Predictive Score: Genetics behind anti-cancer drug resistance

Last Updated on December 16, 2024 by Joseph Gut – thasso

December 15 2024 – Eighty-seven years after launching the National Cancer Act in the United Stated with the intention of a national combat against cancer, and despite despite significant progress, cancer treatment often falls short, with 50% to 80% of patients not responding to treatment and more than 600,000 cancer deaths annually in the US. One of the biggest problems in fighting cancer is its tendency to develop anti-cancer drug  resistance under treatment.

Very new genetic research should help to overcome such problems with a 36-gene predictive score of anti-cancer drug resistance which anticipates and/or predicts cancer therapy outcomes. The aim is that clinicians could predict the success of personalised cancer treatments, ensuring that each patient receives the most effective care.

The challenge for the researchers was the diverse nature of the disease. There are hundreds of different types of cancers, characterized by the specific type of cell from which they originate. Even patients with the same cancer type require personalised treatments due to unique factors like genetic predisposition, lifestyle and immune response, i.e., the many confounding factors besides genetics. They developed a workflow to identify genes whose expression positively correlated with anti-cancer drug resistance in cancer cell lines in order to tackle the therapeutic outcomes from complete remission to resistance to treatment, which largely are unpredictable to date.

To tackle this complexity, a research team at the University of Alabama at Birmingham recently published their study which sought to genetic identify patterns within this apparent randomness. They 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) in order to investigate whether gene expression levels correlate with drug response across various cancer cell lines. GDSC and CTRP

provide information on how sensitive different cell lines are to various anti-cancer drugs, whereas COSMIC catalogs their gene expression. The researches  studied 777 cancer cell lines that were present in both databases and found 36 genes linked with anti-cancer drug resistance.

In fact, one of these genes, FAM129B, was found to be particularly important in drug resistance by cancer cells. This finding aligns with previous experimental studies on FAM129B, validating the efficacy of the analytical approach employed in this so-called UAB study. The research group developed a combined score, named UAB36, using the 36 genes most linked to drug resistance. They found that the polygenic score UAB36 showed superior correlation with relative resistance to various anti-cancer drugs compared to existing polygenic scores. The researchers applied UAB36 to predict the expression of genes linked with the resistance of breast cancer against tamoxifen, a drug widely used for breast cancer treatment. UAB36 consistently showed a higher efficacy compared to a single gene approach. UAB36 also outperformed established gene signatures like ENDORSE and PAM50 in its correlation with tamoxifen resistance in breast cancer cells.

The study crossed from the cell-line studies to application as a prognostic tool when the 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 showed poorer survival independent of patient’s age and tumor stage, consistent with the expectation that this score predicts higher resistance to tamoxifen. The tumors with high UAB36 showed enrichment of gene sets associated with multiple drug resistance. This establishes UAB36 as a promising biomarker for predicting anti-cancer drug resistance and poor survival.

UAB36 has potential as a tool for personalized and theragenomic medicine, helping 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, though this has to be validated by a prospective clinical trial.

See here a sequence on anti-cancer drug resistance:

Disclaimer: Images and/or videos (if any) in this blog may be copyrighted. All rights remain with the owner of such rights.