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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| 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. |
| doi_str_mv | 10.1371/journal.pone.0235490 |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0235490</identifier><identifier>PMID: 32628708</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Biology and Life Sciences ; Biomarkers ; Breast cancer ; Cancer ; Cancer metastasis ; Cohort analysis ; Colon ; Colorectal cancer ; Colorectal carcinoma ; Computer and Information Sciences ; Development and progression ; Diet ; Epidermal growth factor receptors ; Gene mutation ; Genes ; Genetic aspects ; Genetics ; Health aspects ; K-Ras protein ; Laboratories ; Learning algorithms ; Machine learning ; Mathematical models ; Medical prognosis ; Medical research ; Medicine and Health Sciences ; Metastases ; Metastasis ; Monoclonal antibodies ; Mutation ; Neural networks ; Parameters ; Physical Sciences ; Prediction models ; Prognosis ; Ras genes ; Research and Analysis Methods ; Small intestine ; Statistical analysis ; Statistical tests ; Studies ; Tumors</subject><ispartof>PloS one, 2020-07, Vol.15 (7), p.e0235490-e0235490</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Sanchez-Ibarra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Sanchez-Ibarra et al 2020 Sanchez-Ibarra et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c669t-750af62842ca838caa5e074217813b40b00c205373f3862ff6b87abb7d53ca4b3</citedby><cites>FETCH-LOGICAL-c669t-750af62842ca838caa5e074217813b40b00c205373f3862ff6b87abb7d53ca4b3</cites><orcidid>0000-0002-9991-267X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7337295/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7337295/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2100,2926,23864,27922,27923,53789,53791,79370,79371</link.rule.ids></links><search><contributor>Toland, Amanda Ewart</contributor><creatorcontrib>Sanchez-Ibarra, Hector Eduardo</creatorcontrib><creatorcontrib>Jiang, Xianli</creatorcontrib><creatorcontrib>Gallegos-Gonzalez, Elena Yareli</creatorcontrib><creatorcontrib>Cavazos-González, Adriana Carolina</creatorcontrib><creatorcontrib>Chen, Yenho</creatorcontrib><creatorcontrib>Morcos, Faruck</creatorcontrib><creatorcontrib>Barrera-Saldaña, Hugo Alberto</creatorcontrib><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><title>PloS one</title><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). 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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.</description><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Breast cancer</subject><subject>Cancer</subject><subject>Cancer metastasis</subject><subject>Cohort analysis</subject><subject>Colon</subject><subject>Colorectal cancer</subject><subject>Colorectal carcinoma</subject><subject>Computer and Information Sciences</subject><subject>Development and progression</subject><subject>Diet</subject><subject>Epidermal growth factor receptors</subject><subject>Gene mutation</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Genetics</subject><subject>Health aspects</subject><subject>K-Ras protein</subject><subject>Laboratories</subject><subject>Learning algorithms</subject><subject>Machine 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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><author>Sanchez-Ibarra, Hector Eduardo ; Jiang, Xianli ; Gallegos-Gonzalez, Elena Yareli ; Cavazos-González, Adriana Carolina ; Chen, Yenho ; Morcos, Faruck ; Barrera-Saldaña, Hugo Alberto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c669t-750af62842ca838caa5e074217813b40b00c205373f3862ff6b87abb7d53ca4b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Biology and Life Sciences</topic><topic>Biomarkers</topic><topic>Breast cancer</topic><topic>Cancer</topic><topic>Cancer metastasis</topic><topic>Cohort analysis</topic><topic>Colon</topic><topic>Colorectal cancer</topic><topic>Colorectal carcinoma</topic><topic>Computer and Information Sciences</topic><topic>Development 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Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sanchez-Ibarra, Hector Eduardo</au><au>Jiang, Xianli</au><au>Gallegos-Gonzalez, Elena Yareli</au><au>Cavazos-González, Adriana Carolina</au><au>Chen, Yenho</au><au>Morcos, Faruck</au><au>Barrera-Saldaña, Hugo Alberto</au><au>Toland, Amanda Ewart</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>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</atitle><jtitle>PloS one</jtitle><date>2020-07-06</date><risdate>2020</risdate><volume>15</volume><issue>7</issue><spage>e0235490</spage><epage>e0235490</epage><pages>e0235490-e0235490</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>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.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>32628708</pmid><doi>10.1371/journal.pone.0235490</doi><tpages>e0235490</tpages><orcidid>https://orcid.org/0000-0002-9991-267X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Biology and Life Sciences Biomarkers Breast cancer Cancer Cancer metastasis Cohort analysis Colon Colorectal cancer Colorectal carcinoma Computer and Information Sciences Development and progression Diet Epidermal growth factor receptors Gene mutation Genes Genetic aspects Genetics Health aspects K-Ras protein Laboratories Learning algorithms Machine learning Mathematical models Medical prognosis Medical research Medicine and Health Sciences Metastases Metastasis Monoclonal antibodies Mutation Neural networks Parameters Physical Sciences Prediction models Prognosis Ras genes Research and Analysis Methods Small intestine Statistical analysis Statistical tests Studies Tumors |
| 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 |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-13T18%3A12%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=KRAS,%20NRAS,%20and%20BRAF%20mutation%20prevalence,%20clinicopathological%20association,%20and%20their%20application%20in%20a%20predictive%20model%20in%20Mexican%20patients%20with%20metastatic%20colorectal%20cancer:%20A%20retrospective%20cohort%20study&rft.jtitle=PloS%20one&rft.au=Sanchez-Ibarra,%20Hector%20Eduardo&rft.date=2020-07-06&rft.volume=15&rft.issue=7&rft.spage=e0235490&rft.epage=e0235490&rft.pages=e0235490-e0235490&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0235490&rft_dat=%3Cgale_plos_%3EA628712478%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2420594156&rft_id=info:pmid/32628708&rft_galeid=A628712478&rft_doaj_id=oai_doaj_org_article_e4705801a02c4fbfb0e94c5dae39b915&rfr_iscdi=true |