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

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 model...

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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f68931067a254b7db52a837c8291c2da
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spelling	oai:doaj.org-article:f68931067a254b7db52a837c8291c2da2021-12-02T11:39:39ZCreation and validation of models to predict response to primary treatment in serous ovarian cancer10.1038/s41598-021-85256-92045-2322https://doaj.org/article/f68931067a254b7db52a837c8291c2da2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85256-9https://doaj.org/toc/2045-2322Abstract 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.Jesus Gonzalez BosquetEric J. DevorAndreea M. NewtsonBrian J. SmithDavid P. BenderMichael J. GoodheartMegan E. McDonaldTerry A. BraunKristina W. ThielKimberly K. LeslieNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q 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 Creation and validation of models to predict response to primary treatment in serous ovarian cancer
description	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.
format	article
author	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
author_facet	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
author_sort	Jesus Gonzalez Bosquet
title	Creation and validation of models to predict response to primary treatment in serous ovarian cancer
title_short	Creation and validation of models to predict response to primary treatment in serous ovarian cancer
title_full	Creation and validation of models to predict response to primary treatment in serous ovarian cancer
title_fullStr	Creation and validation of models to predict response to primary treatment in serous ovarian cancer
title_full_unstemmed	Creation and validation of models to predict response to primary treatment in serous ovarian cancer
title_sort	creation and validation of models to predict response to primary treatment in serous ovarian cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f68931067a254b7db52a837c8291c2da
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Creation and validation of models to predict response to primary treatment in serous ovarian cancer

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