AI Article Synopsis
- Understanding how different cancer therapies work for patients with specific tumor mutations can lead to better treatment outcomes and personalized medicine.
- A comprehensive analysis of data from over 40,000 cancer patients revealed 458 mutations that help predict patient survival based on the type of therapy received.
- The study also explored how interactions between different mutations affect the effectiveness of targeted therapies, showcasing the potential of big data in advancing precision oncology.
Article 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.
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| http://dx.doi.org/10.1038/s41591-022-01873-5 | DOI Listing |
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