Scientists at the University of Colorado Cancer Center have described a new tool that reportedly 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, according to the researchers who published their study (“Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data”) in Bioinformatics.
“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 Aik Choon Tan, Ph.D., investigator at the CU Cancer Center, associate professor of bioinformatics at the CU School of Medicine, and the paper's senior author.
KAR makes its predictions based on two data sources. First is data describing the full spectrum of effects of the drugs known as 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',” continued Dr. Tan.
Looking at the drug crizotinib it was designed, tested, and approved to silence the ALK-EML fusion gene that drives a subset of lung cancers. But it also happens to act against a similar rearrangement of the ROS1 gene. Patients with the ROS1 rearrangement have been treated successfully with crizotinib. In this case, what researchers and doctors first called an “ALK inhibitor” turns out to have other, important uses. And, in fact, for each drug in this class of kinase inhibitors, there is a profile or signature describing the few or many kinases each drug fully or partially inhibits.
Dr. Tan and colleagues combine these kinase inhibition signatures with the results of high-throughput screening against a panel of cancer cells. Specifically, Dr. 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: for any cancer cell line, like those derived from a patient with cancer, the program ranks the kinases that are most important to the growth of the disease; then the program recommends the combination of existing kinase inhibitors drugs (TKIs) that is likely to do the most good against the implicated kinases.
The Bioinformatics paper describes the success of the KAR tool. First, based on samples from 151 leukemia patients, KAR was able to correctly predict the outcomes of patients treated with certain drugs. The same was true in 21 lung cancer cell lines: KAR predicted the degree of sensitivity of these cells to certain drugs, matching the results of experiments that show 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.”
“This is a new tool, a new way of looking at drugs and how we combine drugs to target kinase dependency in cancer,” said Dr. Tan, who added that he tool is available for download as a Python function or MATLAB script at the website of his laboratory (tanlab.ucdenver.edu/KAR).