Repair of leukemia-associated single nucleotide variants via interallelic gene conversion.

CRISPR-Cas9 is a useful tool for inserting precise genetic alterations through homology-directed repair (HDR), although current methods rely on provision of an exogenous repair template. Here, we tested the possibility of repairing heterozygous single nucleotide variants (SNVs) using the cell's own wild-type allele rather than an exogenous template. Using high-fidelity Cas9 to perform allele-specific CRISPR across multiple human leukemia cell lines as well as in primary hematopoietic cells from patients with leukemia, we find high levels of reversion to wild-type in the absence of exogenous template. Moreover, we demonstrate that bulk treatment to revert a truncating mutation in ASXL1 using CRISPR-mediated interallelic gene conversion (IGC) is sufficient to prolong survival in a human cell line-derived xenograft model (median survival 33 days vs 27.5 days; p = 0.0040). These results indicate that IGC can be applied to numerous types of leukemia and can meaningfully alter cellular phenotypes at scale. Because our method targets single-base mutations, rather than larger variants targeted by IGC in prior studies, it greatly expands the pool of risk-increasing genetic lesions which could potentially be targeted by IGC. This technique may reduce cost and complexity for experiments modeling phenotypic consequences of SNVs. The principles of SNV-specific IGC demonstrated in this proof-of-concept study could be applied to investigate the phenotypic effects of targeted clonal reduction of leukemogenic SNV driver mutations.

Excessive concentrations of kinase inhibitors in translational studies impede effective drug repurposing.

Drug repositioning seeks to leverage existing clinical knowledge to identify alternative clinical settings for approved drugs. However, repositioning efforts fail to demonstrate improved success rates in late-stage clinical trials. Focusing on 11 approved kinase inhibitors that have been evaluated in 139 repositioning hypotheses, we use data mining to characterize the state of clinical repurposing. Then, using a simple experimental correction with human serum proteins in in vitro pharmacodynamic assays, we develop a measurement of a drug's effective exposure. We show that this metric is remarkably predictive of clinical activity for a panel of five kinase inhibitors across 23 drug variant targets in leukemia. We then validate our model's performance in six other kinase inhibitors for two types of solid tumors: non-small cell lung cancer (NSCLC) and gastrointestinal stromal tumors (GISTs). Our approach presents a straightforward strategy to use existing clinical information and experimental systems to decrease the clinical failure rate in drug repurposing studies.