Why this study is important?

The rise of personalized medicine has led to the search of biomarkers that can predict chemotherapy outcomes. Resistance to chemotherapy is a critical challenge leading to treatment failure and patient death. Genetic alterations such as acquired mutations, activation of cancer cell survival signaling pathways, drug efflux, loss of DNA damage repair, copy number variations, EMT pathways, or transition to cancer stem cells are shown to be responsible for drug resistance in tumors. However, these factors alone are not sufficient to explain the complexity of drug resistance. This study was motivated by gaps in understanding how gene expression profiles of tumors can determine whether a tumor will respond to a particular drug treatment.

A Novel Predictive Approach

Analyzing gene expression profiles of hundreds of cancer cell-lines and the same cell-lines' responses to drug treatment we identified 36 genes, whose expression was associated with relative drug resistance (increased IC50) to many anti-cancer drugs. We first focused on the best gene from the list, NIBAN2/FAM129B, whose expression correlated with resistance to more than 30 FDA approved drugs. In the process we discovered that integrating the expression of all 36 genes from the cancer cell lines into a polygenic score, termed UAB36 achieved higher predictive ability for drug resistance compared to FAM129B alone.  We then looked at drug resistance of breast cancer cells to Tamoxifen and correlated this to expression of the UAB36 genes.  This helped us establish a polygenic gene signature score that reliably predicts whether a cell line would be relatively more resistant to Tamoxifen. What is exciting is that although the 36 genes and the formula for how to combine them in a polygenic score were selected from the behavior of cell lines in culture, UAB36 successfully predicted the outcome of ER positive and HER2 negative patients (ER+/HER2-) breast cancer patients undergoing Tamoxifen therapy. UAB36 outperformed two established predictive signatures ENDORSE and PAM50 that are in use in the clinic. Interestingly, UAB36 predicted the outcome of Tamoxifen treatment in these breast cancer patients independent of well-known clinical factors that are currently known to help in such prediction.

 From Lab to Clinic

UAB36’s success in predicting Tamoxifen resistance was validated across several independent breast cancer cohorts including METABRIC, GSE9195, and TCGA-BRCA, marking an important step towards its clinical application. In breast cancer, by accurately predicting drug response, UAB36 score can help oncologists personalize therapies for ER positive and HER2 negative patients. This will potentially improve survival rates and reduce tumor relapse.

 What’s Next?

We are currently exploring how UAB36 can be applied to other cancer types and drug treatments. Additionally further investigation into the biological pathways of these 36 genes may reveal deeper insights into cancer resistance mechanisms, paving the way for new treatment strategies.