Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas
Copyright policy )Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas
Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these "hidden responders" may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders.
- Department of Pediatric Oncology Dana-Farber Cancer Institute Harvard Medical School United States
- Centerstone United States
- Centeye (United States) United States
- Dartmouth College United States
- Raymond and Ruth Perelman School of Medicine at the University of Pennsylvania United States
Medical Physiology, PATHOGENESIS, pan-cancer, PROTEIN, Cancer Genome Atlas Research Network, Gene, Machine Learning, Gene expression; HRAS; KRAS; NF1; NRAS; Ras; TCGA; drug sensitivity; machine learning; pan-cancer, Neoplasms, Machine Learning and Artificial Intelligence, Precision Oncology, Precision Medicine, Phase-Ii, PRECISION ONCOLOGY, Biology (General), Previously Treated Patients, Cancer, Tumor, Genome, Signatures, PREVIOUSLY TREATED PATIENTS, Biological Sciences, Gene Expression Regulation, Neoplastic, machine learning, Networking and Information Technology R&D (NITRD), HRAS, PHASE-II, Cell Lung-Cancer, Life Sciences & Biomedicine, Mutations, Human, Signal Transduction, 570, Selumetinib, QH301-705.5, Bioinformatics and Computational Biology, 610, NRAS, Article, Cell Line, BRAF, Cancer Genomics, Cell Line, Tumor, 616, Genetics, drug sensitivity; Gene expression; HRAS; KRAS; machine learning; NF1; NRAS; pan-cancer; Ras; TCGA; Biochemistry, Genetics and Molecular Biology (all), KRAS, Humans, drug sensitivity, SIGNATURES, Neoplastic, Thyroid-Cancer, Science & Technology, MUTATIONS, Genome, Human, Protein, Human Genome, Cell Biology, SELUMETINIB, TCGA, GENE, Good Health and Well Being, Gene Expression Regulation, NF1, ras Proteins, Biochemistry and Cell Biology, Gene expression, Ras
Medical Physiology, PATHOGENESIS, pan-cancer, PROTEIN, Cancer Genome Atlas Research Network, Gene, Machine Learning, Gene expression; HRAS; KRAS; NF1; NRAS; Ras; TCGA; drug sensitivity; machine learning; pan-cancer, Neoplasms, Machine Learning and Artificial Intelligence, Precision Oncology, Precision Medicine, Phase-Ii, PRECISION ONCOLOGY, Biology (General), Previously Treated Patients, Cancer, Tumor, Genome, Signatures, PREVIOUSLY TREATED PATIENTS, Biological Sciences, Gene Expression Regulation, Neoplastic, machine learning, Networking and Information Technology R&D (NITRD), HRAS, PHASE-II, Cell Lung-Cancer, Life Sciences & Biomedicine, Mutations, Human, Signal Transduction, 570, Selumetinib, QH301-705.5, Bioinformatics and Computational Biology, 610, NRAS, Article, Cell Line, BRAF, Cancer Genomics, Cell Line, Tumor, 616, Genetics, drug sensitivity; Gene expression; HRAS; KRAS; machine learning; NF1; NRAS; pan-cancer; Ras; TCGA; Biochemistry, Genetics and Molecular Biology (all), KRAS, Humans, drug sensitivity, SIGNATURES, Neoplastic, Thyroid-Cancer, Science & Technology, MUTATIONS, Genome, Human, Protein, Human Genome, Cell Biology, SELUMETINIB, TCGA, GENE, Good Health and Well Being, Gene Expression Regulation, NF1, ras Proteins, Biochemistry and Cell Biology, Gene expression, Ras
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