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

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.See related commentary by Handley and Sood, p. 9.

Original languageEnglish (US)
Pages (from-to)213-219
Number of pages7
JournalClinical cancer research : an official journal of the American Association for Cancer Research
Volume26
Issue number1
DOIs
StatePublished - Jan 1 2020

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Ovarian Neoplasms
Transcriptome
Genes
Neoplasms
Selection Bias
Atlases
Residual Neoplasm
Gene Expression Profiling
Tumor Biomarkers
Area Under Curve
Theoretical Models
Biomarkers
Genome
Population

Bibliographical note

©2019 American Association for Cancer Research.

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

In: Clinical cancer research : an official journal of the American Association for Cancer Research, Vol. 26, No. 1, 01.01.2020, p. 213-219.

Research output: Contribution to journalArticle

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, Kurzeder, C, Schmalfeldt, B, Braicu, EI, Harter, P, Dowdy, SC, Winterhoff, BJ, Pfisterer, J & 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 : an official journal of the American Association for Cancer Research, vol. 26, no. 1, pp. 213-219. https://doi.org/10.1158/1078-0432.CCR-19-1741
Heitz, Florian ; Kommoss, Stefan ; Tourani, Roshan ; Grandelis, Anthony ; Uppendahl, Locke ; Aliferis, Constantin ; Burges, Alexander ; Wang, Chen ; Canzler, Ulrich ; Wang, Jinhua ; Belau, Antje ; Prader, Sonia ; Hanker, Lars ; Ma, Sisi ; Ataseven, Beyhan ; Hilpert, Felix ; Schneider, Stephanie ; Sehouli, Jalid ; Kimmig, Rainer ; Kurzeder, Christian ; Schmalfeldt, Barbara ; Braicu, Elena I ; Harter, Philipp ; Dowdy, Sean C ; Winterhoff, Boris J ; Pfisterer, Jacobus ; du Bois, Andreas. / 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. In: Clinical cancer research : an official journal of the American Association for Cancer Research. 2020 ; Vol. 26, No. 1. pp. 213-219.
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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 - ©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.

U2 - 10.1158/1078-0432.CCR-19-1741

DO - 10.1158/1078-0432.CCR-19-1741

M3 - Article

C2 - 31527166

VL - 26

SP - 213

EP - 219

JO - Clinical Cancer Research

JF - Clinical Cancer Research

SN - 1078-0432

IS - 1

ER -