Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence

Jesus Gonzalez-Bosquet; Sofia Gabrilovich; Megan E McDonald; Brian J Smith; Kimberly K Leslie; David D Bender; Michael J Goodheart; Eric Devor

doi:10.3390/ijms232416014

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Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence

Journal article

Open access

Peer reviewed

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence

Jesus Gonzalez-Bosquet, Sofia Gabrilovich, Megan E McDonald, Brian J Smith, Kimberly K Leslie, David D Bender, Michael J Goodheart and Eric Devor

International journal of molecular sciences, Vol.23(24), p.16014

12/16/2022

DOI: 10.3390/ijms232416014

PMCID: PMC9785370

PMID: 36555654

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Abstract

Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. However, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case-control study evaluating patients with EC, endometrioid type: 7 with recurrence of disease (cases), and 55 without (controls). RNA was extracted from frozen specimens and sequenced (RNAseq). Genomic features from RNAseq included transcriptome expression, genomic, and structural variation. Feature selection for variable reduction was performed with univariate ANOVA with cross-validation. Selected variables, informative for EC recurrence, were introduced in multivariate lasso regression models. Validation of models was performed in machine-learning platforms (ML) and independent datasets (TCGA). The best performing prediction models (out of >170) contained the same lncRNA features (AUC of 0.9, and 95% CI: 0.75, 1.0). Models were validated with excellent performance in ML platforms and good performance in an independent dataset. Prediction models of EC recurrence containing lncRNA features have better performance than models with clinical data alone.

Endometrial Cancer

Machine Learning

recurrence

prediction

Details

Title: Subtitle: Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence
Creators: Jesus Gonzalez-Bosquet - Department of Obstetrics and Gynecology, University of Iowa, 200 Hawkins Dr., Iowa City, IA 52242, USA
Sofia Gabrilovich - Department of Obstetrics and Gynecology, University of Iowa, 200 Hawkins Dr., Iowa City, IA 52242, USA
Megan E McDonald - University of Iowa
Brian J Smith - Department of Biostatistics, University of Iowa, 145 N Riverside Dr., Iowa City, IA 52242, USA
Kimberly K Leslie - University of New Mexico
David D Bender - Department of Obstetrics and Gynecology, University of Iowa, 200 Hawkins Dr., Iowa City, IA 52242, USA
Michael J Goodheart - University of Iowa
Eric Devor - Department of Obstetrics and Gynecology, University of Iowa, 200 Hawkins Dr., Iowa City, IA 52242, USA
Resource Type: Journal article
Publication Details: International journal of molecular sciences, Vol.23(24), p.16014
DOI: 10.3390/ijms232416014
PMID: 36555654
PMCID: PMC9785370
NLM abbreviation: Int J Mol Sci
ISSN: 1422-0067
eISSN: 1422-0067
Grant note: OC190352 / Department of Defence 5R01CA99908-18 / National Institute of Health 2014 / Obstetrics & Gynecology department Research Funds
Language: English
Date published: 12/16/2022
Academic Unit: Biostatistics; Obstetrics and Gynecology; Holden Comprehensive Cancer Center
Record Identifier: 9984339316202771

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