One of the most elusive challenges oncologists encounter is why some patients respond to a particular therapy while others do not. Thus, optimizing a personalized treatment regimen that gives a patient the best odds of success has become a cornerstone of cancer research. The desire to implement more individualized therapies has brought about an increasing the focus on personalized medicine. This promising approach uses specific patient characteristics, including genetic makeup, environment, and lifestyle, to develop an individualized treatment plan.
Working towards improving the speed and accuracy of genetic screening to inform personalized medicine, a team of researchers conducted a comprehensive study. The journal NPJ Precision Oncol recently published the results. The researchers meticulously investigated the gene expression of almost 800 cancer cell lines and their response to treatment. With this thorough process, the researchers identified specific genetic patterns that correlated with drug resistance.
The study identified 36 genes correlating to resistance to multiple anti-cancer drugs. The researchers calculated a score, called UAB36, based on the correlation coefficient of the 36 genes identified. This UAB36 score, a novel predictive tool, accurately forecasted resistance to tamoxifen, an anti-cancer drug used to treat some types of breast cancer and prevent cancer progression in women with ductal carcinoma in situ (DCIS).
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