The effectiveness of targeted anti-cancer therapeutics is frequently reduced by the acquisition of drug- resistance. A comprehensive understanding of the mutations compatible with oncogene function should define the mutations available to direct drug-resistance and provide a guide for the rational development of improved inhibitors with reduced probability of resistance. Traditional approaches to analyze the functional effects of mutations rely on randomly generated mutants and typically can only identify a handful of mutations with a strongly selected phenotype. We propose an approach to systematically analyze the functional effects of all possible single-nucleotide substitutions for entire oncogenes both in the presence and absence of inhibitors. Our approach will systematically define the positive or negative impact of each mutation on cell growth. Only mutations that are compatible with oncogene function should be available to drug resistance evolution. Therefore, identifying the set of functional mutations provides a powerful structural guide that can be incorporated in the early stages of future drug design efforts.
The emergence of drug-resistance can limit the effectiveness of anti-cancer drugs with frightening consequences for patients. Traditional approaches to study drug-resistance have not been powerful enough to provide a good guide for developing new drugs that are less prone to resistance. We are developing a new technology to rapidly and systematically analyze the functional effects of all mutations in oncogenes. The results of this approach will provide a detailed mutational map that defines the mutations available to evolve drug-resistance. By systematically identifying these mutations, they can be accounted for during the design of future inhibitors. Inhibitors designed to block oncogenes in spite of any of these mutations should be less prone to drug resistance, and should result in improved cancer treatments and improved human health.
| Ma, Leyuan; Boucher, Jeffrey I; Paulsen, Janet et al. (2017) CRISPR-Cas9-mediated saturated mutagenesis screen predicts clinical drug resistance with improved accuracy. Proc Natl Acad Sci U S A 114:11751-11756 |
