ArticleScience translational medicine2018
A machine learning approach for somatic mutation discovery.
Article in Science translational medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- Metastatic Colorectal Cancer Treatment Response Evaluation by Ultra-Deep Sequencing of Cell-Free DNA and Matched White Blood Cells.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Trial
- VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data.Genome medicine · 2026Article
- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
- Deciphering the oncogenic role of key genes in HNSC: insights from multi-omics and functional studies.Discover oncology · 2025Article
- Molecular and immunological features associated with long-term benefits in metastatic NSCLC patients undergoing immune checkpoint blockade.Oncoimmunology · 2025Article
- Detection of Brain Cancer Using Genome-wide Cell-free DNA Fragmentomes.Cancer discovery · 2025Article
- Of Context, Quality, and Complexity: Fine-Combing Tumor Mutational Burden in Immunotherapy-Treated Cancers.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Review
- Improvement of the sensitivity of circulating tumor DNA-based liquid biopsy: current approaches and future perspectives.Exploration of targeted anti-tumor therapy · 2025Review
- Effectiveness of Artificial Intelligence Technologies in Cancer Treatment for Older Adults: A Systematic Review.Journal of clinical medicine · 2024Review
- Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.Frontiers in oncology · 2024Review
- Personalized cancer vaccine design using AI-powered technologies.Frontiers in immunology · 2024Review
- The mutational pattern of homologous recombination repair genes in urothelial carcinoma and its correlation with immunotherapeutic response.Cancer medicine · 2023Article
- Validation of a Molecular Diagnostic Test for Circulating Tumor DNA by Next-Gen Sequencing.International journal of molecular sciences · 2023Article
- Data mining on identifying diagnosis and prognosis biomarkers in head and neck squamous carcinoma.Scientific reports · 2023Article
- RFcaller: a machine learning approach combined with read-level features to detect somatic mutations.NAR genomics and bioinformatics · 2023Article
- Review of diagnosis, differential diagnosis, and management of retroperitoneal lymphangioma.Japanese journal of radiology · 2023Review
- Fast, accurate, and racially unbiased pan-cancer tumor-only variant calling with tabular machine learning.NPJ precision oncology · 2023Article
- Universal cutoff for tumor mutational burden in predicting the efficacy of anti-PD-(L)1 therapy for advanced cancers.Frontiers in cell and developmental biology · 2023Article
- Comprehensive NGS profiling to enable detection ofFrontiers in oncology · 2023Article
- DeepSom: a CNN-based approach to somatic variant calling in WGS samples without a matched normal.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning that outperformed existing methods in identifying experimentally validated tumor alterations (sensitivity of 97% versus 90 to 99%; positive predictive value of 98% versus 34 to 92%). Analysis of paired tumor-normal exome data from 1368 TCGA (The Cancer Genome Atlas) samples using this method revealed concordance for 74% of mutation calls but also identified likely false-positive and false-negative changes in TCGA data, including in clinically actionable genes. Determination of high-quality somatic mutation calls improved tumor mutation load-based predictions of clinical outcome for melanoma and lung cancer patients previously treated with immune checkpoint inhibitors. Integration of high-quality machine learning mutation detection in clinical next-generation sequencing (NGS) analyses increased the accuracy of test results compared to other clinical sequencing analyses. These analyses provide an approach for improved identification of tumor-specific mutations and have important implications for research and clinical management of cancer patients.
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