Researchers identify 36 genes linked to resistance to cancer drugs

A new study from the University of Alabama at Birmingham (UAB) has identified 36 different genes that can help predict resistance to cancer drugs, offering the potential for more personalized cancer treatments. The study, led by Anindya Dutta, PhD, professor and chair of the UAB Department of Genetics, was published in npj precision oncologyThe goal is to better understand the genetic factors that contribute to drug resistance in cancer cells.

Cancer is a complex disease with hundreds of different types, each characterized by the cells from which they originate. Even among patients with the same type of cancer, treatment results can vary greatly due to factors such as genetics, immune response and lifestyle. This unpredictability arises in part because cancer cells can develop genetic mutations that allow them to resist treatment, rendering therapies ineffective.

To address this challenge, Dutta and his team launched research to identify patterns within the apparent randomness of cancer drug resistance. The team analyzed data from several large cancer cell databases, including Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Therapeutics Response Portal (CTRP), and Catalog of Somatic Mutations in Cancers (COSMIC). These databases contain data on how cancer cell lines respond to various drugs and catalog their genetic mutations.

Using this data, Divya Sahu, a researcher in Dutta’s lab, examined 777 cancer cell lines present in both the GDSC and CTRP databases and identified 36 genes strongly linked to anticancer drug resistance. One gene, FAM129B, was found to be particularly important in drug resistance, a finding consistent with previous research and supporting the validity of their analytical approach.

Based on these 36 genes, researchers developed a polygenic risk score called UAB36, which demonstrated superior ability to predict resistance to various cancer drugs compared to existing methods. The team then applied UAB36 to predict the expression of genes associated with breast cancer resistance to the selective estrogen receptor modulator (SERM) tamoxifen, a drug commonly used to combat the disease. The researcher’s analysis showed that UAB36 has higher predictive power for tamoxifen resistance than other established gene signatures, including ENDORSE and PAM50.

Going beyond cell lines, the team then applied UAB36 to predict patient outcomes in three separate cohorts of breast cancer patients treated with tamoxifen. The results showed that patients with higher UAB36 levels tended to have worse survival chances, regardless of factors such as age and tumor stage. This finding suggests that the UAB36 score could be used as a biomarker to identify patients at higher risk of treatment resistance and poor prognosis.

The study authors believe that UAB36 may be a promising tool for developing personalized cancer treatment plans in the clinic. By identifying patients who are more likely to develop resistance to tamoxifen, UAB36 could help physicians adjust treatment strategies and explore alternative therapies for these patients. However, the researchers emphasize that further validation in prospective clinical trials is needed to confirm the clinical utility of the score.

“This approach should provide promising polygenic biomarkers of the resistance of many cancers to certain drugs and can be further improved by incorporating machine learning methods into the analysis,” Dutta said.