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

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Zwimpfer , Sian Fereday , Ahwan Pandey , Dinuka Ariyaratne , Madawa W. Jayawardana , Laura Twomey , Céline M. Laumont , Catherine J. Kennedy , Adelyn Bolithon , Nicola S. Meagher , Katy Milne , Phineas Hamilton , Jennifer Alsop , Antonis C. Antoniou , George Au-Yeung , Matthias W. Beckmann , Amy Berrington de Gonzalez , Christiani Bisinotto , Freya Blome , Clara Bodelon , Jessica Boros , Alison H. Brand , Michael E. Carney , Alicia Cazorla-Jiménez , Derek S. Chiu , Elizabeth L. Christie , Anita Chudecka-Głaz , Penny Coulson , Kara L. Cushing-Haugen , Cezary Cybulski , Kathleen M. Darcy , Cath David , Trent Davidson , Arif B. Ekici , Esther Elishaev , Julius Emons , Tobias Engler , Rhonda Farrell , Anna Fischer , View ORCID Profile Montserrat García-Closas , Aleksandra Gentry-Maharaj , Prafull Ghatage , Rosalind Glasspool , Philipp Harter , Andreas D. Hartkopf , Arndt Hartmann , Sebastian Heikaus , Brenda Y. Hernandez , Anusha Hettiaratchi , Sabine Heublein , David G. Huntsman , Mercedes Jimenez-Linan , View ORCID Profile Michael E. Jones , Eunyoung Kang , Ewa Kaznowska , Tomasz Kluz , Felix K.F. Kommoss , Gottfried Konecny , Roy F.P.M. Kruitwagen , Jessica Kwon , Diether Lambrechts , Cheng-Han Lee , Jenny Lester , Samuel C.Y. Leung , Yee Leung , Anna Linder , Jolanta Lissowska , Liselore Loverix , Jan Lubiński , Constantina Mateoiu , View ORCID Profile Iain A. McNeish , Malak Moubarak , Gregg S. Nelson , Nikilyn Nevins , Alexander B. Olawaiye , Siel Olbrecht , Sandra Orsulic , Ana Osorio , Carmel M. Quinn , Ganendra Raj Mohan , Isabelle Ray-Coquard , Cristina Rodríguez-Antona , Patricia Roxburgh , Matthias Ruebner , Stuart G. Salfinger , Spinder Samra , Minouk J. Schoemaker , Hans-Peter Sinn , Gabe S. Sonke , Linda Steele , Colin J.R. Stewart , Aline Talhouk , Adeline Tan , Christopher M. Tarney , Sarah E. Taylor , Koen K. Van de Vijver , Maaike A. van der Aa , Toon Van Gorp , Els Van Nieuwenhuysen , Lilian van-Wagensveld , Andrea E. Wahner-Hendrickson , Christina Walter , View ORCID Profile Chen Wang , Julia Welz , Nicolas Wentzensen , Lynne R. Wilkens , Stacey J. Winham , Boris Winterhoff , View ORCID Profile Michael S. Anglesio , Andrew Berchuck , Francisco J. Candido dos Reis , Paul A. Cohen , View ORCID Profile Thomas P. Conrads , Philip Crowe , Jennifer A. Doherty , Peter A. Fasching , Renée T. Fortner , María J. García , Simon A. Gayther , Marc T. Goodman , Jacek Gronwald , Holly R. Harris , Florian Heitz , Hugo M. Horlings , Beth Y. Karlan , Linda E. Kelemen , G. Larry Maxwell , Usha Menon , Francesmary Modugno , Susan L. Neuhausen , Joellen M. Schildkraut , Annette Staebler , Karin Sundfeldt , Anthony J. Swerdlow , Ignace Vergote , Anna H. Wu , View ORCID Profile James D. Brenton , View ORCID Profile Paul D.P. Pharoah , Celeste Leigh Pearce , Malcolm C. Pike , Ellen L. Goode , Susan J. Ramus , View ORCID Profile Martin Köbel , Brad H. Nelson , Anna DeFazio , Michael L. Friedlander , David D.L. Bowtell , View ORCID Profile Dale W. Garsed doi: https://doi.org/10.1101/2025.09.17.25335919 Tibor A. Zwimpfer 1 Peter MacCallum Cancer Centre , Melbourne, Victoria, Australia , 3000 2 Department of Biomedicine, University of Basel , Basel, Switzerland , 4031 3 Gynecological Cancer Centre, University Hospital Basel , Basel, Switzerland , 4031 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tibor A. Zwimpfer For correspondence: Dale.Garsed{at}petermac.org tibor.zwimpfer{at}petermac.org Sian Fereday 1 Peter MacCallum Cancer Centre , Melbourne, Victoria, Australia , 3000 4 The Sir Peter MacCallum Department of Oncology, The University of Melbourne , Parkville, Victoria, Australia , 3010 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ahwan Pandey 1 Peter MacCallum Cancer Centre , Melbourne, Victoria, Australia , 3000 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dinuka Ariyaratne 1 Peter MacCallum Cancer Centre , Melbourne, Victoria, Australia , 3000 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Madawa W. Jayawardana 1 Peter MacCallum Cancer Centre , Melbourne, Victoria, Australia , 3000 4 The Sir Peter MacCallum Department of Oncology, The University of Melbourne , Parkville, Victoria, Australia , 3010 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Laura Twomey 1 Peter MacCallum Cancer Centre , Melbourne, Victoria, Australia , 3000 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Céline M. Laumont 5 Deeley Research Centre , BC Cancer. Victoria, BC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Catherine J. Kennedy 6 Centre for Cancer Research, The Westmead Institute for Medical Research , Sydney, New South Wales, Australia , 2145 7 Department of Gynaecological Oncology, Westmead Hospital , Sydney, New South Wales, Australia , 2145 8 Faculty of Medicine and Health, The University of Sydney , Sydney, New South Wales, Australia , 2006 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Adelyn Bolithon 9 School of Clinical Medicine, UNSW Medicine and Health, University of NSW Sydney , Sydney, New South Wales, Australia , 2052 10 Adult Cancer Program, Lowy Cancer Research Centre, University of NSW Sydney , Sydney, New South Wales, Australia , 2052 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nicola S. Meagher 9 School of Clinical Medicine, UNSW Medicine and Health, University of NSW Sydney , Sydney, New South Wales, Australia , 2052 11 The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW , Sydney, New South Wales, Australia , 2006 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katy Milne 5 Deeley Research Centre , BC Cancer. Victoria, BC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Phineas Hamilton 5 Deeley Research Centre , BC Cancer. Victoria, BC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jennifer Alsop 12 Department of Oncology, University of Cambridge. Cambridge , UK , CB2 0QQ Find this author on Google Scholar Find this author on PubMed Search for this author on this site Antonis C. Antoniou 13 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge , Cambridge, UK , CB1 8RN Find this author on Google Scholar Find this author on PubMed Search for this author on this site George Au-Yeung 1 Peter MacCallum Cancer Centre , Melbourne, Victoria, Australia , 3000 4 The Sir Peter MacCallum Department of Oncology, The University of Melbourne , Parkville, Victoria, Australia , 3010 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Matthias W. Beckmann 14 Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg. University Hospital Erlangen , Erlangen, Germany , 91054 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amy Berrington de Gonzalez 15 Division of Genetics and Epidemiology, The Institute of Cancer Research , London, UK , SW7 3RP Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christiani Bisinotto 16 Department of Gynecology and Obstetrics, Ribeirão Preto Medical School, University of São Paulo. Ribeirão Preto , Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Freya Blome 17 Institute of Pathology and Neuropathology, Tuebingen University Hospital , Tuebingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clara Bodelon 18 Department of Population Science, American Cancer Society , Atlanta, GA, USA , 30303 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jessica Boros 6 Centre for Cancer Research, The Westmead Institute for Medical Research , Sydney, New South Wales, Australia , 2145 7 Department of Gynaecological Oncology, Westmead Hospital , Sydney, New South Wales, Australia , 2145 8 Faculty of Medicine and Health, The University of Sydney , Sydney, New South Wales, Australia , 2006 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alison H. Brand 7 Department of Gynaecological Oncology, Westmead Hospital , Sydney, New South Wales, Australia , 2145 8 Faculty of Medicine and Health, The University of Sydney , Sydney, New South Wales, Australia , 2006 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael E. Carney 19 Department of Obstetrics and Gynecology, John A. Burns School of Medicine, University of Hawaii , Honolulu, HI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alicia Cazorla-Jiménez 20 Pathology Department, Fundación Jiménez Díaz . Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Derek S. Chiu 21 British Columbia’s Gynecological Cancer Research Team OVCARE, University of British Columbia, BC Cancer, and Vancouver General Hospital , Vancouver, BC, Canada , V5Z 4E6 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elizabeth L. Christie 1 Peter MacCallum Cancer Centre , Melbourne, Victoria, Australia , 3000 4 The Sir Peter MacCallum Department of Oncology, The University of Melbourne , Parkville, Victoria, Australia , 3010 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anita Chudecka-Głaz 22 Department of Gynecological Surgery and Gynecological Oncology of Adults and Adolescents, Pomeranian Medical University , Szczecin, Poland , 70-111 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Penny Coulson 15 Division of Genetics and Epidemiology, The Institute of Cancer Research , London, UK , SW7 3RP Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kara L. Cushing-Haugen 23 Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Center , Seattle, WA, USA , 98109 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cezary Cybulski 24 Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University , Mississippi State, MS, USA , 39762 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kathleen M. Darcy 25 Gynecologic Cancer Center of Excellence, Department of Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Walter Reed National Military Medical Center , Bethesda, MD, USA 26 Henry M Jackson Foundation for the Advancement of Military Medicine, Inc . Bethesda, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cath David 27 Gynaecological Cancer Centre, Royal Hospital for Women , Randwick, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Trent Davidson 28 NSW Health Pathology, Prince of Wales Hospital. Sydney , New South Wales, Australia 29 School of Medicine, Western Sydney University , Penrith, New South Wales, Australia , 2751 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Arif B. Ekici 30 Institute of Human Genetics. Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg FAU , Erlangen, Germany , 91054 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Esther Elishaev 31 Department of Pathology, University of Pittsburgh School of Medicine , Pittsburgh, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Julius Emons 14 Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg. University Hospital Erlangen , Erlangen, Germany , 91054 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tobias Engler 32 Department of Women’s Health, Tuebingen University Hospital , Tuebingen, Germany , 72076 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rhonda Farrell 8 Faculty of Medicine and Health, The University of Sydney , Sydney, New South Wales, Australia , 2006 33 Prince of Wales Private Hospital , Randwick, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna Fischer 17 Institute of Pathology and Neuropathology, Tuebingen University Hospital , Tuebingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Montserrat García-Closas 15 Division of Genetics and Epidemiology, The Institute of Cancer Research , London, UK , SW7 3RP Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Montserrat García-Closas Aleksandra Gentry-Maharaj 34 MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London , London, UK , WC1V 6LJ 35 Department of Women’s Cancer, Elizabeth Garrett Anderson Institute for Women’s Health, University College London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Prafull Ghatage 36 Department of Oncology, Division of Gynecologic Oncology, Cumming School of Medicine, University of Calgary. Calgary , AB, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rosalind Glasspool 37 Beatson West of Scotland Cancer Centre and School of Cancer Sciences, University of Glasgow , Glasgow, UK , G12 0YN Find this author on Google Scholar Find this author on PubMed Search for this author on this site Philipp Harter 38 Department of Gynecology and Gynecologic Oncology, Evangelische Kliniken Essen-Mitte , Essen, Germany , 45136 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andreas D. Hartkopf 32 Department of Women’s Health, Tuebingen University Hospital , Tuebingen, Germany , 72076 39 Department of Gynecology and Obstetrics, University Hospital of Ulm , Ulm, Germany , 89075 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Arndt Hartmann 40 Institute of Pathology, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen , Erlangen, Germany , 91054 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sebastian Heikaus 41 Center for Pathology, Evangelische Kliniken Essen-Mitte , Essen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brenda Y. Hernandez 42 University of Hawaii Cancer Center , Honolulu, HI, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anusha Hettiaratchi 43 The Health Precincts Biobank, UNSW Biospecimen Services, Mark Wainwright Analytical Centre, UNSW , Sydney, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sabine Heublein 44 Department of Obstetrics and Gynecology, University Hospital Heidelberg , Heidelberg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site David G. Huntsman 45 Department of Obstetrics and Gynecology, University of British Columbia , Vancouver, BC, Canada , V5Z 1L3 46 Department of Molecular Oncology, BC Cancer Research Centre , Vancouver, BC, Canada , V5Z 4E6 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mercedes Jimenez-Linan 47 Department of Histopathology, Addenbrooke’s Hospital , Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael E. Jones 15 Division of Genetics and Epidemiology, The Institute of Cancer Research , London, UK , SW7 3RP Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael E. Jones Eunyoung Kang 48 Department of Surgery, Seoul National University Bundang Hospital , Seongnam, Korea, 13260 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ewa Kaznowska 49 Department of Pathology, Institute of Medical Sciences, Medical College of Rzeszow University , Rzeszow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tomasz Kluz 50 Department of Gynecology, Gynecology Oncology and Obstetrics, Institute of Medical Sciences, Medical College of Rzeszów University , Rzeszów, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Felix K.F. Kommoss 51 Institute of Pathology, Heidelberg University Hospital , Heidelberg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gottfried Konecny 52 David Geffen School of Medicine, Department of Obstetrics and Gynecology, University of California at Los Angeles , Los Angeles, CA, USA , 90095 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Roy F.P.M. 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Leung 21 British Columbia’s Gynecological Cancer Research Team OVCARE, University of British Columbia, BC Cancer, and Vancouver General Hospital , Vancouver, BC, Canada , V5Z 4E6 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yee Leung 58 Department of Gynaecological Oncology, King Edward Memorial Hospital , Subiaco, Western Australia , Australia 59 Division of Obstetrics and Gynaecology, Medical School, University of Western Australia , Crawley, Western Australia , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna Linder 60 Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Center for Cancer Research, University of Gothenburg , Gothenburg, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jolanta Lissowska 61 Department of Cancer Epidemiology and Prevention, M Sklodowska-Curie National Research Oncology Institute , Warsaw, Poland , 02-034 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Liselore Loverix 62 Division of Gynecologic Oncology, Department of Gynecology and Obstetrics. 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McNeish Malak Moubarak 38 Department of Gynecology and Gynecologic Oncology, Evangelische Kliniken Essen-Mitte , Essen, Germany , 45136 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gregg S. Nelson 36 Department of Oncology, Division of Gynecologic Oncology, Cumming School of Medicine, University of Calgary. Calgary , AB, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nikilyn Nevins 6 Centre for Cancer Research, The Westmead Institute for Medical Research , Sydney, New South Wales, Australia , 2145 7 Department of Gynaecological Oncology, Westmead Hospital , Sydney, New South Wales, Australia , 2145 8 Faculty of Medicine and Health, The University of Sydney , Sydney, New South Wales, Australia , 2006 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alexander B. 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University Hospital Erlangen , Erlangen, Germany , 91054 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stuart G. Salfinger 70 Department of Gynaecological Oncology, St John of God Subiaco Hospital , Subiaco, Western Australia , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Spinder Samra 6 Centre for Cancer Research, The Westmead Institute for Medical Research , Sydney, New South Wales, Australia , 2145 8 Faculty of Medicine and Health, The University of Sydney , Sydney, New South Wales, Australia , 2006 73 Tissue Pathology and Diagnostic Oncology, Westmead Hospital , Sydney, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Minouk J. Schoemaker 15 Division of Genetics and Epidemiology, The Institute of Cancer Research , London, UK , SW7 3RP Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hans-Peter Sinn 51 Institute of Pathology, Heidelberg University Hospital , Heidelberg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gabe S. Sonke 74 Department of Medical Oncology, Netherlands Cancer Institute , Amsterdam, the Netherlands , 1066 CX Find this author on Google Scholar Find this author on PubMed Search for this author on this site Linda Steele 75 Department of Population Sciences, Beckman Research Institute of City of Hope , Duarte, CA, USA , 91010 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Colin J.R. Stewart 59 Division of Obstetrics and Gynaecology, Medical School, University of Western Australia , Crawley, Western Australia , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aline Talhouk 21 British Columbia’s Gynecological Cancer Research Team OVCARE, University of British Columbia, BC Cancer, and Vancouver General Hospital , Vancouver, BC, Canada , V5Z 4E6 45 Department of Obstetrics and Gynecology, University of British Columbia , Vancouver, BC, Canada , V5Z 1L3 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Adeline Tan 59 Division of Obstetrics and Gynaecology, Medical School, University of Western Australia , Crawley, Western Australia , Australia 76 Gynaepath WA, Clinipath Sonic Healthcare , Osbourne Park, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christopher M. 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Anglesio 21 British Columbia’s Gynecological Cancer Research Team OVCARE, University of British Columbia, BC Cancer, and Vancouver General Hospital , Vancouver, BC, Canada , V5Z 4E6 45 Department of Obstetrics and Gynecology, University of British Columbia , Vancouver, BC, Canada , V5Z 1L3 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael S. Anglesio Andrew Berchuck 85 Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Duke University Medical Center , Durham, NC, USA , 27710 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Francisco J. Candido dos Reis 16 Department of Gynecology and Obstetrics, Ribeirão Preto Medical School, University of São Paulo. Ribeirão Preto , Brazil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paul A. Cohen 58 Department of Gynaecological Oncology, King Edward Memorial Hospital , Subiaco, Western Australia , Australia 59 Division of Obstetrics and Gynaecology, Medical School, University of Western Australia , Crawley, Western Australia , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thomas P. Conrads 25 Gynecologic Cancer Center of Excellence, Department of Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Walter Reed National Military Medical Center , Bethesda, MD, USA 86 Women’s Health Integrated Research Center, Women’s Service Line, Inova Health System , Falls Church, VA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thomas P. Conrads Philip Crowe 9 School of Clinical Medicine, UNSW Medicine and Health, University of NSW Sydney , Sydney, New South Wales, Australia , 2052 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jennifer A. Doherty 87 Huntsman Cancer Institute, Department of Population Health Sciences, University of Utah , Salt Lake City, UT, USA , 84112 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peter A. Fasching 14 Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg. University Hospital Erlangen , Erlangen, Germany , 91054 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Renée T. Fortner 88 Division of Cancer Epidemiology, German Cancer Research Center DKFZ , Heidelberg, Germany , 69120 89 Department of Research, Cancer Registry of Norway, Norwegian Institute of Public Health , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site María J. García 90 Genomic Biomarkers and Precision Oncology Group, Sols-Morreale Biomedical Research Institute IIBM, Consejo Superior de Investigaciones Cientficas & Universidad Autónoma de Madrid CSIC-UAM , Madrid, Spain , 28029 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Simon A. Gayther 91 Medicine, University of Texas Health , San Antonio, San Antonio, USA , 78229 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marc T. Goodman 92 Cancer Prevention and Control Program, Cedars-Sinai Cancer, Cedars-Sinai Medical Center , Los Angeles, CA, USA , 90048 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jacek Gronwald 63 Department of Genetics and Pathology, Pomeranian Medical University , Szczecin, Poland , 71-252 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Holly R. Harris 23 Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Center , Seattle, WA, USA , 98109 93 Department of Epidemiology, University of Washington School of Public Health , Seattle, WA, USA , 98195 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Florian Heitz 38 Department of Gynecology and Gynecologic Oncology, Evangelische Kliniken Essen-Mitte , Essen, Germany , 45136 41 Center for Pathology, Evangelische Kliniken Essen-Mitte , Essen, Germany 94 Department of Gynecology and Gynecological Oncology, HSK, Dr Horst-Schmidt Klinik , Wiesbaden, Wiesbaden, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hugo M. Horlings 95 Department of Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital , Amsterdam, the Netherlands , 1066 CX Find this author on Google Scholar Find this author on PubMed Search for this author on this site Beth Y. Karlan 52 David Geffen School of Medicine, Department of Obstetrics and Gynecology, University of California at Los Angeles , Los Angeles, CA, USA , 90095 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Linda E. Kelemen 96 Communicable Disease Epidemiology Section, South Carolina Department of Public Health , Columbia, SC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site G. Larry Maxwell 25 Gynecologic Cancer Center of Excellence, Department of Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Walter Reed National Military Medical Center , Bethesda, MD, USA 86 Women’s Health Integrated Research Center, Women’s Service Line, Inova Health System , Falls Church, VA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Usha Menon 34 MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London , London, UK , WC1V 6LJ Find this author on Google Scholar Find this author on PubMed Search for this author on this site Francesmary Modugno 67 Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine , Pittsburgh, PA, USA , 15213 97 Department of Epidemiology, University of Pittsburgh School of Public Health , Pittsburgh, PA, USA , 15213 98 Women’s Cancer Research Center, Magee-Womens Research Institute and Hillman Cancer Center , Pittsburgh, PA, USA , 15213 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Susan L. Neuhausen 75 Department of Population Sciences, Beckman Research Institute of City of Hope , Duarte, CA, USA , 91010 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joellen M. Schildkraut 99 Department of Epidemiology, Rollins School of Public Health, Emory University , Atlanta, GA, USA , 30322 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Annette Staebler 17 Institute of Pathology and Neuropathology, Tuebingen University Hospital , Tuebingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karin Sundfeldt 60 Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Center for Cancer Research, University of Gothenburg , Gothenburg, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anthony J. Swerdlow 15 Division of Genetics and Epidemiology, The Institute of Cancer Research , London, UK , SW7 3RP 100 Division of Breast Cancer Research, The Institute of Cancer Research , London, UK , SW7 3RP Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ignace Vergote 62 Division of Gynecologic Oncology, Department of Gynecology and Obstetrics. Leuven Cancer Institute. Leuven , Belgium , 3000 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna H. Wu 101 Department of Population Health and Public Health Sciences, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center , Los Angeles, CA, USA , 90033 Find this author on Google Scholar Find this author on PubMed Search for this author on this site James D. Brenton 102 Cancer Research UK Cambridge Institute, University of Cambridge , Cambridge, UK , CB2 0RE Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for James D. Brenton Paul D.P. Pharoah 103 Department of Computational Biomedicine, Cedars-Sinai Medical Center , West Hollywood, CA, USA , 90069 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paul D.P. Pharoah Celeste Leigh Pearce 104 Department of Epidemiology, University of Michigan School of Public Health , Ann Arbor, MI, USA , 48109 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Malcolm C. Pike 101 Department of Population Health and Public Health Sciences, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center , Los Angeles, CA, USA , 90033 105 Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center , New York, NY, USA , 10065 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ellen L. Goode 106 Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic , Rochester, MN, USA , 55905 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Susan J. Ramus 9 School of Clinical Medicine, UNSW Medicine and Health, University of NSW Sydney , Sydney, New South Wales, Australia , 2052 10 Adult Cancer Program, Lowy Cancer Research Centre, University of NSW Sydney , Sydney, New South Wales, Australia , 2052 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martin Köbel 107 Department of Pathology and Laboratory Medicine, University of Calgary, Foothills Medical Center , Calgary, AB, Canada , T2N 2T9 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martin Köbel Brad H. Nelson 5 Deeley Research Centre , BC Cancer. Victoria, BC, Canada 108 Department of Biochemistry and Microbiology, University of Victoria , Victoria, BC, Canada 109 Department of Medical Genetics, University of British Columbia , Victoria, BC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna DeFazio 6 Centre for Cancer Research, The Westmead Institute for Medical Research , Sydney, New South Wales, Australia , 2145 7 Department of Gynaecological Oncology, Westmead Hospital , Sydney, New South Wales, Australia , 2145 8 Faculty of Medicine and Health, The University of Sydney , Sydney, New South Wales, Australia , 2006 11 The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW , Sydney, New South Wales, Australia , 2006 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael L. Friedlander 27 Gynaecological Cancer Centre, Royal Hospital for Women , Randwick, New South Wales, Australia 110 Nelune Comprehensive Cancer Centre, Prince of Wales Hospital , Sydney, New South Wales, Australia 111 Prince of Wales Clinical School, UNSW Medicine and Health, University of NSW Sydney , Sydney, New South Wales, Australia , 2052 Find this author on Google Scholar Find this author on PubMed Search for this author on this site David D.L. Bowtell 1 Peter MacCallum Cancer Centre , Melbourne, Victoria, Australia , 3000 4 The Sir Peter MacCallum Department of Oncology, The University of Melbourne , Parkville, Victoria, Australia , 3010 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dale W. Garsed 1 Peter MacCallum Cancer Centre , Melbourne, Victoria, Australia , 3000 4 The Sir Peter MacCallum Department of Oncology, The University of Melbourne , Parkville, Victoria, Australia , 3010 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dale W. Garsed For correspondence: Dale.Garsed{at}petermac.org tibor.zwimpfer{at}petermac.org Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT BRCA -associated homologous recombination deficiency (HRD) is present in ∼50% of high-grade serous carcinomas (HGSC) and predicts sensitivity to platinum-based therapy. However, there is little understanding of why some patients with BRCA -deficient tumors experience unexpectedly poor outcomes. We profiled 154 tumors, enriched for patients with BRCA -deficient tumors that experienced short overall survival (≤3 years, n=42), using whole-genome, transcriptome, and methylation analyses. All but one BRCA -deficient tumor exceeded an accepted HRD genomic scarring threshold. However, patients with BRCA1 -deficient HGSC with a more elevated HRD score survived significantly longer. Patients with BRCA2 -deficient HGSC and loss of NF1 survived twice as long as those without NF1 loss, whereas PIK3CA or RAD21 amplification defined BRCA2 -deficient HGSC with exceptionally short survival. BRCA1 -deficient tumors in short survivors had evidence of immunosuppressive c-kit signaling and EMT. In a large HGSC cohort (n=1,389) including 282 individuals with pathogenic germline BRCA variants (g BRCA pv), the location of the mutation within functional domains stratified clinical outcomes. Notably, residual disease after primary surgery had limited prognostic effect in g BRCA pv-carriers compared to non-carriers. Our findings indicate that tumor HR proficiency in the context of therapy response and survival is not a binary property, and highlight genomic and immune modifiers of outcomes in BRCA -deficient HGSC. INTRODUCTION The identification of clinical and molecular determinants of survival in patients with cancer has the dual benefits of finding biomarkers that may guide patient management or provide novel therapeutic opportunities. Until relatively recently, the identification of prognostic biomarkers in ovarian cancer has been confounded by a lack of appreciation of the distinctly different molecular characteristics of the various histologic subtypes that make up epithelial ovarian cancer 1 . Evaluating histologically homogenous sets of ovarian tumors has been critical in deciphering the prognostic importance of proteins such as p53 2 , 3 and WT1 4 , and identifying genetic risk loci 5 – 12 . High-grade serous carcinoma (HGSC) is the most common histotype, accounting for approximately 70% of ovarian cancer deaths in Western countries 13 – 16 . Homologous recombination-mediated DNA repair deficiency (HRD) is frequent in HGSC and is most often associated with mutations in BRCA1 and BRCA2 17 – 19 . Approximately fifty percent of HGSC are regarded to have HRD, a feature that can be inferred through specific patterns of genomic scarring in tumor cells 13 , 20 – 25 . HRD leads to genomic instability and tumorigenesis, providing a vulnerability in tumor cells with increased sensitivity to double-strand DNA breaks that can be exploited therapeutically 26 – 28 . As a result, platinum-based chemotherapy and poly (ADP-ribose) polymerase inhibitor (PARPi) maintenance therapy are generally more effective in patients with HRD tumors 28 – 33 . While HRD status is informative, accurate prediction of treatment response and survival in HGSC cannot be simply determined by the presence or absence of mutations in genes associated with HR DNA repair. The initial survival advantage for carriers of pathogenic germline BRCA1 variants (g BRCA1 pv) diminishes over time, with fewer g BRCA1 pv-carriers surviving 10 years after diagnosis than either g BRCA2 pv-carriers or non-carriers 33 – 35 . Factors associated with survival outcome in HGSC include residual disease following cytoreductive surgery 16 , 36 – 38 , the molecular subtype of the tumor 39 , age at diagnosis 40 , and the extent of T- and B-cell infiltration into tumors 41 , 42 . In germline pathogenic variant carriers, the location of mutations within BRCA1 or BRCA2 or the retention of the wildtype allele in the tumor can result in a hypomorphic phenotype associated with resistance to platinum-based therapy 43 – 47 . Furthermore, revertant mutations restoring BRCA1 and BRCA2 function contribute to acquired resistance to platinum-based therapy and PARPis, impacting treatment response and patient outcomes 48 – 50 . Comparing patients who represent the extremes of survival outcomes may provide increased sensitivity to identify prognostic biomarkers that are relevant to a wider patient population 51 . Using this approach, we have recently shown that plasma cell infiltration and other molecular changes, including co-loss of BRCA and the tumor suppressor RB1 , are associated with especially long-term survival in HGSC 22 , 52 , 53 . The current study evaluates BRCA -deficient HGSC by first focusing on g BRCA pv-carriers and then expanding to include somatic mutations and promoter methylation in BRCA1/2, and other key HR genes, as well as evaluating tumor HRD status. We focus on patients with either poor or favorable survival outcomes, harnessing the value of analyzing patients with exceptional survival outcomes while comparing cohorts that are as similar as possible in other respects. RESULTS Association of residual disease with prognosis is attenuated in g BRCA pv-carriers Pathogenic germline BRCA variants (g BRCA pv) were identified in 20% of patients in the Australian Ovarian Cancer Study (AOCS) cohort ( n =282/1389) ( Table 1 , Supplementary Tables S1 and S2). In applying a survival model, there was evidence that the proportional hazards assumption did not hold ( P 1 indicates longer time to progression or death, and a TR < 1 indicates shorter survival or time to progression. Patients with g BRCA pvs exhibited improved overall survival (OS; TR: 1.53, 95% CI: 1.33-1.76, P <0.001) and progression-free survival (PFS; TR: 1.34 95% CI: 1.28-1.53, P <0.001) compared with non-carriers (Supplementary Tables S3 and S4). View this table: View inline View popup Download powerpoint Table 1: Baseline characteristics of the clinicopathological features from patients with HGSC of the Australian Ovarian Cancer Study (AOCS) cohort. G2=Grade 2, G3=Grade 3, OS=Overall survival, gBRCApv=pathogenic germline BRCA variant We considered whether clinical characteristics differed by germline BRCA status and found a statistically significant interaction with residual disease status ( P -interaction=0.011; Supplementary Table S3). Using this interaction term, we found that the negative effect of residual disease after cytoreductive surgery on OS was less pronounced in g BRCA pv-carriers (TR: 0.87, 95% CI: 0.72-1.06, P =0.162) than in non-carriers (TR: 0.51, 95% CI: 0.44-0.59, P <0.001; Fig. 1a , Table 2 ) . The importance of residual disease for survival in non-carriers was confirmed in the independent OTTA cohort ( n =1004, g BRCA pv-carriers=221, 22%; Fig. 1b , Extended Data Figs. 1 and 2a). Download figure Open in new tab Fig. 1 BRCA status and residual disease as predictors of overall survival in HGSC. Kaplan-Meier survival curve for the interaction term BRCA and Residual status from patients of a , the Australian Ovarian Cancer Study (AOCS) cohort and b , the Ovarian Tumor Tissue Consortium (OTTA) cohort. P values calculated by log-rank test. R=Residual disease, R0=No residual disease, gBRCApv=pathogenic germline BRCA variant, n=Number of patients, OS=Overall survival View this table: View inline View popup Download powerpoint Table 2: Multivariable Accelerated Failure Time (AFT) model of BRCA and residual disease status and clinicopathological predictive features on overall survival in patients from the AOCS cohort. The model was fitted using a log-logistic distribution. Results are expressed as Time Ratios (TR) with corresponding 95% confidence intervals (CI) and P -values derived from Wald tests. A TR > 1 indicates a longer survival time, whereas a TR < 1 indicates a shorter survival time. Age at diagnosis was modeled using restricted cubic splines with 3 knots and is presented as two spline terms. We examined the relationship of residual disease and BRCA status to known immune and molecular features associated with survival, including tumor-infiltrating lymphocytes (TIL) 42 , 55 , RB1 loss 22 , 52 , 56 , and transcriptional molecular subtypes 39 . Non-carriers with residual disease had an inverse association with high CD8+ TIL density ( P =0.016), with 38.3% of tumors classified as having low or no TIL (Extended Data Fig. 2b, Supplementary Table S5). This group also showed an inverse association with the C4/differentiated (C4.DIF) molecular subtype ( P =0.010; Extended Data Fig. 2b). We observed an association between the C1/mesenchymal (C1.MES) molecular subtype and residual disease as previously reported 57 , but this was only statistically significant among non-carriers ( P =0.005). RB1 loss was associated with g BRCA pv-carriers without residual disease ( P <0.001; Extended Data Fig. 2b). Although no statistically significant interaction between neoadjuvant chemotherapy (NACT) and BRCA status was observed ( P -interaction=0.12; Supplementary Table S3), there was evidence of heterogeneity of effect in these subgroups. Among participants who did not receive NACT, g BRCA pv-carriers showed a survival benefit compared to non-carriers (TR: 1.60, 95% CI: 1.37-1.87, P <0.001; Supplementary Table S6, Extended Data Fig. 3). In contrast, the overall survival benefit in g BRCA pv-carriers versus non-carriers was not statistically significant in the NACT group (TR: 1.39 and 1.17, 95% CI: 0.75-2.60 and 0.62-2.21, P =0.298 and P =0.634 respectively, compared to non-carriers who did not receive NACT). g BRCA pv location and type are associated with survival and therapy response Mutations located in various functional domains of BRCA1 and BRCA2 have been associated with differences in survival and responses to PARPi in ovarian cancer 43 , 44 . The mutation type and location of g BRCA pvs was ascertained for 240 of the patients in the AOCS cohort from their clinical records and/or previous sequencing analyses 22 , 56 , 58 , 59 (Extended Data Figs. 4a,b and Supplementary Table S2). Following adjustment for FIGO stage, residual disease status, primary site, age, and first-line treatment, patients with g BRCA1 pvs in exon 10 had a statistically significant improved OS and PFS (TR: 1.54 and 1.49, 95% CI: 1.19-2.00 and 1.16-1.91, P <0.001 and P =0.002, respectively), but the association was attenuated for those with variants outside exon 10 (TR: 1.21 and 1.18, 95% CI: 0.97-1.51 and 0.96-1.46, P =0.09 and P =0.12, respectively) compared to non-carriers ( Table 3 ). More specifically, pathogenic variants in the DNA binding domain (DBD) of BRCA1 , located in exon 10, were associated with an OS and PFS benefit compared to non-carriers (TR: 1.60 and 1.58, 95% CI: 1.14-2.25 and 1.15-2.18, P =0.005 and P =0.006, respectively; Table 3 ). In contrast, the OS and PFS benefit was not statistically significant for patients with pathogenic variants in the Really Interesting New Gene (RING) (TR: 1.28 and 1.15, 95% CI: 0.87-1.90 and 0.82-1.61, P =0.216 and P =0.419, respectively) and C-terminal domains of BRCA1 (BRCT) (TR: 1.35 and 1.43, 95% CI: 0.83-2.20 and 0.90-2.26, P =0.222 and P =0.126, respectively), located outside of exon 10. View this table: View inline View popup Download powerpoint Table 3: Multivariable Accelerated Failure Time (AFT) model analysis of germline BRCA pathogenic variant (g BRCA pv) location and progression-free survival and overall survival in patients from the AOCS cohort. Models were adjusted for FIGO stage, residual disease status, primary tumor site, type of surgery, age at diagnosis (modeled with restricted cubic splines, 3 knots), use of neoadjuvant chemotherapy, tumor grade, and PARP inhibitor use in first-line treatment. AFT models were fitted using a log-logistic distribution. Results are presented as Time Ratios (TR) with 95% confidence intervals (CI) and P -values derived from Wald tests. A TR > 1 indicates an association with longer time to progression or death, while a TR < 1 reflects shorter survival. The reference group for all comparisons is non-carriers of g BRCA pv. Patients with BRCA1 variants in exon 10 have been reported to have poorer outcomes 46 due to expression of an alternative splice isoform called BRCA1 -delta11q (Δ11q) that bypasses almost all of exon 10 of BRCA1 (historically referred to as exon 11). To explore this further, we assessed BRCA1 isoform expression in our multi-omics cohort ( n =154) using the bulk RNA sequencing reads spanning the exon 10 to exon 11 junction ( Fig. 2a , Supplementary Tables S7 and S8, Supplementary Information). The Δ11q isoform was widely expressed regardless of BRCA- status, but patients with BRCA1 variants in exon 10 had significantly higher proportions of Δ11q transcripts relative to canonical transcripts ( P =0.011; Fig. 2b ). Patients with BRCA1 variants in exon 10 were classified as having high ( n = 10) or low (n = 9) BRCA1 Δ11q expression, according to the median. Patients with high Δ11q expression had a shorter survival (median OS 2.74 years) compared to those with low Δ11q expression (median OS not reached), although this was not statistically significant ( P =0.083) and was not associated with differences in the HRD sum score ( Figs. 2c,d and Supplementary Table S9). Download figure Open in new tab Fig. 2 Analysis of pathogenic germline BRCA1 and BRCA2 variants and isoform expression on survival in HGSC. a , Illustrates the RNA-seq coverage and splice junction reads across the BRCA1 gene for two samples (BRCA-7 and BRCA-14). The top and middle panels show the expression levels, with BRCA-7 and BRCA-14 indicating overall expression coverage. The bottom panel depicts the structure of the BRCA1 isoforms, where the canonical isoform includes exon 10, while the Δ11q isoform excludes it. Grey arcs in the top and middle panels represent splice junction reads supporting the canonical isoform, while red arcs indicate reads supporting the Δ11q isoform. The higher expression of the Δ11q isoform in BRCA-14 compared to BRCA-7 highlights differential splicing events between these samples. b , Illustrates a comparison of BRCA1 Δ11q expression among patients with mutations in BRCA1 exon 10 and outside exon 10, BRCA2 exon 11 and outside exon 11, and patients with BRCA wildtype. Kruskal–Wallis test P value is reported as well as pairwise Wilcoxon rank-sum test P values. c , Shows HRD sum score distribution among patients with mutations in BRCA1 exon 10 (high and low Δ11q expression) and outside exon 10, BRCA2 exon 11 and outside exon 11 and BRCA wildtype tumors. Kruskal–Wallis test P value is reported as well as pairwise Wilcoxon rank-sum test P values. d , Kaplan-Meier analysis of overall survival comparing high vs low Δ11q expression (divided by median) in patients with a BRCA1 mutation on Exon 10. P value calculated by log-rank test. The distribution of mutation types within BRCA1 outside exon 10 vs. on exon 10 and for BRCA2 outside exon 11 vs. on exon 11 is presented in e and f , respectively. Fisher’s exact test P values are reported. BRCAwt=BRCA wildtype, HR=Hazard ratio, n=Number of patients, SV= Structural variants, n= number of patients, LST=Large scale transitions, LOH= Loss of heterozygosity, AI= Allelic imbalance Overall, patients with g BRCA2 pv had an improved OS compared to non-carriers, regardless of mutation location ( Table 3 ). The only exception was the small group ( n =13) with pathogenic variants in the DNA binding domain (DBD) of BRCA2, located outside of exon 11, who did not show a statistically significant OS or PFS benefit compared to non-carriers (TR: 0.79 and 0.81, 95% CI: 0.39-1.63 and 0.43-1.51, P =0.528 and P =0.506, respectively). The type of mutation in BRCA1 and BRCA2 also plays a predictive role in response to PARPi therapy in ovarian cancer 43 . In our analysis, pathogenic variants in BRCA1 exon 10 and BRCA2 exon 11 were more likely to be truncating (98.6% and 92.3%) than those outside these exons (60% and 76.3%, P <0.001 and P =0.032 respectively; Figs. 2e,f ). BRCA1 and BRCA2 domains associated with prolonged survival were more likely to have truncating variants than missense or splice site variants ( P <0.001 and P =0.067, respectively; Extended Data Figs. 4c,d). NF1 gene alterations are associated with improved survival in BRCA2 -deficient HGSC To identify genomic features associated with short survival in HRD tumors, we compared tumor genomes and transcriptomes between short (OS ≤3 years, STS) and long-term (OS >3 years, LTS) survival groups ( Fig. 3a ). Tumor genomes were classified as either BRCA1 -deficient, BRCA2 -deficient or BRCA -proficient, which incorporated germline and somatic alterations in BRCA1 and BRCA2 , as well as other well-defined HR genes, and tumor HRD status as determined by a mutational signature-based classifier (CHORD, Classifier of HOmologous Recombination Deficiency) 60 (Supplementary Information and Supplementary Tables S10-S12). CCNE1 amplifications (gene level copy number ≥7) were associated with BRCA -proficiency, and particularly the short-survival BRCA- proficient group (50%, P adj <0.001; Fig. 3b ). BRCA -proficient tumors had less genomic scarring and were associated with an older age at diagnosis compared to BRCA1 -deficient and BRCA2 -deficient tumors (Extended Data Figs. 5a,b). Gene methylation has been identified as a prognostic factor in HGSC 61 , but no significantly differentially methylated genes with corresponding up- or down-regulated gene expression were observed between STS and LTS groups in BRCA1 - and BRCA2 -deficient tumors (Supplementary Table S13 and Supplementary Information). Download figure Open in new tab Fig. 3 Genetic landscape of HGSC stratified by BRCA status and survival. a , Oncoprint showing germline and somatic alterations of homologous recombination (HR) genes and other genes of interest stratified by BRCA -status and survival group. The distribution of the mutation type within the BRCA survival group is shown for b CCNE1 , c NF1 , d PIK3CA , and e RAD21. P -values were calculated by the Fisher’s exact test and Benjamini-Hochberg (BH) adjusted ( P adj ). BRCA status group: Long-term survivor (LTS) = OS >3 years, Short-term survivor (STS) = OS ≤3 years, BRCA-P=BRCA-proficient, HRD score: High= ≥ 63 HRD Sum, Moderate=42-62, Low= ≤41 HRD Sum, HRD= Homologous recombination deficiency, CHORD= Classifier of HOmologous Recombination Deficiency, SV=Structural variant, WG=Whole gene, BH=Benjamini-Hochberg Alterations in NF1 were most common in BRCA -deficient tumors, regardless of survival group ( BRCA1 STS 43.8%, BRCA1 LTS 33.3%, BRCA2 STS 30%, BRCA2 LTS 37.5%, BRCA -P STS 21.4%, BRCA -P LTS 14.3%, P adj =0.061; Fig. 3c and Supplementary Table S14). Notably, gene breakage caused by large-scale deletions was enriched in BRCA2 -deficient tumors in the LTS group. We hypothesized that not all alteration types equivalently disrupt gene function. Indeed, only 54.2% (26/48) of NF1 alterations showed a locus-specific loss of heterozygosity (LOH) suggesting a loss-of-function (Supplementary Table S14 and Supplementary Information). Concordantly, NF1 mRNA expression varied in tumors according to the type of NF1 alteration and was particularly depleted in those with locus-specific LOH ( P <0.0001; Extended Data Fig. 6a). Patients with tumors that harbored loss-of-function NF1 alterations showed an improved survival compared to non-loss-of-function NF1 alterations (median OS 11.92 years vs 5.17 years, P =0.032; Extended Data Fig. 6b). In particular, the combination of both BRCA2 -deficiency and loss-of-function NF1 alteration ( n =11) was associated with the best survival outcome (median OS 16.96 years), almost twice as long as those with BRCA2 -deficient tumors with no loss-of-function NF1 alteration (median OS 8.84 years; Extended Data Fig. 6c and Supplementary Table S9). NF1 protein expression was assessed by IHC in a larger cohort enriched for long-term survivors ( n =658; Extended Data Fig. 1). NF1 protein loss was observed in 13.37% ( n =88/658) of patients and was associated with improved survival compared to retained NF1 expression (median OS 4.70 vs. 3.58 years, P =0.028; Extended Data Fig. 7a). Although there were few patients with NF1 protein loss and germline BRCA1 ( n =21) or BRCA2 ( n =6) pathogenic variants, NF1 loss was associated with better survival in g BRCA2 pv-carriers (median OS 8.05 years NF1 loss vs. 5.72 years NF1 retained) but not in g BRCA1 pv-carriers (median OS 4.74 years NF1 loss vs. 4.69 years NF1 retained; Extended Data Fig. 7b). NF1 loss also was associated with a longer survival among non-carriers (median OS 5.01 years NF1 loss vs. 3.36 years NF1 retained; Extended Data Fig. 7b). In the independent OTTA cohort with NF1 mRNA expression and survival data available (n=5666), low NF1 expression (lowest quantile) was associated with improved survival compared to high expression (2 nd to 5 th quantiles) (median OS 4.19 vs. 3.56 years, P <0.0001; Extended Data Figs. 1 and 7c). Consistent with the other cohorts, g BRCA2 pv-carriers with low NF1 expression (n=36) showed an improved survival (median OS 6.42 years NF1 low vs. 5.66 years NF1 high), while there was no effect in g BRCA1 pv-carriers (median OS 5.41 years NF1 low vs. 5.65 years NF1 high, Extended Data Fig. 7d). PIK3CA and RAD21 amplifications are associated with short survival in BRCA2 -deficient HGSC We found an enrichment of PIK3CA and RAD21 gene amplifications in BRCA2 -deficient tumors in patients with short compared to long survival ( PIK3CA: 5/10, 50% vs 4/24, 16.7%, P adj =0.232 and RAD21: 5/10, 50% vs 4/24, 16.7%, P adj =0.105, respectively; Fig. 3d,e ). Co-occurrence of RAD21 and PIK3CA amplification was observed in 8.8% (3/34) patients with BRCA2 -deficiency (Supplementary Tables S15 and S16). PIK3CA and RAD21 mRNA expression was highly correlated with copy number ( P <0.0001), and tumors with gene amplification (≥7 copies) had a significantly higher expression ( P <0.001 and P =0.02, respectively) (Extended Data Fig. 8a,b and Supplementary Tables S17 and S18). Patients with combined BRCA2 -deficiency and PIK3CA amplification ( n =9, median OS 2.89 years) or RAD21 amplification ( n =9, median OS 2.89 years) had a significantly worse prognosis compared to patients with BRCA2 -deficient tumors without PIK3CA amplification (n=25, median OS 11.92 years) or RAD21 amplification ( n =25, median OS 11.53 years; Extended Data Fig. 8c,d and Supplementary Table S9). PI-3 kinase pathway activity is thought to contribute to tolerance to genome doubling and PIK3CA amplification in whole-genome duplicated tumors is a frequent event in HRD end-stage HGSC 49 , 62 . The STS BRCA2 -deficient group was characterized by high ploidy ( P adj =0.0073) and whole-genome duplication ( P adj =0.0404), in contrast to BRCA1 -deficient and BRCA -proficient tumors where the LTS groups tended to have higher ploidy (Extended Data Fig. 5a). The association between PIK3CA and survival by BRCA status was further corroborated in the OTTA cohort, where g BRCA2 pv carriers with high PIK3CA RNA expression (highest quantile) had shorter survival relative to their counterparts with low expression (median OS 4.09 vs 7.43 years, P <0.0001; Extended Data Fig. 8e). By contrast, g BRCA1 pv carriers with high PIK3CA RNA expression showed improved survival (median OS 7.67 vs 5.23 years). Elevated HRD scarring is prognostic for survival in BRCA -deficient HGSC High tumor mutation burden has been shown to be associated with long-term survival in ovarian cancer 22 . However, we found that tumor mutation burden and predicted neoantigen counts were equivalent in BRCA1 -deficient and BRCA2 -deficient tumors between STS and LTS groups ( Fig. 4a-c , Extended Data Fig. 5a, and Supplementary Table S19). Among various genomic features that were compared between these groups (Extended Data Fig. 5a), the HRD score 27 was elevated in BRCA1 -deficient tumors with long survival times compared to those with short survival times ( P =0.017; Fig. 4d ). HRD score is a measure of genomic scarring associated with impaired HR repair, suggesting a more profound inactivation of the HR pathway in patients with good outcome. Retention of the wildtype allele with absence of locus specific LOH has been reported to influence outcomes in g BRCA pv-carriers in ovarian and breast cancer 63 – 66 . However, in our cohort there was only one g BRCA2 pv carrier without loss of the wildtype allele (patient BRCA_9; Supplementary Table S11 and Supplementary Information). Concordantly this tumor was HR-proficient with an HRD score of 27 (HRP ≤42 HRD sum score) and CHORD score of 0 (HRP ≤0.5 CHORD score), and the patient had short OS (<3 years). Download figure Open in new tab Fig. 4 Influence of homologous recombination deficiency in HGSC independent of BRCA status. Comparison of a SV total counts, b SNV counts per megabase, c neoantigen counts, and d HRD sum score between BRCA survival groups ( BRCA1=BRCA1 -deficient; BRCA2=BRCA2 -deficient; BRCA-P = BRCA -proficient; Long term survivor (LTS) = OS >3 years; Short term survivor (STS) = OS ≤3 years). P -values were calculated by the Kruskal Wallis test and Benjamini-Hochberg (BH) adjusted ( P adj ). e , Kaplan-Meier analysis of overall survival stratified by different thresholds of the HRD sum score (High ≥63, Moderate 42-62, Low ≤42) in 154 patients with BRCA -deficient and BRCA -proficient HGSC. P value calculated by log-rank test. f , Kaplan-Meier analysis of overall survival in patients with HGSC stratified by BRCA -status and high (High ≥63) or low (Low < 63) HRD sum score. P value calculated by log-rank test. g , Clustered heatmap summarizing gene set enrichment analysis (GSEA) using the hallmark Molecular Signatures Database (MSigDB) gene sets. Direction and color of triangles relate to the normalized enrichment score (NES) as generated by FGSEA. P values (two-sided) were calculated using the FGSEA default Monte Carlo method; the size of the triangles corresponds to the negative log10 Benjamini-Hochberg (BH) adjusted P value ( P adj ). Columns are separated by BRCA- status and HRD score groups ( BRCA1 ; BRCA2 ; BRCA -P, BRCA -proficient, High ≥ 63; Low <63) with the direction of enrichment indicated by the first group mentioned in the x-axis label. SV=Structural variants, SNV=Single nucleotide variant, MB=Megabase, HRD=Homologous recombination deficiency, HRP=Homologous recombination proficiency, BRCA-P=BRCA-proficient, LST=Large scale transitions, LOH= Loss of heterozygosity, AI= Allelic imbalance We observed a dynamic range in HRD scores, even among tumors with pathogenic BRCA mutations, suggesting a non-equivalence of alterations. The cutoff of the HRD score has been debated, with 42 mainly used in recent clinical trials 67 – 71 , and a more stringent threshold of 63 has been proposed for ovarian cancer 72 . Indeed, patients whose tumors had a high HRD score (≥63) had longer OS (median OS 10 years) compared to those with HRD scores of 42-62 (median OS 2.66 years) and ≤41 (median OS 2.5 years), regardless of BRCA -status ( P =0.039; Fig. 4e and Supplementary Table S9). Applying a threshold of 63 to divide samples into high and low HRD, all BRCA -proficient tumors had a low HRD score. Furthermore, patients with BRCA1 - and BRCA2 -deficient tumors and HRD scores ≥63 had longer OS compared to patients with lower HRD scores (median OS 6.76 vs. 2.01 years and 11.88 vs. 6.73 years, respectively; Fig. 4f and Supplementary Table S9). Notably, patients with BRCA1 -deficient tumors with HRD scores <63 had similar OS to patients with BRCA -proficient tumors (median OS 2.01 years vs 2.21 years). Gene set enrichment analysis 73 (GSEA; Methods) revealed distinct patterns of pathway regulation based on HRD scores and BRCA status in patients with HGSC. Specifically, pathway activation in BRCA1 - and BRCA2 -deficient patients with low HRD (<63) closely resembled those of BRCA -proficient patients ( Fig. 4g ). In contrast, BRCA1 -deficient patients with high (≥63) HRD scores showed an upregulation of several pathways, including interferon-gamma and inflammatory response. These pathways are primarily involved in host defense and immune surveillance 74 , underscoring their potential role in modulating the tumor microenvironment and influencing immune response in patients with BRCA1 -deficient tumors. CD8+ PD-1+ T cells are prognostic for survival in g BRCA pv-carriers We considered whether BRCA -deficient cases with shorter survival would have fewer mutation-associated neoantigens to drive anti-tumor responses, but there was no difference in neoantigen counts between the STS and LTS groups for both BRCA1 and BRCA2 ( P =0.51 and P =0.39, respectively; Fig. 4c ). Tumor samples from 143 HGSC g BRCA pv-carriers were analyzed by multi-color immunofluorescence to determine the epithelial and stromal immune cell densities and their associations with survival groups (Extended Data Fig. 1). Aside from intraepithelial B cells and CD4+ T cells (OR=1.0), all other immune cell subsets had a positive association with survival (OR<1.0; Supplementary Table S20). Only intrastromal and intraepithelial CD8+ PD-1+ T cells were significantly more abundant in g BRCA pv-carriers with LTS compared to those with STS ( P =0.043 and P =0.029, respectively; Supplementary Table S20). The mesenchymal features c-KIT and mast cells are associated with poor outcome in HGSC Immune cell abundance was estimated in 154 HGSC tumor samples using CIBERSORTx 75 . Unsupervised clustering of the inferred immune cell densities identified six groups of patients ( Fig. 5a , and Supplementary Table S21) associated with differential survival outcomes ( P =0.0053; Fig. 5b ). The IMMB.1 ( n =30) and IMMB.6 ( n =25) clusters had exceptionally long survival (median OS 14.87 and 10.45 years, respectively; Supplementary Table S9). The group with the shortest survival (cluster IMMB.5, n =24, median OS 2.03 years) was enriched with activated dendritic cells and resting mast cells, a feature associated with the C1.MES subtype ( P =0.0021; Fig. 5c ). Multivariable Cox regression analysis showed that resting mast cells (HR: 1.26, 95% CI 1.06-1.5, P =0.009) were the immune cell type most strongly associated with short survival (Extended Data Fig. 9a). BRCA1 -deficient tumors in patients with STS had increased expression of the mast cell growth factor receptor c-KIT (CD117) compared to those with LTS ( P =0.003, P adj =0.101; Extended Data Fig. 9b). Patients with high c-KIT tumor expression had significantly shorter OS than those with low c-KIT tumor expression, regardless of BRCA and HRD status (HR: 1.71, 95% CI 1.16-2.53, P =0.0071; Extended Data Fig. 9c). The C1.MES subtype showed higher expression of c-KIT , together with an upregulation of the epithelial mesenchymal transition (EMT) pathway, compared to the C2.IMM subtype ( P adj <0.001) (Extended Data Fig. 9d,e). Download figure Open in new tab Fig. 5 Integration of immune cell profiling by CIBERSORTx and survival analysis in HGSC. a , Summary of the immune cell types arising from the CIBERSORTx analysis from BRCA- deficient and BRCA -proficient samples (n = 153 patients). Tumors fell into 6 major clusters (IMM.1-IMM.6) of immune cell types associated with survival. Each patient is annotated with survival group, status at last follow-up, CIBERSORTx absolute immune scores, molecular subtype, HRD score, BRCA status and CHORD score. b , Kaplan-Meier analysis of overall survival stratified by immune clusters. P value calculated by log-rank test. c , Boxplots summarize the absolute cell enrichment score of mast cells resting markers across the molecular subtype (C1.MES; C2.IMM; C4.DIF; C5.PRO); points represent each sample, boxes show the interquartile range (25–75th percentiles), central lines indicate the median, and whiskers show the smallest/largest values within 1.5 times the interquartile range. Kruskal– Wallis test P value is reported as well as pairwise Wilcoxon rank-sum test P values comparing molecular subtypes (C2.IMM; C4.DIF; C5.PRO) to C1.MES (**, P <0.01, ***, P 3 years, Short term survivor (STS)= OS ≤3 years, HRD=Homologous recombination deficiency, HRD score: High= ≥ 63 HRD Sum, Moderate=42-62, Low= ≤41 HRD Sum, Molecular subtypes: C1.MES=C1 mesenchymal subtype, C2.IMM=C2 immunoreactive subtype, C4.DIF=C4 differentiated subtype, C5.PRO=C5 proliferative subtype, Status: D=Dead, PF=Progression-free, P=Progression, IMMB=Immune cluster BadBRCA (IMMB.1-IMMB.6), DISCUSSION Our study highlights the complexity of survival determinants in patients with HGSC, demonstrating that it is the intersection of multiple factors, including surgical residual disease, immune response, and somatic gene alterations, which may influence outcome rather than BRCA mutation status alone. This interplay was particularly apparent in the diminished adverse impact of surgical residual disease in g BRCA pv-carriers compared to non-carriers. Previous reports have suggested that surgery in a BRCA -deficient setting may have a lesser impact on survival in both first-line and platinum-sensitive setting 33 , 47 , 76 , indicating that it may be particularly important to achieve complete resection of BRCA -proficient tumors. In addition, an exploratory analysis of the PAOLA-1/ENGOT-ov25 trial 77 showed that patients with BRCA -proficient tumors classified as higher risk (FIGO stage III with primary cytoreductive surgery and residual disease, or NACT; FIGO stage IV) had notably worse PFS compared to lower-risk patients, while this difference was less pronounced in patients with BRCA -deficient tumors. These results emphasize the importance of primary cytoreductive surgery with complete resection for non-carriers, who may also benefit more from secondary cytoreductive surgery in contrast to g BRCA pv-carriers 78 . Equally, it may be that the positive effect of optimal cytoreduction is not as apparent in BRCA carriers, due to the chemotherapy (platinum) sensitivity associated with BRCA -deficiency. In the current study, the association between NACT and survival appeared to differ by g BRCA pv status, with a potential attenuation of survival benefit among g BRCA pv-carriers who received NACT. However, the subgroup analyses by g BRCA pv status and treatment type were likely underpowered, limiting definitive conclusions regarding potential interactions. Given the rapid increase in the uptake of NACT in recent years 79 , it will be important to determine if patients with BRCA -deficient tumors may be negatively impacted by NACT 80 . The acquisition of BRCA reversion mutations is frequent 48 – 50 , and it is plausible that reversion events may be more common where chemotherapy commences with a large tumor volume from which resistant clones could emerge under selection 58 . This is especially important in the PARPi era, where the early development of platinum resistance could negatively impact on the potential benefit gained from PARPi treatment. While the impact of NACT on outcomes according to BRCA status is not yet known, it is becoming increasingly important to more rapidly determine the BRCA and broader HR status of a patient’s tumor at diagnosis to make the most informed decisions at primary treatment. Our study highlights the spectrum of HRD scores seen in patients with BRCA -deficient tumors. While all but two exceeded a threshold (>42) required for classification as HRD, the improved OS and PFS seen with a more stringent threshold (≥63) shows that HRD should not be considered a binary classification but rather appears to be a continuous variable. This finding is consistent with a previous analysis of 537 HGSC cases from The Cancer Genome Atlas which showed that patients with HRD scores ≥63 were associated with better survival outcomes, while those with intermediate (42-62) and low (≤42) HRD scores had overlapping survival curves 72 . It is important to mention that in our study, samples were collected over nearly 20 years, a timeframe that encompasses changes in treatment practices, making it challenging to determine how evolving therapies, particularly the introduction of PARPi, may have influenced outcomes. It is notable that the HRD score threshold of 42 was originally established to predict response to neoadjuvant platinum-containing chemotherapy in patients with breast cancer 81 , which tends to have less genomic scarring compared to ovarian cancer 27 , 72 . As HRD scores ≥63 strongly predicted better outcomes in BRCA -deficient HGSC, our findings support the prognostic value of HRD score thresholds. However, it is premature to conclude that a higher threshold should alter therapy selection. To establish this, a comprehensive analysis of maintenance PARPi trials, incorporating HRD scores, would be necessary to confirm their predictive role in guiding treatment decisions. Furthermore, it would be ideal to extend this investigation to include other relevant genomic alterations identified in trial samples to refine patient stratification further. This refinement would help identify patients for whom no maintenance therapy or additional targeted therapy may be more appropriate, while avoiding potentially ineffective treatments for those with lower HRD scores, thereby personalizing therapy to maximize efficacy and minimize unnecessary side effects. Our analyses corroborated Labidi-Galy et al.’s findings that pathogenic variants in the RAD51-BD of BRCA2 44 and the DBD of BRCA1 43 are associated with improved outcomes in HGSC. By contrast, alterations outside BRCA1 exon 10, particularly in the BRCT and RING regions, are not associated with a significantly improved survival compared to non-carriers and in some cases may confer platinum and PARPi resistance 45 . While BRCA1 exon 10 mutations have been associated with improved outcomes in multiple studies, including ours, there is evidence that tumors may express the BRCA1 -Δ11q splice isoform, which bypasses exon 10 mutations and results in a shorter but partially functional protein that is permissive of treatment resistance 43 , 46 . In a relatively small sample size for which we had RNA-seq data (n=19 BRCA1 exon 10 mutated tumors), we found that patients with a pathogenic BRCA1 variant in exon 10 and high Δ11q expression had a shorter survival. We were unable to measure Δ11q expression during or following treatment. This is important because Δ11q expression may increase or fluctuate under the selective pressure of treatment, which would influence treatment response and survival outcomes. CD8+ PD1+ T cells are associated with improved outcomes in ovarian cancer 82 , contributing to enhanced anti-tumor immunity. In our analysis, the presence of these cells in tumors were prognostic for survival in g BRCA pv-carriers, although to a lesser extent. This suggests that while cytotoxic T-cell activity remains important in BRCA -deficient tumors, additional factors may influence survival. Given the established association between BRCA and HR status and increased TMB 22 , it is possible that immune exhaustion, suppressive signaling or tumor-intrinsic immune resistance pathways may counteract the expected immunogenicity. Intriguingly, BRCA1 -deficient tumors with high HRD scores had evidence of enhanced immune-related gene transcription. In addition, while our study did not include cigarette smoking in the survival models, smoking has been identified as a potential factor influencing survival in g BRCA pv-carriers 83 , which may also influence the immune response. Further research into markers of T-cell exhaustion and other immune regulators is needed to better understand the differential immune responses in these patients. NF1 gene loss-of-function emerged as a good prognostic factor in BRCA2 -deficient HGSC. Loss-of-function of NF1 is common in epithelial ovarian cancer with a prevalence of 12–31% 13 , 20 , 22 , 58 , 84 , 85 . NF1 inactivation by gene breakage or mutations may contribute to initial good prognosis but later chemoresistance in patients with HGSC and BRCA -deficiency 84 . This is consistent with recent findings that deleterious NF1 mutations are associated with improved PFS in ovarian cancer 20 and low mRNA expression of NF1 predicts longer overall survival 22 . In contrast, PIK3CA amplification and high mRNA expression were associated with shorter survival in patients with BRCA2 -deficient HGSC. As a major regulator of the phosphoinositide 3-kinase (PI3K) pathway, PIK3CA activation promotes cell proliferation and survival, especially in genomically unstable cancers 49 , 62 . Its amplification may enhance tolerance to genome doubling and contribute to the aggressive nature of BRCA2 -deficient tumors. The contrasting survival outcomes between PIK3CA amplification and NF1 loss-of-function underscore the heterogeneity of HGSC tumors, highlighting the need for personalized therapeutic strategies, even within the BRCA2 -deficient subgroup. METHODS Ethics statement Written informed consent or an approved waiver of consent was obtained at each participating study site for patient recruitment and the use of samples and linked clinical information (Supplementary Table S22). Investigations were performed after approval by local human research ethics/institutional review board committees at each site. This study was conducted in accordance with the principles of Good Clinical Practice, the Declaration of Helsinki and local regulations. Study population This retrospective, multi-center study included patients diagnosed with HGSC between 2002 and 2019. The Australian Ovarian Cancer Study (AOCS) cohort (n=1389) included all stages (FIGO I-IV), and the Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) cohort (n=154) was restricted to advanced stage disease (FIGO III and IV; Table 1 , Extended Data Fig. 1, and Supplementary Table S22). Patients were categorized based on OS into short (<3 years) and long (≥3 years) OS groups (Supplementary Information). For multi-omics analysis, 154 patients had fresh-frozen tumor obtained during primary cytoreductive surgery and matched blood samples, or were previously analyzed 22 , 58 . Findings were validated in an independent HGSC cohort (n=5875) from the Ovarian Tumor Tissue Analysis Consortium (OTTA) for which g BRCA pv status was available. Molecular data Single-nucleotide polymorphism (SNP) arrays Tumor and matched normal DNA was analyzed with the Infinium OmniExpress-24 BeadChip arrays as described previously 22 . The concordance of normal and tumor DNA was assessed using HYSYS 86 . Tumor DNA samples with estimated tumor cellularity >40% (determined by qPure 87 and ASCAT 88 ) were considered appropriate for whole genome sequencing and methylation arrays. Whole genome sequencing (WGS) For WGS, libraries were generated from tumor and matched normal genomic DNA from peripheral blood mononuclear cells with a minimum base coverage of 60x and 30x, respectively. FASTQ files were assessed for sequencing quality using FASTQC (v0.11.8) and, for contaminants using FastQ Screen 89 (v0.11.4). Adapters, N-content and low-quality bases were trimmed using fastq-mcf (v1.05). Sequenced data was mapped to the human genome reference GRCh37 b37 using the aligner BWA mem 90 (v.0.7.17-r1188). Aligned BAM files per lane were then sorted, merged and duplicates marked using Picard Tools (v.2.17.3). Further processing of the aligned files included base recalibration using GATK BaseRecalibrator (v4.0.10.1). Coverage calculation was performed using GATK DepthOfCoverage (v3.8-1-0-gf15c1c3ef). GATK HaplotypeCaller (v.4.0.10.1) was used on germline BAMs to generate Genomic Variant Call Format (GVCF) files which were used as the Panel of Normals (PoN) in the Mutect2 somatic variant calling workflow. Tumor purity and ploidy were estimated using FACETS 91 . RNA-sequencing (RNA-seq) Extracted RNA from tumor tissue samples underwent RNA-seq, with initial quality control checks on raw FASTQ files performed using FastQC 89 (v0.11.8). Adapter, poly (A) tails, N content and low quality base trimming was done using fastq-mcf (v1.05), and contamination was assessed using FastQ Screen 89 (v(0.11.4). Reads were then mapped to the human reference GRCh37.92 using the STAR 92 (v2.6.0b) two-pass method. The mapped reads were then sorted using Picard Tools (v2.17.3). Counts were generated using HTSeq 93 (v0.10.0) on the GRCh37.92 Ensembl release gene annotation. Raw count data was then subsetted to protein coding genes and lowly expressed genes were removed using the following strategy. First, raw counts were converted to CPM (counts per million) and only protein coding genes with a CPM of greater than 0.5 in at least 10 samples were retained. The resulting raw count matrix was then normalized using the trimmed mean of M values (TMM) method using edgeR 94 (v3.28.1). Batch effects were removed using limma’s 95 (v3.48.2) removeBatchEffect function. Batch effect removal was done by applying batch correction on the library type (stranded/unstranded) while preserving the survival group (long/short). Methylation arrays The generation and processing of methylation array data was performed as previously described by Garsed et al. 22 . Briefly, initial quality control was performed by QuantiFluor (Promega). Subsequently, 500 ng tumor DNA was converted using the EZ DNA Methylation kit (Zymo Research) and analyzed using the Infinium MethylationEPIC BeadChip arrays. The R package minfi 96 (v1.32.0) was then used for quality control assessment and processing of the methylation data as previously described 22 . Immunofluorescence (IF) data Tissue microarrays (TMAs) were constructed from formalin-fixed paraffin-embedded (FFPE) blocks of tumor tissue and stained by IF with two panels of antibodies against immune markers of interest. Panel 1 detected CD3, CD8, CD20, FOXP3 and CD79; panel 2 detected CD3, CD8, PD-1, PD-L1 and CD68. Both panels also detected pan-cytokeratin to identify tumor epithelium. Automated cell scoring, including separation of epithelial and stromal regions, was performed using QuPath (v0.2m2), with extensive manual training and validation. CD4+ T cells were defined as CD3+CD8-cells, as previously 97 . Immunohistochemistry (IHC) Sections of 4 µm thickness were cut from previously constructed TMAs of FFPE tumor samples. Deparaffinized sections were stained with the C-terminal NF1 antibody (clone NFC, SIGMA #MABE1820; St. Louis, MO, USA) using our previously described protocol on a DAKO Omnis platform: 30 min of pre-treatment heat-induced antigen retrieval in Tris-EDTA buffer, pH = 9.0; primary antibody incubation for 1h at dilution 1/50, 10 min of a mouse linker, and 30 min for the peroxidase labelled Dako EnVision+polymer-based detection system (Dako protocol 1 h-10M-30, Agilent, Santa Clara, CA, USA) 85 . Samples were scored as follows: inactivated (loss of expression with retained internal control), normal retained expression, subclonal loss, uninterpretable (loss of tumor expression but no internal control present), and exclude (no tumor in core) ( Supplementary Information ). mRNA expression data by NanoString Tumor mRNA expression data for genes of interest ( NF1, PIK3CA, c-KIT, and RB1 ) and transcriptional molecular subtypes in the OTTA cohort were determined using NanoString, as previously described 98 , 99 . Measurements Variant detection and annotation Variant calling was performed for: germline base substitution and INDEL variants by VarDictJava 100 (v1.5.7 with –r = 2 –Q = 10 –f = 0.1). somatic base substitution and INDEL variants using four separate variant callers as follows: by Mutect2 101 (v4.0.11.0 with defaults), VarDictJava 100 (v1.5.7 with –r = 2 –Q = 10 –V = 0.05 –f = 0.01), Strelka2 102 (v2.9.9 with defaults), and VarScan2 103 (SAMtools 104 ) v1.9 for mpileup and VarScan2 v2.4.3 with -min-coverage 7 -min-var-freq = 0.05 -min-freq-for-hom = 0.75 -p-value = 0.99 -somatic-p-value =0.05 -strand-filter =0). Variant calls were decomposed and normalized using vt 105 GATKs ReadBackPhasing tool (v3.8-1-0-gf15c1c3ef with -phaseQualityThresh = 10 – enableMergePhasedSegregatingPolymorphismsToMNP -min_base_quality_score =10 - min_mapping_quality_score = 10 -maxGenomicDistanceForMNP = 2) was applied on the passing variants per tool to combine contiguous SNVs to MNVs (multi-nucleotide variants). GATK’s CombineVariants (v3.8-1-0-gf15c1c3ef with -genotypeMergeOptions UNIQUIFY -priority Strelka2, Mutect2, VarScan2, VarDictJava) was used to merge the variant calls from all four callers into a consensus variant call set. The resulting variant call format (VCF) file was once again decomposed and normalized using vt. Forward and reverse strand counts for the reference and alternate alleles were calculated using bam-readcount (v0.8.0). Finally, all variants were annotated for Duke and DAC blacklisted regions. Any variants that were passed in at least two callers, had at least one variant read in each strand, and were not in the database of FrequentLy mutAted GeneS (FLAGS) 106 or the Duke and DAC blacklist regions were deemed high-confidence. structural variants (SV) using four separate callers Manta 107 + BreakPointInspector (v1.5.0), GRIDSS 108 (v2.0.1), Smoove (v0.2.2) and SvABA 109 (v134). The SV calls were split into germline and somatic VCFs per caller. The findBreakpointsOverlaps method of the R library StructuralVariantAnnotation (v1.3.1) with a value of 10 for the ‘maxgap’ parameter was used to intersect common breakpoints between the callers. SVs were annotated to constituent types (duplication, deletion, inversion or translocation) using a simple annotation script provided by the GRIDSS tool. High-confidence SVs were categorized as those called by two or more callers. copy number variations (CNV) detection by FACETS 91 and cnv_facets (v0.13.0) as described previously 22 . The detected variants were filtered for variants with a high probability of pathogenicity as described in detail before 22 . Mutation burden and downsampling We downsampled the higher coverage tumor BAM files using Picard DownsampleSam (v2.17.3) to achieve balanced median coverage sequencing batches, to compare mutation burden across samples with inconsistent coverage 22 . The median coverage of the International Cancer Genome Consortium (ICGC) tumors was 52.15x, the MOCOG tumors was 77.81x and the short survival BRCA dataset tumors was 64.98x. So, to get the same median coverage across the three batches we downsampled the MOCOG and short survival BRCA dataset tumors to the ICGC median by specifying downsampling fractions of 0.67 and 0.8 respectively. See Supplementary Table S19 for details on the tumor sample coverage before and after downsampling and the number of SNVs, MNVs, indels and SVs called after downsampling. Neoantigen prediction Neoantigen prediction was performed as previously reported by Garsed et al. 22 . Briefly, HLA-VBSeq 110 (v11_22_2018) was used to generate HLA types which were then used to identify and construct neoantigen using pVACtools 111 pVACseq (v1.3.5). Homologous recombination deficiency (HRD) HRD status was determined using 1) scarHRD 112 , which uses loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large scale state transition (LST) in tumor genomes to generate a HRD sum score, and 2) CHORD (Classifier of Homologous Recombination Deficiency) 60 , which uses specific base substitution, indel and structural rearrangement signatures detected in tumor genomes to generate BRCA1 -type and BRCA2 -type HRD scores. RNA-seq data analysis Raw count data was subsetted to protein coding genes and lowly expressed genes were removed using the following strategy. First, raw counts were converted to CPM (counts per million) and only protein coding genes with a CPM of greater than 0.5 in at least 10 samples were retained. The resulting raw count matrix was then normalized using the trimmed mean of M values (TMM) method using edgeR 94 (v3.28.1). Batch effects were removed using limma’s 95 (v3.48.2) removeBatchEffect function. Batch effect removal was done by applying batch correction on the library type (stranded/unstranded) while preserving the survival group (long-term/short-term). RNA differential expression and pathway analysis by grouping Groupings For differential expression and pathway analysis, various groupings were used alone or in combination, namely 1) BRCA -deficiency status, 2) HRD groups, survival groups, and 3) molecular subtypes ( Supplementary Information ). Differential expression analysis To identify differentially expressed protein-coding genes between the comparison groups of interest, DESeq2 (v1.26.0) 113 was applied. Raw counts were filtered to remove low expressed genes prior to analysis and batch effects were accounted for in the model 22 . Gene Set Enrichment Analysis (GSEA) FGSEA v1.15.1 was used to calculate gene set enrichment across the comparison groups. P -values obtained from DESeq2 were transformed to signed P -values and then sorted and fed into FGSEA to generate enrichment scores and FDR-adjusted P -values across the Hallmark gene sets in the MSigDB database49 (v7.4) via its function fgseaMultilevel (minSize=15, maxSize = 500, gseaParam = 0, eps = 0) 22 . CIBERSORTx CIBERSORTx analysis was performed as previously described 22 . Briefly, CIBERSORTx 75 with the LM22 matrix was used on RNA-seq data for immune cell deconvolution. Immune clusters were then generated with k-means clustering of the generated absolute cell abundances using ConsensusClusterPlus 114 (Supplementary Information). Immunofluorescence Data were categorized based on epithelial content, measured directly by pan-cytokeratin positivity and cell morphology (assessed by automated image analysis). Epithelium-negative, cellular (i.e., non-necrotic) tumor regions were defined as stroma. Immunomarker density (D; cells/mm 2 ) for a given marker was calculated separately for epithelial and stromal compartments. For cases with multiple cores, the epithelial area was taken as the sum of all their individual TMA epithelial areas and similarly for the stromal area. We categorized marker D values into quartiles (separately for epithelial and stromal markers) to provide categorical comparisons for ease of interpretation of the odds ratios (ORs). Conditional logistic regression models were fitted for the long survival group vs short survival group. Logistic regression analyses were performed with the quartile values (scored as 1, 2, 3, 4). Immune clusters were then generated by k-means clustering of the immune cell type densities using ConsensusClusterPlus 114 . Statistical analyses Continuous variables were compared between groups using the Kruskal-Wallis test and the difference between proportions of categorical data were assessed using the Chi-squared or Fisher’s exact test. Correlations between continuous variables were assessed using Spearman correlation. Benjamini-Hochberg adjusted P -values are reported as P adj to account for multiple testing. Median PFS and OS were estimated using the Kaplan-Meier method and survival distribution were compared using the log-rank (Mantel-Cox) test. For the AOCS cohort, univariable and multivariable survival analyses were performed using Accelerated Failure Time (AFT) models 54 with a log-logistic distribution to evaluate associations between clinical and molecular variables and time-to-event outcomes. Results were reported as Time Ratios (TR) with 95% confidence intervals (CI), where a TR > 1 indicates longer time to progression or death, and a TR < 1 indicates shorter survival. Wald tests were used to compute P -values for individual covariates and interaction terms. Age at diagnosis was modelled using restricted cubic splines with three knots to allow for potential non-linear effects. Model assumptions were assessed using quantile-quantile plots of deviance residuals and Cox-Snell residuals to evaluate overall model fit. The Akaike Information Criterion (AIC) was used to compare alternative parametric distributions and confirm the suitability of the log-logistic model 115 . For survival analyses of the OTTA cohort, Cox proportional hazards models were applied. Left truncation was used to account for delayed study enrolment at some sites, and follow-up time was right-censored at 10 years from diagnosis to minimize the influence of non-ovarian cancer-related deaths. P -values from Cox models correspond to Wald and log-rank tests. The proportional hazards assumption was assessed using the Grambsch-Therneau test based on scaled Schoenfeld residuals and further evaluated through graphical inspection of Schoenfeld residual plots 115 , 116 . All statistical tests were two sided and considered significant when P < 0.05 or P adj <0.1. All analyses were performed using the statistical software R version 4.1.3 117 . Data availability Short survival BRCA dataset WGS, RNA-seq and SNP array data from short-term survivors generated as part of the current study have been deposited in the European Genome-phenome Archive (EGA) repository ( https://ega-archive.org ) under accession code EGAS00001008059. WGS and RNA-seq data are available as raw FASTQ files for each sample type (tumor/normal) and SNP array data are available as raw signal intensity files in text format for each sample type (tumor/normal). Access to patient sequence data can be gained for academic use through application to the independent Data Access Committee ( DGO{at}petermac.org ). Responses to data requests will be provided within two weeks. Information on how to apply for access is available at the EGA under accession code EGAS00001008059. The raw methylation data sets have been submitted to the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/ ) under accession code GSE292140 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE292140 ) with no access restrictions. no access restrictions. ICGC dataset Previously published WGS and RNA-seq data generated as part of the ICGC Ovarian Cancer project 58 are available from the EGA repository as a single bam file for each sample type (tumor/normal), under the accession code EGAD00001000877 (“ https://ega-archive.org/datasets/EGAD00001000877 “ https://ega-archive.org/datasets/EGAD00001000877 ). Due to the sensitive nature of these patient datasets, access is subject to approval from the ICGC Data Access Compliance Office ( https://docs.icgc.org/download/data-access/ ), an independent body who authorizes controlled access to ICGC sequencing data. ICGC SNP array and methylation data sets have been deposited into the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/ ) under accession code GSE65821 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65821 ), without access restrictions. ICGC gene count level transcriptomic data has been deposited into the GEO under accession code GSE209964 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE209964 ). ICGC SNP array and methylation data sets have been deposited into GEO ( https://www.ncbi.nlm.nih.gov/geo/ ) under accession code GSE65821 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65821 ), without access restrictions. ICGC gene count level transcriptomic data has been deposited into the GEO under accession code GSE209964 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE209964 ). MOCOG dataset WGS, RNA-seq and SNP array data from long-term survivors generated as part of the MOCOG study 22 have been deposited in the EGA repository under accession code EGAS00001005984. WGS and RNA-seq data are available as raw FASTQ files for each sample type (tumor/normal) and SNP array data are available as raw signal intensity files in text format for each sample type (tumor/normal). Access to patient sequence data can be gained for academic use through application to the independent Data Access Committee DGO{at}petermac.org ). Responses to data requests will be provided within two weeks. Information on how to apply for access is available at the EGA under accession code EGAS00001005984. The MOCOG cohort raw methylation data sets have been submitted to the GEO under accession code GSE211687 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE211687 ), with no access restrictions. OTTA dataset Participants of this study did not agree to their data being shared publicly; accordingly, the data used in this research will not be made available. Uniformly processed somatic variant data from the ICGC, MOCOG, and short survival BRCA cohorts is deposited in Synapse under accession code syn65463502 and processed expression and methylation data from all cohorts has been submitted into the GEO under accession code GSE292140 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE292140 ) and GSE292142 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE292142 ) , without access restrictions. All other data are available within the article (and its Supplementary Information files) or from the corresponding authors on request. Population frequencies of genetic variants can be accessed via the Genome Aggregation Database (gnomAD) at https://gnomad.broadinstitute.org/ . Supporting evidence for pathogenicity of genomic alterations can be accessed via ClinVar ( https://www.ncbi.nlm.nih.gov/clinvar/ ), BRCA Exchange ( https://brcaexchange.org/ ) and the TP53 Database ( https://tp53.isb-cgc.org/ ). The Ensembl ranked order of severity of variant consequences is available at: https://m.ensembl.org/info/genome/variation/prediction/predicted_data.html . Mutational signature reference databases can be accessed via COSMIC ( https://cancer.sanger.ac.uk/signatures/ ) and Signal ( https://signal.mutationalsignatures.com/ ). The LM22 signature matrix used for immune cell deconvolution can be downloaded here: https://cibersortx.stanford.edu/ . MSigDB hallmark gene sets can be accessed here: https://www.gsea-msigdb.org/gsea/msigdb/ . Illumina methylation probes that were filtered out due to poor performance (e.g. cross reactive or non-specific probes) can be found here: https://github.com/sirselim/illumina450k_filtering . Germline polymorphic sites for reference and variant allele read counts used in FACETS analysis can be found at ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b151_GRCh37p13/VCF/common_all_20180423.vcf.gz . The GTF used for annotation and RNA-seq counts is available here: ftp://ftp.ensembl.org/pub/grch37/release-92/ . Code availability No custom code or software was used in the data analyses and for the figures. All results can be replicated using publicly available tools and software. The tools and versions used are described in the Methods and Supplementary Information. Authors contributions T.A.Z.: Conceptualization, data curation, formal analysis, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. S.F.: Conceptualization, data curation, validation, methodology, writing–original draft, writing–review and editing. A.P.: Conceptualization, data curation, formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. D.A.: Data curation, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. M.W.J.: Formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. L.T.: Formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing– review and editing. A.F.: Conceptualization, data curation, investigation, writing–review and editing. C.M.L.: Formal analysis, validation, investigation, methodology, writing–review and editing. C.J.K.: Resources, data curation, methodology, writing–original draft, writing–review and editing. A.B.: Resources, writing–review and editing. N.S.M.: Resources, writing–review and editing. K.M.: Resources, writing–review and editing. P.H.: Data curation, formal analysis, validation, investigation, methodology, writing–review and editing. J.A.: Resources, writing– review and editing. A.C.A.: Resources, writing–review and editing. G.A-Y.: Resources, writing–review and editing. M.W.B.: Resources, writing–review and editing. A.B.: Resources, writing–review and editing. C.B.: Resources, writing–review and editing. F.B.: Resources, writing–review and editing. C.B.: Resources, writing–review and editing. J.B.: Resources, writing–review and editing. A.H.B.: Resources, writing–review and editing. M.E.C.: Resources, writing–review and editing. A.C-J.: Resources, writing–review and editing. D.S.C.: Resources, writing–review and editing. E.L.C.: Resources, writing–review and editing. A.C-G.: Resources, writing–review and editing. P.C.: Resources, writing–review and editing. K.L.C-H.: Resources, writing–review and editing. C.C.: Resources, writing–review and editing. K.M.D.: Resources, writing–review and editing. C.D.: Resources, writing–review and editing. T.D.: Resources, writing–review and editing. A.B.E.: Resources, writing–review and editing. E.E.: Resources, writing–review and editing. J.E.: Resources, writing–review and editing. T.E.: Resources, writing–review and editing. R.F.: Resources, writing–review and editing. A.F.: Resources, writing–review and editing. M.G-C.: Resources, writing–review and editing. A.G-M.: Resources, writing–review and editing. P.G.: Resources, writing–review and editing. R.G.: Resources, writing–review and editing. P.H.: Resources, writing–review and editing. A.D.H.: Resources, writing–review and editing. A.H.: Resources, writing–review and editing. S.H.: Resources, writing–review and editing. B.Y.H.: Resources, writing–review and editing. A.H.: Resources, writing–review and editing. S.H.: Resources, writing–review and editing. D.G.H.: Resources, writing–review and editing. M.J-L.: Resources, writing–review and editing. M.E.J.: Resources, writing–review and editing. E.K.: Resources, writing–review and editing. E.K.: Resources, writing–review and editing. T.K.: Resources, writing–review and editing. F.K.F.K.: Resources, writing–review and editing. G.K.: Resources, writing–review and editing. R.F.P.M.K.: Resources, writing–review and editing. J.K.: Resources, writing– review and editing. D.L.: Resources, writing–review and editing. C-H.L.: Resources, writing– review and editing. J.L.: Resources, writing–review and editing. S.C.Y.L.: Resources, writing– review and editing. Y.L.: Resources, writing–review and editing. A.L.: Resources, writing– review and editing. J.L.: Resources, writing–review and editing. L.L.: Resources, writing– review and editing. J.L.: Resources, writing–review and editing. C.M.: Resources, writing– review and editing. I.A.M.: Resources, writing–review and editing. M.M.: Resources, writing– review and editing. G.S.N.: Resources, writing–review and editing. N.N.: Resources, writing– review and editing. A.O.: Resources, writing–review and editing. S.O.: Resources, writing– review and editing. A.O.: Resources, writing–review and editing. C.M.Q.: Resources, writing– review and editing. G.RM.: Resources, writing–review and editing. I.R-C.: Resources, writing– review and editing. C.R-A.: Resources, writing–review and editing. P.R.: Resources, writing– review and editing. M.R.: Resources, writing–review and editing. S.G.S.: Resources, writing– review and editing. S.S.: Resources, writing–review and editing. M.J.S.: Resources, writing– review and editing. H-P.S.: Resources, writing–review and editing. G.S.S.: Resources, writing– review and editing. L.S.: Resources, writing–review and editing. C.J.R.S.: Resources, writing– review and editing. A.T.: Resources, writing–review and editing. A.T.: Resources, writing– review and editing. C.M.T.: Resources, writing–review and editing. S.E.T.: Resources, writing–review and editing. K.K.V.: Resources, writing–review and editing. M.A.A.: Resources, writing–review and editing. T.G.: Resources, writing–review and editing. E.N.: Resources, writing–review and editing. L.W.: Resources, writing–review and editing. A.E.W-H.: Resources, writing–review and editing. C.W.: Resources, writing–review and editing. C.W.: Resources, writing–review and editing. J.W.: Resources, writing–review and editing. N.W.: Resources, writing–review and editing. L.R.W.: Resources, writing–review and editing. S.J.W.: Resources, writing–review and editing. B.W.: Resources, writing–review and editing. M.S.A.: Resources, writing–review and editing. A.B.: Resources, writing–review and editing. F.J.C-R.: Resources, writing–review and editing. P.A.C.: Resources, writing–review and editing. T.P.C.: Resources, writing–review and editing. P.C.: Resources, writing–review and editing. J.A.D.: Resources, writing–review and editing. P.A.F.: Resources, writing–review and editing. R.T.F.: Resources, writing–review and editing. M.J.G.: Resources, writing–review and editing. S.A.G.: Resources, writing–review and editing. M.T.G.: Resources, writing–review and editing. J.G.: Resources, writing–review and editing. H.R.H.: Resources, writing–review and editing. F.H.: Resources, writing–review and editing. H.M.H.: Resources, writing–review and editing. B.Y.K.: Resources, writing–review and editing. L.E.K.: Resources, writing– review and editing. G.L.M.: Resources, writing–review and editing. U.M.: Resources, writing– review and editing. F.M.: Resources, writing–review and editing. S.L.N.: Resources, writing– review and editing. J.M.S.: Resources, writing–review and editing. A.S.: Resources, writing– review and editing. A.J.S.: Resources, writing–review and editing. I.V.: Resources, writing– review and editing. A.H.W.: Resources, writing–review and editing. J.D.B.: Resources, writing–review and editing. P.D.P.P.: Resources, writing–review and editing. C.L.P.: Resources, writing–review and editing. M.C.P.: Resources, writing–review and editing. E.L.G.: Resources, writing–review and editing. S.J.R.: Conceptualization, resources, data curation, supervision, funding acquisition, validation, writing–original draft, project administration, writing–review and editing. M.K.: Conceptualization, resources, data curation, investigation, formal analysis, validation, visualization, supervision, methodology, writing– original draft, writing–review and editing. B.N.: Resources, data curation, investigation, formal analysis, validation, visualization, methodology, writing–review and editing. A.DF.: Conceptualization, Resources, writing–review and editing. M.L.F.: Conceptualization, Resources, writing–review and editing. D.D.L.B.: Conceptualization, resources, supervision, funding acquisition, writing–original draft, writing–review and editing. D.W.G.: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. Competing interests T.A.Z. reports personal consulting fees from AbbVie that are outside the submitted work. D.D.L.B. reports research support grants from AstraZeneca, Roche-Genentech and BeiGene paid to institution outside the submitted work; also, personal consulting fees from Exo Therapeutics that are outside the submitted work. G.A.-Y. reports research support grants from AstraZeneca and Roche-Genentech paid to institution outside the submitted work; also, personal consulting fees from Incyclix Bio that are outside the submitted work. A.DeF. reports research support from AstraZeneca and Illumina. N.N. reports research support from Illumina. P.A.C. reports speakers’ honoraria from AstraZeneca, Merck Sharpe and Dohme, and GlaxoSmithKline, and personal consulting fees from Astra Zeneca outside the remit of the submitted work. U.M. and A.G.M. report personal consulting fees from Mercy BioAnalytics Ltd and research support grants from Intelligent Lab on Fiber, RNA Guardian, and MercyBio Analytics that are all outside the remit of the submitted work. E.L.C. reports research support from AstraZeneca paid to institution outside the submitted work and speakers’ honoraria from AstraZeneca and GSK. S.E.T reports consulting fees from AstraZeneca and IntegraConnect outside the submitted work. P.H. reports honoraria and consulting fees from Amgen, Astra Zeneca, GSK, Roche, Immunogen, Sotio, Stryker, ZaiLab, MSD, Clovis, Miltenyi, Eisai, Mersana, Exscientia, Daiichi Sankyo, Karyopharm, Abbvie, Novartis, Corcept, BionTech, Zymeworks and Research funding (Institutional) from Astra Zeneca, Roche, GSK, Genmab, Immunogen, Seagen, Clovis, Novartis, Immatics, Abbvie, MSD. I.V. has participated in consulting advisory boards for Akesobio, Bristol Myers Squibb, Eisai, F. Hoffmann-La Roche, Genmab, GSK, ITM, Karyopharm, MSD, Novocure, Oncoinvent, Sanofi, Regeneron, and Seagen, and has participated in consulting data monitoring committees for Abbvie, Agenus, AstraZeneca, Corcept, Daiichi, F. Hoffmann-La Roche, Immunogen, Kronos Bio, Mersana, Novartis, OncXerna, Verastem Oncology, and Zentalis. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Extended Data Figure legends Extended Data Fig. 1 | Patient cohorts and case selection: Overview for clinical, molecular, and validation analysis. Overview of patient cohorts and case selection for the clinical, molecular and validation analysis. HGSC=Tubo-ovarian high-grade serous carcinoma, AOCS =Australian Ovarian Cancer Study, MOCOG=Multidisciplinary Ovarian Cancer Outcome Group, gBRCApv=pathogenic germline BRCA variant, mcIF=multicolor immunofluorescence, OTTA = Ovarian Tumor Tissue Analysis, mRNA=messenger ribonucleic acid, OS=overall survival Extended Data Fig. 2 | Association of BRCA status and residual disease with survival and distribution of molecular features in HGSC: Insights from the OTTA cohort. a, Multivariable Cox proportional hazards model of the interaction term BRCA and residual disease status and clinicopathological and molecular predictive features on overall survival with patients from the OTTA cohort. P values were derived using the Wald test; values < 0.05 are colored red (*, P < 0.05; **, P < 0.01; ***, P < 0.001; **** P < 0.0001). b , Distribution of molecular features (CD8+ TIL density, molecular subtypes, and RB1 loss) within the BRCA and residual groups by odds ratios. P -values were calculated based on odds ratios (OR). R=Residual disease, R0=No residual disease, gBRCApv=pathogenic germline BRCA variant, HR=Hazard ratio, CI=confidence interval, TIL= tumor-infiltrating lymphocyte Extended Data Fig. 3 | Association of BRCA status and neoadjuvant chemotherapy on survival in HGSC. Kaplan-Meier analysis of overall survival stratified by the interaction term BRCA and neoadjuvant chemotherapy status from patients of the Australian Ovarian Cancer Study (AOCS) cohort. P value calculated by log-rank test. gBRCApv=pathogenic germline BRCA variant, NACT=neoadjuvant chemotherapy, n=Number of patients, OS=Overall survival Extended Data Fig. 4 | Prognostic significance of pathogenic germline BRCA1 and BRCA2 variant by domain location and mutation type on outcome in HGSC. a, and b , shows the distribution of pathogenic germline mutations on the BRCA1 and BRCA2 gene, respectively. c , and d , show the distribution of mutation types within the different BRCA1 and BRCA2 functional domains. Fisher’s exact test P value is reported. gBRCApv=pathogenic germline BRCA variant, DBD=DNA binding domain, RING=Really Interesting New Gene domain, RAD51-BD=RAD51-binding domain, BRCT=BRCA c-terminal domain, n=Number of patients Extended Data Fig. 5 | Distribution analysis of clinical and molecular features by BRCA and survival groups in HGSC. a , Box plots summarizing numerical, clinical and genomic characteristics by BRCA and survival groups; dots represent each sample, boxes show the interquartile range (25-75th percentiles), central lines show the median, whiskers show the smallest/largest values within 1.5 times the interquartile range. Kruskal-Wallis test Benjamini-Hochberg adjusted P -values and pairwise Mann-Whitney-Wilcoxon test P values (two sided) are reported (ns, P >0.1; ., P <0.1; *, P < 0.05; **, P < 0.01; ***, P < 0.001). Features are ranked according to their significance. b , Proportion of patients with categorical characteristics per BRCA and survival group. Features are ordered by significance using Fisher’s exact test (two-tailed) and clusters are ordered by proportion of long-term survivors. Fisher’s test P -values shown are Benjamini-Hochberg adjusted P -values. Features are ordered by significance. LST=Large scale transitions, LOH= Loss of heterozygosity, SV= Structural variants, DEL=Deletion, DUP=Duplication, AI=Allelic imbalance, INV=Inversion; Long term survivor (LTS)= OS >3 years, Short term survivor (STS)= OS ≤3 years, BRCA-P=BRCA-proficient; HR status= Homologous recombination status, P=Progression, PF= Progression-free; R=residual disease, R0=no residual disease; High ≥63, Moderate= 42-62, Low= ≤41, IMMB=Immune clusters (by CIBERSORTx), Molecular subtypes: C1.MES=C1 mesenchymal subtype, C2.IMM=C2 immunoreactive subtype, C4.DIF=C4 differentiated subtype, C5.PRO=C5 proliferative subtype Extended Data Fig. 6 | NF1 gene alterations and expression. a, Scatter graphs (right) show NF1 expression (y-axis) plotted against copy number (x-axis) in primary tumors (n=153, Spearman correlation analysis). Boxplots (left) summarize NF1 expression by NF1 alterations with and without locus specific loss of heterozygosity (LOH); lines indicate median, and whiskers show range. Kruskal–Wallis test P value is reported as well as pairwise Wilcoxon rank-sum test P values comparing altered groups to wildtype (non-significant (ns), P >0.05; ****, P <0.0001). b , Kaplan-Meier analysis of overall survival in patients stratified by NF1 alterations exhibiting locus specific LOH and c , in patients with BRCA1 -and BRCA2 -deficient tumors stratified by NF1 alteration status. P values calculated by log-rank test. TMM=Trimmed Mean of M-values Extended Data Fig. 7 | Survival analysis by NF1 expression HGSC, with stratification by BRCA status: Findings from MOCOG and OTTA cohorts. Kaplan-Meier curves for overall survival (OS) comparing a patients with HGSC from the MOCOG cohort by NF1 protein expression status (NF1 retained vs loss) and in b additionally stratified by germline BRCA mutation status. c , Kaplan-Meier curve comparing the overall survival of patients with HGSC from the OTTA cohort by NF1 RNA expression status (low=lowest quantile, high=2 nd to 5 th quantiles) and in d additionally stratified by germline BRCA mutation status. P values calculated by log-rank test Extended Data Fig. 8 | PIK3CA and RAD21 gene alterations in HGSC. a, b, Scatter graphs (right) of the expression (y-axis) of PIK3CA (a) and RAD21 (b) plotted against copy number (x-axis) in primary tumors (n=153, Spearman correlation analysis). Boxplots (left) summarize expression by mutation type; lines indicate median, and whiskers show range. Kruskal–Wallis test P value is reported as well as pairwise Wilcoxon rank-sum test P values comparing altered groups to wildtype (non-significant (ns), P >0.05; ****, P <0.0001; ***, P <0.001; **, P <0.01). c , Kaplan-Meier analysis of overall survival in patients with HGSC stratified by BRCA -status and PIK3CA amplification vs no amplification . P value calculated by log-rank test. d , Kaplan-Meier analysis of overall survival in patients with HGSC stratified by BRCA -status and RAD21 amplification vs no amplification. P value calculated by log-rank test. e , Kaplan-Meier analysis of overall survival in patients with HGSC from the OTTA cohort stratified by PIK3CA RNA expression status (high=highest quantile, low=1 st to 4 th quantiles) and stratified by germline BRCA mutation status. P value calculated by log-rank test. SV=Structural variants, amp=amplification, WG=Whole gene, BRCA-P=BRCA-proficient, TMM=Trimmed Mean of M-values Extended Data Fig. 9 | c-KIT gene expression in HGSC: Association with survival, molecular subtypes, and BRCA status. a , Forest plot (left) indicates the hazard ratio (HR, squares) and 95% confidence interval (CI; whiskers) for overall survival (OS) calculated using a multivariable Cox proportional hazard regression model based on the LM22 immune cell types detected by CIBERSORTx analysis (n = 153 patients). Cell types are arranged by HR. P values were derived by Wald test; values < 0.05 are colored red (* P < 0.05, ** P < 0.01). b , Differential expression analysis was performed using DESeq2 to determine fold change (right) of gene expression between the BRCA survival groups ( BRCA1=BRCA1 -deficient; BRCA2=BRCA2 -deficient; BRCA-P = BRCA -proficient; Long term survivor (LTS) = OS >3 years; Short term survivor (STS) = OS ≤3 years) (two-tailed Wald test, both unadjusted P values and Benjamini-Hochberg adjusted P values ( P adj ) are shown). c , Multivariable Cox proportional hazards model for OS comparing c-KIT with high vs low RNA expression levels by median and adjusted for HRD sum score and BRCA HRD status. P values were derived by Wald test; values < 0.05 are colored red (* P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001). d , Boxplots summarize RNA expression of the c-KIT gene marker across the molecular subtypes (C1.MES=C1 mesenchymal subtype, C2.IMM=C2 immunoreactive subtype, C4.DIF=C4 differentiated subtype, C5.PRO=C5 proliferative subtype); points represent each sample, boxes show the interquartile range (25–75th percentiles), central lines indicate the median, and whiskers show the smallest/largest values within 1.5 times the interquartile range. Differential expression analysis was performed using DESeq2 to determine fold change (right) of gene expression between the molecular subtypes (two-tailed Wald test, both unadjusted P values and Benjamini-Hochberg adjusted P values ( P adj ) are shown). e , Clustered heatmap summarizing gene set enrichment analysis (GSEA) using the hallmark Molecular Signatures Database (MSigDB) gene sets. Direction and color of triangles relate to the normalized enrichment score (NES) as generated by FGSEA. P values (two-sided) were calculated using the FGSEA default Monte Carlo method; the size of the triangles corresponds to the negative log 10 Benjamini-Hochberg adjusted P value ( P adj ). Columns are separated by molecular subtypes with the direction of enrichment indicated by the first group mentioned in the x-axis label. BRCA-P= BRCA-proficient, Survival group: Long-term survivor (LTS)= OS >3 years, Short-term survivor (STS)= OS ≤3 years, TMM=Trimmed Mean of M-values Supplementary Tables Table S1 Clinical data of the AOCS cohort Table S2 Mutation type and location g BRCA pv-carriers AOCS cohort Table S3 Univariable and multivariable Accelerated Failure Time (AFT) model results of clinical features, including interaction analyses with gBRCApv status, on overall survival in patients with HGSC from the AOCS cohort. Table S4 Univariable and multivariable Accelerated Failure Time (AFT) model results of clinical features, including interaction analyses with gBRCApv status, on progression-free survival in patients with HGSC from the AOCS cohort. Table S5 Distribution of molecular subtypes, tumor-infiltrating lymphocytes, RB1 protein expression status, and CCNE1 amplification status stratified by residual disease or no residual disease, and by g BRCA pv carrier or non-carrier status. Percentages are given in parentheses and calculated within each subgroup. Table S6 Univariable and multivariable Accelerated Failure Time (AFT) model of gBRCApv and neoadjuvant chemotherapy status and clinicopathological predictive features on overall survival in patients with HGSC from the AOCS cohort. Table S7 Clinical data of the multi-omics cohort Table S8 Mutation location with Δ11q proportion and Δ11q BRCA1 expression Table S9 Results of the Kaplan–Meier analysis of overall survival in 154 patients with HGSC from the multi-omics cohort stratified by the main features of interest. Table S10 BRCA groups, HRD score, CHORD scores, whole genome duplication, and molecular signatures multi-omics cohort Table S11 Germline alterations in genes of interest with loss of wildtype allele and clonality Table S12 Somatic alterations in genes of interest Table S13 Frequency of genes identified as significant in both differential methylation and differential expression analyses. Table S14 NF1 alteration type, segment copy number, clonality, loss of heterozygosity and, and RNA expression. Table S15 Mutual exclusivity and co-occurrence analysis whole multi-omics cohort Table S16 Mutual exclusivity and co-occurrence analysis short survival BRCA group Table S17 PIK3CA segment copy number, RNA expression, and alteration type Table S18 RAD21 segment copy number, RNA expression, and alteration type Table S19 Mutation and neoantigen burden Table S20 Quartile odds ratios of immune cell subsets comparing short-term (n=36) vs long-term (n=106) survival group Table S21 Relative CIBERSORTx abundance of LM22 cell types of the multi-omics cohort with cluster Table S22 Details of participating study sites and ethics approvals from the AOCS, MOCOG and OTTA cohort. Acknowledgments We thank A. Freimund, R. Lupat, J. Ellul, and the Peter MacCallum Cancer Centre Research Computing Facility for their contributions to the study. This work was supported by the National Health and Medical Research Council (NHMRC) of Australia (GNT1186505 and GNT2029088), the US Army Medical Research and Materiel Command Ovarian Cancer Research Program (Award No. W81XWH-16-2-0010 and W81XWH-21-1-0401), the National Institutes of Health (NIH) (R21-CA267050, K07-CA080668, R01-CA95023, R01-CA248288, P50-CA136393, P30-CA015083, MO1-RR000056), the Swiss National Foundation (P500PM_20726); Bangerter-Rhyner Stiftung (0297); Margarete and Walter Lichtenstein-Stiftung; and Freie Gesellschaft Basel. The Gynaecological Oncology Biobank at Westmead was funded by the NHMRC (ID310670, ID628903); the Cancer Institute NSW (12/RIG/1-17, 15/RIG/1-16); the Department of Gynaecological Oncology, Westmead Hospital; and acknowledges financial support from the Sydney West Translational Cancer Research Centre, funded by the Cancer Institute NSW (15/TRC/1-01). Direct funding for the generation of the NanoString data for OTTA was provided by the NIH (R01-CA172404, and R01-CA168758), the Canadian Institutes for Health Research (Proof-of-Principle I program) and the United States Department of Defense Ovarian Cancer Research Program (OC110433). T.A.Z. is supported by the Swiss National Foundation Return CH Postdoc.Mobility (P5R5PM_222151). D.W.G. is supported by a Victorian Cancer Agency/Ovarian Cancer Australia Low-Survival Cancer Philanthropic Mid-Career Research Fellowship (MCRF22018) and the Ovarian Cancer Research Foundation (2025/OCRF0071). S.J.R. is supported by the NHMRC (2009840). M.J.G is supported by the Ministerio de Ciencia, Innovación y Universidades (MICIU)/AEI/10.13039/501100011033 and ERDF, EU (Project PID2023-151298OB-I00). A.O. is partially funded by Ministerio de Ciencia e Innovación, Instituto de Salud Carlos III (PI23/01235) supported by FEDER funds and the Spanish Network on Rare Diseases (CIBERER). K.M.D., T.P.C., and G.L.M. were supported by awards from the Uniformed Services University of the Health Sciences and the Defense Health Program to the Henry M Jackson Foundation (HJF) for the Advancement of Military Medicine Inc. to the Gynecologic Cancer Center of Excellence Program including HU0001-16-2-0006 (PIs: Chad A. Hamilton and G. Larry Maxwell), HU0001-19-2-0031, HU0001-20-2-0033, and HU0001-21-2-0027 (PIs: Yovanni Casablanca and G. Larry Maxwell), HU0001-22-2-0016 and HU0001-23-2-0038 (PIs: Neil T. Phippen and G. Larry Maxwell), as well as HU0001-23-2-0038 and HU0001-24-2-0047 (PIs Christopher M Tarney and G. Larry Maxwell). T.V.G. is a Senior Clinical Investigator of the Fund for Scientific Research-Flanders (FWO Vlaanderen 18B2921N). A.DeF. is supported by the NHMRC (2033042). The AOV study was funded by the Canadian Institutes for Health Research (MOP-86727). 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You are going to email the following Beyond BRCA deficiency: Clinical and molecular predictors of survival in patients with BRCA-deficient tubo-ovarian high-grade serous carcinoma Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Beyond BRCA deficiency: Clinical and molecular predictors of survival in patients with BRCA -deficient tubo-ovarian high-grade serous carcinoma Tibor A. Zwimpfer , Sian Fereday , Ahwan Pandey , Dinuka Ariyaratne , Madawa W. Jayawardana , Laura Twomey , Céline M. Laumont , Catherine J. Kennedy , Adelyn Bolithon , Nicola S. Meagher , Katy Milne , Phineas Hamilton , Jennifer Alsop , Antonis C. Antoniou , George Au-Yeung , Matthias W. Beckmann , Amy Berrington de Gonzalez , Christiani Bisinotto , Freya Blome , Clara Bodelon , Jessica Boros , Alison H. Brand , Michael E. Carney , Alicia Cazorla-Jiménez , Derek S. Chiu , Elizabeth L. Christie , Anita Chudecka-Głaz , Penny Coulson , Kara L. Cushing-Haugen , Cezary Cybulski , Kathleen M. Darcy , Cath David , Trent Davidson , Arif B. Ekici , Esther Elishaev , Julius Emons , Tobias Engler , Rhonda Farrell , Anna Fischer , Montserrat García-Closas , Aleksandra Gentry-Maharaj , Prafull Ghatage , Rosalind Glasspool , Philipp Harter , Andreas D. Hartkopf , Arndt Hartmann , Sebastian Heikaus , Brenda Y. Hernandez , Anusha Hettiaratchi , Sabine Heublein , David G. Huntsman , Mercedes Jimenez-Linan , Michael E. Jones , Eunyoung Kang , Ewa Kaznowska , Tomasz Kluz , Felix K.F. Kommoss , Gottfried Konecny , Roy F.P.M. Kruitwagen , Jessica Kwon , Diether Lambrechts , Cheng-Han Lee , Jenny Lester , Samuel C.Y. Leung , Yee Leung , Anna Linder , Jolanta Lissowska , Liselore Loverix , Jan Lubiński , Constantina Mateoiu , Iain A. McNeish , Malak Moubarak , Gregg S. Nelson , Nikilyn Nevins , Alexander B. Olawaiye , Siel Olbrecht , Sandra Orsulic , Ana Osorio , Carmel M. Quinn , Ganendra Raj Mohan , Isabelle Ray-Coquard , Cristina Rodríguez-Antona , Patricia Roxburgh , Matthias Ruebner , Stuart G. Salfinger , Spinder Samra , Minouk J. Schoemaker , Hans-Peter Sinn , Gabe S. Sonke , Linda Steele , Colin J.R. Stewart , Aline Talhouk , Adeline Tan , Christopher M. Tarney , Sarah E. Taylor , Koen K. Van de Vijver , Maaike A. van der Aa , Toon Van Gorp , Els Van Nieuwenhuysen , Lilian van-Wagensveld , Andrea E. Wahner-Hendrickson , Christina Walter , Chen Wang , Julia Welz , Nicolas Wentzensen , Lynne R. Wilkens , Stacey J. Winham , Boris Winterhoff , Michael S. Anglesio , Andrew Berchuck , Francisco J. Candido dos Reis , Paul A. Cohen , Thomas P. Conrads , Philip Crowe , Jennifer A. Doherty , Peter A. Fasching , Renée T. Fortner , María J. García , Simon A. Gayther , Marc T. Goodman , Jacek Gronwald , Holly R. Harris , Florian Heitz , Hugo M. Horlings , Beth Y. Karlan , Linda E. Kelemen , G. Larry Maxwell , Usha Menon , Francesmary Modugno , Susan L. Neuhausen , Joellen M. Schildkraut , Annette Staebler , Karin Sundfeldt , Anthony J. Swerdlow , Ignace Vergote , Anna H. Wu , James D. Brenton , Paul D.P. Pharoah , Celeste Leigh Pearce , Malcolm C. Pike , Ellen L. Goode , Susan J. Ramus , Martin Köbel , Brad H. Nelson , Anna DeFazio , Michael L. Friedlander , David D.L. Bowtell , Dale W. Garsed medRxiv 2025.09.17.25335919; doi: https://doi.org/10.1101/2025.09.17.25335919 Share This Article: Copy Citation Tools Beyond BRCA deficiency: Clinical and molecular predictors of survival in patients with BRCA -deficient tubo-ovarian high-grade serous carcinoma Tibor A. Zwimpfer , Sian Fereday , Ahwan Pandey , Dinuka Ariyaratne , Madawa W. Jayawardana , Laura Twomey , Céline M. Laumont , Catherine J. Kennedy , Adelyn Bolithon , Nicola S. Meagher , Katy Milne , Phineas Hamilton , Jennifer Alsop , Antonis C. Antoniou , George Au-Yeung , Matthias W. Beckmann , Amy Berrington de Gonzalez , Christiani Bisinotto , Freya Blome , Clara Bodelon , Jessica Boros , Alison H. Brand , Michael E. Carney , Alicia Cazorla-Jiménez , Derek S. Chiu , Elizabeth L. Christie , Anita Chudecka-Głaz , Penny Coulson , Kara L. Cushing-Haugen , Cezary Cybulski , Kathleen M. Darcy , Cath David , Trent Davidson , Arif B. Ekici , Esther Elishaev , Julius Emons , Tobias Engler , Rhonda Farrell , Anna Fischer , Montserrat García-Closas , Aleksandra Gentry-Maharaj , Prafull Ghatage , Rosalind Glasspool , Philipp Harter , Andreas D. Hartkopf , Arndt Hartmann , Sebastian Heikaus , Brenda Y. Hernandez , Anusha Hettiaratchi , Sabine Heublein , David G. Huntsman , Mercedes Jimenez-Linan , Michael E. Jones , Eunyoung Kang , Ewa Kaznowska , Tomasz Kluz , Felix K.F. Kommoss , Gottfried Konecny , Roy F.P.M. Kruitwagen , Jessica Kwon , Diether Lambrechts , Cheng-Han Lee , Jenny Lester , Samuel C.Y. Leung , Yee Leung , Anna Linder , Jolanta Lissowska , Liselore Loverix , Jan Lubiński , Constantina Mateoiu , Iain A. McNeish , Malak Moubarak , Gregg S. Nelson , Nikilyn Nevins , Alexander B. Olawaiye , Siel Olbrecht , Sandra Orsulic , Ana Osorio , Carmel M. Quinn , Ganendra Raj Mohan , Isabelle Ray-Coquard , Cristina Rodríguez-Antona , Patricia Roxburgh , Matthias Ruebner , Stuart G. Salfinger , Spinder Samra , Minouk J. Schoemaker , Hans-Peter Sinn , Gabe S. Sonke , Linda Steele , Colin J.R. Stewart , Aline Talhouk , Adeline Tan , Christopher M. Tarney , Sarah E. Taylor , Koen K. Van de Vijver , Maaike A. van der Aa , Toon Van Gorp , Els Van Nieuwenhuysen , Lilian van-Wagensveld , Andrea E. Wahner-Hendrickson , Christina Walter , Chen Wang , Julia Welz , Nicolas Wentzensen , Lynne R. Wilkens , Stacey J. Winham , Boris Winterhoff , Michael S. Anglesio , Andrew Berchuck , Francisco J. Candido dos Reis , Paul A. Cohen , Thomas P. Conrads , Philip Crowe , Jennifer A. Doherty , Peter A. Fasching , Renée T. Fortner , María J. García , Simon A. Gayther , Marc T. Goodman , Jacek Gronwald , Holly R. Harris , Florian Heitz , Hugo M. Horlings , Beth Y. Karlan , Linda E. Kelemen , G. Larry Maxwell , Usha Menon , Francesmary Modugno , Susan L. Neuhausen , Joellen M. Schildkraut , Annette Staebler , Karin Sundfeldt , Anthony J. Swerdlow , Ignace Vergote , Anna H. Wu , James D. Brenton , Paul D.P. Pharoah , Celeste Leigh Pearce , Malcolm C. Pike , Ellen L. Goode , Susan J. Ramus , Martin Köbel , Brad H. Nelson , Anna DeFazio , Michael L. Friedlander , David D.L. Bowtell , Dale W. 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