Using CRISPR/Cas9 Screens to Define Organizing Principles that Govern Drug Sensitivity in Acute Myeloid Leukemia

Abstract

In 2019, the cornerstone of the clinical management of acute myeloid leukemia (AML) is chemotherapy. In fact, for the majority of patients diagnosed with AML, their best chance at achieving complete remission is a regimen of cytotoxic chemotherapies approved more than three decades ago. Most of those patients will eventually succumb to their cancer. This bleak reality belies an abundance of preclinical and clinical drug development in the myeloid leukemia space. The problem that faces many oncologists today is not whether we have potent, selective drugs, but rather, how our armamentarium of effective chemotherapeutics should be best deployed. To adequately address this question, a detailed understanding of drug mechanism and cellular response alike is required. Unfortunately, drugs tend to be more complicated and less specific than physicians are willing to consider; cancer cells tend to be more adaptable and durable than pharmacologists and medicinal chemists think they are. Our inability to effectively bridge this gap has led some to declare that cancer will never be cured and that targeted therapies, the christened ‘silver bullet’ in the fight against cancer, to be another false hope.This dissertation seeks to reconcile the nuances of drug mechanism with the adaptability of cancer cells using functional, loss and gain-of-function genomic screens to annotate a series of gene-drug interactions which can then be recast to address the question(s) at hand. Because gene-drug interactions are inherently functional, the data produced by such studies can often be rapidly translated to clinically relevant models. Here we provide four examples, presented as discrete case studies, that illustrate the ability of gene-drug interactions to predict trends in tumor evolution, nominate synergistic drug combinations, and approximate disparate fields of biology. Taken together, the studies presented here highlight the potential for using functional genomics to more fully map the effects of drugs on cancer cells.