RBH, and two cases with hemizygous deletion of \u003cem\u003eRB1\u003c/em\u003e harbored single nucleotide variants in \u003cem\u003eRB1\u003c/em\u003e: (1) TCGA-24-1469 harbored R763T and had an RB1 gene expression of 1644 nTPM and (2) TCGA-29-1703 harbored a hotspot splice site mutation (X405) and had an RB1 gene expression of 717 nTPM. Just 4 of 59 (6.8%) of \u003cem\u003eRB1\u003c/em\u003e copy number neutral cases were RBL, and 3 of these 4 (75%) had detectable, pathogenic SNVs in \u003cem\u003eRB1\u003c/em\u003e: (1) TCGA-61-1741 harbored a splice site alteration (X474) and had an RB1 gene expression of 319 nTPM , (2) TCGA-24-1849 harbored a frameshift mutation (F526Lfs*) and had an RB1 gene expression of 612 nTPM, and (3) TCGA-24-1463 harbored a frameshift mutation (C553Wfs*) and had an RB1 gene expression of 480 nTPM). None of the cases with amplification of \u003cem\u003eRB1\u0026nbsp;\u003c/em\u003ewere RBL or harbored SNVs in \u003cem\u003eRB1\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHR gene status is associated with divergent immune gene expression profiles\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe next investigated associations between \u003cem\u003eRB1\u003c/em\u003e and HRD status in a separate cohort of HGOSC that were processed for clinical CGIP by our laboratory. We identified 406 ovarian high grade serous tumors from 249 provider facilities across the United States that had CGIP performed by OmniSeq and met inclusion criteria, including the presence of a pathogenic mutation in \u003cem\u003eTP53\u0026nbsp;\u003c/em\u003eto exclude low grade serous tumors. Our final dataset included samples subjected to the following tests: \u0026nbsp;OmniSeq Comprehensive (OC, n=64), OmniSeq Immune Report Card (IRC, n=26), OminSeq Advance (OA, n=97), and OmniSeq INSIGHT (OI, n=219). The most common pathogenic alterations were observed in the following genes (\u003cstrong\u003eFigure 3\u003c/strong\u003e): \u003cem\u003eBRCA1\u0026nbsp;\u003c/em\u003e(21.93%), \u003cem\u003ePIK3R1\u0026nbsp;\u003c/em\u003e(18.23%), \u003cem\u003eNF1\u0026nbsp;\u003c/em\u003e(17.98%), \u003cem\u003eAPC\u003c/em\u003e (15.76%), \u003cem\u003eZFHX3\u0026nbsp;\u003c/em\u003e(15.02%), and \u003cem\u003eBRCA2\u0026nbsp;\u003c/em\u003e(14.53%). To evaluate the effect of HR gene alterations on molecular characteristics, we segregated our cases into three groups: (1) BRCAa, which included cases with alterations in \u003cem\u003eBRCA1\u0026nbsp;\u003c/em\u003eand/or \u003cem\u003eBRCA2\u0026nbsp;\u003c/em\u003e(n=128, 31.5%); (2) NBHRa, which included cases with alterations in any non-\u003cem\u003eBRCA\u003c/em\u003e HR-related genes (n=114, 28.1%), and (3) HRi, defined as cases without alterations in HR-related genes (n=164, 40.4%). Patients had a median age at test ordering of 65 years (range: 25-90). The BRCAa group contained younger patients than the NBHRa (median 65 vs 71 years, P\u0026lt;0.0001) and the HRi groups (65 vs 70 years, P=0.0003). There was no statistical difference between patient age in the NBHRa and HRi groups (P\u0026gt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA comparison of CGIP results from the three HR-related groups revealed significant differences across several variables (\u003cstrong\u003eSupplemental Table 3\u003c/strong\u003e). The mean TMB for all patients was 6.4 mut/Mb, with BRCAa patients showing a significantly higher mean TMB (12.1±72.5) compared to either NBHRa (4.4±2.48, P=0.0052) or HRi (3.9±2.3, P=0.0059) groups, and the percentage of patients with high TMB (≥10 mutations/Mb) was notably higher in the BRCAa group (3.5%) compared to the HRi group (0.7%, P=0.0043). Although PD-L1 IHC expression is not a predictive biomarker associated with sensitivity to FDA approved therapies for HGSOC patients, PD-L1 expression levels using TPS scoring criteria were available for all patients in this cohort. We observed no differences in PD-L1 TPS scores among the three groups (P\u0026gt;0.05), and the proportion of PD-L1 positive patients (≥1% TPS) was similar across all groups, with 48.0% in BRCAa, 37.4% in NBHRa, and 44.1% in HRi, with no significant differences seen in pairwise comparisons. Tumor Immunogenicity Score (TIGS), a composite immune gene expression score, was significantly lower in the BRCAa group (40.8±17.7) compared to the HRi group (46.5±21.8, P=0.033). BRCAa patients also had a lower frequency of strong TIGS (14.0%) compared to HRi (30.8%, P=0.0022). CP expression score was significantly higher in the BRCAa group (51.8±23.3) compared to the HRi group (44.4±24, P=0.036); however, no significant differences were observed across semi-quantitative CP score distributions (P\u0026gt;0.05). CTAB expression score was higher for the HRi group (317.4±236.5) compared to BRCAa tumors (285.2±225, P=0.014).\u003c/p\u003e\n\u003cp\u003eDifferential gene expression analysis revealed marked differences in gene expression between the BRCAa and HRi cohorts, with 14 DEGs elevated in BRCAa vs 97 elevated in the HRi group (\u003cstrong\u003eFigure 4, Supplemental Table 4\u003c/strong\u003e). The NBHRa group showed fewer gene expression changes, with 5 DEGs elevated in BRCAa vs 6 in NBHRa and with no elevated DEGs with FDR \u0026lt;0.25 when comparing the NBHRa and HRi groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRB1 expression status is associated with distinct immune gene expression profiles\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the effect of RB1 mRNA expression on genomic features and immune profiles in the cohort, we stratified each HR-related groups based on \u003cem\u003eRB1\u003c/em\u003e mRNA expression levels, designating the lower quartile as RB1 “low” and combining the upper three quartiles as RB1 “high”.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe identified 332 cases from the HGOSC cohort that completed gene expression profiling. The cohort was comprised of 114 (34.3%) RBL and 218 (65.7%) RBH malignancies, which included 44 (47.3%) RBL and 49 (52.7%) RBH BRCAa tumors, 28 (26.4%) RBL and 78% (73.6%) RBH NBHRa tumors, and 42 (31.6%) RBL and 91 (68.4%) RBH HRi tumors (\u003cstrong\u003eSupplemental Table 5\u003c/strong\u003e). Chi-Square analysis demonstrated that HRi (P=0.0184) and NBHRa (P=0.0030) cohorts were both enriched for RBH tumors vs. the BRCAa cohort. In general, RBL status was associated with lower TIGS (37.7 vs 49.0, P\u0026lt;0.0001), lower CP (44.3 vs 50.9, P=0.033), and reduced expression of BRCA2 (P\u0026lt;0.0001), CD3 (P=0.0002), CD20 (P\u0026lt;0.0001), CD68 (P=0.0012), and PTEN (P\u0026lt;0.0001). No differences in Ki-67 mRNA expression were observed among groups in the cohort (P\u0026gt;0.05). Within the BRCAa subgroup, RBL vs. RBH tumors showed no statistically significant differences in TIGS (38.9 vs 42.6, P\u0026gt;0.05); in contrast, RBH status was associated with higher TIGS in HRi tumors (36.7 vs 51.0, P=0.0009). Regarding individual immune-related gene differences, RB1-status in BRCAa tumors was not associated with differences in CD3, CD20, and CD68 expression (P\u0026gt;0.05); in contrast, RBL tumors in the HRi cohort were associated with lower CD3 (P=0.0078), CD20 (P=0.0130), and CD68 (P=0.045) mRNA expression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGene expression analysis revealed a smaller number of elevated DEGs in RBL vs RBH tumors (9 vs 276) in the OmniSeq cohort (\u003cstrong\u003eFigure 5, Supplemental Table 6\u003c/strong\u003e). Among BRCAa tumors, RBL status was associated with 10 elevated DEGs in the BRCAa-RBL vs. 96 in the BRCAa-RBH samples. Among HRi tumors, RBL status revealed 5 DEGs with elevated fold change vs. 213 in the HRi-RBH group. Chi-square analysis demonstrated that the distribution of DEGs was different for BRCAa and HRi tumors when considering RB1 status, with a relatively greater proportion of DEGs occurring in the HRi-RBH vs. the BRCAa-RBH subgroup (P=0.0024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further explore the differences in immune expression profiles, we compared molecular results between the HR-related subgroups restricted to either the RBL (\u003cstrong\u003eSupplemental Table 7\u003c/strong\u003e) or RBH subsets (\u003cstrong\u003eSupplemental Table 8\u003c/strong\u003e). Among RBL tumors, we observed a higher TMB (mut/Mb) in BRCAa vs HRi tumors (5.2 vs 3.9, P=0.0058) and in BRCAa vs NBHRa tumors (5.2 vs 5, P=0.039). In contrast, examination of the RBH tumors revealed that BRCAa tumors had lower TIGS than HRi tumors (42.6 vs 51.0, P=0.025) and lower CTAB than either HRi (232.6 vs 327.6, P=0.011) or NBHRa (232.6 vs 362.8, P=0.026) tumors. Among individual immune-related genes, CD68 mRNA expression was greater in HRi-RBH than BRCAa-RBH tumors (3266.4 vs 2022.6 nRPM, P=0.02).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we evaluated fold changes in gene expression to compare the HR-related groups stratified by RB1 status (i.e., BRCAa-RBL vs HRi-RBL and BRCAa-RBH vs. HRi-RBH). Among RBL tumors (\u003cstrong\u003eSupplemental Table 9\u003c/strong\u003e) BRCAa-RBL tumors exhibited 7 DEGs with elevated fold change compared to zero elevated DEGs in the HRi-RBL group. Among RBH tumors (\u003cstrong\u003eSupplemental Table 10\u003c/strong\u003e), BRCAa-RBH tumors had 8 DEGs with elevated fold change compared to 80 DEGs elevated in HRi-RBH tumors. Chi-square analysis showed that the proportion of elevated DEGs was significantly greater for HRi-RBH tumors (P\u0026gt;0.0001).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePathway analysis of differentially-expressed immune-related genes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe performed pathway analysis of DEGs from the OmniSeq cohort using the PANTHER classification system to identify overrepresented Reactome pathways and calculate fold enrichment (FE) over expected values based on the reference gene list. DEG lists and pathways analysis results are detailed in \u003cstrong\u003eSupplemental Table 11\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cem\u003eStratification by HR status\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eConsidering DEGs related to HR gene status, BRCAa tumors showed enrichment of pathways related to interferon signaling, including interferon\u0026nbsp;α/β\u0026nbsp;signaling (FE\u0026gt;100, FDR=7.72x10\u003csup\u003e-22\u003c/sup\u003e) and ISG15 antiviral mechanisms (FE=62.1, FDR=6.08x10\u003csup\u003e-3\u003c/sup\u003e). Conversely, HRi tumors exhibited overrepresentation of epithelial-mesenchymal transition (EMT; FE\u0026gt;100, FDR=0.0143), adaptive immune regulation (IL-4/IL-13 signaling; FE=24.9, FDR=2.21x10\u003csup\u003e-12\u003c/sup\u003e), and anti-inflammatory pathways including CD163 (FE=70.7, FDR=1.46x10\u003csup\u003e-3\u003c/sup\u003e) and IL-10 signaling (FE=33, FDR=5.32x10\u003csup\u003e-7\u003c/sup\u003e). Additionally, HRi tumors demonstrated DEGs of kinase pathways including PI3K/AKT signaling in cancer (FE=11.5, FDR=8.88x10\u003csup\u003e-3\u003c/sup\u003e), ALK (FE=22.7, P=0.0276), and NTRK1 (FE=8.1, P=0.0330).\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u003cem\u003eStratification by RB1 mRNA expression\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNext, we stratified tumors according to RB1 mRNA expression status. We found no significantly overrepresented pathways in the RBL group, whereas the RBH group demonstrated enrichment of multiple signaling pathways: EMT (FE=36.8, FDR=0.0191), interferon\u0026nbsp;α/β\u0026nbsp;(FE=14.5, FDR=1.30x10\u003csup\u003e-11\u003c/sup\u003e), interferon γ\u0026nbsp;(FE=15.3, FDR=4.12x10\u003csup\u003e-16\u003c/sup\u003e), chemokine receptor (FE=33.5, FDR=3.38x10\u003csup\u003e-31\u003c/sup\u003e), PD-1 (FE=33.0, FDR=3.79x10\u003csup\u003e-15\u003c/sup\u003e), and interleukin pathways (FE=10.1, FDV=5.82x10\u003csup\u003e-42\u003c/sup\u003e), including IL-4/13 (FE=20.5, FDR=1.11x10\u003csup\u003e-29\u003c/sup\u003e), IL-6 (FE=12.3, FDR=6.22x10\u003csup\u003e-3\u003c/sup\u003e), IL-10 (FE=35.9, FDR=3.87x10\u003csup\u003e-27\u003c/sup\u003e), IL-12 (FE=11.8, FDR=3.71x10\u003csup\u003e-6\u003c/sup\u003e), and IL-21 (FE=44.1, FDR=9.03x10\u003csup\u003e-8\u003c/sup\u003e). Additionally, kinase signaling pathways were represented in RBH tumors, such as PIK3K cascade (FE=8.2, FDR=0.0240), MET signaling of PI3K/AKT (FE=29.4, FDR=0.0299), and STAT3 nuclear events downstream of ALK signaling (FE=33.4, FDR=1.11x10\u003csup\u003e-5\u003c/sup\u003e).\u003c/p\u003e\n\u003col start=\"100\"\u003e\n \u003cli\u003e\u003cem\u003eStratification of BRCAa tumors by RB1 status\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAmong BRCAa tumors, we identified no pathways enriched in RBL tumors compared to RB1 high tumors. However, BRCAa-RBH tumors showed enrichment in interferon\u0026nbsp;α/β\u0026nbsp;(FE=19.8, FDR=1.41x10\u003csup\u003e-5\u003c/sup\u003e), trafficking and processing of endosomal toll-like receptor (FE=49.5, FDR=1.93x10\u003csup\u003e-3\u003c/sup\u003e), chemokine receptor (FE=30.1,FDR=9.96x10\u003csup\u003e-8\u003c/sup\u003e), and interleukin signaling pathways, including IL-2 (FE=35.7, FDR=0.0481), IL-4/IL-13 (FE=21.2, FDR=2.13x10\u003csup\u003e-9\u003c/sup\u003e), IL-10 (FE=33.4, FDR=5.65x10\u003csup\u003e-7\u003c/sup\u003e), and IL-12 (FE=15.3, FDR=0.0074). Additionally, BRCAa-RBH tumors demonstrated DEGs of kinase pathways including PI3K/AKT (FE=16.3, FDR=4.92x10\u003csup\u003e-5\u003c/sup\u003e) and ALK (FE=45.9, FDR=9.98x10\u003csup\u003e-7\u003c/sup\u003e).\u003c/p\u003e\n\u003col start=\"500\"\u003e\n \u003cli\u003e\u003cem\u003eStratification of HRi tumors by RB1 status\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn an analysis restricted to HRi tumors alone, we identified no pathways enriched in HRi-RBL tumors over HRi-RBH tumors. However, HRi-RBH tumors showed enrichment of EMT (FE=48.5, FDR=0.0139) and interleukin-related signaling pathways, including IL-6 (FE=35.3, FDR=1.52x10\u003csup\u003e-4\u003c/sup\u003e), IL-9 (FE=43.1, FDR=6.80x10\u003csup\u003e-5\u003c/sup\u003e), IL-10 (FE=41.0, FDR=7.81x10\u003csup\u003e-24\u003c/sup\u003e), IL-12 (FE=12.1, FDR=8.30x10\u003csup\u003e-5\u003c/sup\u003e), and IL-21 (FE=38.8, FDR=1.02x10\u003csup\u003e-4\u003c/sup\u003e). Additionally, HRi-RBH tumors showed activation of the following kinase pathways over HRi-RBL tumors: PI3K/AKT (FE=10.6, FDR=2.70x10\u003csup\u003e-6\u003c/sup\u003e), ALK (FE=15.3, FDR=5.90x10\u003csup\u003e-11\u003c/sup\u003e), and MET (FE=6.4, FDR=0.0221).\u003c/p\u003e\n\u003col start=\"5\"\u003e\n \u003cli\u003e\u003cem\u003eStratification of RBL and RBH tumors by HR status\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFinally, we stratified tumors by RB1 expression status and compared DEGs elevated in BRCAa and HRi groups. Among RBL tumors, BRCAa was associated with interferon\u0026nbsp;α/β\u0026nbsp;signaling (FE\u0026gt;100, FDR=3.29x10\u003csup\u003e-8\u003c/sup\u003e), cytokine signaling (FE=23.0, FDR=1.57x10\u003csup\u003e-5\u003c/sup\u003e), and immune system activation (FE=8.4, FDR=4.61x10\u003csup\u003e-3\u003c/sup\u003e); HRi-RBL tumors showed no elevated differentially expressed genes with an FDR \u0026lt;0.25 when compared to BRCAa-RBL tumors. Among RBH tumors, BRCAa was again associated with interferon\u0026nbsp;α/β\u0026nbsp;signaling genes (FE\u0026gt;100, FDR=7.00x10\u003csup\u003e-3\u003c/sup\u003e). Compared to BRCAa-RBH tumors, HRi-RBH tumors were associated with a variety of overrepresented pathways including EMT (FE\u0026gt;100, FDR=0.0179), IL-4/IL-13 (FE=25.5, FDR=2.27x1-\u003csup\u003e-10\u003c/sup\u003e), IL-10 (FE=4.6, FDR=2.61x10\u003csup\u003e-9\u003c/sup\u003e), and general receptor tyrosine kinase signaling (FE=44.9, FDR=0.0229).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this study was to explore the relationship between HR status and RB1 mRNA expression in HGSOC. As summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, our results show that HRP-RBH tumors are a distinct molecular subgroup within HGSOC, characterized by aneuploidy, unique gene expression patterns, and poorer survival outcomes compared to other subgroups. These observations highlight the potential of HR and RB1 stratification to improve prognostic assessments and potentially tailor future therapeutic strategies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe loss of RB1, commonly observed in HGSOC,\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e has long been implicated as a core driver of malignant transformation.\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e We confirmed that RB1 expression levels closely correlate with RB1 copy number, where homozygous deletions lead to the lowest RB1 mRNA expression, and amplifications result in the highest levels. This gene dosage effect aligns with prior studies,\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e highlighting RB1 deletion as a major mechanism for its loss of function. However, in tumors with hemizygous deletions or low RB1 expression, epigenetic mechanisms, such as promoter hypermethylation, may silence the remaining allele, contributing to RB1 loss as seen in other tumor types.\u003csup\u003e\u003cspan additionalcitationids=\"CR42 CR43 CR44\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e Moreover, our analysis identified several inactivating single nucleotide variants (SNVs) in RB1, which, although infrequent, may substantially impair its function. These variants typically involved frameshift or splice site mutations, aligning with the role of RB1 as a critical protein-binding cell cycle checkpoint protein.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e Notably, we also observed concordance between RB1 mRNA and protein levels within the TCGA cohort, supporting the use of mRNA as a surrogate marker for protein expression. Future studies incorporating immunohistochemistry or proteomic profiling may help validate the mRNA cutoffs used and potentially enable RB1 stratification for clinical purposes.\u003c/p\u003e\u003cp\u003eRBH tumors, particularly within the HRi group, showed elevated DEGs of immune signaling pathways (e.g., IL-10, IL-4/13) and oncogenic kinase signaling pathways (e.g., PI3K/AKT, MET). These pathways suggest that RBH tumors are not only driven by tumor-protective immune regulation but also by pro-survival signaling. In contrast, RBL tumors lacked DEGs of specific pathways, possibly reflecting a more quiescent signaling environment driven by the absence of cell cycle regulation. Of note is that patients with HRP-RBH tumors in the TCGA cohort demonstrated the poorest survival metrics across all subgroups, but the mechanisms driving poor survival in this context are uncertain and should be investigated further. Future studies should investigate the relationship between RB1 expression and platinum resistance.\u003c/p\u003e\u003cp\u003eRB1-status in HGSOC may have therapeutic implications because inhibition of CDK4/6 has been proposed as a therapeutic target.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e In normal cell cycle regulation, phosphorylation of RB1 by CDK4/6 results in its disassociation from E2F1/2/3, resulting in transcriptional activation. Thus, inhibition of these RB1 regulators prevents inhibitory phosphorylation allowing RB1 to inhibit cellular proliferation and delay disease progression.\u003csup\u003e\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e However, there is strong evidence that loss of RB1 removes the evolutionary advantage provided by CDK4/6 deregulation;\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e in other words, CDK4/6 inhibitors may not have any effect in tumors with loss of RB1 expression. Therefore, RB1 loss should be considered in exclusion criteria for trials that target CDK4/6 activity.\u003c/p\u003e\u003cp\u003eOur study is orthogonal and complementary to existing molecular classifications of HGSOC,\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e notably the four major transcriptomic subtypes (immunoreactive, differentiated, proliferative, mesenchymal), by evaluating just two variables: HR status and RB1 expression. BRCAa tumors, resembling the immunoreactive subtype, exhibited a favorable prognosis and enhanced activation of innate immune responses, particularly through interferon signaling. This immune activation is likely driven by genomic instability, leading to the accumulation of neoantigens and cytosolic DNA.\u003csup\u003e\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e In contrast, HRi-RBH tumors shared similarities with the mesenchymal subtype, demonstrating upregulation of immune evasion mechanisms, including anti-inflammatory pathways (e.g., IL-10, CD163) and EMT, suggesting these tumors rely on these mechanisms to promote tumor progression and evade immune detection. Of note, we observed a lack of significant gene expression changes in the NBHRa subgroup, suggesting that NBHRa included both HR-deficient and HR-proficient tumors, supporting its exclusion from our downstream analyses.\u003c/p\u003e\u003cp\u003eThe current study has several important limitations. As a retrospective study, it is inherently limited by the quality and completeness of the available data. While HR status was available for the TCGA cohort, we had to rely on gene alterations to assess HR status in the OmniSeq cohort, which may not be as reliable. The potential misclassification of HR status based on gene alterations alone may not capture the full complexity of gene expression changes associated with HR proficiency, leading to possible inaccuracies. Furthermore, our study's findings are based on a cohort of advanced HGSOC undergoing CGIP, and external validation is needed to ensure generalizability. Future studies should aim to replicate our findings in larger, more diverse datasets with well-defined HR status for each case.\u003c/p\u003e\u003cp\u003eIn conclusion, our study provides evidence for the molecular subtyping of HGSOC based on HR status and RB1 expression. We identified HRP-RBH tumors as the largest subgroup, characterized by the poorest prognosis and a distinct genomic instability profile marked by high aneuploidy and particularly low levels of HR deficiency. Molecular stratification of HGSOC based on HR status and RB1 expression may have implications for prognosis and therapeutic decision-making, with the potential to identify patients with platinum-resistant tumors. Our findings highlight the potential of RB1 mRNA expression as a prognostic biomarker, particularly for patients with HRP HGSOC. Given the distinct molecular, immune and prognostic profiles of HRP-RBH tumors, these patients may benefit from alternative follow-up strategies. CDK4/6 inhibitors, which target cell cycle regulation, could be promising for RB1-intact HGSOC, although further studies are needed to confirm their efficacy, particularly considering resistance mechanisms associated with RB1 loss.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKCS, ZDW, HK, MFG, AD, SH, TJJ, ME, BJC, SR, EAS, and RAP are all employees of Labcorp and declare stock ownership from Labcorp. AR is an employee of Thermo Fisher Scientific and declares stock ownership from Thermo Fisher Scientific. Any remaining authors declare no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eEthics Declaration\u003c/h2\u003e\n\u003cp\u003eApproval for this study was obtained from an independent institutional review board (IRB), the Western Copernicus Group (WCG) (www.wcgclinical.com), under protocol # 1340120. The WCG IRB determined this study met requirements for a waiver of informed consent under 45 CFR 164.512.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eKCS, ZDW, SP, EAS, and RAP conceptualized the study. Data acquisition, curation, and formal analyses were performed by KCS and ZDW. Data were interpreted by KCS, HK, MFG, AD, SR, EAS, and RAP. Funding and supervision were provided by TJJ, ME, BJC, SP, SR, EAS, and RAP. KCS, ZDW, and RAP wrote the main manuscript text. All authors thoroughly reviewed the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData from the TCGA cohort is publicly accessible through The Cancer Genome Atlas repository (http://cancergenome.nih.gov). Clinical data from the OmniSeq cohort are not publicly available due to patient confidentiality and data-sharing agreements. Researchers may contact the corresponding author for inquiries regarding potential collaboration and data sharing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLisio, M. A., Fu, L., Goyeneche, A., Gao, Z. H. \u0026amp; Telleria, C. High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 952. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms20040952\u003c/span\u003e\u003cspan address=\"10.3390/ijms20040952\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBowtell, D. D. et al. Rethinking ovarian cancer II: reducing mortality from high-grade serous ovarian cancer. \u003cem\u003eNat. Rev. 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While homologous recombination (HR) deficiency and retinoblastoma gene (\u003cem\u003eRB1\u003c/em\u003e) expression have been implicated in prognosis, their combined role in shaping tumor biology and survival outcomes is not well defined. To investigate the relationship between HR status and RB1 expression and explore their potential as a combined prognostic marker, we analyzed data from two cohorts: (1) 272 HGSOC cases from The Cancer Genome Atlas (TCGA) with \u003cem\u003eRB1\u003c/em\u003e mRNA expression data and HR status previously annotated by Takaya et al. (HR-deficient, HRD; HR-proficient, HRP), and (2) 226 clinical HGSOC cases profiled by comprehensive genomic and immune profiling (CGIP) at OmniSeq, categorized as either HR-intact (HRi) or harboring \u003cem\u003eBRCA1/2\u003c/em\u003e alterations (BRCAa). Cases were additionally stratified according to RB1 mRNA expression level as RB1-high (\u0026gt;\u0026thinsp;25th percentile; RBH) or or RB1-low (\u0026le;\u0026thinsp;25th percentile; RBL). HRP-RBH tumors (n\u0026thinsp;=\u0026thinsp;120, 44.1%) were associated with significantly worse overall survival (OS) and progression free survival (PFS) compared to all other subgroups. Median OS for HRP-RBH was 35.9 mo, shorter than HRP-RBL (52.0 mo), HRD-RBL (57.1 mo), and HRD-RBH subgroups (53.3 mo; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and PFS demonstrated a similar trend (15.1 mo vs. 20.6, 20.2 and 20.4 mo, respectively, p\u0026thinsp;=\u0026thinsp;0.0021). HRP-RBH tumors also showed higher aneuploidy scores (median 18 vs\u0026thinsp;\u0026le;\u0026thinsp;10.5 in other subgroups, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). HRi-RBH tumors exhibited a distinct immune gene signature, including elevated mRNA expression of 213 differentially expressed genes and enrichment of pathways such as EMT, PI3K/AKT signaling, and interleukin signaling. Overall, this study suggests that molecular subtyping of HGSOC based on HR status and \u003cem\u003eRB1\u003c/em\u003e expression may provide valuable prognostic insight. HRP tumors with high \u003cem\u003eRB1\u003c/em\u003e expression represent a high-risk subgroup with a distinct molecular profiles and poor clinical outcomes, underscoring the need for novel therapeutic strategies targeting this aggressive subset. These findings provide a foundation for future studies aimed at developing biomarkers and treatments tailored to this challenging subset of HGSOC patients.\u003c/p\u003e","manuscriptTitle":"RB1 expression and HR proficiency define a poor-outcome molecular subtype of high grade serous ovarian carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 05:11:16","doi":"10.21203/rs.3.rs-5523163/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-29T07:17:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-11T15:05:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104065012179831252570450538653624357270","date":"2025-07-09T16:38:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T16:05:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-04T07:59:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-02T20:20:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6aefc3aa-8708-4d82-bef2-e0c7fd7950f8","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51295758,"name":"Health sciences/Oncology/Cancer/Gynaecological cancer/Ovarian cancer"},{"id":51295759,"name":"Biological sciences/Genetics/Cancer genomics"}],"tags":[],"updatedAt":"2025-08-18T16:04:30+00:00","versionOfRecord":{"articleIdentity":"rs-5523163","link":"https://doi.org/10.1038/s41598-025-15156-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-08-12 15:57:14","publishedOnDateReadable":"August 12th, 2025"},"versionCreatedAt":"2025-07-14 05:11:16","video":"","vorDoi":"10.1038/s41598-025-15156-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-15156-9","workflowStages":[]},"version":"v1","identity":"rs-5523163","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5523163","identity":"rs-5523163","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}