Photo courtesy of the
University of Colorado
Researchers say they have developed a tool that allows us to determine which tyrosine kinase inhibitor (TKI) will be most effective against a certain type of cancer.
The tool, known as the Kinase Addiction Ranker (KAR), predicts the genetic abnormalities that are driving the cancer in any population of cells and chooses the best TKI or combination of TKIs to target these abnormalities.
The researchers described the tool in Bioinformatics.
“A lot of [TKIs] inhibit a lot more than what they’re supposed to inhibit,” said study author Aik Choon Tan, PhD, of the University of Colorado Anschutz Medical Campus in Aurora.
“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.’”
For each TKI, there is a signature describing the kinases each drug fully or partially inhibits. Dr Tan and his colleagues combined these kinase inhibition signatures with the results of high-throughput screening. They used the Genomics of Drug Sensitivity in Cancer database to determine which TKIs have already proven active against which cancer cell lines.
The result is KAR, which does 2 things. For any cancer cell line, the program ranks the kinases that are most important to the growth of the disease. Then, the program recommends the combination of existing TKIs that is likely to do the most good against the implicated kinases.
Dr Tan and his colleagues tested KAR using samples from 151 leukemia patients and found that, among the kinases analyzed, FLT3 had the highest variance in sensitivity to TKIs.
But EPHA5, EPHA3, and BTK were the kinases most commonly associated with drug sensitivity. They had significant associations in 72%, 58%, and 54% of the patient samples, respectively.
The researchers said the frequency of BTK dependence they observed is interesting given the fact that the BTK inhibitor ibrutinib produced favorable results in a phase 1b/2 trial of patients with chronic lymphocytic leukemia (CLL). The progression-free survival rate at 26 months was 75% in that trial.
Dr Tan and his colleagues said this was consistent with their findings, which showed that 70% of CLL patient data had a significant association between BTK inhibition and drug sensitivity.
The researchers also found that KAR could predict TKI sensitivity in 21 lung cancer cell lines. In addition, the tool was able to recommend a combination of TKIs that hindered proliferation in the lung cancer cell line H1581. KAR suggested ponatinib and the experimental anticancer agent AZD8055, and experiments showed that these drugs synergistically reduced proliferation in H1581.
KAR is available for download on the Tan lab’s website.
