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KRAS、NRAS和BRAF基因突变患病率、临床病理关联及其在墨西哥转移性结直肠癌患者预测模型中的应用:一项回顾性队列研究。

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.

作者信息

Sanchez-Ibarra Hector Eduardo, Jiang Xianli, Gallegos-Gonzalez Elena Yareli, Cavazos-González Adriana Carolina, Chen Yenho, Morcos Faruck, Barrera-Saldaña Hugo Alberto

机构信息

Genetics Laboratory, Vitagénesis SA de CV, Monterrey, Nuevo Leon, Mexico.

Evolutionary Information Laboratory, Department of Biological Sciences, the University of Texas at Dallas, Richardson, Texas, United States of America.

出版信息

PLoS One. 2020 Jul 6;15(7):e0235490. doi: 10.1371/journal.pone.0235490. eCollection 2020.

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.

摘要

KRAS、NRAS和BRAF(RAS/BRAF)基因的突变是转移性结直肠癌(mCRC)中抗表皮生长因子受体单克隆抗体(MAb)靶向治疗反应的主要预测生物标志物。这项回顾性研究旨在报告这些基因的突变状态患病率,探索它们与临床病理特征的可能关联,并建立和验证一个预测模型。为实现这些目标,对500例墨西哥mCRC患者进行了RAS/BRAF基因临床相关突变的筛查。这些标本中有52%在至少一个筛查基因中存在临床相关突变。其中,86%的患者KRAS发生突变,7%的患者NRAS发生突变,6%的患者BRAF发生突变,2%的患者NRAS和BRAF均发生突变。仅近端结肠的肿瘤位置与KRAS和BRAF突变状态存在显著相关性(p值分别为0.0414和0.0065)。对191个标本进行了进一步的t-SNE分析,以揭示临床参数和KRAS突变状态患者之间的模式。然后,在经典统计测试和t-SNE分析结果的指导下,神经网络模型利用实体嵌入来学习模式,并使用最少数量的可训练参数建立预测模型。这项研究可能是从肿瘤特征预测RAS/BRAF突变状态的第一步,并可能为受益于机器学习方法的更详细、更多样化的数据集开辟道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d506/7337295/f9944b5bcd4a/pone.0235490.g001.jpg

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