TY - JOUR
T1 - Dilution of Molecular-Pathologic Gene Signatures by Medically Associated Factors Might Prevent Prediction of Resection Status After Debulking Surgery in Patients With Advanced Ovarian Cancer
AU - Heitz, Florian
AU - Kommoss, Stefan
AU - Tourani, Roshan
AU - Grandelis, Anthony
AU - Uppendahl, Locke
AU - Aliferis, Constantin
AU - Burges, Alexander
AU - Wang, Chen
AU - Canzler, Ulrich
AU - Wang, Jinhua
AU - Belau, Antje
AU - Prader, Sonia
AU - Hanker, Lars
AU - Ma, Sisi
AU - Ataseven, Beyhan
AU - Hilpert, Felix
AU - Schneider, Stephanie
AU - Sehouli, Jalid
AU - Kimmig, Rainer
AU - Kurzeder, Christian
AU - Schmalfeldt, Barbara
AU - Braicu, Elena I
AU - Harter, Philipp
AU - Dowdy, Sean C
AU - Winterhoff, Boris J
AU - Pfisterer, Jacobus
AU - du Bois, Andreas
N1 - Publisher Copyright:
©2019 American Association for Cancer Research.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - PURPOSE: Predicting surgical outcome could improve individualizing treatment strategies for patients with advanced ovarian cancer. It has been suggested earlier that gene expression signatures (GES) might harbor the potential to predict surgical outcome.EXPERIMENTAL DESIGN: Data derived from high-grade serous tumor tissue of FIGO stage IIIC/IV patients of AGO-OVAR11 trial were used to generate a transcriptome profiling. Previously identified molecular signatures were tested. A theoretical model was implemented to evaluate the impact of medically associated factors for residual disease (RD) on the performance of GES that predicts RD status.RESULTS: A total of 266 patients met inclusion criteria, of those, 39.1% underwent complete resection. Previously reported GES did not predict RD in this cohort. Similarly, The Cancer Genome Atlas molecular subtypes, an independent de novo signature and the total gene expression dataset using all 21,000 genes were not able to predict RD status. Medical reasons for RD were identified as potential limiting factors that impact the ability to use GES to predict RD. In a center with high complete resection rates, a GES which would perfectly predict tumor biological RD would have a performance of only AUC 0.83, due to reasons other than tumor biology.CONCLUSIONS: Previously identified GES cannot be generalized. Medically associated factors for RD may be the main obstacle to predict surgical outcome in an all-comer population of patients with advanced ovarian cancer. If biomarkers derived from tumor tissue are used to predict outcome of patients with cancer, selection bias should be focused on to prevent overestimation of the power of such a biomarker.See related commentary by Handley and Sood, p. 9.
AB - PURPOSE: Predicting surgical outcome could improve individualizing treatment strategies for patients with advanced ovarian cancer. It has been suggested earlier that gene expression signatures (GES) might harbor the potential to predict surgical outcome.EXPERIMENTAL DESIGN: Data derived from high-grade serous tumor tissue of FIGO stage IIIC/IV patients of AGO-OVAR11 trial were used to generate a transcriptome profiling. Previously identified molecular signatures were tested. A theoretical model was implemented to evaluate the impact of medically associated factors for residual disease (RD) on the performance of GES that predicts RD status.RESULTS: A total of 266 patients met inclusion criteria, of those, 39.1% underwent complete resection. Previously reported GES did not predict RD in this cohort. Similarly, The Cancer Genome Atlas molecular subtypes, an independent de novo signature and the total gene expression dataset using all 21,000 genes were not able to predict RD status. Medical reasons for RD were identified as potential limiting factors that impact the ability to use GES to predict RD. In a center with high complete resection rates, a GES which would perfectly predict tumor biological RD would have a performance of only AUC 0.83, due to reasons other than tumor biology.CONCLUSIONS: Previously identified GES cannot be generalized. Medically associated factors for RD may be the main obstacle to predict surgical outcome in an all-comer population of patients with advanced ovarian cancer. If biomarkers derived from tumor tissue are used to predict outcome of patients with cancer, selection bias should be focused on to prevent overestimation of the power of such a biomarker.See related commentary by Handley and Sood, p. 9.
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U2 - 10.1158/1078-0432.CCR-19-1741
DO - 10.1158/1078-0432.CCR-19-1741
M3 - Article
C2 - 31527166
SN - 1078-0432
VL - 26
SP - 213
EP - 219
JO - Clinical cancer research : an official journal of the American Association for Cancer Research
JF - Clinical cancer research : an official journal of the American Association for Cancer Research
IS - 1
ER -