Details

Autor(en) / Beteiligte

• Wood, Derrick E
• White, James R
• Georgiadis, Andrew
• Van Emburgh, Beth
• Parpart-Li, Sonya
• Mitchell, Jason
• Anagnostou, Valsamo
• Niknafs, Noushin
• Karchin, Rachel
• Papp, Eniko
• McCord, Christine
• LoVerso, Peter
• Riley, David
• Diaz, Jr, Luis A
• Jones, Siân
• Sausen, Mark
• Velculescu, Victor E
• Angiuoli, Samuel V

Titel

A machine learning approach for somatic mutation discovery

Ist Teil von

• Science translational medicine, 2018-09, Vol.10 (457)

Ort / Verlag

United States

Erscheinungsjahr

2018

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.