A new tool 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. It was described in a recent article in Bioinformatics (2015; doi:10.1093/bioinformatics/btv427).

Targeted therapies attack a cancer’s genetic sensitivities. However, determining which genetic processes are driving a patient’s cancer can be difficult, and the effects of drugs designed to target a genetic abnormality often go beyond their intended target. 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.

“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,” said senior author Aik Choon Tan, PhD, investigator at the University of Colorado Cancer Center in Aurora.

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KAR makes its predictions based on two data sources: first, data describing the full spectrum of effects of tyrosine kinase inhibitors (TKIs).

“A lot of these kinase inhibitors inhibit a lot more than what they’re supposed to inhibit. Maybe drug A was designed to inhibit kinase B, but it also inhibits kinase C and D as well. Our approach centers on exploiting the promiscuity of these drugs, the drug spillover,” said Tan.

Tan and colleagues combine these kinase inhibition signatures with the results of high-throughput screening, which is a method for testing hundreds of drugs against a panel of cancer cells. Specifically, Tan used the publicly available Genomics of Drug Sensitivity in Cancer database to discover which compounds have been shown to be active against which cancer cell lines.

The result is KAR, which does two things: ranks the kinases most important to the growth of the cancer, then recommends the combination of existing TKIs likely to do the most good against the implicated kinases.

Based on samples from 151 leukemia patients, KAR was able to correctly predict the outcomes for patients treated with certain drugs in this study. The same was true in 21 lung cancer cell lines; KAR predicted the degree of sensitivity of these cells to certain drugs that matched the results of experiments showing these sensitivities.

Finally, the researchers asked KAR to rank the kinases most important to the proliferation of the lung cancer cell line H1581 and to recommend a combination of targeted treatments to attack these cells. KAR suggested the combination of ponatinib with experimental anticancer agent AZD8055, and, in fact, this combination proved highly effective at controlling these cells, creating what the researchers call a “synergistic reduction in proliferation.”