New UAB tool predicts cancer drug resistance in patients

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In 1937, President Franklin Roosevelt signed the National Cancer Act, launching a nationwide effort to combat the disease. Eighty-seven years later, despite significant advances, cancer treatment is often unsuccessful, with 50 to 80 percent of patients not responding to treatment and more than 600,000 cancer deaths in the United States each year.

What if doctors could predict the success of every cancer treatment to ensure every patient receives the most effective treatment?

The challenge lies in the diversity of the disease. There are hundreds of different types of cancer, characterized by the specific cell type from which they arise. Even patients with the same type of cancer require personalized treatments due to unique factors such as genetics, lifestyle and immune response. Therapeutic outcomes – from complete remission to treatment resistance – are unpredictable because cancer cells can develop resistance to drugs through genetic mutations, rendering therapy ineffective.

To address this complexity, a research team at the University of Alabama at Birmingham led by Anindya Dutta, Ph.D., professor and chair of the UAB Department of Genetics, sought to identify patterns within this apparent randomness. Using established databases on cancer cells – including Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Therapeutics Response Portal (CTRP) and Catalog of Somatic Mutations in Cancers (COSMIC) – the team investigated “whether gene expression levels are related to cancer cells.” Drugs correlate “response” across different cancer cell lines.

GDSC and CTRP provide information about how sensitive different cell lines are to different cancer drugs, while COSMIC catalogs their gene expression. Divya Sahu in Dutta’s lab examined 777 cancer cell lines present in both databases and found 36 genes associated with resistance to cancer drugs. One of these genes, FAM129B, was found to be particularly important for drug resistance in cancer cells. This result is consistent with previous experimental studies on FAM129B and confirms the effectiveness of the analytical approach used in this UAB study.

The research group developed a composite score called UAB36 that used the 36 genes most strongly linked to drug resistance. They found that the UAB36 polygene score had a better correlation with relative resistance to various cancer drugs compared to existing polygene scores.

The study moved from cell line studies to use as a prognostic tool when researchers used the UAB36 score to predict patient outcome in three different cohorts of actual breast cancer patients treated with tamoxifen. They found that patients with high UAB36 scores had worse survival regardless of the patient’s age and tumor stage, consistent with the expectation that this score predicts higher resistance to tamoxifen. The UAB36-high tumors showed enrichment of gene sets associated with multiple drug resistance. UAB36 is thus establishing itself as a promising biomarker for predicting resistance to cancer drugs and poor chances of survival.

UAB36 has potential as a tool for personalized medicine and helps identify patients at higher risk of tamoxifen resistance and poor survival, suggesting that these patients will benefit from alternative treatment strategies. The study provides a map to help doctors choose the best cancer treatment and predict outcomes for each patient. However, this needs to be validated by a prospective clinical trial.

“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.

Reference: Sahu D, Shi J, Segura Rueda IA, Chatrath A, Dutta A. Development of a polygenic score to predict drug resistance and patient outcome in breast cancer. npj Precis Onc. 2024;8(1):219. doi: 10.1038/s41698-024-00714-7

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