1. 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
- Author
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Hector E. Sanchez-Ibarra, Hugo A. Barrera-Saldaña, Yenho Chen, Faruck Morcos, Adriana Carolina Cavazos-González, Xian-Li Jiang, and Elena Yareli Gallegos-Gonzalez
- Subjects
0301 basic medicine ,Neuroblastoma RAS viral oncogene homolog ,Oncology ,Male ,Mutation rate ,endocrine system diseases ,Colorectal cancer ,medicine.medical_treatment ,medicine.disease_cause ,Targeted therapy ,GTP Phosphohydrolases ,Cohort Studies ,0302 clinical medicine ,Mathematical and Statistical Techniques ,Mutation Rate ,Adenocarcinomas ,Medicine and Health Sciences ,Multidisciplinary ,Statistics ,Middle Aged ,medicine.anatomical_structure ,030220 oncology & carcinogenesis ,Physical Sciences ,Medicine ,Female ,KRAS ,Anatomy ,Colorectal Neoplasms ,Cohort study ,Research Article ,Proto-Oncogene Proteins B-raf ,medicine.medical_specialty ,Computer and Information Sciences ,Histology ,Neural Networks ,Colon ,Science ,Rectum ,Research and Analysis Methods ,Carcinomas ,Proto-Oncogene Proteins p21(ras) ,03 medical and health sciences ,Internal medicine ,medicine ,Genetics ,Humans ,Statistical Methods ,neoplasms ,Mexico ,Retrospective Studies ,Colorectal Cancer ,Models, Statistical ,business.industry ,Membrane Proteins ,Biology and Life Sciences ,Cancers and Neoplasms ,Retrospective cohort study ,medicine.disease ,digestive system diseases ,Gastrointestinal Tract ,030104 developmental biology ,Mutation ,Neural Networks, Computer ,business ,Digestive System ,Mathematics ,Forecasting ,Neuroscience - 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.
- Published
- 2020