ArticleTranslational cancer research2024
Machine learning-based pan-cancer study of classification and mechanism of BRAF inhibitor resistance.
Article in Translational cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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7 authors.
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Abstract
Background: V-raf murine sarcoma viral oncogene homolog B1 (BRAF) inhibitor (BRAFi) therapy resistance affects approximately 15% of cancer patients, leading to disease recurrence and poor prognosis. The aim of the study was to develop a machine-learning based method to identify patients who are at high-risk of BRAFi resistance and potential biomarker. Methods: From Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases, we collected RNA sequencing and half maximal inhibitory concentration (IC Results: Conclusions: We established a gene-expression model using ML methods, consisting of 37 variables based on RNA-seq database, which was externally validated and could be used to predict BRAFi resistance. Meanwhile, our findings provide valuable insights into the molecular mechanisms of BRAFi resistance, enabling the identification of high-risk patients.
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