{"paper_id":"430a0121-7dd6-4a51-85a8-5aa69a25ef9f","body_text":"1 \nConcurrent RB1 loss and BRCA-deficiency predicts enhanced immunological response 1 \nand long-term survival in tubo-ovarian high-grade serous carcinoma 2 \n 3 \nFlurina A. M. Saner1,2,†, Kazuaki Takahashi1,3,†, Timothy Budden4,5, Ahwan Pandey1, Dinuka 4 \nAriyaratne1, Tibor A. Zwimpfer1, Nicola S. Meagher4,6, Sian Fereday1,7, Laura Twomey1, 5 \nKathleen I. Pishas1,7, Therese Hoang1, Adelyn Bolithon4,8, Nadia Traficante1,7, Kathryn 6 \nAlsop1,7, Elizabeth L. Christie1,7, Eun-Young Kang9, Gregg S. Nelson10, Prafull Ghatage10, 7 \nCheng-Han Lee11, Marjorie J. Riggan12, Jennifer Alsop13, Matthias W. Beckmann14, Jessica 8 \nBoros15-17, Alison H. Brand16,17, Angela Brooks-Wilson18, Michael E. Carney19, Penny 9 \nCoulson20, Madeleine Courtney-Brooks21, Kara L. Cushing-Haugen22, Cezary Cybulski23, 10 \nMona A. El-Bahrawy24, Esther Elishaev25, Ramona Erber26, Simon A. Gayther27, Aleksandra 11 \nGentry-Maharaj28,29, C. Blake Gilks30, Paul R. Harnett17,31, Holly  R. Harris22,32, Arndt 12 \nHartmann26, Alexander Hein14, Joy Hendley1, AOCS Group1,16,33, Brenda Y. Hernandez34, 13 \nAnna Jakubowska23,35, Mercedes Jimenez-Linan36, Michael E. Jones20, Scott H. Kaufmann37, 14 \nCatherine J. Kennedy15,17, Tomasz Kluz38, Jennifer M. Koziak39, Björg Kristjansdottir40, Nhu  15 \nD. Le41, Marcin Lener42, Jenny Lester43, Jan Lubiński23, Constantina Mateoiu44, Sandra 16 \nOrsulic43, Matthias Ruebner14, Minouk J. Schoemaker21, Mitul Shah13, Raghwa Sharma45, 17 \nMark E. Sherman46, Yurii B. Shvetsov34, Naveena Singh30, T. Rinda Soong25, Helen 18 \nSteed47,48, Paniti Sukumvanich21, Aline Talhouk49,50, Sarah E. Taylor21, Robert  A.  19 \nVierkant51, Chen Wang52, Martin Widschwendter53, Lynne R. Wilkens34, Stacey J. 20 \nWinham52, Michael S. Anglesio49,50, Andrew Berchuck12, James D. Brenton54, Ian 21 \nCampbell1,7, Linda S. Cook55,56, Jennifer A. Doherty57, Peter A. Fasching14, Renée T. 22 \nFortner58,59, Marc T. Goodman60, Jacek Gronwald23, David G. Huntsman30,49,50,61, Beth Y.  23 \nKarlan43, Linda E. Kelemen62, Usha Menon28, Francesmary Modugno21,63,64 Paul D.P. 24 \nPharoah13,65,66, Joellen M. Schildkraut67, Karin Sundfeldt40, Anthony J. Swerdlow20,68, Ellen 25 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n   \n \n 2 \nL. Goode69, Anna DeFazio6,15-17, Martin Köbel9,‡, Susan J. Ramus4,8,‡, David D. L. 26 \nBowtell1,7,‡, and Dale W. Garsed1,7,‡,* 27 \n 28 \n1Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia. 29 \n2Department of Obstetrics and Gynecology, Bern University Hospital and University of Bern, 30 \nBern, Switzerland. 31 \n3Department of Obstetrics and Gynecology, The Jikei University School of Medicine, Tokyo, 32 \nJapan. 33 \n4School of Clinical Medicine, UNSW Medicine and Health, University of NSW Sydney, 34 \nSydney, New South Wales, Australia. 35 \n5Skin Cancer and Ageing Lab, Cancer Research United Kingdom Manchester Institute, The 36 \nUniversity of Manchester, Manchester, UK. 37 \n6The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, 38 \nSydney, New South Wales, Australia. 39 \n7Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, 40 \nVictoria, Australia. 41 \n8Adult Cancer Program, Lowy Cancer Research Centre, University of NSW Sydney, Sydney, 42 \nNew South Wales, Australia. 43 \n9Department of Pathology and Laboratory Medicine, University of Calgary, Foothills 44 \nMedical Center, Calgary, AB, Canada. 45 \n10Department of Oncology, Division of Gynecologic Oncology, Cumming School of 46 \nMedicine, University of Calgary, Calgary, AB, Canada. 47 \n11Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, 48 \nAlberta, Canada. 49 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 3 \n12Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Duke 50 \nUniversity Medical Center, Durham, NC, USA. 51 \n13Centre for Cancer Genetic Epidemiology, Department of Oncology, University of 52 \nCambridge, Cambridge, UK. 53 \n14Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, 54 \nFriedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, 55 \nErlangen, Germany. 56 \n15Centre for Cancer Research, The Westmead Institute for Medical Research, Sydney, New 57 \nSouth Wales, Australia. 58 \n16Department of Gynaecological Oncology, Westmead Hospital, Sydney, New South Wales, 59 \nAustralia. 60 \n17The University of Sydney, Sydney, New South Wales, Australia. 61 \n18Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada. 62 \n19Department of Obstetrics and Gynecology, John A. Burns School of Medicine, University 63 \nof Hawaii, Honolulu, HI, USA. 64 \n20Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK. 65 \n21Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh 66 \nSchool of Medicine, Pittsburgh, PA, USA. 67 \n22Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer 68 \nCenter, Seattle, WA, USA. 69 \n23Department of Genetics and Pathology, International Hereditary Cancer Center, 70 \nPomeranian Medical University, Szczecin, Poland. 71 \n24Department of Metabolism, Digestion and Reproduction, Imperial College London, 72 \nHammersmith Hospital, London, UK. 73 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 4 \n25Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 74 \nUSA. 75 \n26Institute of Pathology, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander 76 \nUniversity Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany. 77 \n27Center for Bioinformatics and Functional Genomics and the Cedars Sinai Genomics Core, 78 \nCedars-Sinai Medical Center, Los Angeles, CA, USA. 79 \n28MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College 80 \nLondon, London, UK. 81 \n29Department of Women’s Cancer, Elizabeth Garrett Anderson Institute for Women’s Health, 82 \nUniversity College London, London, UK 83 \n30Department of Pathology and Laboratory Medicine, University of British Columbia, 84 \nVancouver, BC, Canada. 85 \n31Crown Princess Mary Cancer Centre, Westmead Hospital, Sydney, New South Wales, 86 \nAustralia. 87 \n32Department of Epidemiology, University of Washington, Seattle, WA, USA. 88 \n33QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. 89 \n34University of Hawaii Cancer Center, Honolulu, HI, USA. 90 \n35Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian 91 \nMedical University, Szczecin, Poland. 92 \n36Department of Histopathology, Addenbrooke's Hospital, Cambridge, UK. 93 \n37Division of Oncology Research, Department of Oncology, Mayo Clinic, Rochester, MN, 94 \nUSA. 95 \n38Department of Gynecology and Obstetrics, Gynecology Oncology and Obstetrics, Institute 96 \nof Medical Sciences, Medical College of Rzeszow University, Rzeszów, Poland. 97 \n39Alberta Health Services-Cancer Care, Calgary, AB, Canada. 98 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 5 \n40Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska 99 \nCenter for Cancer Research, University of Gothenburg, Gothenburg, Sweden. 100 \n41Cancer Control Research, BC Cancer Agency, Vancouver, BC, Canada. 101 \n42International Hereditary Cancer Center, Department of Genetics and Pathology, 102 \nPomeranian Medical University in Szczecin, Szczecin, Poland. 103 \n43David Geffen School of Medicine, Department of Obstetrics and Gynecology, University of 104 \nCalifornia at Los Angeles, Los Angeles, CA, USA. 105 \n44Department of Pathology, University of Gothenburg, Gothenburg, Sweden. 106 \n45Tissue Pathology and Diagnostic Oncology, Westmead Hospital, Sydney, New South 107 \nWales, Australia. 108 \n46Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA. 109 \n47Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University 110 \nof Alberta, Edmonton, Alberta, Canada. 111 \n48Section of Gynecologic Oncology Surgery, North Zone, Alberta Health Services, 112 \nEdmonton, Alberta, Canada. 113 \n49British Columbia's Gynecological Cancer Research Team (OVCARE), University of British 114 \nColumbia, BC Cancer, and Vancouver General Hospital, Vancouver, BC, Canada. 115 \n50Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, BC, 116 \nCanada. 117 \n51Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, 118 \nMayo Clinic, Rochester, MN, USA. 119 \n52Department of Quantitative Health Sciences, Division of Computational Biology, Mayo 120 \nClinic, Rochester, MN, USA. 121 \n53EUTOPS Institute, University of Innsbruck, Innsbruck, Austria. 122 \n54Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK. 123 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 6 \n55Epidemiology, School of Public Health, University of Colorado, Aurora, CO, USA. 124 \n56Community Health Sciences, University of Calgary, Calgary, AB, Canada. 125 \n57Huntsman Cancer Institute, Department of Population Health Sciences, University of Utah, 126 \nSalt Lake City, UT, USA. 127 \n58Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, 128 \nGermany. 129 \n59Department of Research, Cancer Registry of Norway, Oslo, Norway. 130 \n60Cancer Prevention and Control Program, Cedars-Sinai Cancer, Cedars-Sinai Medical 131 \nCenter, Los Angeles, CA, USA. 132 \n61Department of Molecular Oncology, BC Cancer Research Centre, Vancouver, BC, Canada. 133 \n62Division of Acute Disease Epidemiology, South Carolina Department of Health & 134 \nEnvironmental Control, Columbia, SC, USA. 135 \n63Department of Epidemiology, University of Pittsburgh School of Public Health, Pittsburgh, 136 \nPA, USA. 137 \n64Women's Cancer Research Center, Magee-Womens Research Institute and Hillman Cancer 138 \nCenter, Pittsburgh, PA, USA. 139 \n65Department of Computational Biomedicine, Cedars-Sinai Medical Center, West 140 \nHollywood, CA, USA. 141 \n66Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, 142 \nUniversity of Cambridge, Cambridge, UK. 143 \n67Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, 144 \nGA, USA. 145 \n68Division of Breast Cancer Research, The Institute of Cancer Research, London, UK. 146 \n69Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, 147 \nRochester, MN, USA. 148 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 7 \n 149 \n*Correspondence to: Dr Dale W. Garsed, Peter MacCallum Cancer Centre, 305 Grattan St, 150 \nMelbourne, 3000, Australia, +61 3 855 96512, Dale.Garsed@petermac.org 151 \n 152 \n†These authors contributed equally to the work. 153 \n‡These authors contributed equally to the work. 154 \n 155 \nABSTRACT 156 \nBackground: Somatic loss of the tumour suppressor RB1 is a common event in tubo-ovarian 157 \nhigh-grade serous carcinoma (HGSC), which frequently co -occurs with alterations in 158 \nhomologous recombination DNA repair genes including BRCA1 and BRCA2 (BRCA). We 159 \nexamined whether tumour expression of RB1 was associated with survival across ovarian 160 \ncancer histotypes (HGSC, endometrioid (ENOC), clear cell (CCOC), mucinous (MOC), low -161 \ngrade serous  carcinoma (LGSC)), and how co-occurrence of germline BRCA pathogenic 162 \nvariants and RB1 loss influences long-term survival in a large series of HGSC. 163 \nPatients and m ethods: RB1 protein expression patterns were classified by 164 \nimmunohistochemistry in epithelial ovarian carcinomas of 7436 patients from 20 studies 165 \nparticipating in the Ovarian Tumor Tissue Analysis consortium and assessed for associations 166 \nwith overall survival  (OS), accounting for patient age at diagnosis  and FIGO stage. We 167 \nexamined RB1 expression and germline BRCA status in a subset of 1 134 HGSC, and related 168 \ngenotype to survival, tumour infiltrating  CD8+ lymphocyte counts and transcriptomic 169 \nsubtypes.  Using CRISPR-Cas9, we deleted RB1 in HGSC cell lines with and without BRCA1 170 \nmutations to model co-loss with treatment response. We also performed genomic analyses on 171 \n126 primary HGSC to explore the molecular characteristics of concurrent homologous 172 \nrecombination deficiency and RB1 loss. 173 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 8 \nResults: RB1 protein loss was most frequent in HGSC (16.4%) and was highly correlated with 174 \nRB1 mRNA expression. RB1 loss was associated with longer OS in HGSC (hazard ratio [HR] 175 \n0.74, 95% confidence interval [ CI] 0.66-0.83, P = 6.8 x10-7), but with poorer prognosis in 176 \nENOC (HR 2.17, 95% CI 1.17-4.03, P = 0.0140). Germline BRCA mutations and RB1 loss co-177 \noccurred in HGSC (P < 0.0001). Patients with both RB1 loss and germline BRCA mutations 178 \nhad a superior OS (HR 0.38, 95% CI 0.25-0.58, P = 5.2 x10-6) compared to patients with either 179 \nalteration alone, and their median OS was three times longer than non-carriers whose tumours 180 \nretained RB1 expression (9.3 years vs. 3.1 years). Enhanced sensitivity to cisplatin (P < 0.01) 181 \nand paclitaxel (P < 0.05) was seen in BRCA1 mutated cell lines with RB1 knockout. Among 182 \n126 patients with whole-genome and transcriptome sequence data , combined RB1 loss and 183 \ngenomic evidence of homologous recombination deficiency was correlated with transcriptional 184 \nmarkers of enhanced interferon response, cell cycle deregulation, and reduced epithelial -185 \nmesenchymal transition in primary HGSC. CD8+ lymphocytes were most prevalent in BRCA-186 \ndeficient HGSC with co-loss of RB1. 187 \nConclusions: Co-occurrence of RB1 loss and  BRCA mutation was associated with 188 \nexceptionally long survival in patients with HGSC, potentially due to better treatment response 189 \nand immune stimulation. 190 \n 191 \nINTRODUCTION 192 \nDespite a high response rate to primary treatment, the progressive development of acquired 193 \ndrug resistance is common in tubo-ovarian high-grade serous carcinoma (HGSC), a histotype 194 \nthat is associated with approximately 70% of ovarian cancer deaths1. The frequent acquisition 195 \nof resistance-conferring alterations in HGSC2-4 suggests that the development of drug 196 \nresistance may be inevitable when curative surgery is not achieved in these patients. Countering 197 \nthat view, however, is the observation that a small subset of  patients with HGSC advanced 198 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 9 \ndisease experience an exceptional response to treatment, survive well beyond a median of 3 .4 199 \nyears5, and in some cases, remain disease free 6,7. Interest in studying long-term cancer 200 \nsurvivors is growing as they may assist the discovery of prognostic biomarkers, novel 201 \ntreatments, and approaches to limit the development of resistance8. 202 \nSeveral clinical and molecular factors that influence treatment response and overall 203 \nsurvival (OS) in HGSC have been described. Complete surgical debulking is associated with a 204 \nmore favourable outcome compared to patients  left with residual disease 9-11. Molecular 205 \nsubtypes defined by distinct gene expression patterns in primary HGSC are associated with 206 \ndifferent outcomes12, including the poor survival C1/mesenchymal subtype that is more often 207 \nseen in patients where complete surgical tumour resection cannot be achieved13-15. By contrast, 208 \nthe C2/immunoreactive subtype is typified by extensive infiltration of intraepithelial T cells12, 209 \na feature known to be strongly associated with improved survival 16,17. Tumours arising in 210 \nindividuals with germline or somatic alterations in BRCA1 or BRCA2 genes are typically more 211 \nresponsive to conventional chemotherapy and poly(ADP-ribose) polymerase ( PARP) 212 \ninhibitors, whereas those tumours with intact homologous recombination (HR) DNA repair are 213 \nmore often resistant to treatment 18-20. Patients with germline BRCA1 or BRCA2 pathogenic 214 \nvariants show more favourable survival at five years post-diagnosis compared to non-carriers, 215 \nwith BRCA2 mutation carriers retain ing a long -term (>10 year) survival advantage 21-23. 216 \nAlthough deleterious mutations in BRCA1, BRCA2 and other genes involved in HR DNA repair 217 \nare associated with a favourable response to treatment, these are not sufficient alone to confer 218 \nlong-term survival and a large proportion of such patients experience a typical disease 219 \ntrajectory. A differential outcome in mutation carriers can in part be ascribed to alternative 220 \nsplicing24 or retention of the wild-type BRCA allele in tumours25, both of which appear to limit 221 \nthe effectiveness of chemotherapy. 222 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 10 \nWe previously characterised a small series  of HGSC exceptional survivors and found 223 \nthat co-occurring loss of function alterations in both BRCA and RB1 were associated with 224 \nunusually favourable survival7,26. Disruption of the RB pathway is found in many cancer types 225 \nbut with variable impacts on patient outcome. For example , co -loss of RB1 and BRCA is 226 \nassociated with shorter survival in breast and prostate cancer, possibly due to lineage switching 227 \nand resistance to hormonal therapy 27-29. A transcriptomic signature of RB1 loss was recently 228 \ndescribed to be associated with poor outcomes across cancer types30. We have previously found 229 \nthat chromosomal breakage is the most common mechanism of RB1 inactivation in HGSC 3, 230 \naccounting for approximately 80% of all RB1 alterations. In addition to its crucial role in cell 231 \ncycle regulation, RB1 is involved in non-canonical functions in a context- and tissue-dependent 232 \nmanner31-33, including HR mediated DNA repair. Loss of RB1 expression in HGSC has been 233 \nassociated with a survival benefit 34, including  in the context of abnormal block -like p16 234 \nstaining35. 235 \nFactors underlying the association of RB1 loss with improved outcome in HGSC are 236 \nunknown. Here, we contrast the pattern and clinical consequences of RB1 loss in HGSC with 237 \nother epithelial ovarian cancer subtypes, investigate the relevance of co-occurring BRCA1 or 238 \nBRCA2 mutations and RB1 loss in HGSC patients, and explore the functional effects of 239 \ncombined BRCA and RB1 impairment in HGSC cell lines. 240 \n 241 \nPATIENTS AND METHODS 242 \nPatient cohorts 243 \nThe study population consisted of 7436 patients diagnosed with invasive epithelial ovarian, 244 \nperitoneal or fallopian tube cancer from 20 studies or biobanks participating in the Ovarian 245 \nTumor Tissue Analysis (OTTA) consortium36 (Supplementary Fig. S1) . Written informed 246 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 11 \nconsent or IRB approved waiver of consent  was obtained at each site for patient recruitmen t, 247 \nsample collection, and study protocols (Supplementary Table S1). 248 \nWhole-genome sequence and matched transcriptome sequence data of primary HGSC 249 \ntumours were available from 126 patients from the Multidisciplinary Ovarian Cancer 250 \nOutcomes Group (MOCOG) study 26 (Supplementary Fig. S1) . This cohort consisted of 34 251 \nshort-term survivors (OS <2 years), 32 moderate-term survivors (OS ≥2 and <10 years) and 60 252 \nlong-term survivors ( OS ≥10 years) with advanced stage ( IIIC/IV) disease, enrolled in the 253 \nAustralian Ovarian Cancer Study (AOCS), the Gynaecological Oncology Biobank at 254 \nWestmead Hospital (Sydney) or the Mayo Clinic Study. 255 \n 256 \nMolecular analyses 257 \nRB1 protein expression was determined by immunohistochemistry (IHC) staining and scoring 258 \nof tissue microarrays (TMAs) from formalin-fixed paraffin-embedded (FFPE) tumour samples, 259 \nusing our previously described protocol 7 (RB1 antibody clone 13A10, Leica Biosystems; 260 \nSupplementary Material). Subsets of HGSC patients had additional molecular or immune data 261 \navailable (Supplementary Fig. S1), including tumour p53 protein expression status previously 262 \nclassified37 as normal (wild -type) or abnormal (overexpression, complete absence, and 263 \ncytoplasmic), germline BRCA1 and BRCA2 pathogenic variant status obtained from OTTA , 264 \nRB1 mRNA tumour expression obtained using Nano String (ref34 and unpublished data) , 265 \ntranscriptional subtypes of tumours using NanoString 38 and CD8+ tumour infiltrating 266 \nlymphocyte (TIL) density was previously classified39 based on the number of CD8+ TILs per 267 \nhigh-powered field: negative (no TILs), low (<3 TILs), moderate (3-19 TILs) or high (≥20 268 \nTILs). 269 \nThe MOCOG whole -genome and transcriptome sequencing  dataset of 126 short -, 270 \nmoderate- and long-term survivors was uniformly processed as previously described26, and 271 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 12 \nincluded detailed characterisation of each tumour sample for inactivating alterations in RB1 272 \nand HR pathway genes, including germline and/or somatic mutations in BRCA1, BRCA2, 273 \nBRIP1, PALB2, RAD51C and RAD51D, or promoter methylation of BRCA1 and RAD51C. 274 \nHomologous recombination deficiency (HRD) status  was assessed using the CHORD 275 \n(Classifier of Homologous Recombination Deficiency) method40, which uses specific base 276 \nsubstitution, indel and structural rearrangement signatures detected in tumo ur genomes to 277 \ngenerate BRCA1-type and BRCA2-type HRD scores. Primary tumours were classified as either 278 \nBRCA1-HRD & RB1 altered; BRCA1-HRD & RB1 wild-type; BRCA2-HRD & RB1 altered; 279 \nBRCA2-HRD & RB1 wild-type; homologous recombination proficient (HRP)  & RB1 altered, 280 \nor HRP  & RB1 wild-type. For details on differential gene expression analyses, see 281 \nSupplementary Material. 282 \n 283 \nCell culture 284 \nThe AOCS  patient-derived cell lines (AOCS1, AOCS3, AOCS7.2 AOCS9, AOCS11.2, 285 \nAOCS14, AOCS16, AOCS22, AOCS30) were established from ascites drained from patients 286 \nwith HGSC, as previously described4. All AOCS cell lines were authenticated against matched 287 \npatient germline DNA using short tandem repeat markers (STR, GenePrint10 System, 288 \nPromega). Commercial cell lines OAW28 and CAOV3, categori sed as likely HGSC41, were 289 \npurchased from the American Type Culture Collection (ATCC) , and JHOS2 and OVCAR4 290 \nwere obtained from the National Cancer Institute Repository . Commercial lines were  291 \nauthenticated by comparing STR profiles (GenePrint10 System, Promega) to those published 292 \nby online repositories (Cancer Cell Line Encyclopaedia, The Cancer Genome Atlas) before use 293 \nin experiments. Cell lines were confirmed to be free of Mycoplasma by PCR at each revival 294 \nand after finishing experiments. For details on cell growth conditions, CRISPR-mediated gene 295 \nknockout, and molecular and functional cell line characterisation, see Supplementary Material. 296 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 13 \n 297 \nStatistical analyses 298 \nCox proportional hazards models were used to estimate  hazard ratio s (HRs) with 95% 299 \nconfidence intervals (CIs) using the ‘coxph’ function of the R package survival (v3.2-7). Final 300 \nmodels were fitted using Cox regression adjusted for age at diagnosis and FIGO stage. A spline 301 \nfunction was used for age at diagnosis with degree of freedom (df) 5 to account for the non-302 \nlinear effect of the continuous variable. Regression models were fitted separately by histotype. 303 \nThe HGSC regression models were also  stratified by site of participant recruitment, and sites 304 \nwith fewer than 10 events within the study period were excluded. The ENOC regression model 305 \nwas not stratified by site due to the limited number of overall patients per site. The OTTA 306 \nsurvival dataset was right censored at 10 years from diagnosis to reduce the number of non -307 \novarian cancer related deaths. In the final Cox regression model, there was evidence for 308 \ndeviation from the proportional hazard assumption, but the degree of deviation was not 309 \nsubstantial when considered alongside the large sample size and Schoenfeld residuals. The 310 \nKaplan–Meier method was used to estimate and plot progression -free and overall survival 311 \nprobabilities, and the log -rank (Mantel –Cox) test used to compare the survival duration 312 \nbetween subgroups. In the Kaplan-Meier curves, the number of patients at risk on the date of 313 \ndiagnosis (time = 0) may be fewer than subsequent time intervals, owing to left truncation of 314 \nfollow-up resulting from delayed study enrolment at some OTTA sites . Differences in 315 \nproportions of categorical features were assessed by either the chi-square or Fisher’s exact test 316 \nas indicated. Differences in continuous variables were assessed by either a Wilcoxon Rank 317 \nSum Test or a Kruskal -Wallis test. All in vitro assays were performed across at least three 318 \nindependent experiments, and data are expressed as mean ± standard error of the mean (SEM) 319 \nas indicated, from a minimum of three independent measurements. All statistical tests were 320 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 14 \ntwo-sided and considered significant when P < 0.05. Statistical analyses were performed using 321 \neither Prism (v9.3.1) or R (v3.6.3). 322 \n 323 \nRESULTS 324 \nLoss of RB1 expression is most frequent in HGSC 325 \nRB1 protein expression was assessed by IHC in tumour samples from 7436 ovarian cancer 326 \npatients using TMAs from 20 centres participating in the OTTA consortium (Supplementary 327 \nTables S1 and  S2). RB1 tumour expression was classified as either retained or lost in 6564 328 \nsamples, with 872 samples excluded that had either subclonal loss (n = 66), cytoplasmic (n = 329 \n17), or uninterpretable results (n = 789) due to either sample drop out or the absence of an 330 \ninternal positive control (Fig. 1A, Supplementary Material). 331 \nRB1 loss was most frequent in HGSC (16.4%), followed by endometrioid ovarian 332 \ncancer (ENOC; 4.1%, Chi-square P < 0.0001, Fig. 1B). Loss of RB1 expression was less 333 \nfrequent in all other histotypes (1.8% to 2.8%). RB1 mRNA expression was also assessed by 334 \nNanoString in a subset of HGSC tumours (n = 2552) and was significantly associated with RB1 335 \nprotein expression (Fig. 1C, P < 0.0001). 336 \n 337 \nRB1 loss is associated with longer survival in HGSC 338 \nLoss of RB1 protein expression was associated with longer OS in patients with HGSC (HR 339 \n0.74, 95% CI 0.66 -0.83, P = 6.8x10-7; Table 1) following multivariate analysis adjusting for 340 \nstage and age at diagnosis and stratified by study. Patients with HGSC were comparable in 341 \nterms of stage regardless of RB1 loss or retained expression (P = 0.9246), however those with 342 \nRB1 loss had a younger age at diagnosis (median 59 years  versus 61 years, P = 0.0003; 343 \nSupplementary Table S3). Median OS was 4.7 years for patients with RB1 loss compared to 344 \n3.6 years for those with retained RB1 expression (Fig. 1D). 345 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 15 \nIn contrast to HGSC, loss of RB1 expression in tumours from patients with ENOC was 346 \nassociated with advanced stage (P = 0.0003) and poorer survival (HR 2.17, 95% CI 1.17-4.03, 347 \nP = 0.0140; Table 1, Fig. 1E, Supplementary Table S4). RB1 loss and abnormal p53 protein 348 \nexpression, which is highly predictive of TP53 mutation42, were strongly correlated (chi-square 349 \nP < 0.0001; Supplementary Fig. 2A). While TP53 mutation is known to be associated with  350 \ninferior survival in patients with ENOC 37,43, we note that  combined RB1 loss and abnormal 351 \np53 expression w ere associated with the shortest  patient survival (median OS 3. 0 years; 352 \nSupplementary Fig. 2B), suggesting that loss of RB1 and TP53 mutation have a compounding 353 \nnegative impact on survival in patients with ENOC. 354 \n 355 \nCombined RB1 loss and germline BRCA mutation is associated with exceptional ly good 356 \nsurvival 357 \nWe previously observed that co-occurrence of somatic RB1 protein loss and BRCA1 or BRCA2 358 \nalteration (somatic or germline) was associated with longer progression-free survival (PFS) 359 \nand OS in HGSC7. Here, germline BRCA1 and BRCA2 status was available for 1134 HGSC 360 \npatients for which we had RB1 IHC data (Supplementary Fig. S1). Consistent with having a 361 \nyounger age of diagnosis, patients with RB1 loss were more likely to have concurrent germline 362 \nBRCA1 or BRCA2 mutations than those with retained RB1 expression (Fig. 1F, Chi-square P 363 \n< 0.0001). Patients with both RB1 loss and a germline BRCA mutation had a 62% reduced risk 364 \nof death compared with non -carriers with retained RB1  (HR 0.38, 95% CI 0.25 -0.58, P = 365 \n5.2x10-6; Table 1). The median OS of BRCA germline carriers with RB1 loss was three times 366 \nlonger than  non-carriers with RB1 retained tumours (median OS 9.3 years vs. 3. 1 years, 367 \nrespectively), while median OS was 5.2 years for BRCA carriers with retained RB1 expression 368 \nand 4.5 years for non-carriers with RB1 loss (Fig. 1G; Supplementary Table S5). 369 \n 370 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 16 \nEnhanced response to chemotherapy in cells with impaired BRCA and RB1 function 371 \nTo investigate whether co -occurrence of RB1 and BRCA alterations enhances sensitivity to 372 \nstandard-of-care ovarian cancer drugs, nine patient -derived HGSC cell lines with confirmed 373 \npathogenic TP53 mutation and known RB1 and BRCA status were treated with cisplatin, 374 \npaclitaxel and olaparib (Supplementary Fig. S 3A-C). AOCS14, the only cell line with a 375 \ngermline BRCA1 mutation and concomitant loss of RB1 expression, showed the best response 376 \nto cisplatin and olaparib, and was the second most sensitive cell line to paclitaxel. In contrast 377 \nAOCS11.2, a line with BRCA1 promoter methylation and loss of RB1 expression, was 378 \nrelatively resistant to paclitaxel and olaparib. A mong cell lines with intact RB1 protein 379 \nexpression and BRCA wildtype background, AOCS3 was resistant to cisplatin, paclitaxel and 380 \nolaparib.  381 \nExcept for the chemo-naïve cell line s AOCS30 and AOCS14 , all other lines were 382 \nderived from patients previously treated with chemotherapy. Since the evaluation of HGSC 383 \ncell lines with existing RB1 mutations may have been confounded by  their prior, differential 384 \nexposure to chemotherapy we therefore characterised responses in isogenically matched lines 385 \ndeleted of RB1 and/or BRCA1. We first inactivated RB1 in two BRCA1-mutant (AOCS7.2, 386 \nAOCS16) and one wild-type line (AOCS1) using CRISPR-Cas9 (Fig. 2A, Supplementary Fig. 387 \nS4A). RB1 knockout clones of the BRCA1-mutant cell line AOCS7.2 had enhanced sensitivity 388 \nto cisplatin and paclitaxel compared to RB1 wild-type clones, which was observed both in 389 \nshort-term drug assays (72  hours, Fig. 2 B) and long er-term clonogenic survival assays (12 390 \ndays, Fig. 2C). In this cell line , sensitivity to paclitaxel and olaparib was increased after RB1 391 \nknockout (paclitaxel IC50 92.0 nM versus 11.8 nM, P < 0.0001; olaparib IC50 6.1 versus 1.1 392 \nnM, P < 0.0001). Further, significantly fewer colonies grew in this BRCA1-mutant cell line 393 \nafter RB1 knockout upon treatment with cisplatin ( P = 0.01), paclitaxel ( P = 0.02) or a 394 \ncombination of both drugs (P = 0.067) in a clonogenic survival assay (n = 3). This effect was 395 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 17 \nnot apparent in the BRCA-wild-type line (AOCS1) or the other BRCA1-mutant line (AOCS16). 396 \nWestern blot and IHC analysis ( Supplementary Fig. S 4A) found that AOCS16 lacked 397 \nexpression of p16, which may functionally disrupt the RB1 pathway irrespective of an RB1 398 \nknockout44. 399 \nGiven that RB1 plays a central role in the negative control of the cell cycle 44,45, we 400 \ntested whether the enhanced chemosensitivity of RB1 knockout AOCS 7.2 cells was associated 401 \nwith increased cell division. Live cell imaging showed similar growth rates of RB1 wildtype 402 \nand knockout clones of all three isogenically matched  HGSC cell lines ( Supplementary Fig. 403 \nS4B). In both BRCA wild-type and BRCA1 mutant cell lines, RB1 knockout did not alter cell 404 \ncycle distribution at baseline or after 24 hours of cisplatin treatment (Supplementary Fig. S4C). 405 \nPaclitaxel treatment resulted in a larger proportion of cells with a tetraploid DNA content in 406 \nRB1 knockout cells compared to RB1 wild-type cells, indicating arrest in the G2 or M phase of 407 \nthe cell cycle. This effect was observed in all cell lines independent of BRCA or p16 status, 408 \nhowever the arrest was more profound in the AOCS7.2 cell line (AOCS1, G2/M difference 409 \n8.59% ± 4.73%, P = 0.144; AOCS16, G2/M difference 8.13% ± 4.45%, P = 0.142; AOCS7.2: 410 \nG2/M difference 14.49% ± 3.99%, P = 0.022; Supplementary Fig. S4C). 411 \nWe extended our analysis of isogenically matched pairs by inactivating BRCA1 and/or 412 \nRB1 in the chemo-naïve cell line AOCS30. While we were  readily able to establish RB1 413 \nknockout lines, all BRCA1 targeted clones were hemizygous for BRCA1 deletion and retained 414 \nBRCA1 expression (Supplementary Table S6), suggesting that engineered homozygous loss of 415 \nBRCA1 was cell lethal, even in a tumour type where BRCA1 loss is frequently observed46. 416 \n 417 \nGenomic and transcriptional landscape of HGSC with combined inactivation of BRCA and 418 \nRB1 419 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 18 \nTo further understand how RB1 loss may impact the biology of HGSC with co-loss of BRCA1 420 \nor BRCA2, we explored matched whole -genome and transcriptome data of primary HGSC 421 \ntumours in the MOCOG cohort26 of 126 short- (OS <2 years), moderate- (OS ≥2 to <10 years) 422 \nand long-term (OS ≥10 years) survivor patients (Supplementary Fig. S1). Each tumour genome 423 \nwas classified according to their HRD and RB1 status, resulting in 6 groups: BRCA1-HRD & 424 \nRB1 altered (n = 13); BRCA1-HRD & RB1 wild-type (n = 36); BRCA2-HRD & RB1 altered (n 425 \n= 8); BRCA2-HRD & RB1 wild-type (n = 20); HRP & RB1 altered (n = 4), or HR P & RB1 426 \nwild-type (n = 45; Fig. 3A). 427 \nThe cohort had been selected for a long-term survivor study26 and hence was enriched 428 \nfor patients with very long survival. Among BRCA2-HRD patients, those with RB1 alterations 429 \nhad longer OS (median OS 17.0 years) compared with those without RB1 alterations (median 430 \nOS 11.7 years, P = 0.0004; Fig. 3B). Similarly, BRCA1-HRD patients with RB1 alterations 431 \nsurvived longer (median OS 10.4 years) than those with an intact RB1 gene (median OS 7.1 432 \nyears). There were few HRP tumours with RB1 alterations, however these patients had a worse 433 \nsurvival (median OS 1.4 years) compared to the HRP group with no RB1 alteration (median 434 \nOS 2.4 years). 435 \nExamination of genomic features revealed relatively similar patterns within BRCA1-436 \nHRD and BRCA2-HRD groups, although there were a few discriminatory features identified 437 \nbetween those with and without RB1 alterations (Supplementary Figs. S5 and S6). For example, 438 \nthe BRCA1-associated rearrangement signature Ovary_G47 was more enriched in BRCA1-HRD 439 \ntumours with RB1 alterations compared to those without ( P = 0.039). Among BRCA2-HRD 440 \ntumours, the mutational signatures DBS6 ( unknown etiology) and SBS3 (associated with 441 \nHRD)48 were higher in RB1-altered tumours compared to non-altered tumours, although this 442 \nwas not significant ( P = 0.082 and P = 0.1 respectively). Concordantly, the average BRCA1-443 \ntype and BRCA2-type CHORD scores40 were highest in  BRCA1- and BRCA2-HRD tumours 444 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 19 \nwith RB1 alterations respectively, indicating a higher probability of HRD . As described 445 \npreviously49, CCNE1 gene amplifications were absent in tumours with both HRD and RB1 446 \nalterations (P = 0.0006; Supplementary Fig. S7). 447 \nWe hypothesised that tumours with combined HRD and RB1 loss may have unique 448 \ntranscriptional profiles. To explore this, we compared gene expression profiles between each 449 \nHRD/RB1 group and the reference set of tumours that were HR P and RB1 wild-type 450 \n(Supplementary Table S7, Supplementary Fig. S8 ). There was significant enrichment of 451 \nMSigDB hallmark gene sets  among genes differentially expressed in BRCA1-HRD tumours 452 \nwith RB1 alterations, the most prominent being interferon gamma response (up), interferon 453 \nalpha response (up), oxidative phosphorylation (up), and E2F targets (up; adjusted P < 0.0001; 454 \nFig. 4A). The differentially expressed genes identified between BRCA2-HRD / RB1 altered 455 \ntumours and the reference set were significantly enriched for the MSigDB hallmark gene sets: 456 \nE2F targets (up), epithelial mesenchymal transition (down), G2M checkpoint (up), and TNF 457 \nalpha signalling via NF-kB (up; adjusted P < 0.0001).  458 \nSince enhanced tumour cell proliferation has  been associated with long -term survival 459 \nin HGSC7,26, and loss of RB1 might accelerate proliferation31, we evaluated the expression of 460 \nproliferation markers across the RB1 and BRCA subgroups. BRCA1-HRD tumours with RB1 461 \nalterations had significantly higher mRNA levels of the cell proliferation related genes PCNA 462 \n(proliferating cell nuclear antigen) and MCM3 (minichromosome maintenance complex 463 \ncomponent 3) compared to BRCA1-HRD tumours without RB1 alterations ( P < 0.0001, 464 \nSupplementary Fig. S 6). However, there were no significant differences in the proportion of  465 \nKi-67 positive cancer cell nuclei (P = 0.3297) across the subgroups (Supplementary Fig. S 6), 466 \nwhich was previously quantified by immunohistochemistry7 in a subset of primary tumours (n 467 \n= 59). 468 \n 469 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 20 \nGermline BRCA mutation carriers with somatic loss of  RB1 tumour expression  show 470 \nelevated immune activity 471 \nHaving observed that HGSC with combined RB1 loss and HRD have enrichment of 472 \ntranscriptional signatures associated with an enhanced immune response, we accessed existing 473 \nimmunohistochemical data39 to determine the prevalence of CD8+ TILs in HGSC samples that 474 \nalso had RB1 protein expression and BRCA germline mutation status (n = 868). BRCA carriers 475 \nwith RB1 loss had a significantly higher proportion of tumours ( 79.6%) with moderate and 476 \nhigh densities of CD8+ TILs, compared to BRCA carriers with retained RB1 (6 4.9%), non-477 \ncarriers with RB1 loss (72. 4%) and non-carriers with retained RB1 (63. 6%, P = 0.0264; Fig. 478 \n4B). Tumours with complete absence of CD8+ TILs were the least frequent in BRCA carriers 479 \nwith RB1 loss (4. 1%) compared to the other groups (1 3.8 % of BRCA carriers with retained 480 \nRB1 tumour expression, 14. 6% of non-carriers with RB1 tumour loss, 18. 8% of non-carriers 481 \nwith retained RB1 tumour expression). 482 \nGene expression-based molecular subtypes12,38 also differed by RB1 and BRCA status 483 \n(P = 0.0271, n = 601; Fig. 4C). As expected, there was enrichment for the C2/immunoreactive 484 \nsubtype, a subtype characterised by the presence of intratumoural CD8+ T cells and good 485 \nsurvival, in germline BRCA carriers with RB1 loss ( 32.4%) compared to the other subgroups 486 \n(between 19.8% and 23.4%). Additionally, tumours with RB1 loss were enriched for the 487 \nC4/differentiated molecular subtype, a subtype characterised by cytokine expression and good 488 \nsurvival, regardless of BRCA status (45.9% in BRCA carriers with RB1 loss, 50.0% in non -489 \ncarriers with RB1 loss, 39.5% in BRCA carriers with retained RB1, 32.1% of non-carriers with 490 \nretained RB1). BRCA carriers with RB1 loss also had the lowest proportion of the 491 \nC5/proliferative molecular subtype (2. 7% versus 1 7.2% to 20.3% in the other groups), a 492 \nsubtype associated with diminished immune cell infiltration and poor survival12,19. 493 \n 494 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 21 \nDISCUSSION 495 \nIdentifying the determinants of long-term patient survival, particularly in  cancers with a  496 \ngenerally unfavourable prognosis such as HGSC, may reveal novel therapeutic targets and 497 \ninform personalised treatment strategies8. Improved survival associated with RB1 loss has been 498 \ndescribed previously in HGSC7,34,35,50 but the underlying factors contributing to this survival 499 \nbenefit have not been studied to date. We assessed tumour samples from a cohort of more than 500 \n7,000 women with ovarian cancer , including a subset with high resolution genomic data , to 501 \nunderstand how RB1 loss may impact on therapeutic response and patient survival. 502 \nAlteration of the RB1 pathway is a frequent event in tumourigenesis, including loss of 503 \nregulators such as p16, activation of D - and E -type cyclins and their associated cyclin 504 \ndependent kinases, and loss of RB1 itself (reviewed in 51). Our study showed that RB1 loss is 505 \nassociated with longer survival in patients with advanced stage HGSC, but by contrast, loss of 506 \nRB1 in ENOC was associated with a shorter survival , particularly in combination with p53 507 \nmutation. Similar to ENOC , i n endocrine -driven breast and prostate cancer, RB1 loss is 508 \nassociated with poorer survival : early co -loss of BRCA2 and RB1 is associated with  an 509 \naggressive, castration-resistant prostate cancer subtype (CRPC) characterised by epithelial-to-510 \nmesenchymal transition and shorter survival29. RB1 loss facilitates lineage plasticity and, with 511 \np53-comutation, leads to an androgen-independent phenotype52,53 and consequently resistance 512 \nto anti-androgen therapy. In estrogen-receptor (ER) positive breast cancer, CDK4/6 inhibitor 513 \nresistance is associated with RB1 loss and cyclin E2 activation54,55. 514 \nTriple negative breast cancer (TNBC) provides an important contrast to the findings for 515 \nRB1 loss in ER-positive breast cancer. In TNBC, RB1 loss is most common in the basal-like 516 \nsubtype, where BRCA1 mutation and promoter hypermethylation is associated with  frequent 517 \nRB1 gene disruption and RB1 loss 28. RB1 loss alone, as well as co -occurrence with BRCA1 518 \npromoter hypermethylation , is associated with a favourable chemotherapy response and 519 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 22 \noutcome27,56-58. Notably, TNBC and HGSC are more similar than the cancers that they are 520 \ngrouped with anatomically, sharing gene expression patterns, genetic drivers including BRCA1 521 \nand BRCA2, ubiquitous loss of TP53, extensive copy number variation, and susceptibility to 522 \nplatinum-based chemotherapy 59,60. Taken together, the relationship between RB1 loss and 523 \npatient survival appears to be dependent on cancer type and molecular context61. 524 \nSome, but not all TNBC and early metastatic prostate cancer s are associated with 525 \ngermline variants in BRCA1, BRCA2 and other genes involved in HR DNA repair. However, 526 \nprevious tumour studies of RB1 expression have not also defined the HRD status of individual 527 \nsamples. A strength of this study was the known BRCA germline status of 1134 of the HGSC 528 \npatients for which we also had RB1 protein expression, and this revealed the strong association 529 \nof co-mutation in either BRCA1 or BRCA2 and RB1 with survival. In addition to germline 530 \nmutations in BRCA1 or BRCA2, germline or somatic mutations, and promoter methylation of 531 \nother genes involved in HR DNA repair, such as RAD51C, can result in a similar molecular 532 \nphenotype, characterised by distinct genomic scarring26. Using whole-genome sequence data, 533 \nwe determined the likely tumour HRD status in a subset of 126 tumours using  an algorithm 534 \nthat recogni ses genomic scarring associated with HRD (Fig. 3A), rather than simply 535 \ndesignating BRCA mutation status, which does not account for all mechanisms of HR repair 536 \ninactivation. Although the number of samples with RB1 loss and HR  proficiency was small, 537 \nthe very poor outcome we observed with this group indicated that for RB1 to impart a survival 538 \nbenefit in HGSC, it must occur in a n HRD background. Validation of this finding in a larger 539 \ncohort may further inform  how RB1 loss could favourably influence survival in certain 540 \nhistological and molecular contexts.  541 \nWe have previously noted that enhanced proliferation in HGSC is associated with long-542 \nterm survival7,26 and it is reasonable to suggest that  RB1 loss may be imparting an effect 543 \nthrough deregulating the cell cycle. However, data on the effect of RB1 loss on proliferation in 544 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 23 \nHGSC tumours and cancer cell lines is inconsistent. RB1 knockout in our HGSC cell lines did 545 \nnot cause cell cycle alterations  in the absence of treatment,  and despite differences in 546 \nproliferative markers at the mRNA level, there was no significant difference in the proportion 547 \nof Ki-67 positive nuclei between tumours with or without RB1 protein expression. In a recent 548 \nOTTA study, Ki-67 expression was not associated with survival in HGSC; however, there was 549 \nstrong correlation between loss of RB1 and the proliferative marker MCM3 62, which may 550 \nprovide a more accurate measure of tumour cell proliferation than Ki-6763. 551 \nIn addition to its role in driving progression through the G1 stage of the cell cycle, RB1 552 \nhas non-canonical functions. RB1 has been shown to participate in HR DNA repair through 553 \ninteractions with BRG1 and ATM64. A recent pan-cancer study65 found that combined loss of 554 \nTP53 and RB1 was associated with a particularly high genome -wide loss-of-heterozygosity 555 \nscore, one of the key elements of genomic scarring associated with HRD. In our whole-genome 556 \nanalysis, HGSC tumours with dual loss of HRD and RB1 did not exhibit overall higher 557 \nmutation burden; however, we did observe elevated levels of mutational signatures associated 558 \nwith HRD, which may be evidence of  compounding DNA repair defects . It remains possible 559 \nthat the combined inactivation of RB1 and HR genes contribute to enhanced chemotherapy 560 \nresponse and/or an impaired ability for tumour cells to develop therapy resistance. 561 \nWhen we evaluated a set of patient  derived HGSC lines, those with germline BRCA1 562 \nmutation and RB1 alteration were most sensitive to cisplatin and olaparib. Knockout of RB1 in 563 \nthe AOCS 7.2 cell line which had a pre-existing BRCA1 mutation, resulted in an increase in 564 \nchemosensitivity, consistent with the notion that co -mutation enhances chemotherapy 565 \nresponse7. Unfortunately , despite considerable effort s, we were unable to generate a larger 566 \nseries of isogenically matched cell lines with combinations of conditional knockout s of RB1 567 \nand BRCA1 as all surviving clones retained at least one BRCA1 allele. BRCA1 loss is embryonic 568 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 24 \nlethal and engineered loss in cell lines has been reported as lethal elsewhere including in the 569 \nhuman haploid cell line, HAP146. 570 \nOur data provides evidence of an enhanced immunogenicity in HGSC with RB1 loss, 571 \nwith higher CD8+ TIL counts and upregulated expression of IFN -γ signalling pathways. RB1 572 \nhas been shown to inhibit innate IFN -β production in immunocompetent mice 66 and RB1 573 \ndeficiency triggered an increased IFN -β and IFN-α secretion. Co-mutation of RB1 and TP53 574 \nwas recently found to be associated with an enhanced response to the immune checkpoint 575 \ninhibitor atezolizumab in metastatic urothelial bladder cancer 67. Similarly, a case report 576 \ndescribed a complete response to atezolizumab in heavily pre-treated, RB1-negative TNBC68. 577 \nThis generates the hypothesis that RB1 loss could predict response to such therapies in HGSC, 578 \nsince this tumour type ubiquitously harbours TP53 mutations69. However, a recent biomarker 579 \nstudy in ovarian cancer  patients treated with atezolizumab or placebo and standard 580 \nchemotherapy found that deleterious mutations in RB1 were prognostic for a better PFS, 581 \nregardless of the addition of atezolizumab 70. While it appears RB1 loss alone may not be  582 \npredictive of response to the PD -L1 inhibitor  atezolizumab, response rates to PD -1/PD-L1 583 \npathway checkpoint inhibitors are generally quite low in HGSC, with the best objective 584 \nresponse rates between 8% and 15% 71. Our study has identified a subset of patients with 585 \ncombined RB1 and BRCA inactivation who demonstrate exceptional immune responses and 586 \nmay provide clues for the  development of new immunotherapeutic strategies for HGSC that 587 \nextend beyond targeting PD-L1/PD-1. 588 \nOur work highlights the importance of RB1 loss to treatment response and survival and 589 \nfocuses attention on other therapeutic opportunities in this subset of HGSC patients. 590 \nApproximately 20 percent of HGSC patients have somatic loss of RB1 assessed using genomic 591 \ndata3,26, a figure that is consistent with the immunohistochemical results obtained in the large 592 \npatient cohort described here. Both approaches indicate that RB1 loss is generally clonal, 593 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 25 \nenhancing its value as a therapeutic target if selective inhibitors can be identified. Casein kinase 594 \n2 (CK2) inhibitors have been reported to enhance the sensitivity of RB1-deficient TNBC and 595 \nHGSC cells to carboplatin and niraparib 72. In addition, Aurora kinase A and B inhibition is 596 \nsynthetically lethal in combination with RB1 loss in breast and lung cancer cells 73-75. 597 \nIrrespective of HRD status, RB1 mutations correlate with sensitivity to WEE1  inhibition in 598 \nTP53 mutant TNBC and HGSC patient-derived xenografts76, indicating additional treatment 599 \noptions that exploit RB1 inactivation in these tumours. In this study, t he BRCA1-mutant cell 600 \nline AOCS7.2 with induced RB1 knockout was more sensitive to olaparib suggesting that RB1 601 \nloss may also predict responses to PARP inhibitors in HGSC. RB1 staining of tumour tissue 602 \nby IHC is a relatively low-cost pathology-based assay that could be used in prospective studies 603 \nto test whether RB1 expression is predictive of responses to PARP inhibitors, either alone or 604 \nin combination with approved HRD tests. 605 \n 606 \nACKNOWLEDGMENTS 607 \nWe thank J. Beach and L. Bowes for their contributions to the study. This work was supported 608 \nby the National Health and Medical Research Council (NHMRC) of Australia (1186505 to 609 \nDWG; 1092856, 1117044 and 2008781 to DDLB; 2009840 to SJR), the National Institutes of 610 \nHealth (NIH) / National Cancer Institute (R01CA172404 to SJR, P50 CA136393 to SHK) and 611 \nthe U.S. Army Medical Research and Materiel Command Ovarian Cancer Research Program 612 \n(Award No. W81XWH -16-2-0010 and W81XWH -21-1-0401). DWG is supported by a 613 \nVictorian Cancer Agency / Ovarian Cancer Australia Low-Survival Cancer Philanthropic Mid-614 \nCareer Research Fellowship ( MCRF22018). FAMS is supported by a Swiss National 615 \nFoundation Early Postdoc Mobility Fellowship (P2BEP3 -172246), a Swiss Cancer League 616 \ngrant BIL KFS-3942-08-2016 and a Prof. Max Cloëtta foundation grant.  KIP is supported by 617 \na NHMRC CJ Martin Overseas Biomedical Fellowship (APP1111032). ELC is supported by a 618 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 26 \nVictorian Cancer Agency Mid -Career Fellowship ( MCRF21004). MW is supported by the 619 \nEuropean Research Council under the European Union’s Horizon 2020 Research and 620 \nInnovation Programme grant agreement No 742432 (BRCA -ERC). KS is supported by the 621 \nSwedish Cancer Foundation. MSA is funded through a Michael Smith Health Research BC 622 \nScholar Award (18274) and the Janet D. Cottrelle Foundation Scholars program managed by 623 \nthe BC Cancer Foundation.  624 \nBC’s Gynecological Cancer Research team (OVCARE) receives support through the 625 \nBC Cancer Foundation and the VGH & UBC Hospitals Foundation. The Gynaecological 626 \nOncology Biobank at Westmead was funded by the NHMRC (ID310670, ID628903); the 627 \nCancer Institute NSW (12/RIG/1 -17, 15/RIG/1 -16); and acknowledges support from the 628 \nDepartment of Gynaecological Oncology, Westmead Hospital, and the Sydney West 629 \nTranslational Cancer Research Centre (Cancer Institute NSW 15/TRC/1 -01). The Women's 630 \nCancer Research Program at Cedars-Sinai Medical Center (LAX) is supported by The National 631 \nCenter for Advancing Translational Sciences (NCATS) Grant UL1TR000124 . The Study of 632 \nEpidemiology and Risk Factors in Cancer Heredity (SEARCH) is funded by Cancer Research 633 \nUK (C490/A10119 C490/A10124 C490/A16561) and the UK National Institute for Health 634 \nResearch Biomedical Research Centre at the University of Cambridge. The UKOPS study was 635 \nfunded by The Eve Appeal (The Oak Foundation) with contribution to authors’ salary through 636 \nMRC core funding MC_UU_00004/01 and the National Institute for Health Research 637 \nUniversity College London Hospitals Biomedical Research Centre. 638 \nThe investigators also acknowledge generous contributions from the Border Ovarian 639 \nCancer Awareness Group, the Peter MacCallum Cancer Foundation, the Graf Family 640 \nFoundation, Wendy Taylor, Arthur Coombs and family, and the Piers K Fowler Fund. The 641 \ncontents of the published material are solely the responsibility of the authors and do not reflect 642 \nthe views of the NHMRC, NIH, and other funders. 643 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 27 \n 644 \nAUTHOR CONTRIBUTIONS 645 \nMK, SJR, DDLB and DWG conceived the study design. FAMS, KT, KP, JB and TH carried 646 \nout experiments, and analysed and interpreted results along with TB, AP, DA, TZ, NSM, SF, 647 \nAD, MK, SJR, DDLB and DWG. MK assessed and interpreted immunohistochemical scores. 648 \nAll authors contributed through recruitment and consenting of patients, collection and 649 \nprocessing of biological samples, clinical care, abstraction and curation of clinical data and 650 \nmaintenance of follow -up. DDLB and DWG supervised the study and together with FAMS 651 \nand KT wrote the manuscript. All authors contributed to writing, review and revision of the 652 \nmanuscript and approved the final submitted version. 653 \n 654 \nCOMPETING INTERESTS 655 \nDDLB is an Exo Therapeutics advisor and has received research grant funding from 656 \nAstraZeneca, Genentech -Roche and BeiGene for unrelated work. SF, NT, KA, and ADeF 657 \nreceived grant funding from AstraZeneca for unrelated work . AGM and UM report funded 658 \nresearch collaborations for unrelated work with industry: Intelligent Lab on Fiber, RNA 659 \nGuardian, Micronoma and Mercy BioAnalytics.  UM had stock ownership (2011 -2021) 660 \nawarded by University College London (UCL) in Abcodia, which held the licence for the Risk 661 \nof Ovarian Cancer Algorithm (ROCA). UM reports research collaboration contracts with 662 \nCambridge University and QIMR Berghofer Medical Research Institute. UM holds patent 663 \nnumber EP10178345.4 for Breast Cancer Diagnostics. UM is a member of Tina's Wish 664 \nScientific Advisory Board (USA) and Research Advisory Panel, Yorkshire Cancer Research 665 \n(UK). The remaining authors declared no conflicts of interest. 666 \n 667 \nFigure legends: 668 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 28 \nFigure 1. Expression of RB1 and survival associations across ovarian cancer histotypes.  669 \n(A) Representative images of immunohistochemical detection of RB1  expression in ovarian 670 \ncarcinoma tissues, showing examples of the three most common expression patterns: retained, 671 \nlost and subclonal loss. (B) Proportion of patients with loss  or retention of RB1 protein 672 \nexpression in tumour samples by ovarian cancer histotype s. Chi-square P value reported for 673 \ndifference in proportion s across all histotypes . HGSC, tubo-ovarian high-grade serous 674 \ncarcinoma; LGSC, low -grade serous carcinoma; MOC, mucinous ovarian cancer; ENOC, 675 \nendometrioid ovarian cancer; CCOC, clear cell ovarian cancer. (C) Boxplots show RB1 mRNA 676 \nexpression (NanoString) by RB1 protein expression status; lines indicate median and whiskers 677 \nshow range (Mann-Whitney test P value reported). Kaplan-Meier analysis of overall survival 678 \nin patients diagnosed with HGSC (D) and ENOC (E) stratified by tumour RB1 expression. (F) 679 \nLoss of RB1 tumour expression is more common in germline BRCA1 and BRCA2 mutation 680 \ncarriers than retained RB1 expression. Chi -square P value is reported. (G) Kaplan-Meier 681 \nestimates of overall survival in HGSC patients by combined germline BRCA and tumour RB1 682 \nexpression status. 683 \n 684 \nFigure 2. Sensitivity to therapeutic agents in BRCA1-mutant cell lines with RB1 knockout.  685 \n(A) RB1 was knocked out using CRISPR/Cas9 in 3 patient-derived Australian Ovarian Cancer 686 \nStudy (AOCS) HGSC cell lines with either wild -type or mutant  BRCA1 background. 687 \nRepresentative Western Blots show protein levels of RB1 and phosphorylated RB1 (pRB1) 688 \ncompared to GAPDH loading control in single cell cloned, homozygous RB1 wildtype (WT) 689 \nand knockout (KO) colonies in comparison to heterogeneous populations with a scramble 690 \nsingle guide RNA (sgRNA). Independent blots were used for RB1 and pRB1. (B) Cell viability 691 \nwas compared between RB1 WT and KO clones following treatment with cisplatin (72 hours), 692 \npaclitaxel (72 hours) or olaparib (120 hours). Nonlinear regression drug curves  are shown; P 693 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 29 \nvalues of a curve fit, extra sum -of squares F test (ns, not significant; ** P < 0.01; **** P < 694 \n0.0001; n = 3). Error bars indicate ± SEM ; for some values error bars are shorter than the 695 \nsymbols and thus are not visible. (C) Proportion of surviving colonies following 16 days of 696 \ntreatment with cisplatin, paclitaxel or a combination of both (with half of the IC50 determined 697 \nper drug and cell line respectively) relative to DMF vehicle control (n = 3 replicates). Data are 698 \npresented as mean ± SEM. Mean values were compared by student's t-test (ns, not significant; 699 \n*P < 0.05; **P < 0.01). Representative scans of the fixed cell colonies stained with crystal 700 \nviolet are shown for each condition. 701 \n 702 \nFigure 3. Genomic landscape of high -grade serous ovarian tumours with co -occurring 703 \nBRCA and RB1 alterations.  704 \n(A) Pathogenic germline and somatic alterations in homologous recombination (HR) and DNA 705 \nrepair genes detected by whole -genome sequencing  and DNA methylation analysis  of 126 706 \nprimary HGSC samples26 are shown, as well as alterations in immune genes and CCNE1. 707 \nSamples are grouped by HR D and RB1 status (wt, wild-type; mut, mutation). Bars at the top 708 \nindicate the number of alterations in each listed gene per patient. Patients are annotated with 709 \nsurvival group (LTS, long -term survivor, OS >10  years; MTS, mid -term survivor, OS 2 -10 710 \nyears; STS, short-term survivor, OS <2 years), tumour CHORD40 scores, and the proportion of 711 \nstructural variant (SV) type ( DUP, duplication; DEL, deletion; INV, inversion; ITX, intra -712 \nchromosomal translocation ). (B) Kaplan -Meier estimates of progression -free and overall 713 \nsurvival of patients with according to HR status ( BRCA1-type HRD, BRCA2-type HRD or 714 \nhomologous recombination proficient tumours) and RB1 status (mut, mutation; wt, wild-type). 715 \n 716 \nFigure 4. Characterisation of HGSC with co-loss of RB1 and BRCA.  717 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 30 \n(A) Gene set enrichment analysis indicating up - and downregulated pathways in tumours 718 \naccording to BRCA and RB1 status. HRP, homologous recombination proficient; HRD, 719 \nhomologous recombination deficient; RB1wt, RB1 wild-type; RB1m, RB1 altered. (B) 720 \nProportion of tumour infiltrating lymphocytes  (TILs) in HGSC tumours grouped by RB1 721 \nexpression and BRCA germline mutation  status (Chi-square P value is indicated). (C) 722 \nProportion of tumours classified as each HGSC molecular subtype 12 grouped by RB1 723 \nexpression and BRCA germline mutation status (Chi -square P value is indicated;  C5.PRO, 724 \nC5/proliferative subtype ; C4.DIF, C4/differentiated subtype ; C2.IMM, C2/immunoreactive 725 \nsubtype; C1.MES, C1/mesenchymal subtype). 726 \n 727 \nSupplementary Figure S1. Patients and tumour samples analysed in this study. 728 \nNumber of patients included in each molecular analysis. HGSC, tubo -ovarian high -grade 729 \nserous ovarian carcinoma; ENOC, endometrioid ovarian carcinoma; OS, overall survival.  730 \n 731 \nSupplementary Figure S2. Combined p53 and RB1 protein expression in ENOC. 732 \n(A) Correlation between RB1 and p53 tumour expression in patients with endometrioid ovarian 733 \ncarcinoma (ENOC). Chi -square P value is reported. (B) Kaplan -Meier estimates of overall 734 \nsurvival in patients with ENOC by combined RB1 and p53 tumour expression status. 735 \n 736 \nSupplementary Figure S3. Drug sensitivity in HGSC cell lines with innate RB1 and/or 737 \nBRCA1 alterations.  738 \n(A) Summary of the molecular features of innate HGSC cell models, including mutations in 739 \nkey genes (TP53, CDKN2A, BRCA1, BRCA2), copy number alterations in CCNE1, and protein 740 \nexpression of RB1 and p16. (B) IC50 of high grade serous ovarian cancer cell lines after 741 \ntreatment with cisplatin (72 hours), paclitaxel (72 hours), or olaparib (120 hours). ND, Not 742 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 31 \ndetermined. (C) Viability of high -grade serous ovarian cancer cell lines after treatment with 743 \ncisplatin (72 hours), paclitaxel (72 hours), or olaparib (120 hours). Data are expressed as mean 744 \n(n = 3 replicates) ± standard error of the mean (SEM). For some points, error bars are shorter 745 \nthan the height of the symbol and are not visible.  746 \n 747 \nSupplementary Figure S4. Cell proliferation and cell cycle distribution of HGSC cell lines 748 \nwith RB1 knockout. 749 \n(A) CRISPR/Cas9 knockout of RB1 in 3 patient-derived ovarian cancer cell lines with different 750 \nBRCA1/2 and p16 background s. The bar graph indicates RB1 mRNA expression levels 751 \ndetermined by RT -PCR ( n = 3) in single -cell clones confirming RB1 wildtype (WT) and 752 \nknockout (KO) compared to heterozygous colonies without gene editing (Scramble). 753 \nRepresentative Western Blots show p16 protein levels compared to GAPDH loading controls 754 \nin each cell line and clone. Images of p16 IHC in AOCS parental cell lines are included 755 \nconfirming the respective p16 status. (B) Proliferative capacity of 3 patient-derived HGSC cell 756 \nlines (RB1 wild-type, WT and RB1 knockout, KO clones) measured by IncuCyte Zoom live -757 \ncell imaging. Data represent mean ± SEM confluency after 20 -25% starting confluency from 758 \nthree to six independent experiments. Dashed line denotes 75% confluency. (C) Cell cycle 759 \ndistribution following RB1 CRISPR knockout. Proportion of cells in G0G1, S or G2/M phase 760 \n24 hours after treatment with DMF, cisplatin or paclitaxel at half the IC50 determined per cell 761 \nline and drug, analysed by flow cytometry. Mean proportion ± SEM of three independently 762 \nperformed experiments are shown. Distribution was compared between RB1 WT and KO 763 \nclones using unpaired t test (ns, not significant; *P < 0.05). 764 \n 765 \nSupplementary Figure S5. Mutational signatures in homologous recombination 766 \ndeficiency and RB1 subgroups. 767 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 32 \nBoxplots show the relative proportion (y -axis) of genome -wide mutational signatures 26 768 \naccording to homologous recombination deficiency (HRD) and RB1 status. Boxes show the 769 \ninterquartile range (25-75th percentiles), central lines indicate the median, dots represent each 770 \nsample, whiskers show the smallest and largest values within 1.5 times the interquartile range, 771 \nred triangles indicate the mean, and dotted lines join the mean of each subgroup to visualise 772 \nthe trend. The Kruskal–Wallis test P values displayed are Benjamini -Hochberg adjusted and 773 \nthe signatures are ordered by their significance.  Pair-wise Mann -Whitney-Wilcoxon test 774 \nadjusted P values are also reported. HRP, homologous recombination proficient. 775 \n 776 \nSupplementary Figure S6. Genomic and clinical characteristics by combined homologous 777 \nrecombination deficiency and RB1 status. 778 \nBoxplots show numerical clinical and genomic features (y-axis) according to homologous 779 \nrecombination deficiency (HRD) and RB1 status. Boxes show the interquartile range (25 -75th 780 \npercentiles), central lines indicate the median, dots represent each sample, whiskers show the 781 \nsmallest and largest values within 1.5 times the interquartile range, red triangles indicate the 782 \nmean, and dotted lines join the mean of each subgroup  to visualise the trend . The Kruskal–783 \nWallis test P values displayed are Benjamini -Hochberg adjusted and the features are ordered 784 \nby their significance.  Pair-wise Mann -Whitney-Wilcoxon test adjusted P values are also 785 \nreported. Features include BRCA1- and BRCA2-type CHORD ( Classifier of HOmologous 786 \nRecombination Deficiency) scores; mean HRD scores ( scarHRD); absolute numbers of 787 \nstructural variants (SVs), including deletions (DEL), duplications (DUP), intrachromosomal 788 \nrearrangements (ITX), and inversions (INV); relative expression levels of PCNA and MCM3; 789 \nproportion of whole-genome loss-of-heterozygosity (LOH); number of predicted neoantigens 790 \nand variants per megabase (Mb);  age of patients at diagnosis ; progression-free and overall 791 \nsurvival; cancer cell purity and ploidy; absolute CIBERSORTx scores; proportion of Ki -67 792 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 33 \npositive tumour cells were available for n = 59 primary tumours as previously measured by 793 \nimmunohistochemistry7. HRP, homologous recombination proficient. 794 \n 795 \nSupplementary Figure S7. Gene alterations across BRCA and RB1 altered subgroups. 796 \nProportion of tumours with alterations in genes of interest for each subgroup. WT, wild -type; 797 \nMUT, mutation ; HRP, homologous recombination proficient . Genes are ordered by 798 \nsignificance using Fisher's exact test; Benjamini-Hochberg adjusted P values are reported. 799 \n 800 \nSupplementary Figure S8. Differentially expressed genes. 801 \nBars indicate the number of differentially expressed genes  (Benjamini-Hochberg adjusted P 802 \nvalue < 0.05)  between HGSC tumours grouped by HRD and/or RB1 status as 803 \nshown. Differential gene expression analysis was performed using DESeq2 to determine fold 804 \nchange of gene expression between groups  (see Supplementary Table 7 for full DESeq2 805 \nresults). HRP, homologous recombination proficient; HRD, homologous recombination 806 \ndeficient; RB1wt, RB1 wild-type; RB1m, RB1 altered. 807 \n 808 \nSupplementary Table captions: 809 \nSupplementary Table S1. 810 \nDetails of participating Ovarian Tumor Tissue Analysis (OTTA) consortium studies and ethics 811 \napproval. 812 \nSupplementary Table S2. 813 \nNumber of patients by study and histotype. 814 \nSupplementary Table S3. 815 \nClinical characteristics of patients diagnosed with high-grade serous ovarian cancer. 816 \nSupplementary Table S4. 817 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 34 \nClinical features of patients with endometrioid ovarian cancer 818 \nSupplementary Table S5. 819 \nClinical characteristics of patients with high -grade serous ovarian cancer according to BRCA 820 \nand RB1 status. 821 \nSupplementary Table S6. 822 \nRelative expression of BRCA1 and RB1 by qPCR in AOCS30 CRISPR knockout model. 823 \nSupplementary Table S7. 824 \nDifferential gene expression analysis comparing transcriptomes of tumours based on BRCA 825 \nand RB1 alteration status. 826 \nSupplementary Table S8. 827 \nSummary of cell lines used in this study. 828 \nSupplementary Table S9. 829 \nSummary of gene alterations and expression found in cell lines. 830 \nSupplementary Table S10. 831 \nSequence of single guide RNA used for CRISPR gene knockout. 832 \nSupplementary Table S11. 833 \nAntibodies and reagents used for this project. 834 \nSupplementary Table S12. 835 \nList of primer sequences used in the study. 836 \n 837 \nREFERENCES 838 \n1. Bowtell DD, Böhm S, Ahmed AA, et al. Rethinking ovarian cancer II: reducing 839 \nmortality from high-grade serous ovarian cancer. Nature Reviews Cancer 2015; 15(11): 668-840 \n79. 841 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 35 \n2. Norquist B, Wurz KA, Pennil CC, et al. Secondary somatic mutations restoring 842 \nBRCA1/2 predict chemotherapy resistance in hereditary ovarian carcinomas. Journal of 843 \nClinical Oncology 2011; 29(22): 3008-15. 844 \n3. Patch AM, Christie EL, Etemadmoghadam D, et al. Whole-genome characterization of 845 \nchemoresistant ovarian cancer. Nature 2015; 521(7553): 489-94. 846 \n4. Christie EL, Pattnaik S, Beach J, et al. Multiple ABCB1 transcriptional fusions in drug 847 \nresistant high-grade serous ovarian and breast cancer. Nature communications 2019; 10(1): 848 \n1295-. 849 \n5. Gockley A, Melamed A, Bregar AJ, et al. Outcomes of Women With High -Grade and 850 \nLow-Grade Advanced -Stage Serous Epithelial Ovarian Cancer. Obstetrics and gynecology  851 \n2017; 129(3): 439-47. 852 \n6. Dao F, Schlappe BA, Tseng J, et al. Characteristics of 10-year survivors of high-grade 853 \nserous ovarian carcinoma. Gynecologic Oncology 2016; 141(2): 260-3. 854 \n7. Garsed DW, Alsop K, Fereday S, et al. Homologous recombination DNA repair 855 \npathway disruption and retinoblastoma protein loss are associated with exceptional survival in 856 \nhigh-grade serous ovarian cancer. Clinical Cancer Research 2018; 24(3): 569-80. 857 \n8. Saner FAM, Herschtal A, Nelson BH, et al. Going to extremes: determinants of 858 \nextraordinary response and survival in patients with cancer. Nature Reviews Cancer  2019; 859 \n19(6): 339-48. 860 \n9. du Bois A, Reuss A, Pujade -Lauraine E, Harter P, Ray-Coquard I, Pfisterer J. Role of 861 \nsurgical outcome as prognostic factor in advanced epithelial ovarian cancer: A combined 862 \nexploratory analysis of 3 prospectively randomized phase 3 multicenter trials. Cancer 2009; 863 \n115(6): 1234-44. 864 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 36 \n10. Wallace S, Kumar A, Mc Gree M, et al. Efforts at maximal cytoreduction improve 865 \nsurvival in ovarian cancer patients, even when complete gross resection is not feasible. 866 \nGynecologic Oncology 2017; 145(1): 21-6. 867 \n11. Harter P, Sehouli J, Vergote I, et al. Randomized Trial of Cytoreductive Surgery for 868 \nRelapsed Ovarian Cancer. The New England journal of medicine 2021; 385(23): 2123-31. 869 \n12. Tothill RW, Tinker AV, George J, et al. Novel molecular subtypes of serous and 870 \nendometrioid ovarian cancer linked to clinical outcome. Clinical Cancer Research  2008; 871 \n14(16): 5198-208. 872 \n13. Liu Z, Beach JA, Agadjanian H, et al. Suboptimal cytoreduction in ovarian carcinoma 873 \nis associated with molecular pathways characteristic of increased stromal activation. 874 \nGynecologic Oncology 2015; 139(3): 394-400. 875 \n14. Wang C, Armasu SM, Kalli KR, et al. Pooled Clustering of High-Grade Serous Ovarian 876 \nCancer Gene Expression Leads to Novel Consensus Subtypes Associated with Survival and 877 \nSurgical Outcomes. Clinical cancer research : an official journal of the American Association 878 \nfor Cancer Research 2017; 23(15): 4077-85. 879 \n15. Torres D, Kumar A, Bakkum-Gamez JN, et al. Mesenchymal molecular subtype is an 880 \nindependent predictor of severe postoperative complications after primary debulking surgery 881 \nfor advanced ovarian cancer. Gynecologic Oncology 2019; 152(2): 223-7. 882 \n16. Zhang L, Conejo-Garcia JR, Katsaros D, et al. Intratumoral T Cells, Recurrence, and 883 \nSurvival in Epithelial Ovarian Cancer. New England Journal of Medicine  2003; 348(3): 203-884 \n13. 885 \n17. Hwang WT, Adams SF, Tahirovic E, Hagemann IS, Coukos G. Prognostic significance 886 \nof tumor-infiltrating T cells in ovarian cancer: A meta -analysis. Gynecologic Oncology 2012; 887 \n124(2): 192-8. 888 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 37 \n18. Fong PC, Yap TA, Boss DS, et al. Poly(ADP) -ribose polymerase inhibition: frequent 889 \ndurable responses in BRCA carrier ovarian cancer correlating with platinum -free interval. 890 \nJournal of clinical oncology : official journal of the American Society of Clinical Oncology  891 \n2010; 28(15): 2512-9. 892 \n19. The Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian 893 \ncarcinoma. Nature 2011; 474(7353): 609-15. 894 \n20. Pennington KP, Walsh T, Harrell MI, et al. Germline and somatic mutations in 895 \nhomologous recombination genes predict platinum response and survival in ovarian, fallopian 896 \ntube, and peritoneal carcinomas. Clinical cancer research : an official journal of the American 897 \nAssociation for Cancer Research 2014; 20(3): 764-75. 898 \n21. Bolton KL, Chenevix -Trench G, Goh C, et al. Association between BRCA1 and 899 \nBRCA2 mutations and survival in women with invasive epithelial ovarian cancer. JAMA 2012; 900 \n307(4): 382-90. 901 \n22. Alsop K, Fereday S, Meldrum C, et al. BRCA mutation frequency and patterns of 902 \ntreatment response in BRCA mutation-positive women with ovarian cancer: A report from the 903 \nAustralian ovarian cancer study group. Journal of Clinical Oncology 2012; 30(21): 2654-63. 904 \n23. Candido-dos-Reis FJ, Song H, Goode EL, et al. Germline mutation in BRCA1 or 905 \nBRCA2 and ten -year survival for women diagnosed with epithelial ovarian cancer. Clinical 906 \ncancer research 2015; 21(3): 652-7. 907 \n24. Wang Y, Bernhardy AJ, Cruz C, et al. The BRCA1 -Δ11q alternative splice isoform 908 \nbypasses germline mutations and promotes therapeutic resistance to PARP inhibition and 909 \ncisplatin. Cancer Research 2016; 76(9): 2778-90. 910 \n25. Maxwell KN, Wubbenhorst B, Wenz BM, et al. BRCA locus -specific loss of 911 \nheterozygosity in germline BRCA1 and BRCA2 carriers. Nature communications 2017; 8(1): 912 \n319-. 913 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 38 \n26. Garsed DW, Pandey A, Fereday S, et al. The genomic and immune landscape of long-914 \nterm survivors of high-grade serous ovarian cancer. Nature genetics 2022; 54(12): 1853-64. 915 \n27. Stefansson OA, Jonasson JG, Olafsdottir K, et al. CpG island hypermethylation of 916 \nBRCA1 and loss of pRb as co -occurring events in basal/triple -negative breast cancer. 917 \nEpigenetics 2011; 6(5): 638-49. 918 \n28. Jönsson G, Staaf J, Vallon -Christersson J, et al. The Retinoblastoma Gene Undergoes 919 \nRearrangements in BRCA1 -Deficient Basal -like Breast Cancer. Cancer Research  2012; 920 \n72(16): 4028-36. 921 \n29. Chakraborty G, Armenia J, Mazzu YZ, et al. Significance of BRCA2 and RB1 co-loss 922 \nin aggressive prostate cancer progression. Clinical Cancer Research 2020; 26(8): 2047-64. 923 \n30. Chen WS, Alshalalfa M, Zhao SG, et al. Novel RB1-Loss Transcriptomic Signature Is 924 \nAssociated with Poor Clinical Outcomes across Cancer Types. Clinical Cancer Research 2019; 925 \n25(14): 4290-9. 926 \n31. Burkhart DL, Sage J. Cellular mechanisms of tumour suppression by the retinoblastoma 927 \ngene. Nature Reviews Cancer 2008; 8(9): 671-82. 928 \n32. Knudsen ES, Knudsen KE. Tailoring to RB: tumour suppressor status and therapeutic 929 \nresponse. Nature reviews Cancer 2008; 8(9): 714-24. 930 \n33. Vélez-Cruz R, Manickavinayaham S, Biswas AK, et al. RB localizes to DNA double -931 \nstrand breaks and promotes DNA end resection and homologous recombination through the 932 \nrecruitment of BRG1. Genes and Development 2016; 30(22): 2500-12. 933 \n34. Millstein J, Budden T, Goode EL, et al. Prognostic gene expression signature for high-934 \ngrade serous ovarian cancer. Annals of Oncology 2020; 31(9): 1240-50. 935 \n35. Milea A, George SHL, Matevski D, et al. Retinoblastoma pathway deregulatory 936 \nmechanisms determine clinical outcome in high -grade serous ovarian carcinoma. Modern 937 \nPathology 2014; 27(7): 991-1001. 938 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 39 \n36. Sieh W, Köbel M, Longacre TA, et al. Hormone-receptor expression and ovarian cancer 939 \nsurvival: An Ovarian Tumor Tissue Analysis consortium study. The Lancet Oncology  2013; 940 \n14(9): 853-62. 941 \n37. Köbel M, Kang EY, Weir A, et al. p53 and ovarian carcinoma survival: an Ovarian 942 \nTumor Tissue Analysis consortium study. The Journal of Pathology: Clinical Research 2023; 943 \n9(3): 208-22. 944 \n38. Talhouk A, George J, Wang C, et al. Development and Validation of the Gene 945 \nExpression Predictor of High -grade Serous Ovarian Carcinoma Molecular SubTYPE 946 \n(PrOTYPE). Clinical Cancer Research 2020; 26(20): 5411-23. 947 \n39. Ovarian Tumor Tissue Analysis (OTTA) Consortium, Goode EL, Block MS, et al. 948 \nDose-Response Association of CD8+ Tumor -Infiltrating Lymphocytes and Survival Time in 949 \nHigh-Grade Serous Ovarian Cancer. JAMA oncology 2017; 3(12): e173290-e. 950 \n40. Nguyen L, W. M. Martens J, Van Hoeck A, Cuppen E. Pan -cancer landscape of 951 \nhomologous recombination deficiency. Nature Communications 2020; 11(1): 1-12. 952 \n41. Domcke S, Sinha R, Levine DA, Sander C, Schultz N. Evaluating cell lines as tumour 953 \nmodels by comparison of genomic profiles. Nature Communications 2013; 4(2126). 954 \n42. Köbel M, Piskorz AM, Lee S, et al. Optimized p53 immunohistochemistry is an 955 \naccurate predictor of TP53 mutation in ovarian carcinoma. The Journal of Pathology: Clinical 956 \nResearch 2016; 2(4): 247-58. 957 \n43. Hollis RL, Thomson JP, Stanley B, et al. Molecular stratification of endometrioid 958 \novarian carcinoma predicts clinical outcome. Nature Communications 2020; 11(1). 959 \n44. Weinberg RA. The retinoblastoma protein and cell cycle control. Cell 1995; 81(3): 323-960 \n30. 961 \n45. Genovese C, Trani D, Caputi M, Claudio PP. Cell cycle control and beyond: emerging 962 \nroles for the retinoblastoma gene family. Oncogene 2006; 25(38): 5201-9. 963 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 40 \n46. Findlay GM, Daza RM, Martin B, et al. Accurate classification of BRCA1 variants with 964 \nsaturation genome editing. Nature 2018; 562(7726): 217-22. 965 \n47. Degasperi A, Amarante TD, Czarnecki J, et al. A practical framework and online tool 966 \nfor mutational signature analyses show intertissue variation and driver dependencies. Nature 967 \nCancer 2020; 1(2): 249-63. 968 \n48. Alexandrov LB, Kim J, Haradhvala NJ, et al. The repertoire of mutational signatures in 969 \nhuman cancer. Nature 2020; 578(7793): 94-101. 970 \n49. Kang EY, Weir A, Meagher NS, et al. CCNE1 and survival of patients with tubo -971 \novarian high-grade serous carcinoma: An Ovarian Tumor Tissue Analysis consortium study. 972 \nCancer 2022; 54(4): 538-45. 973 \n50. da Costa AABA, do Canto LM, Larsen SJ, et al. Genomic profiling in ovarian cancer 974 \nretreated with platinum based chemotherapy presented homologous recombination deficiency 975 \nand copy number imbalances of CCNE1 and RB1 genes. BMC Cancer 2019; 19(1): 422-. 976 \n51. Mandigo AC, Tomlins SA, Kelly WK, Knudsen KE. Relevance of pRB Loss in Human 977 \nMalignancies. Clin Cancer Res 2022; 28(2): 255-64. 978 \n52. Ku SY, Rosario S, Wang Y, et al. Rb1 and Trp53 cooperate to suppress prostate cancer 979 \nlineage plasticity, metastasis, and antiandrogen resistance. Science 2017; 355(6320): 78-83. 980 \n53. Mu P, Zhang Z, Benelli M, et al. SOX2 promotes lineage plasticity and antiandrogen 981 \nresistance in TP53 - and RB1 -deficient prostate cancer. Science 2017; 355(6320): 84-8. 982 \n54. Palafox M, Monserrat L, Bellet M, et al. High p16 expression and heterozygous RB1 983 \nloss are biomarkers for CDK4/6 inhibitor resistance in ER(+) breast cancer. Nat Commun 2022; 984 \n13(1): 5258. 985 \n55. Wander SA, Cohen O, Gong X, et al. The Genomic Landscape of Intrinsic and 986 \nAcquired Resistance to Cyclin -Dependent Kinase 4/6 Inhibitors in Patients with Hormone 987 \nReceptor-Positive Metastatic Breast Cancer. Cancer Discov 2020; 10(8): 1174-93. 988 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 41 \n56. Derenzini M, Donati G, Mazzini G, et al. Loss of Retinoblastoma Tumor Suppressor 989 \nProtein Makes Human Breast Cancer Cells More Sensitive to Antimetabolite Exposure. 990 \nClinical Cancer Research 2008; 14(7): 2199-209. 991 \n57. Treré D, Brighenti E, Donati G, et al. High prevalence of retinoblastoma protein loss in 992 \ntriple-negative breast cancers and its association with a good prognosis in patients treated with 993 \nadjuvant chemotherapy. Annals of Oncology 2009; 20(11): 1818-23. 994 \n58. Patel JM, Goss A, Garber JE, et al. Retinoblastoma protein expression and its predictors 995 \nin triple-negative breast cancer. NPJ breast cancer 2020; 6(1): 19-. 996 \n59. Bowtell DD. The genesis and evolution of high -grade serous ovarian cancer. Nat Rev 997 \nCancer 2010; 10(11): 803-8. 998 \n60. Cancer Genome Atlas N. Comprehensive molecular portraits of human breast tumours. 999 \nNature 2012; 490(7418): 61-70. 1000 \n61. Köbel M, Kalloger SE, Boyd N, et al. Ovarian carcinoma subtypes are different 1001 \ndiseases: implications for biomarker studies. PLoS medicine 2008; 5(12): e232-e. 1002 \n62. Kang EY, Millstein J, Popovic G, et al. MCM3 is a novel proliferation marker 1003 \nassociated with longer survival for patients with tubo -ovarian high-grade serous carcinoma. 1004 \nVirchows Archiv 2022; 480(4): 855-71. 1005 \n63. Zhao Y, Wang Y, Zhu F, Zhang J, Ma X, Zhang D. Gene expression profiling revealed 1006 \nMCM3 to be a better marker than Ki67 in prognosis of invasive ductal breast carcinoma 1007 \npatients. Clinical and Experimental Medicine 2020; 20(2): 249-59. 1008 \n64. Velez-Cruz R, Manickavinayaham S, Biswas AK, et al. RB localizes to DNA double -1009 \nstrand breaks and promotes DNA end resection and homologous recombination through the 1010 \nrecruitment of BRG1. Genes Dev 2016; 30(22): 2500-12. 1011 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 42 \n65. Westphalen CB, Fine AD, André F, et al. Pan -cancer Analysis of Homologous 1012 \nRecombination Repair –associated Gene Alterations and Genome -wide Loss -of-1013 \nHeterozygosity Score. Clinical Cancer Research 2022; 28(7): 1412-21. 1014 \n66. Meng J, Liu X, Zhang P, et al. Rb selectively inhibits innate IFN -β production by 1015 \nenhancing deacetylation of IFN -β promoter through HDAC1 and HDAC8. Journal of 1016 \nAutoimmunity 2016; 73: 42-53. 1017 \n67. Manzano RG, Catalan-Latorre A, Brugarolas A. RB1 and TP53 co-mutations correlate 1018 \nstrongly with genomic biomarkers of response to immunity checkpoint inhibitors in urothelial 1019 \nbladder cancer. BMC Cancer 2021; 21(432). 1020 \n68. Molinero L, Li Y, Chang C -W, et al. Tumor immune microenvironment and genomic 1021 \nevolution in a patient with metastatic triple negative breast cancer and a complete response to 1022 \natezolizumab. Journal for ImmunoTherapy of Cancer 2019; 7(274). 1023 \n69. Ahmed AA, Etemadmoghadam D, Temple J, et al. Driver mutations in TP53 are 1024 \nubiquitous in high grade serous carcinoma of the ovary. Journal of Pathology  2010; 221(1): 1025 \n49-56. 1026 \n70. Landen CN, Molinero L, Hamidi H, et al. Influence of Genomic Landscape on Cancer 1027 \nImmunotherapy for Newly Diagnosed Ovarian Cancer: Biomarker Analyses from the 1028 \nIMagyn050 Randomized Clinical Trial. Clinical Cancer Research 2023; 29(9): 1698-707. 1029 \n71. Kandalaft LE, Odunsi K, Coukos G. Immune Therapy Opportunities in Ovarian 1030 \nCancer. American Society of Clinical Oncology Educational Book 2020; 3(40): e228-e40. 1031 \n72. Bulanova D, Akimov Y, Senkowski W, et al. A synthetic lethal dependency on casein 1032 \nkinase 2 in response to replication-perturbing drugs in RB1-deficient ovarian and breast cancer 1033 \ncells. bioRxiv 2022: 1-22. 1034 \n73. Gong X, Du J, Parsons SH, et al. Aurora A Kinase Inhibition Is Synthetic Lethal with 1035 \nLoss of the RB1 Tumor Suppressor Gene. Cancer Discov 2019; 9(2): 248-63. 1036 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\n   \n \n 43 \n74. Lyu J, Yang EJ, Zhang B, et al. Synthetic lethality of RB1 and aurora A is driven by 1037 \nstathmin-mediated disruption of microtubule dynamics. Nat Commun 2020; 11(1): 5105. 1038 \n75. Oser MG, Fonseca R, Chakraborty AA, et al. Cells Lacking the RB1 Tumor Suppressor 1039 \nGene Are Hyperdependent on Aurora B Kinase for Survival. Cancer Discov 2019; 9(2): 230-1040 \n47. 1041 \n76. Serra V, Wang AT, Castroviejo -Bermejo M, et al. Identification of a Molecularly -1042 \nDefined Subset of Breast and Ovarian Cancer Models that Respond to WEE1 or ATR 1043 \nInhibition, Overcoming PARP Inhibitor Resistance. Clinical Cancer Research 2022; 28(20): 1044 \n4536-50. 1045 \n 1046 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\nHGSC (\nn = 4482)\nLGSC (\nn = 181)\nMOC (\nn = 326)\nENOC (\nn = 908)\nCCOC (\nn = 667)\n0\n20\n40\n60\n80\n100Proportion (%)\nRB1 loss\nRB1 retained\nChi-square \nP < 0.0001\nRB1 retained (\nn = 2183)\nRB1 loss (\nn = 369)\n-8\n-6\n-4\n-2\n0\nRB1 mRNA expression Mann-Whitney\nP < 0.0001 \n✱✱✱✱\nFigure 1.\nB C\nD\n E\nF G\nRB1 loss (\nn = 218)\nRB1 retained (\nn = 916)\n0\n20\n40\n60\n80\n100Proportion (%)\nBRCA1 mutation carriers\nBRCA2 mutation carriers\nNon-carriers\nChi-square\nP < 0.0001\nRB1 retained RB1 loss RB1 subclonal loss\nA\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\nTable 1. Multivariate analysis of molecular alterations and overall survival in patients with HGSC and ENOC\nHistotype Feature Category No. patients (events, %) HR (95% CI) P P  for interaction\nHGSCa,b RB1 Retained 3453 (71.3) 1 [Reference]\nLoss 686 (61.1) 0.74 (0.66-0.83) 6.8 x 10-7\nENOCa RB1 Retained 649 (22.7) 1 [Reference]\nLoss 28 (39.3) 2.17 (1.17-4.03) 0.014\nHGSCa,b RB1 and BRCA status RB1 retained & non-carrier 714 (76.3) 1 [Reference] 0.24\nRB1 loss & non-carrier 135 (60.7) 0.74 (0.57-0.96) 0.023\nRB1 retained & BRCA  carrier 159 (67.9) 0.69 (0.55-0.86) 0.001\nRB1 loss & BRCA  carrier 70 (42.9) 0.38 (0.25-0.58) 5.2 x 10-6\nENOCa RB1 and p53 RB1 retained & p53 normal 492 (17.5) 1 [Reference] 0.698\nRB1 retained & p53 abnormal 58 (36.2) 2.26 (1.38-3.71) 0.001\nRB1 loss & p53 normal 11 (27.3) 1.77 (0.56-5.65) 0.332\nRB1 loss & p53 abnormal 12 (58.3) 5.34 (2.43-11.8) <0.001\naAdjusted for stage and age at diagnosis. bStratified by study. \nHR, hazard ratio, CI, confidence interval; HGSC, tubo-ovarian high-grade serous carcinoma; ENOC, endometrioid ovarian cancer.\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\nC\nFigure 2.\nA B\nRB1\nWT\nRB1\nKO\nRB1\nWT\nRB1\nKO\nRB1 \nWT\nRB1\nKO\nDMF Cisplatin Paclitaxel Cis/Pac\n-6 -4 -2 0\n0.0\n0.5\n1.0\nLog[Cisplatin], µM\nViability\nAOCS1 (BRCA1/2 WT, p16 normal)\n-6 -4 -2 0\n0.0\n0.5\n1.0\nLog[Cisplatin], µM\nViability\nAOCS16 (BRCA1 mut, p16 absent)\n-6 -4 -2 0\n0.0\n0.5\n1.0\nLog[Cisplatin], µM\nViability\nAOCS7.2 (BRCA1 mut, p16 normal)\n-8 -6 -4 -2 0\n0.0\n0.5\n1.0\n1.5\nLog[Paclitaxel], µM\nViability\n-8 -6 -4 -2 0\n0.0\n0.5\n1.0\n1.5\nLog[Paclitaxel], µM\nViability\n-8 -6 -4 -2 0\n0.0\n0.5\n1.0\n1.5\nLog[Paclitaxel], µM\nViability\nns\n**\nns\nns\n**** ****\n-6 -4 -2 0\n0.0\n0.5\n1.0\nLog[Olaparib], µM\nViability\nRB1 WT\nRB1 KO\n-6 -4 -2 0\n0.0\n0.5\n1.0\nLog[Olaparib], µM\nViability\nRB1 WT\nRB1 KO\n-6 -4 -2 0\n0.0\n0.5\n1.0\nLog[Olaparib], µM\nViability\nRB1 WT\nRB1 KO\nns\n****\n**\nDMF\nCisplatin Paclitaxel Cis/Pac\n0\n50\n100% Clonogenic Survival\nDMF\nCisplatin Paclitaxel Cis/Pac\n0\n50\n100% Clonogenic Survival\nDMF\nCisplatinPaclitaxel Cis/Pac\n0\n50\n100% Clonogenic Survival\nAOCS1 \nBRCA1/2 WT, p16 normal\nAOCS7.2 \nBRCA1 mut, p16 normal\nAOCS16 \nBRCA1 mut, p16 absent\n✱✱ ✱ ns\nns ns ns\nns ns ns\nRB1 WT\nRB1 KO\nRB1\nGAPDH\nScramble\nRB1 WT\nRB1 KO\nAOCS1\n(BRCA1/2 WT\np16 normal) pRB1\nScramble\nRB1 WT\nRB1 KO\nAOCS7.2\n(BRCA1 mut\np16 normal)\nScramble\nRB1 WT\nRB1 KO\nAOCS16\n(BRCA1 mut\np16 absent)\nRB1\nGAPDH\npRB1\nRB1\nGAPDH\npRB1\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\nHRP & \nRB1wild−type (n=45)\nHRP & \nRB1altered (n=4)\nBRCA1 −HRD & \nRB1wild−type (n=36)\nBRCA1 −HRD & \nRB1altered (n=13)\nBRCA2 −HRD & \nRB1wild−type (n=20)\nBRCA2 −HRD & \nRB1altered (n=8)\n2\n4\n6\nSurvival group\nCHORD score\nSV %\n0.25\n0.5\n0.75\n1\n25\n50\n75\n100\n41%\n14%\n12%\n6%\n5%\n4%\n3%\n2%\n2%\n2%\n2%\n2%\n2%\n1%\n1%\n1%\n1%\n1%\n1%\n1%\n20%\n17%\n11%\n5%\n3%\n3%\n2%\n2%\nBRCA1\nBRCA2\nRAD51B\nRAD51C\nBRIP1\nA TM\nP ALB2\nBLM\nFANCD2\nA TR\nBARD1\nFANCI\nFANCM\nCHEK2\nFANCA\nFANCE\nMSH2\nMSH6\nPMS1\nRAD51D\nRB1\nCCNE1\nPTEN\nCDK12\nCXCL9\nCXCL10\nCXCL11\nIFNG\n0 25 50 0 50 100\nAlteration\nGermline duplication\nGermline deletion\nGermline inversion\nSomatic duplication\nSomatic deletion\nSomatic inversion\nSomatic interchromosomal translocation\nSomatic amplification\nGermline frameshift indel\nGermline nonsense\nGermline missense\nSomatic nonsense\nSomatic frameshift indel\nSomatic in−frame indel\nSomatic splice site\nPromoter methylation\nSurvival group\nLTS\nMTS\nSTS\nCHORD\nNone\nBRCA2 −type\nBRCA1 −type\nSV %\nDUP\nDEL\nINV\nITX\nAlteration\ncount\nAlteration\n%\nFigure 3.\nA\nB\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\nE2f Targets\nG2m Checkpoint\nInterferon Gamma Response\nInterferon Alpha Response\nOxidative Phosphorylation\nEstrogen Response Early\nEstrogen Response Late\nTnfa Signaling Via Nfkb\nReactive Oxygen Species Pathway\nAdipogenesis\nMyc Targets V1\nP53 Pathway\nFatty Acid Metabolism\nMtorc1 Signaling\nAllograft Rejection\nInflammatory Response\nComplement\nIl6 Jak Stat3 Signaling\nEpithelial Mesenchymal Transition\nProtein Secretion\nMitotic Spindle\nSpermatogenesis\nCoagulation\nPeroxisome\nIl2 Stat5 Signaling\nXenobiotic Metabolism\nApoptosis\nPi3k Akt Mtor Signaling\nCholesterol Homeostasis\nMyc Targets V2\nUv Response Up\nDna Repair\nGlycolysis\nKras Signaling Dn\nBile Acid Metabolism\nHypoxia\nUv Response Dn\nKras Signaling Up\nWnt Beta Catenin Signaling\nMyogenesis\nApical Junction\nHeme Metabolism\nHRD/HRP (sans RB1 sig)\nBRCA2/HRP (sans RB1 sig)\nBRCA1/HRP (sans RB1 sig)\nBRCA2.RB1w/HRP.RB1wt\nBRCA1.RB1w/HRP.RB1wt\nBRCA2.RB1m/HRP.RB1wt\nBRCA1.RB1m/HRP.RB1wt\nRB1m/RB1wt (sans HRD type)\n−log10(padj)\n10\n20\n30\n40\n−4\n−2\n0\n2\n4\nNES\nDirection\nUp\nDown\nMSigDB HALLMARK fGSEA Results (P.adj <= 0.05)\nA\nFigure 4.\nB\nE2f Targets\nG2m Checkpoint\nInterferon Gamma Response\nInterferon Alpha Response\nOxidative Phosphorylation\nEstrogen Response Early\nEstrogen Response Late\nTnfa Signaling Via Nfkb\nReactive Oxygen Species Pathway\nAdipogenesis\nMyc Targets V1\nP53 Pathway\nFatty Acid Metabolism\nMtorc1 Signaling\nAllograft Rejection\nInflammatory Response\nComplement\nIl6 Jak Stat3 Signaling\nEpithelial Mesenchymal Transition\nProtein Secretion\nMitotic Spindle\nSpermatogenesis\nCoagulation\nPeroxisome\nIl2 Stat5 Signaling\nXenobiotic Metabolism\nApoptosis\nPi3k Akt Mtor Signaling\nCholesterol Homeostasis\nMyc Targets V2\nUv Response Up\nDna Repair\nGlycolysis\nKras Signaling Dn\nBile Acid Metabolism\nHypoxia\nUv Response Dn\nKras Signaling Up\nWnt Beta Catenin Signaling\nMyogenesis\nApical Junction\nHeme Metabolism\nHRD/HRP (sans RB1 sig)\nBRCA2/HRP (sans RB1 sig)\nBRCA1/HRP (sans RB1 sig)\nBRCA2.RB1w/HRP.RB1wt\nBRCA1.RB1w/HRP.RB1wt\nBRCA2.RB1m/HRP.RB1wt\nBRCA1.RB1m/HRP.RB1wt\nRB1m/RB1wt (sans HRD type)\n−log10(padj)\n10\n20\n30\n40\n−4\n−2\n0\n2\n4\nNES\nDirection\nUp\nDown\nMSigDB HALLMARK fGSEA Results (P.adj <= 0.05)\nE2f Targets\nG2m Checkpoint\nInterferon Gamma Response\nInterferon Alpha Response\nOxidative Phosphorylation\nEstrogen Response Early\nEstrogen Response Late\nTnfa Signaling Via Nfkb\nReactive Oxygen Species Pathway\nAdipogenesis\nMyc Targets V1\nP53 Pathway\nFatty Acid Metabolism\nMtorc1 Signaling\nAllograft Rejection\nInflammatory Response\nComplement\nIl6 Jak Stat3 Signaling\nEpithelial Mesenchymal Transition\nProtein Secretion\nMitotic Spindle\nSpermatogenesis\nCoagulation\nPeroxisome\nIl2 Stat5 Signaling\nXenobiotic Metabolism\nApoptosis\nPi3k Akt Mtor Signaling\nCholesterol Homeostasis\nMyc Targets V2\nUv Response Up\nDna Repair\nGlycolysis\nKras Signaling Dn\nBile Acid Metabolism\nHypoxia\nUv Response Dn\nKras Signaling Up\nWnt Beta Catenin Signaling\nMyogenesis\nApical Junction\nHeme Metabolism\nHRD/HRP (sans RB1 sig)\nBRCA2/HRP (sans RB1 sig)\nBRCA1/HRP (sans RB1 sig)\nBRCA2.RB1w/HRP.RB1wt\nBRCA1.RB1w/HRP.RB1wt\nBRCA2.RB1m/HRP.RB1wt\nBRCA1.RB1m/HRP.RB1wt\nRB1m/RB1wt (sans HRD type)\n−log10(padj)\n10\n20\n30\n40\n−4\n−2\n0\n2\n4\nNES\nDirection\nUp\nDown\nMSigDB HALLMARK fGSEA Results (P.adj <= 0.05)\nE2f Targets\nG2m Checkpoint\nInterferon Gamma Response\nInterferon Alpha Response\nOxidative Phosphorylation\nEstrogen Response Early\nEstrogen Response Late\nTnfa Signaling Via Nfkb\nReactive Oxygen Species Pathway\nAdipogenesis\nMyc Targets V1\nP53 Pathway\nFatty Acid Metabolism\nMtorc1 Signaling\nAllograft Rejection\nInflammatory Response\nComplement\nIl6 Jak Stat3 Signaling\nEpithelial Mesenchymal Transition\nProtein Secretion\nMitotic Spindle\nSpermatogenesis\nCoagulation\nPeroxisome\nIl2 Stat5 Signaling\nXenobiotic Metabolism\nApoptosis\nPi3k Akt Mtor Signaling\nCholesterol Homeostasis\nMyc Targets V2\nUv Response Up\nDna Repair\nGlycolysis\nKras Signaling Dn\nBile Acid Metabolism\nHypoxia\nUv Response Dn\nKras Signaling Up\nWnt Beta Catenin Signaling\nMyogenesis\nApical Junction\nHeme Metabolism\nHRD/HRP (sans RB1 sig)\nBRCA2/HRP (sans RB1 sig)\nBRCA1/HRP (sans RB1 sig)\nBRCA2.RB1w/HRP.RB1wt\nBRCA1.RB1w/HRP.RB1wt\nBRCA2.RB1m/HRP.RB1wt\nBRCA1.RB1m/HRP.RB1wt\nRB1m/RB1wt (sans HRD type)\n−log10(padj)\n10\n20\n30\n40\n−4\n−2\n0\n2\n4\nNES\nDirection\nUp\nDown\nMSigDB HALLMARK fGSEA Results (P.adj <= 0.05)\nC\nRB1 retained & non-carrier (\nn = 602)\nRB1 retained & \nBRCA\n carrier (\nn = 94)\nRB1 loss & non-carrier (\nn = 123)\nRB1 loss & \nBRCA\n carrier (\nn = 49)\n0\n20\n40\n60\n80\n100Proportion Negative\nLow\nModerate\nHigh\nChi-square\nP = 0.0264\nNumber of TILs\nRB1 retained & non-carrier (\nn = 414)\nRB1 retained & \nBRCA\n carrier (\nn = 86)\nRB1 loss & non-carrier (\nn = 64)\nRB1 loss & \nBRCA\n carrier (\nn = 37)\n0\n20\n40\n60\n80\n100Proportion C1.MES\nC2.IMM\nC4.DIF\nC5.PRO\nChi-square\nP = 0.0271 \nMolecular subtype\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\nSupplementary Figure S1.\n7436 patients with ovarian \ncarcinomas assessed by \nimmunohistochemistry for \ntumour RB1 protein \nexpression\n6564 patients classified as \nhaving retained or lost RB1 \ntumour protein expression\n872 excluded\n66 had subclonal RB1 loss\n17 had cytoplasmic RB1 expression\n789 uninterpretable expression\n4482 HGSC patients with \nretained or lost RB1 protein \nexpression\nSurvival analyses:\n4256 univariate\n4139 multivariate\n2552 patients with tumour\nRB1 mRNA expression \ndetermined by NanoString\n1134 BRCA germline \nmutation status established\nSurvival analyses:\n1119 univariate\n1078 multivariate\n908 ENOC patients with \nretained or lost RB1 protein \nexpression\nSurvival analyses:\n718 univariate\n677 multivariate\n868 patients with CD8+ \ntumour infiltrating lymphocyte \ncounts\n601 patients with HGSC \nmolecular subtypes from \nadnexal and presumed \nadnexal specimens\nOvarian Tumor Tissue Analysis (OTTA) consortium\nMultidisciplinary Ovarian Cancer Outcomes Group (MOCOG) study \n126 patients with advanced \nstage (IIIC/IV) HGSC \nassessed by whole-genome \nsequencing and RNA \nsequencing of primary \ntumours\n34 short-term \nsurvivors \n(OS <2 years)\n32 moderate-term \nsurvivors (OS ≥2 \nand <10 years)\n60 long-term \nsurvivors \n(OS ≥10 years)\n759 with p53 abnormal or \nnormal protein expression\nSurvival analyses:\n609 univariate\n573 multivariate\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\nSupplementary Figure S2.\nA\nB\nRB1 loss (\nn = 28)\nRB1 retained (\nn = 731)\n0\n20\n40\n60\n80\n100Proportion (%)\np53 abnormal\np53 normal\nChi-square\nP < 0.0001\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\nA\nB\nC\nSupplementary Figure S3.\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\nIHC p16\n0.0\n0.5\n1.0\n1.5\nRB1 ex-\npression\nScramble\nRB1 WT\nRB1 KO\nAOCS1\n(BRCA1/2 WT)\nScramble\nRB1 WT\nRB1 KO\nAOCS7.2\n(BRCA1 mut)\nScramble\nRB1 WT\nRB1 KO\nAOCS16\n(BRCA1 mut)\np16\nGAPDH\nA\nB\nC\nSupplementary Figure S4.\n0 24 48 72 96 120 144 168 192 216\n0\n25\n50\n75\n100\nAOCS7.2 (BRCA1 mut, p16 normal)\nTime (hrs)\nConfluence (%)\n0 24 48 72 96 120 144 168 192 216\n0\n25\n50\n75\n100\nAOCS1 (BRCA1/2 WT, p16 normal)\nTime (hrs)\nConfluence (%)\n0 24 48 72 96 120 144 168 192 216\n0\n25\n50\n75\n100\nAOCS16 (BRCA1 mut, p16 absent)\nTime (hrs)\nConfluence (%)\nRB1 Wildtype\nRB1 Knockout\nRB1 WT RB1 KO\n0\n20\n40\n60\n80\n100% cells\nDMF\nAOCS1 (BRCA1/2 WT, p16 normal)\nRB1 WT RB1 KO\n0\n20\n40\n60\n80\n100% cells\nAOCS16 (BRCA1 mut, p16 absent)\nRB1 WT RB1 KO\n0\n20\n40\n60\n80\n100% cells\nAOCS7.2 (BRCA1 mut, p16 normal)\nRB1 WT RB1 KO\n0\n20\n40\n60\n80\n100% cells\nCisplatin\nRB1 WT RB1 KO\n0\n20\n40\n60\n80\n100% cells\nRB1 WT RB1 KO\n0\n20\n40\n60\n80\n100% cells\nRB1 WT RB1 KO\n0\n20\n40\n60\n80\n100% cells\nPaclitaxel\nRB1 WT RB1 KO\n0\n20\n40\n60\n80\n100% cells\nRB1 WT RB1 KO\n0\n20\n40\n60\n80\n100% cells\nSub G0G1 %\nG0G1 %\nS %\nG2M %\nSub G2M %\nns\nns\n*\nns\nns\nns\nns\nns ns\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\nSupplementary Figure S5.\n<0.0001\n<0.0001\n<0.0001\n<0.0001\n0.5990\n<0.0001\n<0.0001\n<0.0001\n<0.0001\n0.2420\nKruskal, P < 0.0001\n0.0\n0.5\n1.0\n1.5\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nid.ID6\n<0.0001\n<0.0001\n0.1960\n0.0290\n0.0390\n<0.0001\n<0.0001\n<0.0001\n0.0003\n0.2350\nKruskal, P < 0.0001\n0\n1\n2\n3\n4\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nsv.Ovary_G\n<0.0001\n<0.0001\n<0.0001\n<0.0001\n0.3180\n0.0004\n0.1390\n0.0004\n0.0280\n0.1510\nKruskal, P < 0.0001\n0.0\n0.3\n0.6\n0.9\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nid.ID8\n<0.0001\n<0.0001\n<0.0001\n<0.0001\n0.6460\n0.4870\n0.1850\n0.4770\n0.1850\n0.4870\nKruskal, P < 0.0001\n0.3\n0.6\n0.9\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nid.ID1\n<0.0001\n<0.0001\n<0.0001\n<0.0001\n0.9020\n0.1180\n0.7700\n0.1560\n0.9720\n0.1000\nKruskal, P < 0.0001\n0.0\n0.2\n0.4\n0.6\n0.8\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nsbs.SBS3\n<0.0001\n<0.0001\n<0.0001\n<0.0001\n0.9860\n0.9860\n0.9860\n0.9860\n0.9860\n0.9860\nKruskal, P < 0.0001\n0.0\n0.2\n0.4\n0.6\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nsbs.SBS1\n0.1110\n0.5160\n<0.0001\n<0.0001\n0.5160\n<0.0001\n<0.0001\n<0.0001\n0.0003\n0.5160\nKruskal, P < 0.0001\n0\n1\n2\n3\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nsv.Ovary_A\n<0.0001\n<0.0001\n<0.0001\n0.0008\n0.7270\n0.8670\n0.3380\n0.8700\n0.8670\n0.7270\nKruskal, P < 0.0001\n0.5\n1.0\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nsbs.SBS5\n<0.0001\n0.0060\n0.0500\n0.1380\n0.0820\n0.0010\n0.0020\n0.3460\n0.5070\n0.8590\nKruskal, P < 0.0001\n0.0\n0.4\n0.8\n1.2\n1.6\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\ndbs.DBS2\n<0.0001\n0.0008\n<0.0001\n<0.0001\n0.9220\n0.3010\n0.2800\n0.2800\n0.2800\n0.9800\nKruskal, P < 0.0001\n0.0\n0.2\n0.4\n0.6\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nid.ID4\n0.0002\n<0.0001\n0.0002\n0.0002\n0.4250\n0.7670\n0.2680\n0.8420\n0.4250\n0.4250\nKruskal, P < 0.0001\n0.4\n0.8\n1.2\n1.6\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nid.ID2\n0.0007\n0.0010\n0.0007\n0.0100\n0.8840\n0.9280\n0.8840\n0.8840\n0.8840\n0.8840\nKruskal, P < 0.0001\n0\n1\n2\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nsv.Ovary_C\n0.0040\n0.0850\n0.0004\n0.0030\n0.8380\n0.0850\n0.0850\n0.1030\n0.0850\n0.8380\nKruskal, P < 0.0001\n0.0\n0.2\n0.4\n0.6\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nid.ID10\n0.0140\n0.4870\n<0.0001\n0.0090\n0.3980\n0.0090\n0.2030\n0.0060\n0.1060\n0.6360\nKruskal, P < 0.0001\n0.00\n0.25\n0.50\n0.75\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nid.ID12\n0.0170\n0.7220\n0.0010\n0.0130\n0.0730\n0.0030\n0.0730\n0.0050\n0.0210\n0.5120\nKruskal, P < 0.0001\n0.0\n0.1\n0.2\n0.3\n0.4\n0.5\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\ndbs.DBS4\n0.0001\n0.0130\n0.1530\n0.1420\n0.6630\n0.0130\n0.3820\n0.1420\n0.6440\n0.3820\nKruskal, P < 0.0001\n0.0\n0.5\n1.0\n1.5\n2.0\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nsv.Ovary_F\n0.0050\n0.0040\n0.0690\n0.1080\n0.0690\n0.8580\n0.8580\n0.0700\n0.1080\n0.8580\nKruskal, P = 0.0003\n0\n1\n2\n3\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nsv.Ovary_B\n0.0010\n0.0180\n0.1010\n0.2610\n0.8730\n0.2610\n0.2610\n0.2610\n0.2610\n0.7210\nKruskal, P = 0.0006\n0\n1\n2\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nsv.Ovary_D\n0.0070\n0.4220\n0.0630\n0.3030\n0.4220\n0.6840\n1.0000\n0.4640\n0.4640\n0.6960\nKruskal, P = 0.0089\n0.0\n0.1\n0.2\n0.3\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nid.ID5\n0.5190\n0.0820\n0.2500\n0.0820\n0.0850\n0.5900\n0.0820\n0.2500\n0.2500\n0.0820\nKruskal, P = 0.0258\n0.0\n0.2\n0.4\n0.6\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\ndbs.DBS6\n0.1770\n0.1770\n0.1770\n0.2280\n0.6210\n0.6210\n0.6210\n1.0000\n1.0000\n1.0000\nKruskal, P = 0.0464\n0\n1\n2\n3\n4\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nsv.Ovary_E\n0.1470\n0.1470\n0.1470\n0.1470\n0.9310\n0.9790\n0.8600\n0.9310\n0.8600\n0.8600\nKruskal, P = 0.0637\n0.0\n0.2\n0.4\n0.6\n0.8\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\ndbs.DBS7\n0.0800\n0.5950\n0.5150\n0.5950\n0.5950\n0.5950\n0.9070\n1.0000\n1.0000\n1.0000\nKruskal, P = 0.0984\n0.0\n0.4\n0.8\n1.2\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\ndbs.DBS11\n0.5480\n0.6970\n0.1030\n0.6970\n0.9200\n0.1800\n0.9760\n0.2890\n0.9200\n0.5320\nKruskal, P = 0.0987\n0.0\n0.1\n0.2\n0.3\n0.4\n0.5\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nsbs.SBS39\n0.0810\n0.8330\n0.5570\n0.5570\n0.5570\n0.5760\n0.8330\n0.8330\n0.8330\n0.8620\nKruskal, P = 0.0987\n0.0\n0.1\n0.2\n0.3\n0.4\n0.5\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nsbs.SBS8\n0.0750\n0.5630\n0.9450\n0.7860\n0.9450\n0.5450\n0.7860\n0.7270\n0.7860\n0.7860\nKruskal, P = 0.1059\n0.0\n0.1\n0.2\n0.3\n0.4\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\nsbs.SBS40\n0.9500\n0.5550\n0.5550\n0.5550\n0.5550\n0.5550\n0.5550\n0.9500\n0.9500\n0.9500\nKruskal, P = 0.5160\n0.00\n0.25\n0.50\n0.75\n1.00\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nSignature Relative Enrichment\ndbs.DBS9\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\nSupplementary Figure S6.\n<0.0001\n<0.0001\n<0.0001\n<0.0001\n0.2100\n<0.0001\n<0.0001\n<0.0001\n0.0002\n0.4160\nKruskal, P < 0.0001\n0\n1\n2\n3\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\n \nCHORD BRCA1 Signature proportion\n<0.0001\n<0.0001\n<0.0001\n<0.0001\n0.2670\n<0.0001\n<0.0001\n<0.0001\n<0.0001\n0.1630\nKruskal, P < 0.0001\n0\n1\n2\n3\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\nCHORD BRCA2 Signature proportion\n<0.0001\n<0.0001\n<0.0001\n<0.0001\n0.5280\n0.5280\n0.0130\n0.9560\n0.0450\n0.0860\nKruskal, P < 0.0001\n0\n1\n2\n3\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\n \nCHORD Total HRD proportion\n<0.0001\n<0.0001\n<0.0001\n0.0010\n0.8560\n0.0940\n0.0180\n0.2090\n0.0650\n0.5290\nKruskal, P < 0.0001\n20\n40\n60\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\nscarHRD Mean\n<0.0001\n<0.0001\n<0.0001\n<0.0001\n0.6340\n0.0020\n0.0110\n0.0140\n0.0110\n0.6210\nKruskal, P < 0.0001\n500\n1000\n1500\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\n \nNumber of SVs (DEL)\n<0.0001\n0.0010\n0.0340\n0.0210\n0.7000\n<0.0001\n<0.0001\n<0.0001\n0.0004\n0.5640\nKruskal, P < 0.0001\n500\n1000\n1500\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\n \nNumber of SVs (DUP)\n<0.0001\n0.0003\n0.0004\n0.0160\n0.9710\n0.9710\n0.5840\n0.9710\n0.3140\n0.5840\nKruskal, P < 0.0001\n500\n1000\n1500\n2000\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\nNumber of\nNot Clustered SVs\n0.0005\n0.0005\n0.0740\n0.1200\n0.1200\n0.5160\n0.4460\n0.0870\n0.0870\n0.8980\nKruskal, P = 0.0001\n0\n100\n200\n300\n400\n500\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\nNumber of\nClustered SVs\n0.4830\n<0.0001\n0.1320\n0.0280\n<0.0001\n0.0650\n0.0250\n0.1830\n0.5000\n0.4760\nKruskal, P = 0.0001\n6\n8\n10\n12\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\n \nPCNA expression\n0.0003\n0.0020\n0.0110\n0.1870\n0.6500\n0.6500\n0.3720\n0.6500\n0.0810\n0.5940\nKruskal, P = 0.0001\n100\n200\n300\n400\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\n \nNumber of SVs (ITX)\n0.4190\n<0.0001\n0.1320\n0.0270\n<0.0001\n0.2880\n0.0270\n0.0100\n0.4160\n0.2600\nKruskal, P = 0.0001\n6\n8\n10\n12\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\n \nMCM3 expression\n0.0005\n0.0005\n0.1040\n0.1570\n0.2260\n0.2260\n0.3660\n0.0900\n0.0900\n0.7460\nKruskal, P = 0.0002\n50\n100\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\n% Whole Genome LOH\n0.0005\n0.0080\n0.0060\n0.0760\n0.9550\n0.9300\n0.6800\n0.9300\n0.6800\n0.6800\nKruskal, P = 0.0002\n500\n1000\n1500\n2000\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\nTotal SVs\n0.0190\n0.1360\n0.0040\n0.0220\n0.5480\n0.1270\n0.2320\n0.1360\n0.0970\n0.5120\nKruskal, P = 0.0006\n200\n400\n600\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\nNumber of Neoantigens\n0.0002\n0.1520\n0.2490\n0.2240\n0.2240\n0.1520\n0.2490\n0.7390\n0.7710\n0.7390\nKruskal, P = 0.0008\n50\n75\n100\n125\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\nAge at diagnosis\n0.0910\n0.1130\n0.0040\n0.0460\n0.5160\n0.0460\n0.1850\n0.5160\n0.6080\n0.9800\nKruskal, P = 0.0018\n20\n40\n60\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\nOverall Survival\n(years)\n0.0060\n0.0280\n0.0060\n0.0690\n0.8850\n0.8850\n0.8850\n0.7060\n0.8850\n0.8850\nKruskal, P = 0.0019\n50\n100\n150\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\n% Whole Genome\nLoss\n0.0250\n0.0250\n0.8190\n0.3130\n0.2450\n0.1380\n0.6540\n0.0370\n0.1510\n0.8190\nKruskal, P = 0.0052\n100\n200\n300\n400\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\nNumber of SVs (INV)\n0.1400\n0.1400\n0.0600\n0.0600\n0.3580\n0.1400\n0.0640\n0.9860\n0.3730\n0.3580\nKruskal, P = 0.0075\n10\n20\n30\n40\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\nProgression−free Survival\n(years)\n0.2190\n0.3470\n0.0300\n0.2620\n0.4090\n0.2190\n0.4090\n0.2940\n0.3080\n0.4090\nKruskal, P = 0.0214\n5\n10\n15\n20\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\nVariants Per Mb\n0.3620\n0.1030\n0.0660\n0.3620\n0.6520\n0.6520\n0.7630\n0.6830\n0.6840\n0.9800\nKruskal, P = 0.0536\n2\n4\n6\n8\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\nPloidy\n0.2100\n0.2100\n0.8160\n0.8160\n0.5860\n0.5860\n0.5860\n0.3500\n0.3500\n0.8160\nKruskal, P = 0.1298\n2.5\n5.0\n7.5\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\nCIBERSORTx\nAbsolute score\n0.3420\n0.6400\n0.6400\n0.6400\n0.9720\n0.9720\n0.9720\n0.9720\n0.9720\n0.9720\nKruskal, P = 0.3297\n0\n50\n100\n150\n200\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\n% Ki67 positive\n0.9490\n0.9490\n0.9490\n0.9490\n0.9490\n0.9490\n0.9490\n0.9490\n0.9720\n0.9490\nKruskal, P = 0.7540\n0.5\n1.0\n1.5\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nGroup\nValue\nPurity\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\nSupplementary Figure S7.\nFisher, p = 0.000009\nFisher, p = 0.074913\nFisher, p = 0.545260\nFisher, p = 0.749909\nFisher, p = 1.000000\nFisher, p = 0.000009\nFisher, p = 0.260249\nFisher, p = 0.567777\nFisher, p = 0.812293\nFisher, p = 1.000000\nFisher, p = 0.000009\nFisher, p = 0.545260\nFisher, p = 0.567777\nFisher, p = 0.812293\nFisher, p = 1.000000\nFisher, p = 0.000607\nFisher, p = 0.545260\nFisher, p = 0.567777\nFisher, p = 0.827347\nFisher, p = 0.012501\nFisher, p = 0.545260\nFisher, p = 0.576173\nFisher, p = 0.854705\nFisher, p = 0.057852\nFisher, p = 0.545260\nFisher, p = 0.729854\nFisher, p = 0.865845\nFANCA FANCE FANCI\nKMT2C MSH6 CHEK2 ATR ATM FANCD2\nBARD1 RAD51D MSH2 PMS1 CDK12 BLM\nPTEN NF1 PIK3CA P ALB2 RAD51B FANCM\nBRCA1 BRCA2 RB1 CCNE1 BRIP1 RAD51C\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\nHRP &\nRB1 wild−type\n(n=45)\nBRCA1−HRD &\nRB1 wild−type\n(n=36)\nBRCA1−HRD &\nRB1 altered (n=13)\nBRCA2−HRD &\nRB1 wild−type\n(n=20)\nBRCA2−HRD &\nRB1 altered\n(n=8)\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\n0.00\n0.25\n0.50\n0.75\n1.00\nStatus\nWT\nMUT\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint \n\nSupplementary Figure S8.\nHRD/HRP (sans RB1 sig)\nBRCA2/HRP (sans RB1 sig)BRCA1/HRP (sans RB1 sig)BRCA2.RB1wt/HRP.RB1wtBRCA1.RB1wt/HRP.RB1wtBRCA2.RB1m/HRP.RB1wtBRCA1.RB1m/HRP.RB1wt\nRB1m/RB1wt (sans HRD type)\n0\n1000\n2000\n3000\n4000\nComparison groups\nNumber of differentially expressed genes \n(adjusted P < 0.05) \nDirection\nDown\nUp\n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted November 10, 2023. ; https://doi.org/10.1101/2023.11.09.23298321doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}