Creation and validation of models to predict response to primary treatment in serous ovarian cancer

Jesus Gonzalez Bosquet; Eric J Devor; Andreea M Newtson; Brian J Smith; David P Bender; Michael J Goodheart; Megan E McDonald; Terry A Braun; Kristina W Thiel; Kimberly K Leslie

doi:10.1038/s41598-021-85256-9

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Creation and validation of models to predict response to primary treatment in serous ovarian cancer

Journal article

Open access

Peer reviewed

Creation and validation of models to predict response to primary treatment in serous ovarian cancer

Jesus Gonzalez Bosquet, Eric J Devor, Andreea M Newtson, Brian J Smith, David P Bender, Michael J Goodheart, Megan E McDonald, Terry A Braun, Kristina W Thiel and Kimberly K Leslie

Scientific reports, Vol.11(1), pp.5957-5957

03/16/2021

DOI: 10.1038/s41598-021-85256-9

PMCID: PMC7971042

PMID: 33727600

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Abstract

Nearly a third of patients with high-grade serous ovarian cancer (HGSC) do not respond to initial therapy and have an overall poor prognosis. However, there are no validated tools that accurately predict which patients will not respond. Our objective is to create and validate accurate models of prediction for treatment response in HGSC. This is a retrospective case-control study that integrates comprehensive clinical and genomic data from 88 patients with HGSC from a single institution. Responders were those patients with a progression-free survival of at least 6 months after treatment. Only patients with complete clinical information and frozen specimen at surgery were included. Gene, miRNA, exon, and long non-coding RNA (lncRNA) expression, gene copy number, genomic variation, and fusion-gene determination were extracted from RNA-sequencing data. DNA methylation analysis was performed. Initial selection of informative variables was performed with univariate ANOVA with cross-validation. Significant variables (p < 0.05) were included in multivariate lasso regression prediction models. Initial models included only one variable. Variables were then combined to create complex models. Model performance was measured with area under the curve (AUC). Validation of all models was performed using TCGA HGSC database. By integrating clinical and genomic variables, we achieved prediction performances of over 95% in AUC. Most performances in the validation set did not differ from the training set. Models with DNA methylation or lncRNA underperformed in the validation set. Integrating comprehensive clinical and genomic data from patients with HGSC results in accurate and robust prediction models of treatment response.

Details

Title: Subtitle: Creation and validation of models to predict response to primary treatment in serous ovarian cancer
Creators: Jesus Gonzalez Bosquet - University of Iowa Hospitals and Clinics
Eric J Devor - University of Iowa Hospitals and Clinics
Andreea M Newtson - University of Iowa Hospitals and Clinics
Brian J Smith - University of Iowa, Biostatistics
David P Bender - University of Iowa Hospitals and Clinics
Michael J Goodheart - University of Iowa Hospitals and Clinics
Megan E McDonald - University of Iowa Hospitals and Clinics
Terry A Braun - University of Iowa Hospitals and Clinics
Kristina W Thiel - University of Iowa Hospitals and Clinics
Kimberly K Leslie - University of Iowa Hospitals and Clinics
Resource Type: Journal article
Publication Details: Scientific reports, Vol.11(1), pp.5957-5957
DOI: 10.1038/s41598-021-85256-9
PMID: 33727600
PMCID: PMC7971042
NLM abbreviation: Sci Rep
ISSN: 2045-2322
eISSN: 2045-2322
Grant note: P30 CA086862 / NCI NIH HHS
Language: English
Date published: 03/16/2021
Academic Unit: Preventive and Community Dentistry; Roy J. Carver Department of Biomedical Engineering; Biostatistics; Obstetrics and Gynecology; Holden Comprehensive Cancer Center
Record Identifier: 9984196988202771

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