Abstract
SummaryThe emergence of mutations that confer resistance to molecularly targeted therapeutics is dependent upon the effect of each mutation on drug affinity for the target protein, the clonal fitness of cells harboring the mutation, and the probability that each variant can be generated by DNA codon base mutation. We present a computational workflow that combines these three factors to identify mutations likely to arise upon drug treatment in a particular tumor type. The Osprey-based workflow is validated using a comprehensive dataset of ERK2 mutations and is applied to small-molecule drugs and/or therapeutic antibodies targeting KIT, EGFR, Abl, and ALK. We identify major clinically observed drug-resistant mutations for drug-target pairs and highlight the potential to prospectively identify probable drug resistance mutations.
Highlights
Targeted cancer therapies, for example, against kinases, hormone receptors, or hormone-synthesizing enzymes, have shown clinical success, many patients develop resistance to treatment and subsequently relapse
We report an in silico cascade that sequentially evaluates the probability of generating any mutant within 5 Aof a bound ligand, the clonal fitness of each mutation, and the effect of each mutation on drug affinity in order to systematically and objectively prioritize mutations that are highly likely to arise under drug treatment
K* scores were computed for the WT protein and all possible amino acid exchanges for both the drug and the endogenous ligand-protein complex with the exception of Pro, which cannot be processed by Osprey
Summary
For example, against kinases, hormone receptors, or hormone-synthesizing enzymes, have shown clinical success, many patients develop resistance to treatment and subsequently relapse. Second- and third-generation drugs are being developed to target these resistant mutants; there is a significant time span between the detection of clinically validated resistance mutations and the availability of suitably targeted treatment options. Several mechanisms underlying resistance to targeted drugs have been described (Holohan et al, 2013), including mutations directly affecting the drug target. Such mutations may, for example, increase affinity for the endogenous co-factor ATP, thereby decreasing the relative affinity of an ATP-competitive drug. Mutations within the binding site may alter drugprotein interactions and directly interfere with drug binding (Barouch-Bentov and Sauer, 2011)
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