Pan-Cancer Analysis of Tumor Mutations Points to Predictive Biomarkers

Quantifying the effectiveness of different cancer therapies in patients with specific tumor mutations is critical for improving patient outcomes and advancing precision medicine. A key goal of precision medicine is to characterize how patients with specific genetic mutations respond to therapies.

Advances in modeling mutation–treatment interactions can potentially improve patient outcomes by recommending promising treatments based on each patient’s distinct tumor mutation profile. These interactions are especially important for guiding therapies for cancer, which is driven by heterogeneous mutations.

Biomedical Scientists at Stanford University (Stanford, CA, USA) and their colleagues used a gene-level analytical strategy, and searched for ties between tumor mutation profiles, cancer treatment histories, and survival patterns. Their dataset included electronic health record (EHR) entries and targeted Foundation Medicine panel sequence profiles for hundreds of cancer-related genes in more than 40,900 de-identified cancer patients who are part of the Flatiron Health-Foundation Medicine (New York, NY, USA) clinicogenomic database.

The participants included more than 12,900 individuals with advanced non-small cell lung cancer (NSCLC), nearly 7,900 metastatic breast cancer patients, almost 3,900 individuals with ovarian cancer, some 3,500 patients with metastatic pancreatic cancer, and thousands more patients with advanced bladder cancer, renal cell carcinoma, or melanoma. The team explained, noting that the results were verified using data for nearly 3,900 additional advanced lung, breast, or colorectal cancer cases from an American Association for Cancer Research (Philadelphia, PA, USA) dataset.

The team flagged 458 apparent mutation markers for survival in cancer patients receiving specific treatment protocols and uncovered specific mutations that typically co-occur with other tumor alterations. The investigators found that mutations in 42 genes tracked with survival outcomes in at least one of the cancer types considered, for example. These genes, in turn, showed almost 100 significant interactions.

Consistent with past studies that showed ties between EGFR inhibitor resistance and KRAS mutations in advanced NSCLCs, they saw shorter-than-usual survival for KRAS-mutated cases treated with EGFR inhibitors and enhanced survival in EGFR inhibitor-treated advanced NSCLC patients with KRAS-wild type tumors. When the team looked at mutation-mutation interactions across genes such as ALK, BRAF, EGFR, MET, RET, ROS1, ERBB2, or PIK3CA that are currently targeted by FDA-approved treatments, it found genes that were more or less likely to be mutated in conjunction with alterations affecting the targeted genes.

The authors concluded that their findings demonstrate that high-quality, real-world clinicogenomic data from patients with cancer can be an important resource for investigating such mutation-treatment interactions by capturing outcome information of patients on diverse treatments. As tumor sequencing data become increasingly linked to the EHR, such data combined with careful computational analysis can greatly benefit precision medicine. The study was published on June 30, 2022 in the journal Nature Medicine.

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