A research team at the University of Alabama at Birmingham (UAB) has developed a 36-gene signature, named UAB36, that can predict resistance to anti-cancer drugs and anticipate cancer therapy outcomes. This polygenic score shows promise in predicting tamoxifen treatment resistance more effectively than conventional methods, potentially paving the way for personalized medicine applications. The study, led by Anindya Dutta, Ph.D., indicates that UAB36 could help clinicians identify patients who are less likely to respond to specific treatments, allowing for more informed decisions about alternative therapies.
Identifying Key Genes Linked to Drug Resistance
To address the challenge of unpredictable therapeutic outcomes in cancer treatment, the UAB team leveraged 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). These databases provide information on the sensitivity of different cell lines to various anti-cancer drugs and catalog their gene expression. Divya Sahu, a member of Dutta’s lab, analyzed 777 cancer cell lines present in both GDSC and CTRP, identifying 36 genes linked with anti-cancer drug resistance. Notably, one of these genes, FAM129B, was found to be particularly significant in drug resistance, aligning with previous experimental studies.
UAB36: A Superior Polygenic Score
The research group developed the UAB36 score using the 36 identified genes. They found that UAB36 showed a superior 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 outperformed both single-gene approaches and established gene signatures like ENDORSE and PAM50 in its correlation with tamoxifen resistance in breast cancer cells.
Clinical Application and Prognostic Potential
The study extended beyond cell-line studies to assess UAB36's prognostic capabilities. Researchers used the UAB36 score to predict patient outcomes in three different cohorts of breast cancer patients treated with tamoxifen. The results indicated that patients with high UAB36 scores exhibited poorer survival, independent of age and tumor stage. This finding aligns with the expectation that the score predicts higher resistance to tamoxifen. Tumors with high UAB36 scores also showed enrichment of gene sets associated with multiple drug resistance, establishing UAB36 as a promising biomarker for predicting anti-cancer drug resistance and poor survival.
Implications for Personalized Medicine
UAB36 holds significant potential as a tool for personalized medicine, aiding in the identification of 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 various cancer types against specific drugs and can be further improved by incorporating machine-learning methods in the analysis. However, prospective clinical trials are needed to validate these findings and fully realize the potential of UAB36 in clinical practice.
