Systematic pan-cancer analysis of mutation-treatment interactions using large real-world clinicogenomics data.

Abstract

Quantifying the effectiveness of different cancer therapies in patients with specific tumor mutations is critical for improving patient outcomes and advancing precision medicine. Here we perform a large-scale computational analysis of 40,903 US patients with cancer who have detailed mutation profiles, treatment sequences and outcomes derived from electronic health records. We systematically identify 458 mutations that predict the survival of patients on specific immunotherapies, chemotherapy agents or targeted therapies across eight common cancer types. We further characterize mutation-mutation interactions that impact the outcomes of targeted therapies. This work demonstrates how computational analysis of large real-world data generates insights, hypotheses and resources to enable precision oncology.

• Authors: Ruishan Liu, Shemra Rizzo, Sarah Waliany, Marius Rene Garmhausen, Navdeep Pal, Zhi Huang, Nayan Chaudhary, Lisa Wang, Chris Harbron, Joel Neal, Ryan Copping, James Zou
• Journal: Nature medicine (Nat Med) Vol. 28 Issue 8 Pg. 1656-1661 (08 2022) ISSN: 1546-170X [Electronic] United States
• PMID: 35773542 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S.)
• Copyright: © 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.

Chemical References

• Antineoplastic Agents

Topics

• Antineoplastic Agents (therapeutic use)
• Humans
• Immunotherapy
• Mutation (genetics)
• Neoplasms (drug therapy, therapy)
• Precision Medicine