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Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence
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 creat...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9785370/ https://www.ncbi.nlm.nih.gov/pubmed/36555654 http://dx.doi.org/10.3390/ijms232416014 |
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author | Gonzalez-Bosquet, Jesus Gabrilovich, Sofia McDonald, Megan E. Smith, Brian J. Leslie, Kimberly K. Bender, David D. Goodheart, Michael J. Devor, Eric |
author_facet | Gonzalez-Bosquet, Jesus Gabrilovich, Sofia McDonald, Megan E. Smith, Brian J. Leslie, Kimberly K. Bender, David D. Goodheart, Michael J. Devor, Eric |
author_sort | Gonzalez-Bosquet, Jesus |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9785370 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97853702022-12-24 Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence Gonzalez-Bosquet, Jesus Gabrilovich, Sofia McDonald, Megan E. Smith, Brian J. Leslie, Kimberly K. Bender, David D. Goodheart, Michael J. Devor, Eric Int J Mol Sci Article 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. MDPI 2022-12-16 /pmc/articles/PMC9785370/ /pubmed/36555654 http://dx.doi.org/10.3390/ijms232416014 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gonzalez-Bosquet, Jesus Gabrilovich, Sofia McDonald, Megan E. Smith, Brian J. Leslie, Kimberly K. Bender, David D. Goodheart, Michael J. Devor, Eric Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence |
title | Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence |
title_full | Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence |
title_fullStr | Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence |
title_full_unstemmed | Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence |
title_short | Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence |
title_sort | integration of genomic and clinical retrospective data to predict endometrioid endometrial cancer recurrence |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9785370/ https://www.ncbi.nlm.nih.gov/pubmed/36555654 http://dx.doi.org/10.3390/ijms232416014 |
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