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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