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KRAS, NRAS, and BRAF mutation prevalence, clinicopathological association, and their application in a predictive model in Mexican patients with metastatic colorectal cancer: A retrospective cohort study

Mutations in KRAS, NRAS, and BRAF (RAS/BRAF) genes are the main predictive biomarkers for the response to anti-EGFR monoclonal antibodies (MAbs) targeted therapy in metastatic colorectal cancer (mCRC). This retrospective study aimed to report the mutational status prevalence of these genes, explore...

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Autores principales: Sanchez-Ibarra, Hector Eduardo, Jiang, Xianli, Gallegos-Gonzalez, Elena Yareli, Cavazos-González, Adriana Carolina, Chen, Yenho, Morcos, Faruck, Barrera-Saldaña, Hugo Alberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7337295/
https://www.ncbi.nlm.nih.gov/pubmed/32628708
http://dx.doi.org/10.1371/journal.pone.0235490
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author Sanchez-Ibarra, Hector Eduardo
Jiang, Xianli
Gallegos-Gonzalez, Elena Yareli
Cavazos-González, Adriana Carolina
Chen, Yenho
Morcos, Faruck
Barrera-Saldaña, Hugo Alberto
author_facet Sanchez-Ibarra, Hector Eduardo
Jiang, Xianli
Gallegos-Gonzalez, Elena Yareli
Cavazos-González, Adriana Carolina
Chen, Yenho
Morcos, Faruck
Barrera-Saldaña, Hugo Alberto
author_sort Sanchez-Ibarra, Hector Eduardo
collection PubMed
description Mutations in KRAS, NRAS, and BRAF (RAS/BRAF) genes are the main predictive biomarkers for the response to anti-EGFR monoclonal antibodies (MAbs) targeted therapy in metastatic colorectal cancer (mCRC). This retrospective study aimed to report the mutational status prevalence of these genes, explore their possible associations with clinicopathological features, and build and validate a predictive model. To achieve these objectives, 500 mCRC Mexican patients were screened for clinically relevant mutations in RAS/BRAF genes. Fifty-two percent of these specimens harbored clinically relevant mutations in at least one screened gene. Among these, 86% had a mutation in KRAS, 7% in NRAS, 6% in BRAF, and 2% in both NRAS and BRAF. Only tumor location in the proximal colon exhibited a significant correlation with KRAS and BRAF mutational status (p-value = 0.0414 and 0.0065, respectively). Further t-SNE analyses were made to 191 specimens to reveal patterns among patients with clinical parameters and KRAS mutational status. Then, directed by the results from classical statistical tests and t-SNE analysis, neural network models utilized entity embeddings to learn patterns and build predictive models using a minimal number of trainable parameters. This study could be the first step in the prediction for RAS/BRAF mutational status from tumoral features and could lead the way to a more detailed and more diverse dataset that could benefit from machine learning methods.
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spelling pubmed-73372952020-07-16 KRAS, NRAS, and BRAF mutation prevalence, clinicopathological association, and their application in a predictive model in Mexican patients with metastatic colorectal cancer: A retrospective cohort study Sanchez-Ibarra, Hector Eduardo Jiang, Xianli Gallegos-Gonzalez, Elena Yareli Cavazos-González, Adriana Carolina Chen, Yenho Morcos, Faruck Barrera-Saldaña, Hugo Alberto PLoS One Research Article Mutations in KRAS, NRAS, and BRAF (RAS/BRAF) genes are the main predictive biomarkers for the response to anti-EGFR monoclonal antibodies (MAbs) targeted therapy in metastatic colorectal cancer (mCRC). This retrospective study aimed to report the mutational status prevalence of these genes, explore their possible associations with clinicopathological features, and build and validate a predictive model. To achieve these objectives, 500 mCRC Mexican patients were screened for clinically relevant mutations in RAS/BRAF genes. Fifty-two percent of these specimens harbored clinically relevant mutations in at least one screened gene. Among these, 86% had a mutation in KRAS, 7% in NRAS, 6% in BRAF, and 2% in both NRAS and BRAF. Only tumor location in the proximal colon exhibited a significant correlation with KRAS and BRAF mutational status (p-value = 0.0414 and 0.0065, respectively). Further t-SNE analyses were made to 191 specimens to reveal patterns among patients with clinical parameters and KRAS mutational status. Then, directed by the results from classical statistical tests and t-SNE analysis, neural network models utilized entity embeddings to learn patterns and build predictive models using a minimal number of trainable parameters. This study could be the first step in the prediction for RAS/BRAF mutational status from tumoral features and could lead the way to a more detailed and more diverse dataset that could benefit from machine learning methods. Public Library of Science 2020-07-06 /pmc/articles/PMC7337295/ /pubmed/32628708 http://dx.doi.org/10.1371/journal.pone.0235490 Text en © 2020 Sanchez-Ibarra et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sanchez-Ibarra, Hector Eduardo
Jiang, Xianli
Gallegos-Gonzalez, Elena Yareli
Cavazos-González, Adriana Carolina
Chen, Yenho
Morcos, Faruck
Barrera-Saldaña, Hugo Alberto
KRAS, NRAS, and BRAF mutation prevalence, clinicopathological association, and their application in a predictive model in Mexican patients with metastatic colorectal cancer: A retrospective cohort study
title KRAS, NRAS, and BRAF mutation prevalence, clinicopathological association, and their application in a predictive model in Mexican patients with metastatic colorectal cancer: A retrospective cohort study
title_full KRAS, NRAS, and BRAF mutation prevalence, clinicopathological association, and their application in a predictive model in Mexican patients with metastatic colorectal cancer: A retrospective cohort study
title_fullStr KRAS, NRAS, and BRAF mutation prevalence, clinicopathological association, and their application in a predictive model in Mexican patients with metastatic colorectal cancer: A retrospective cohort study
title_full_unstemmed KRAS, NRAS, and BRAF mutation prevalence, clinicopathological association, and their application in a predictive model in Mexican patients with metastatic colorectal cancer: A retrospective cohort study
title_short KRAS, NRAS, and BRAF mutation prevalence, clinicopathological association, and their application in a predictive model in Mexican patients with metastatic colorectal cancer: A retrospective cohort study
title_sort kras, nras, and braf mutation prevalence, clinicopathological association, and their application in a predictive model in mexican patients with metastatic colorectal cancer: a retrospective cohort study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7337295/
https://www.ncbi.nlm.nih.gov/pubmed/32628708
http://dx.doi.org/10.1371/journal.pone.0235490
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