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

Florian Heitz, Stefan Kommoss, Roshan Tourani, Anthony Grandelis, Locke Uppendahl, Constantin Aliferis, Alexander Burges, Chen Wang, Ulrich Canzler, Jinhua Wang, Antje Belau, Sonia Prader, Lars Hanker, Sisi Ma, Beyhan Ataseven, Felix Hilpert, Stephanie Schneider, Jalid Sehouli, Rainer Kimmig, Christian KurzederBarbara Schmalfeldt, Elena I. Braicu, Philipp Harter, Sean C. Dowdy, Boris J. Winterhoff, Jacobus Pfisterer, Andreas du Bois

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)213-219
Number of pages7
JournalClinical Cancer Research
Volume26
Issue number1
DOIs
StatePublished - Jan 1 2020

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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    Heitz, F., Kommoss, S., Tourani, R., Grandelis, A., Uppendahl, L., Aliferis, C., Burges, A., Wang, C., Canzler, U., Wang, J., Belau, A., Prader, S., Hanker, L., Ma, S., Ataseven, B., Hilpert, F., Schneider, S., Sehouli, J., Kimmig, R., ... du Bois, A. (2020). 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. Clinical Cancer Research, 26(1), 213-219. https://doi.org/10.1158/1078-0432.CCR-19-1741