A research team at the University of Alabama at Birmingham (UAB) has developed a 36-gene score, named UAB36, that shows promise in predicting resistance to anti-cancer drugs, particularly in breast cancer. The study, published in NPJ Precision Oncology, could lead to more personalized treatment strategies for cancer patients.
Identifying Key Genes Linked to Drug Resistance
To address the challenge of unpredictable therapeutic outcomes in cancer treatment, the UAB team, led by Anindya Dutta, Ph.D., analyzed established cancer cell databases, including the Genomics of Drug Sensitivity in Cancer (GDSC), the Cancer Therapeutics Response Portal (CTRP), and the Catalogue of Somatic Mutations in Cancers (COSMIC). They investigated the correlation between gene expression levels and drug response across various cancer cell lines.
Divya Sahu, a member of Dutta’s lab, studied 777 cancer cell lines present in both GDSC and CTRP databases and identified 36 genes linked with anti-cancer drug resistance. One gene, FAM129B, was found to be particularly important, aligning with previous experimental studies and validating the analytical approach.
UAB36 Outperforms Existing Methods
The research group developed the UAB36 score using these 36 genes. The polygenic score demonstrated a stronger correlation with relative resistance to various anti-cancer drugs compared to existing polygenic scores. When applied to predict the expression of genes linked with resistance to tamoxifen in breast cancer, UAB36 consistently showed higher efficacy than single-gene approaches and outperformed established gene signatures like ENDORSE and PAM50.
Clinical Relevance and Patient Outcomes
The study extended its findings to clinical application by using the UAB36 score to predict patient outcomes in three different cohorts of breast cancer patients treated with tamoxifen. Patients with high UAB36 scores exhibited poorer survival, independent of age and tumor stage, which aligns with the expectation that the score predicts higher resistance to tamoxifen. Tumors with high UAB36 scores showed enrichment of gene sets associated with multiple drug resistance.
Potential for Personalized Medicine
UAB36 holds potential as a tool for personalized medicine, helping to identify patients at higher risk of tamoxifen resistance and poor survival. This suggests that these patients may benefit from alternative treatment strategies. According to Dutta, this approach could provide promising polygenic biomarkers for resistance in many cancer types against specific drugs and can be improved further by incorporating machine-learning methods in the analysis. However, prospective clinical trials are needed to validate these findings.
