Research ResourceCancer

A machine learning approach for somatic mutation discovery

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Science Translational Medicine  05 Sep 2018:
Vol. 10, Issue 457, eaar7939
DOI: 10.1126/scitranslmed.aar7939

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Calling it like the algorithm sees it

Somatic mutation calling is essential for the proper diagnosis and treatment of most cancer patients. Wood et al. developed a machine learning approach called Cerebro that increased the accuracy of calling validated somatic mutations in tumor samples from cancer patients. Cerebro outperformed six other mutation detection methods by better distinguishing technical sequencing artifacts. An analysis of non–small cell lung cancer and melanoma patient samples revealed that Cerebro more accurately classified patients according to their immunotherapy response, suggesting that the authors’ mutation calling approach could favorably affect patient care.

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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