期刊
SCIENCE TRANSLATIONAL MEDICINE
卷 10, 期 457, 页码 -出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/scitranslmed.aar7939
关键词
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资金
- U.S. NIH [CA121113, CA180950]
- Dr. Miriam and Sheldon G. Adelson Medical Research Foundation
- Stand Up to Cancer-Dutch Cancer Society International Translational Cancer Research Dream Team Grant [SU2C-AACR-DT1415]
- Commonwealth Foundation
- Eastern Cooperative Oncology Group-American College of Radiology Imaging Network
- MacMillan Foundation
- LUNGevity Foundation
- NATIONAL CANCER INSTITUTE [U10CA180950, P30CA006973, R01CA121113] Funding Source: NIH RePORTER
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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