A machine learning approach for somatic mutation discovery

Derrick E. Wood

doi:10.1126/scitranslmed.aar7939

A machine learning approach for somatic mutation discovery

2018-09-05

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Victor E. Velculescu *

S

Samuel V. Angiuoli *

DOI：10.1126/scitranslmed.aar7939

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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.

Keywords:

COMPREHENSIVE MOLECULAR CHARACTERIZATION

GENERATION SEQUENCING PANEL

GENOMIC CHARACTERIZATION

CLINICAL VALIDATION

POINT MUTATIONS

READ ALIGNMENT

HUMAN BREAST

OPEN-LABEL

CANCER

TUMOR

A machine learning approach for somatic mutation discovery

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