medwireNews: Researchers have used computer-based analyses to identify and confirm a large number genetic biomarkers that predict cancer treatment outcomes, as well as gene–gene interactions that may modify the effects of targeted treatments.
Writing in Nature Medicine, James Zou (Stanford University, Palo Alto, California, USA) and co-authors say that although “next-generation sequencing has enabled fast and accurate genomics profiling and is becoming a powerful resource in cancer care,” converting the information into treatment recommendations is “a major challenge.”
To better understand the interactions between cancer-related gene alterations and treatment outcomes, the researchers carried out a computational analysis of electronic health record (EHR) data for a large cohort of US patients with advanced malignancies including non-small-cell lung cancer (NSCLC; n=12,934), colorectal cancer (n=8590), breast cancer (n=7877), ovarian cancer (n=3899), pancreatic cancer (n=3505), melanoma (n=1522), bladder cancer (n=1531), and renal cell carcinoma (n=1045). The EHRs included information on tumor mutation profiles, treatments given, and outcomes.
The team initially identified 42 genes that were significantly associated with survival in at least one cancer, including TP53, MYC, and CDKN2A, which is in line with previous findings.
Further analysis revealed 458 significant interactions between tumor mutations and first-line treatment efficacy, 98 of which were in the 42 prognostic genes.
Among them were several mutations that are “consistent with and provide further support for previous studies on smaller cohorts that focused on specific target genes,” the researchers remark.
For example, KRAS mutations were associated with resistance to EGFR inhibitors in advanced NSCLC, with a significant gene-by-treatment hazard ratio (HR) for death of 1.86.
EGFR and PTEN mutations predicted an increased risk for death among people receiving immune checkpoint inhibitors for advanced NSCLC, at HRs of 1.67 and 1.55, respectively, while TP53 mutations predicted better survival with chemotherapy (HR=0.86) but worse survival with ALK inhibitors (HR=2.12).
For metastatic breast cancer, RB1 and CCND1 mutations were associated with improved survival during chemotherapy (HRs=0.74 and 0.73, respectively), which Zou et al say is also in line with previous findings. As was an increased risk for death among people with ERBB2 (HR=1.76) and CDK12 (HR=1.83) mutations who were receiving hormone therapy.
Among the less well-explored associations, the researchers identified mutations in APC as a strong positive predictor of response to immunotherapy in patients with advanced bladder cancer, at a HR of 0.35, while ASXL1 mutations – more commonly seen in myeloid malignancies – predicted positive immunotherapy outcomes (HR=0.68) in people with NSCLC.
The team also explored how co-occurrence of targeted mutations in anchor genes with other mutations in modifier genes impacts outcome.
In all, they identified 61 significant anchor–modifier interactions, of which 25 were positive interactions and 36 were negative interactions.
Examples include better immunotherapy outcomes among people with KRAS-mutated advanced NSCLC and concurrent mutations in MLL3, NBN, or NF1, than in those without the modifier mutations, but worse immunotherapy outcomes in those with co-occurring mutations in KEAP1, STK11, SMARCA4, CDKN2B, or CDKN2A.
Zou et al conclude: “Overall, our global analysis across multiple cancer types, treatment classes and genes provides a more comprehensive picture of potential gene–treatment interactions and generates new hypotheses for future biological and clinical investigations.”
However, they also stress than any “mutations not highlighted in our analysis should not be directly interpreted as non-significant due to cohort size limitations and the non-randomized nature of real-world data.”
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