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New tool uses ‘drug spillover’ to match cancer patients with treatments

Targeted therapies attack a cancer’s genetic sensitivities. However, it can be difficult to discover the genetics driving a patient’s cancer, and the effects of drugs designed to target a genetic abnormality often go beyond their intended target alone. The result is threefold: sometimes a drug is prescribed to treat a target that proves to be irrelevant to the disease, sometimes an existing drug could be used to treat a cancer for which there is no approved targeted therapy, and sometimes a combination of targeted treatments could be used to simultaneously silence more than one genetic cause of a patient’s cancer.

A recent article in the journal Bioinformatics from researchers at the University of Colorado Cancer Center describes a new tool that improves the ability to match drugs to disease: the Kinase Addiction Ranker (KAR) predicts what genetics are truly driving the cancer in any population of cells and chooses the best « kinase inhibitor » to silence these dangerous genetic causes of disease.

« For example, we know that the disease Chronic Myeloid Leukemia is driven by the fusion gene bcr-abl and we can treat this with the tyrosine kinase inhibitor imatinib, which targets this abnormality. But for many other cancers, the genetic cause and best treatments are less distinct. The KAR tool clarifies the drug or combination of drugs that best targets the specific genetic abnormalities driving a patient’s cancer, » says Aik Choon Tan, PhD, investigator at the CU Cancer Center, associate professor of Bioinformatics at the CU School of Medicine, and the paper’s senior author.