Targeted therapies have been a recent focus of drug development for acute myeloid leukemia (AML) and chronic lymphocytic leukemia (CLL), but the majority of patients eventually develop resistance even to these new drugs. There is thus an urgent need to better understand the pathways underlying drug resistance to identify novel drugs or combinations of drugs that can effectively inhibit these pathways. Through our leadership of the Beat AML program as well as other programs in our laboratories oriented towards CLL, we are amassing a large cohort of patient samples with corresponding genomic, functional, clinical and immune annotation. We are developing novel computational tools to extract useful conclusions from these large datasets. The overall goals of this proposal are to leverage our existing cohorts, high-throughput screening tools, and datasets for prediction and pre-clinical testing of novel drug combinations that will eventually be translated into clinical trials.
The specific aims of this project are to: (1) use genome-wide CRISPR screening and mass cytometry to create a discovery resource of genomic and immune profiles of 500 primary samples from leukemia patients; (2) develop an integrated computational framework (called PRECEPTS) to infer the cellular processes driving resistance to perturbagens and predict combination targets that can overcome resistance; (3) identify synergistic drug combinations by combining ex vivo testing of single drugs with CRISPR/Cas synthetic lethality screening with genes prioritized by computational prediction, and identify resistance pathways by using RNAseq to profile any residual resistant cells; and (4) use the data from (3) to identify and test drug combinations. This proposed project will contribute to all 3 areas of research interest for the CTD2 by improving our understanding of the molecular processes underlying drug sensitivity and resistance in leukemias, developing algorithms to predict markers and targets in these processes, and identifying drugs and/or combinations that will maximize drug sensitivity and minimize resistance. The proposed studies have direct translational relevance in selecting novel treatment strategies for clinical trials, and will benefit the CTD2 by generating large-scale data sets and providing novel computational tools that can be applied to future studies and expanded beyond leukemias.
Most leukemia patients eventually succumb to their disease due to resistance to both conventional chemotherapies and newer targeted agents. We propose to perform computational analyses of large-scale functional, genomic and immunologic datasets from primary leukemia patient samples to understand the pathways involved in drug resistance and predict effective drug combinations. We will then test these predictions in the lab using prospective samples from leukemia patients, which will not only validate new treatment strategies, but also help to refine the computational algorithms for a more accurate and thorough understanding of drug resistance in leukemias.
