Quantitative imaging of RAD51 expression as a marker of platinum resistance in ovarian cancer

Article11 March 2021Open Access Transparent process Quantitative imaging of RAD51 expression as a marker of platinum resistance in ovarian cancer Michal M Hoppe orcid.org/0000-0002-0364-6080 Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Patrick Jaynes Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Joanna D Wardyn Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Sai Srinivas Upadhyayula Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Tuan Zea Tan orcid.org/0000-0001-6624-1593 Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Stefanus Lie Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Diana G Z Lim Department of Pathology, National University Hospital, Singapore Search for more papers by this author Brendan N K Pang Cancer Science Institute of Singapore, National University of Singapore, Singapore Department of Pathology, National University Hospital, Singapore Search for more papers by this author Sherlly Lim Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Joe P S Yeong Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Anthony Karnezis British Columbia Cancer Agency, Vancouver, BC, Canada Search for more papers by this author Derek S Chiu British Columbia Cancer Agency, Vancouver, BC, Canada Search for more papers by this author Samuel Leung British Columbia Cancer Agency, Vancouver, BC, Canada Search for more papers by this author David G Huntsman British Columbia Cancer Agency, Vancouver, BC, Canada Search for more papers by this author Anna S Sedukhina Department of Pharmacogenomics, St. Marianna University, Kawasaki, Japan Search for more papers by this author Ko Sato Department of Pharmacogenomics, St. Marianna University, Kawasaki, Japan Search for more papers by this author Monique D Topp The Walter and Eliza Hall Institute of Medical Research, Parkville, Vic., Australia Search for more papers by this author Clare L Scott The Walter and Eliza Hall Institute of Medical Research, Parkville, Vic., Australia Search for more papers by this author Hyungwon Choi orcid.org/0000-0002-6687-3088 Saw Swee Hock School of Public Health, National University of Singapore, Singapore Search for more papers by this author Naina R Patel Division of Cancer, Imperial College London, London, UK Search for more papers by this author Robert Brown orcid.org/0000-0001-7960-5755 Division of Cancer, Imperial College London, London, UK Search for more papers by this author Stan B Kaye Department of Haematology-Oncology, National University Hospital, Singapore Search for more papers by this author Jason J Pitt Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author David S P Tan Corresponding Author [email protected] orcid.org/0000-0001-9087-5262 Cancer Science Institute of Singapore, National University of Singapore, Singapore Department of Haematology-Oncology, National University Hospital, Singapore Search for more papers by this author Anand D Jeyasekharan Corresponding Author [email protected] orcid.org/0000-0001-9816-6137 Cancer Science Institute of Singapore, National University of Singapore, Singapore Department of Haematology-Oncology, National University Hospital, Singapore Search for more papers by this author Michal M Hoppe orcid.org/0000-0002-0364-6080 Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Patrick Jaynes Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Joanna D Wardyn Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Sai Srinivas Upadhyayula Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Tuan Zea Tan orcid.org/0000-0001-6624-1593 Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Stefanus Lie Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Diana G Z Lim Department of Pathology, National University Hospital, Singapore Search for more papers by this author Brendan N K Pang Cancer Science Institute of Singapore, National University of Singapore, Singapore Department of Pathology, National University Hospital, Singapore Search for more papers by this author Sherlly Lim Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Joe P S Yeong Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author Anthony Karnezis British Columbia Cancer Agency, Vancouver, BC, Canada Search for more papers by this author Derek S Chiu British Columbia Cancer Agency, Vancouver, BC, Canada Search for more papers by this author Samuel Leung British Columbia Cancer Agency, Vancouver, BC, Canada Search for more papers by this author David G Huntsman British Columbia Cancer Agency, Vancouver, BC, Canada Search for more papers by this author Anna S Sedukhina Department of Pharmacogenomics, St. Marianna University, Kawasaki, Japan Search for more papers by this author Ko Sato Department of Pharmacogenomics, St. Marianna University, Kawasaki, Japan Search for more papers by this author Monique D Topp The Walter and Eliza Hall Institute of Medical Research, Parkville, Vic., Australia Search for more papers by this author Clare L Scott The Walter and Eliza Hall Institute of Medical Research, Parkville, Vic., Australia Search for more papers by this author Hyungwon Choi orcid.org/0000-0002-6687-3088 Saw Swee Hock School of Public Health, National University of Singapore, Singapore Search for more papers by this author Naina R Patel Division of Cancer, Imperial College London, London, UK Search for more papers by this author Robert Brown orcid.org/0000-0001-7960-5755 Division of Cancer, Imperial College London, London, UK Search for more papers by this author Stan B Kaye Department of Haematology-Oncology, National University Hospital, Singapore Search for more papers by this author Jason J Pitt Cancer Science Institute of Singapore, National University of Singapore, Singapore Search for more papers by this author David S P Tan Corresponding Author [email protected] orcid.org/0000-0001-9087-5262 Cancer Science Institute of Singapore, National University of Singapore, Singapore Department of Haematology-Oncology, National University Hospital, Singapore Search for more papers by this author Anand D Jeyasekharan Corresponding Author [email protected] orcid.org/0000-0001-9816-6137 Cancer Science Institute of Singapore, National University of Singapore, Singapore Department of Haematology-Oncology, National University Hospital, Singapore Search for more papers by this author Author Information Michal M Hoppe1, Patrick Jaynes1, Joanna D Wardyn1, Sai Srinivas Upadhyayula1, Tuan Zea Tan1, Stefanus Lie1, Diana G Z Lim2, Brendan N K Pang1,2, Sherlly Lim1, Joe P S Yeong1, Anthony Karnezis3,†, Derek S Chiu3, Samuel Leung3, David G Huntsman3, Anna S Sedukhina4, Ko Sato4, Monique D Topp5, Clare L Scott5, Hyungwon Choi6, Naina R Patel7, Robert Brown7, Stan B Kaye8, Jason J Pitt1, David S P Tan *,1,8 and Anand D Jeyasekharan *,1,8 1Cancer Science Institute of Singapore, National University of Singapore, Singapore 2Department of Pathology, National University Hospital, Singapore 3British Columbia Cancer Agency, Vancouver, BC, Canada 4Department of Pharmacogenomics, St. Marianna University, Kawasaki, Japan 5The Walter and Eliza Hall Institute of Medical Research, Parkville, Vic., Australia 6Saw Swee Hock School of Public Health, National University of Singapore, Singapore 7Division of Cancer, Imperial College London, London, UK 8Department of Haematology-Oncology, National University Hospital, Singapore †Present address: Pathology and Lab medicine, UC Davis Medical Centre, Sacramento, CA, USA *Corresponding author. Tel: +65 6773 7888; E-mail: [email protected] *Corresponding author. Tel: +65 6516 5094; E-mail: [email protected] EMBO Mol Med (2021)13:e13366https://doi.org/10.15252/emmm.202013366 See also: J Schwickert et al (May 2021) PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Early relapse after platinum chemotherapy in epithelial ovarian cancer (EOC) portends poor survival. A-priori identification of platinum resistance is therefore crucial to improve on standard first-line carboplatin–paclitaxel treatment. The DNA repair pathway homologous recombination (HR) repairs platinum-induced damage, and the HR recombinase RAD51 is overexpressed in cancer. We therefore designed a REMARK-compliant study of pre-treatment RAD51 expression in EOC, using fluorescent quantitative immunohistochemistry (qIHC) to overcome challenges in quantitation of protein expression in situ. In a discovery cohort (n = 284), RAD51-High tumours had shorter progression-free and overall survival compared to RAD51-Low cases in univariate and multivariate analyses. The association of RAD51 with relapse/survival was validated in a carboplatin monotherapy SCOTROC4 clinical trial cohort (n = 264) and was predominantly noted in HR-proficient cancers (Myriad HRDscore < 42). Interestingly, overexpression of RAD51 modified expression of immune-regulatory pathways in vitro, while RAD51-High tumours showed exclusion of cytotoxic T cells in situ. Our findings highlight RAD51 expression as a determinant of platinum resistance and suggest possible roles for therapy to overcome immune exclusion in RAD51-High EOC. The qIHC approach is generalizable to other proteins with a continuum instead of discrete/bimodal expression. Synopsis Quantitative immunohistochemistry (qIHC) reveals that high expression of the DNA repair protein RAD51 in epithelial ovarian cancer is associated with early relapse after platinum chemotherapy, and also with decreased cytotoxic T-cell infiltration into tumors. High nuclear expression score for RAD51 (RAD51NES) was correlated with early relapse and shorter survival in two independent EOC patient cohorts (n = 264 + 284). RAD51NES was prognostically relevant primarily for EOCs that did not have homologous recombination deficiency (HRD). RAD51 expression was correlated with a unique immune phenotype in cancer, with increased chemokines but reduced cytotoxic T-cell infiltration. The paper explained Problem Platinum chemotherapy is the cornerstone of treatment for epithelial ovarian cancer (EOC). While the typical first-line chemotherapy of Carboplatin + Paclitaxel is highly effective in EOC, a subset of patients are resistant to or relapse early after treatment and have poor overall survival. It would be advantageous to identify these cases prior to initiation of treatment, to facilitate the testing of novel agents that can supplement or even supplant platinum chemotherapy. There are no molecular markers currently used in pathology labs to define possible platinum-resistance, in large part due to challenges in quantitating expression of candidate proteins in tissue sections. Results We used a state-of-the-art method for simultaneous staining of multiple proteins in tissue sections along with automated microscopy to quantitatively measure RAD51, a DNA repair protein that is important for the resolution of platinum induced damage. We show using two large independent EOC patient cohorts, cases that expressed a high amount of RAD51 relapsed sooner than those expressing a low amount of RAD51. Furthermore, this phenomenon correlates with an exclusion of anti-tumour immune cells from the microenvironment of cancers with RAD51-High. Impact Our study identifies RAD51 as a bonafide biomarker for increased likelihood for resistance to platinum chemotherapy in ovarian cancer, which can subsequently be developed into a clinical grade assay for routine diagnostic practice. Furthermore, our observation that RAD51 tumours tend to exclude important anti-cancer immune cells sets the stage for developing therapeutic approaches to increase immune infiltration in these cancers. Introduction Epithelial ovarian cancer (EOC) is the most lethal of all female genital tract cancers. Platinum chemotherapy is the cornerstone of treatment for EOC, typically combined with paclitaxel. The duration of disease control after platinum chemotherapy is a strong predictor of overall survival in EOC (Davis et al, 2014). In recurrent EOC, the platinum treatment-free interval strongly correlates with subsequent response to platinum rechallenge therapy (Markman et al, 1991). Platinum resistance (defined as relapse within six months following completion of platinum chemotherapy) occurs in 20–30% of cases. However, recent consensus guidelines highlight the predictive limitations of this “time-based” definition (Colombo et al, 2019). There remains a need to a-priori identify patients who will have platinum resistance, and there are no molecular markers of platinum resistance in current clinical use. The identification of cases for whom first-line carboplatin–paclitaxel chemotherapy is sub-optimal will facilitate trials of early incorporation of novel agents to improve overall survival. The sensitivity of ovarian cancers to platinum chemotherapy is in part due to a high prevalence of aberrations in the DNA repair pathway of homologous recombination (HR; McMullen et al, 2020). Platinum treatment leads to inter-strand cross-links, which are typically repaired by the pathways of nucleotide excision repair (NER) and HR (De Silva et al, 2000; Sarkar et al, 2006). HR deficiency (HRD) e.g., with BRCA mutations, is associated with exquisite platinum sensitivity due to the inability to repair platinum cross-links (Tan et al, 2008). Up to 50% of EOC show HRD through mutations in other HR regulatory genes of the BRCA/Fanconi Anaemia (FA) network (Bell et al, 2011). However, unlike other BRCA/FA genes, RAD51—the central recombinase of the HR pathway—is not commonly mutated in cancer. Depletion or mutation of RAD51 is lethal due to its essential role in cellular replication (Tsuzuki et al, 1996; Sonoda et al, 1998). Conversely, RAD51 is often upregulated in multiple cancer types and is associated with poor survival (Qiao et al, 2005; Mitra et al, 2009; Tennstedt et al, 2013; Alshareeda et al, 2016). As a corollary to platinum sensitivity in ovarian cancers with HRD, it is not known if the overexpression of RAD51 confers platinum resistance. However, evaluating the clinical significance of RAD51 overexpression has been hampered by the lack of quantitative tools for proteomics in situ. In this paper, we utilize quantitative immunohistochemistry (qIHC) through multispectral imaging/ automated analysis to evaluate baseline RAD51 protein levels in formalin-fixed paraffin-embedded (FFPE) tissue. For the discovery cohort, we focused on high-grade serous ovarian carcinomas (HGSOC), the most common and aggressive subtype of EOC. Platinum is typically combined with paclitaxel, the sensitivity to which is not associated with HRD, making it challenging to dissect the contribution of a biomarker to platinum-specific survival. We therefore validated our findings in samples from the SCOTROC4 clinical trial in a REMARK-compliant retrospective biomarker analysis. SCOTROC4 was a phase III trial of carboplatin monotherapy in EOC, assessing two different dosing schedules (Banerjee et al, 2013). While the trial showed no difference between the two arms, it represents a unique cohort of platinum monotherapy in ovarian cancer, with well-annotated survival data and HRD scores (Stronach et al, 2018). Results and Discussion RAD51 forms discrete nuclear foci upon activation of HR, and this is a widely used measure of recombination proficiency in vitro (Graeser et al, 2010). The RAD51 foci counting assay has been evaluated in FFPE and ex vivo samples (Graeser et al, 2010; Naipal et al, 2014; Castroviejo-Bermejo et al, 2018; Tumiati et al, 2018). However, automated quantitation of foci counts in FFPE and ex vivo samples is logistically complex and highly reliant on sample preparation/microscopy setup. Conversely, the quantitation of mean nuclear intensity (nuclear expression score) by qIHC is relatively amenable to automated quantitation and scalability in large data sets. To setup our RAD51 qIHC assay, we first validated a rabbit monoclonal antibody EPR4030(3) (Abcam)—demonstrating specific detection of RAD51 in FFPE cell blocks, ex vivo irradiated patient-derived xenografts and control human tissues (Fig EV1-EV5). We define RAD51 nuclear expression score (RAD51NES) as the average intensity of RAD51 expression measured by qIHC across all imaged tumour cells for a given sample (Fig 1A). In a training cohort of EOC cases (n = 52), RAD51NES showed strong concordance with RAD51 H-Scores obtained independently from two board-certified pathologists (Fig 1B). We evaluated RAD51 expression in a HGSOC cohort of cases treated with standard-of-care protocols at British Columbia Cancer (BCC) Vancouver. We observed that the RAD51NES in this cohort followed a normal distribution (Fig 1C). Unlike markers such as Ki67 or ER/PR which have a distinct bimodal pattern of expression (i.e. a cell is either “positive” or “negative”), many cancer-related proteins display homogenous expression within a sample and normal distribution across samples. To cater for the normal distribution of RAD51 within a clinical cohort, RAD51NES was analysed as either a continuous variable or a categorical variable dividing the cohort into RAD51-Low (RAD51NES first quartile [Q1]), RAD51-High (fourth quartile [Q4]) and RAD51-IQR (IQR [quartiles 2 + 3]). We subsequently applied our optimized protocol for staining, imaging, scoring and analysis to assess the clinical relevance of RAD51 protein expression in the BCC cohort. In a Kaplan–Meier survival analysis, high RAD51NES was associated with poorer progression-free survival (PFS) and overall survival (OS) (Fig 1D). We used the 12-month PFS rate (%) as a surrogate for early relapse after completion of platinum-based chemotherapy. RAD51-High cases showed higher likelihood of progression than RAD51-Low cases at both 12 and 24 months (Fig 1E), pointing to the potential utility of RAD51 in predicting platinum resistance. As RAD51 expression is linked to proliferation through common regulatory pathways (Fischer et al, 2016), a possible explanation for poor survival could be increased proliferation in RAD51-High tumours. We therefore measured the proliferation marker Ki67 in the BCC cohort by qIHC. RAD51NES correlated weakly with Ki67 extent (Fig EV2A). Importantly, the proliferation status of the tumour (i.e. extent of Ki67 positivity) was not associated with survival outcomes (Fig EV2B). Furthermore, in a multivariate cox proportional hazards model (Cox PH) adjusting for Ki67 extent, age and stage, RAD51NES as a continuous variable remained a statistically significant independent predictor of PFS in HGSOC. Comparable results were obtained for OS (Table 1). Click here to expand this figure. Figure EV1. Validation of a RAD51-specific antibody Immunoblot of RAD51 in HEY-T30 control and knock-down cell lines. The EPR4030(3) antibody reveals a single band of 37 kDa size in control cells (top band results from post-translational modification of RAD51), which is not present in RAD51 knock-down cells. Blot is a representation of three independent experiments. Cells from (A) were used to create a FFPE cell block used for RAD51 fluorescent IHC (left). Cells from (A) in an FFPE block stained with an IgG isotype control (right). Scale bar is 50μm. All images are representative of three independent experiments. Fluorescent IHC FFPE block of an ovarian PDX treated ex vivo with γ-radiation and stained for RAD51 and p-H2AX S139 (left). Scale bar is 50 μm. RAD51NES score for seven ovarian PDXs before and after treatment with γ-radiation (IR) (right). Paired t-test. RAD51 fluorescent IHC on normal FFPE tissues. Testis is shown as a positive control and liver a negative control. Scale bar is 50 μm. All images are representative of three independent biological samples. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Correlation of Ki67 % extent and HRD phenotype with RAD51NES Correlation of Ki67 % extent and RAD51NES in the BCC cohort. Spearman correlation (left) and one-way ANOVA with Bonferroni correction (right). Median with interquartile range. Kaplan–Meier plots for PFS (left) and OS (right) stratified according to Ki67 extent quartile in the BCC cohort. Q—quartile. Log-rank test, shading denotes 95% confidence intervals. Correlation of Ki67 extent and RAD51NES in the SCOTROC4 cohort. Spearman correlation (left) and one-way ANOVA with Bonferroni correction (right). Median with interquartile range. Kaplan–Meier plots for PFS (left) and OS (right) stratified according to Ki67 extent quartile in the SCOTROC4 cohort. Q—quartile. Log-rank test, shading denotes 95% confidence intervals. Correlation of RAD51NES with BRCA mutation status in EOC. One-way ANOVA. Median with interquartile range. Linear regression of “genomic scar” HRD score assay and RAD51NES. Vertical dashed line denotes HRD positivity score of 42. Download figure Download PowerPoint Click here to expand this figure. Figure EV3. Exogenous Flag-RAD51 is functional Immunoblot of RAD51 upon overexpression and subsequent RNAi-mediated depletion of total (siRAD51-CDS) or endogenous (siRAD51-3’UTR) RAD51 mRNA. The exogenous stably overexpressed Flag-RAD51 represents the top band. HGSOC cell utilized indicated below the blot. Blots are representative of two independent experiments. Validation of exogenous Flag-RAD51 functionality using a cell viability assay. Flag-RAD51 was stably overexpressed in three HGSOC cell lines which were treated with increasing doses of carboplatin for 96 h. Flag-RAD51 rescues carboplatin sensitivity upon depletion of endogenous RAD51 protein. Mean with standard deviation is shown of at least three biological replicates per point. Statistical comparison is performed at 1μM concentration of carboplatin, t-test. Immunofluorescence of TYK-nu cells with stable Flag-RAD51 overexpression treated with 10μM of carboplatin for 48 h. Cells were co-stained for both RAD51 and Flag. DAPI serves as a nuclear counterstaining. Scale bar is 20 μm. Download figure Download PowerPoint Click here to expand this figure. Figure EV4. Differential gene expression analysis of immune genes between RAD51-High and -Low tumours Differential gene expression analysis of immune genes between RAD51-High (Q4) and -Low (Q1) across three independent mRNA cohorts of EOC (see also Fig 3E). t-Test; dashed line denoted threshold of significance, Bonferroni corrected for multiple testing. Download figure Download PowerPoint Click here to expand this figure. Figure EV5. Multiplexed fluorescent IHC staining for immune markers of the tumour microenvironment Multiplexed fluorescent IHC staining for immune markers of the microenvironment in an EOC patient sample. Unmixed monochrome components are shown along with a false-coloured merge image. Cytokeratin staining was used to differentiate between the tumour and stromal compartments of the sample. Scale bar is 50 μm. Quantitation of immune populations in the BCC cohort. Results for RAD51-High and -Low tumours are shown. T/S—tumour/stroma ratio. Bar is median. Mann–Whitney test. Subset analysis of CD8+ cytotoxic T-cell infiltration in the BCC cohort stratified according to BRCA mutation status. Absolute tumour CD8+ cytotoxic T-cell infiltration numbers and tumour/stroma (T/S) cytotoxic T-cell number ratio in RAD51-High and -Low cases. Bar is median. Mann–Whitney test. Download figure Download PowerPoint Figure 1. RAD51 assay development and testing on BCC cohort Range of example RAD51 nuclear expression score (RAD51NES) values with respective unmixed monochrome fluorescent IHC staining images. EpCAM is used as a tumour marker and an internal sample quality control. Scale bar is 50 μm. Correlation of RAD51NES with two independent pathologist H-scores (top left and top right) and correlation of two pathologist with each other (bottom). Pearson correlation. Distribution of RAD51 nuclear expression score (RAD51NES) in the BCC cohort. The cohort is divided into RAD51-Low expressing cases (first quartile, Q1—blue), intermediate cases (interquartile range, IQR—grey) and RAD51-High expressing cases (fourth quartile, Q4—brown). Dashed line denotes the median RAD51NES in this cohort. Survival analysis of the BCC cohort. Kaplan–Meier plots for progression-free survival (PFS) (left) and overall survival (OS) (right) stratified according to fourth quartile (Q4) and first quartile (Q1) of RAD51NES. Log-rank test, shading denotes 95% confidence intervals. Number of cases with progression at 12 and 24 months. Chi-square test. Download figure Download PowerPoint Table 1. Multivariate analysis of continuous RAD51NES and Ki67 extent as a predictor of PFS and OS in the BCC cohort of HGSOC (Cox proportional hazards model). Variable Total cases (n = 242) missing values (n = 43) Total cases (n = 278) missing values (n = 7) PFS OS HR (95% CI) P-value HR (95% CI) P-value RAD51NES (continuous) 1.4 (1.0 to 1.9) 0.025 1.3 (0.98 to 1.9) 0.066 Ki67% (continuous) 0.97 (0.54 to 1.7) 0.975 0.84 (0.49 to 1.4) 0.529 Age <65 Ref. Ref. ≥65 1.0 (0.79 to 1.4) 0.797 1.3 (1.0 to 1.7) 0.022 Stage 0.048 0.011 I Ref. Ref. II 1.8 (0.70 to 4.4) 0.230 1.4 (0.53 to 3.7) 0.500 III 2.6 (1.2 to 5.6) 0.015 2.7 (1.2 to 6.2) 0.017 IV 2.2 (0.9 to 5.3) 0.086 2.3 (0.91 to 5.7) 0.078 BCC, British Columbia Cancer; CI, Confidence interval; HGSOC, High-grade serous ovarian cancer; HR, Hazard ratio; OS, overall survival; PFS, progression-free survival; RAD51NES, RAD51 nuclear expression score; Ref., Reference sample. Platinum is typically used along with paclitaxel in frontline chemotherapy of ovarian cancer. To negate potential confounding effects of paclitaxel on survival outcomes, we utilized the unique carboplatin monotherapy SCOTROC4 trial as a validation cohort for our findings. RAD51 protein expression within this cohort also followed a normal distribution (Fig 2A). RAD51-High patients again showed poorer PFS and OS after platinum monotherapy in comparison to RAD51-Low cases (Fig 2B). In a Cox PH multivariate analysis of continuous RAD51NES and Ki67 extent controlling for clinical prognosticators (Table 2), RAD51NES was not independently associated with poor PFS, but remained an independent statistically significant predictor of OS. Ki67 extent was not significantly associated with PFS or OS in multivariate analyses (Fig EV2CandD, Table 2). We then used PFS rate (%) at 12 months (calculated from time of randomization) as a surrogate for a shorter platinum-free interval and hence platinum resistance. Similar to the BCC cohort, RAD51-High cases were more likely to relapse within both 12 months and 24 months than RAD51-Low cases (Fig 2C). Overall, in two independent cohorts, a high RAD51NES associates with early relapses after platinum treatment in ovarian cancer and implies a higher risk of primary platinum resistance in RAD51-High tumours. Figure 2. Validation of assay and findings in SCOTROC4 cohort Distribution of RAD51NES in the SCOTROC4 cohort. Dashed line denotes the median RAD51NES in this cohort. Survival analysis of the SCOTROC4 cohort. Kaplan–Meier plots for PFS (left) and OS (right) stratified according to quartiles of RAD51NES. Log-rank test, shading denotes 95% confidence intervals. Number of cases with progression at 12 and 24 months. Chi-square test. Survival analysis of HRD-positive patients according to quartile of RAD51NES. Log-rank test, shading denotes 95% confidence intervals. Survival analysis of HRD-negative patients. Log-rank test, shading denotes 95% confidence intervals. Download figure Download PowerPoint Table 2. Multivariate analysis of continuous RAD51NES and Ki67 extent as a predictor of PFS and OS in the SCOTROC4 cohort (Cox proportional hazards model). Variable Total cases (n = 175) missing values (n = 93) PFS OS HR (95% CI) P-value HR (95% CI) P-value RAD51NES (continuous) 1.2 (0.97 to 1.5) 0.104 1.4 (1.1 to 1.9) 0.007 Ki67% (continuous) 1.0 (0.98 to 1.02) 0.971 0.98 (0.96 to 1.0) 0.227 Age <65 Ref. Ref. ≥65 1.0 (0.64 to 1.6) 0.996 0.99 (0.54 to 1.8) 0.977 Stage <0.001 0.023 I Ref. Ref. II 3.8 (0.99 to 15.0) 0.052 13.7 (1.6 to 120.5) 0.018 III 9.7 (2.8 to 33.1) <0.001 11.2 (1.4 to 88.9) 0.022 IV 5.3 (1.4 to 20.2) 0.014 4.8 (0.54 to 44.0) 0.160 Histology 0.150 <0.001 Serous Ref. Ref. Mucinous 3.9 (1.3 to 12.4) 0.01