Prediction of chemo-response in serous ovarian cancer

Jesus Gonzalez Bosquet; Andreea M Newtson; Rebecca K Chung; Kristina W Thiel; Timothy Ginader; Michael J Goodheart; Kimberly K Leslie; Brian J Smith

doi:10.1186/s12943-016-0548-9

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Prediction of chemo-response in serous ovarian cancer

Journal article

Open access

Peer reviewed

Prediction of chemo-response in serous ovarian cancer

Jesus Gonzalez Bosquet, Andreea M Newtson, Rebecca K Chung, Kristina W Thiel, Timothy Ginader, Michael J Goodheart, Kimberly K Leslie and Brian J Smith

Molecular cancer, Vol.15(1), p.66

10/19/2016

DOI: 10.1186/s12943-016-0548-9

PMCID: PMC5070116

PMID: 27756408

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Abstract

Nearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. Using the Cancer Genome Atlas (TCGA) serous OVCA database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Our objective was to test whether this signature is an accurate and sensitive predictor of chemo-response in serous OVCA. We first constructed prediction models to predict chemo-response using our previously described 422-gene signature that was associated with response to treatment in serous OVCA. Performance of all prediction models were measured with area under the curves (AUCs, a measure of the model's accuracy) and their respective confidence intervals (CIs). To optimize the prediction process, we determined which elements of the signature most contributed to chemo-response prediction. All prediction models were replicated and validated using six publicly available independent gene expression datasets. The 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Optimization of prediction models identified the 34 most important genes in chemo-response prediction. These 34-gene models had improved performance, with AUCs approaching 80 %. Both 422-gene and 34-gene prediction models were replicated and validated in six independent datasets. These prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients.

Area Under Curve

Oligonucleotide Array Sequence Analysis - methods

Humans

Middle Aged

Gene Expression Profiling - methods

Treatment Outcome

Antineoplastic Agents - therapeutic use

Cystadenocarcinoma, Serous - genetics

Gene Regulatory Networks - drug effects

Cystadenocarcinoma, Serous - drug therapy

Ovarian Neoplasms - genetics

Survival Analysis

Female

Antineoplastic Agents - pharmacology

Models, Genetic

Gene Expression Regulation, Neoplastic - drug effects

Ovarian Neoplasms - drug therapy

Precision Medicine

Details

Title: Subtitle: Prediction of chemo-response in serous ovarian cancer
Creators: Jesus Gonzalez Bosquet - Department of Obstetrics and Gynecology, University of Iowa Hospitals and Clinics, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA. jesus-gonzalezbosquet@uiowa.edu
Andreea M Newtson - Department of Obstetrics and Gynecology, University of Iowa Hospitals and Clinics, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
Rebecca K Chung - Department of Obstetrics and Gynecology, University of Iowa Hospitals and Clinics, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
Kristina W Thiel - Department of Obstetrics and Gynecology, University of Iowa Hospitals and Clinics, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
Timothy Ginader - Biostatistics, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, USA
Michael J Goodheart - Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, USA
Kimberly K Leslie - Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, USA
Brian J Smith - Biostatistics, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, USA
Resource Type: Journal article
Publication Details: Molecular cancer, Vol.15(1), p.66
DOI: 10.1186/s12943-016-0548-9
PMID: 27756408
PMCID: PMC5070116
NLM abbreviation: Mol Cancer
ISSN: 1476-4598
eISSN: 1476-4598
Publisher: England
Grant note: R01 CA099908 / NCI NIH HHS P30 CA086862 / NCI NIH HHS
Language: English
Date published: 10/19/2016
Academic Unit: Biostatistics; Obstetrics and Gynecology; Holden Comprehensive Cancer Center
Record Identifier: 9983931497602771

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