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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

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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: CLIN CANCER RES, Vol. 26, No. 1, 01.01.2020, p. 213-219.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

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', CLIN CANCER RES, vol. 26, no. 1, pp. 213-219. https://doi.org/10.1158/1078-0432.CCR-19-1741

APA

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. CLIN CANCER RES, 26(1), 213-219. https://doi.org/10.1158/1078-0432.CCR-19-1741

Vancouver

Heitz F, Kommoss S, Tourani R, Grandelis A, Uppendahl L, Aliferis C et al. 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. CLIN CANCER RES. 2020 Jan 1;26(1):213-219. https://doi.org/10.1158/1078-0432.CCR-19-1741

Bibtex

@article{ae9ca233b78a49f88c9a54c91d626362,
title = "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",
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.",
author = "Florian Heitz and Stefan Kommoss and Roshan Tourani and Anthony Grandelis and Locke Uppendahl and Constantin Aliferis and Alexander Burges and Chen Wang and Ulrich Canzler and Jinhua Wang and Antje Belau and Sonia Prader and Lars Hanker and Sisi Ma and Beyhan Ataseven and Felix Hilpert and Stephanie Schneider and Jalid Sehouli and Rainer Kimmig and Christian Kurzeder and Barbara Schmalfeldt and Braicu, {Elena I} and Philipp Harter and Dowdy, {Sean C} and Winterhoff, {Boris J} and Jacobus Pfisterer and {du Bois}, Andreas",
note = "{\textcopyright}2019 American Association for Cancer Research.",
year = "2020",
month = jan,
day = "1",
doi = "10.1158/1078-0432.CCR-19-1741",
language = "English",
volume = "26",
pages = "213--219",
journal = "CLIN CANCER RES",
issn = "1078-0432",
publisher = "American Association for Cancer Research Inc.",
number = "1",

}

RIS

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 - ©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 - SCORING: Journal article

C2 - 31527166

VL - 26

SP - 213

EP - 219

JO - CLIN CANCER RES

JF - CLIN CANCER RES

SN - 1078-0432

IS - 1

ER -