New Approach for Uncovering Tumors' Molecular Dependencies

A computational method for identifying how much cancer cells depend on specific proteins for survival is reported in PLOS Computational Biology this week, representing a potential new tool for cancer drug development. While chemical perturbations via bioactive compound screening in primary cancer cells represents an important tool for identifying such dependencies, mechanistic understanding of hits and further pharmaceutical development is often complicated by the fact that a chemical compound has affinities to multiple proteins. To overcome this challenge, a group led by scientists from the European Molecular Biology Laboratory developed a framework, dubbed DepInfeR, that identifies tumor-specific dependencies on druggable proteins by integrating drug sensitivity assays and drug-protein affinity profiling. The researchers validated the approach by correctly identifying known kinase dependencies in acute myeloid leukemia and chronic lymphocytic leukemia (CLL), then used it with newly generated drug screening data on primary tumor samples to discover a previously unreported dependence on checkpoint kinase 1 by a molecular subgroup of high-risk CLL. Possible uses for DepInfeR include the discovery of novel disease stratifications by their characteristic dependencies on specific proteins and improved understanding of existing disease stratifications in terms of differential protein dependencies, the study's authors add.