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Dilution of molecular-pathologic gene signatures (MPG) by parameter unrelated to tumor biology (UTB) may prevent prediction of the resection status after debulking surgery in patients with advanced ovarian cancer
  1. F Heitz1,2,
  2. S Kommoss2,3,
  3. R Tourani4,
  4. A Grandelis5,
  5. L Uppendahl5,
  6. C Aliferis4,
  7. A Burges2,6,
  8. C Wang7,
  9. U Canzler2,8,
  10. J Wang4,
  11. A Belau2,9,
  12. S Prader1,
  13. L Hanker2,10,
  14. S Ma4,
  15. B Ataseven1,6,
  16. F Hilpert2,11,
  17. S Schneider1,
  18. J Sehouli12,
  19. R Kimmig2,13,
  20. C Kurzeder14,15,
  21. B Schmalfeldt2,16,17,
  22. I Braicu12,
  23. P Harter1,2,
  24. SC Dowdy7,
  25. BJN Winterhoff5,
  26. J Pfisterer2,18 and
  27. A du Bois1,2
  1. 1Klinik für Gynäkologie und Gynäkologische Onkologie, Kliniken Essen-Mitte, Essen
  2. 2AGO Study Group, Wiesbaden
  3. 3Department of Women’s Health, Tuebingen University Hospital, Tuebingen, Germany
  4. 4Institute for Health Informatics (IHI), Academic Health Center, University of Minnesota, Minneapolis
  5. 5Department of Gynecology, Obstetrics and Women’s Health, Division of Gynecologic Oncology, University of Minnesota, Minnesota, MN, USA
  6. 6Department of Gynecology, University Hospital Munich-Großhadern, Munich, Germany
  7. 7Division of Gynecologic Surgery, Department of Obstetrics and Gynecology, Mayo Clinic Hospital, Rochester, MN, USA
  8. 8Department of Gynecology and Obstetrics, Technische Universität Dresden, Dresden
  9. 9Klinik und Poliklinik für Frauenheilkunde und Geburtshilfe, Ernst Moritz Arndt Universität Greifswald, Greifswald
  10. 10Klinik für Frauenheilkunde und Geburtshilfe, University of Schleswig-Holstein, Lübeck
  11. 11Department for Tumortherapy, Krankenhaus Jerusalem Hamburg, Hamburg
  12. 12Department of Gynecology, Charité – Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin
  13. 13Department of Gynecology and Obstetrics, University of Duisburg-Essen, Essen, Germany
  14. 14Department for Gynecology, Universitätsspital Basel, Basel, Switzerland
  15. 15Department of Obstrics and Gynecology, Universtity of Ulm, Ulm
  16. 6Zentrum für Department of Gynecology, University Medical Center Hamburg-Eppendorf, Hamburg
  17. 17Department of Gynecology and Obstetrics, Technical University of Munich, Munich
  18. 18Gynecologic Oncology Center, Kiel, Germany


Introduction/Background Predicting surgical outcome could improve individualizing treatment strategies for pts with AOC. Several earlier papers have proposed that debulking signatures using RNA expression analyses might help in this regard.

Methodology FFPE tumor tissue of FIGO stage IIIC/IV pts of AGO-OVAR 11 were used to generate whole-exome data. Previously identified molecular signatures (MS), including TCGA molecular subtype and three other published MPG, were tested. We used state-of the art biostatistical approaches for analyses. A theoretical model using data of a tertiary gynecologic cancer center was implemented to evaluate the impact of predictive factors for RD not directly related to tumor biology on the performance of a debulking signature model that predicts RD status (RD0 and RD<1).

Results Of the 266 pts that met inclusion criteria, 104 (39.1%) underwent complete resection. Previously reported MPG did not predict RD in this cohort. Best Area under the Curve (AUC) was generated using LR: 0.53±0.04. Similarly, TCGA molecular subtypes (AUC=0.56±0.04), an independent de novo signature (AUC=0.51±0.04), and the total gene expression data set using all 21,000 genes (AUC=0.55±0.03) were not able to predict RD status. We identified reasons unrelated to tumor biology that serve as potential limiting factors in the ability to use MS for predicting RD. Even in a centre with a complete resection rate higher than 65% in all comers, a debulking signature which perfectly predicts RD (100%) would have a predictive performance of only AUC 0.83, due to UTB.

Conclusion Previously identified MPG cannot be generalized. Our theoretical model showed that factors unrelated to tumor biology limit the ability to identify a molecular debulking signature for RD. UTB may be the main obstacle to predict surgical outcome in an all comer Population.

Disclosure FH: Advisory board: Roche, Tesaro, Honoraria: AstraZeneca, Roche, Tesaro, Clovis, travel/accommodation expenses: PharmaMar; Tesaro; SK: none ; RT: none; AG: none; LU: none; CA: none; AB:none; CW: none; UC: Advisory board/honoraria: Roche, Astra Zeneca; JW:none; AB: none; SP: none; LH:none; SM: none; BA: Advisory board: Roche, Tesaro, Amgen; honoraria: Celgene, Clovis, Astra Zeneca; travel/accommodation expenses: PharmaMar; FH: Honoraria: Roche, Tesaro, Clovis, Astra Zeneca, PharmaMar travel/accommodations/expenses: Astra Zeneca, Pharmamar; SS: none; JS:; RK: Advisory Role: Medtronic, Roche, Teva; Honoraria: AstraZeneca, Intuitive Surgical, Prostrakan, RIEMSER; travel/accommodations/expenses: Cambridge Medical Robotics; CK: none; BS: none; IB: Honoraria: Roche Pharma, CLOVIS, Tesaro, AstraZeneca, Seattle Genetics, Amgen; PH: Advisory Board: Astra Zeneca, Roche, Tesaro, Lilly, Clovis, Immunogen, MSD/Merck; Honoraria: Astra Zeneca, Roche, Sotio, Tesaro, Stryker, ASCO, Zai Lab, MSD; Research funding (Inst): Astra Zeneca, Roche, GSK, Boehringer Ingelheim, Medac, DFG, European Union, DKH, Tesaro, Genmab; SD: none; BW: none; JP: Advisory board: Clovis, Roche, AstraZeneca, Tesaro; Honoraria: Roche, AstraZeneca, Tesaro, Amgen, Clovis, MSD; travel/accommodations/expenses: Roche, Tesaro; AdB: advisory board: Roche, Astra Zeneca, Tesaro, Clovis, Pfizer, Biocad, Genmab, Seattle Genetics, MSD; Honoraria: Roche, Astra Zeneca, Tesaro, Clovis, Pfizer, Biocad, Genmab, Seattle Genetics, MSD.

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