Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening.

Elucidation of the mutational landscape of human cancer has progressed rapidly and been accompanied by the development of therapeutics targeting mutant oncogenes. However, a comprehensive mapping of cancer dependencies has lagged behind and the discovery of therapeutic targets for counteracting tumor suppressor gene loss is needed. To identify vulnerabilities relevant to specific cancer subtypes, we conducted a large-scale RNAi screen in which viability effects of mRNA knockdown were assessed for 7,837 genes using an average of 20 shRNAs per gene in 398 cancer cell lines. We describe findings of this screen, outlining the classes of cancer dependency genes and their relationships to genetic, expression, and lineage features. In addition, we describe robust gene-interaction networks recapitulating both protein complexes and functional cooperation among complexes and pathways. This dataset along with a web portal is provided to the community to assist in the discovery and translation of new therapeutic approaches for cancer.

A chemical genetics approach for the functional assessment of novel cancer genes.

Assessing the functional significance of novel putative oncogenes remains a significant challenge given the limitations of current loss-of-function tools. Here, we describe a method that employs TALEN or CRISPR/Cas9-mediated knock-in of inducible degron tags (Degron-KI) that provides a versatile approach for the functional characterization of novel cancer genes and addresses many of the shortcomings of current tools. The Degron-KI system allows for highly specific, inducible, and allele-targeted inhibition of endogenous protein function, and the ability to titrate protein depletion with this system is able to better mimic pharmacologic inhibition compared with RNAi or genetic knockout approaches. The Degron-KI system was able to faithfully recapitulate the effects of pharmacologic EZH2 and PI3Kα inhibitors in cancer cell lines. The application of this system to the study of a poorly understood putative oncogene, SF3B1, provided the first causal link between SF3B1 hotspot mutations and splicing alterations. Surprisingly, we found that SF3B1-mutant cells are not dependent upon the mutated allele for in vitro growth, but instead depend upon the function of the remaining wild-type alleles. Collectively, these results demonstrate the broad utility of the Degron-KI system for the functional characterization of cancer genes.

Studying clonal dynamics in response to cancer therapy using high-complexity barcoding.

Resistance to cancer therapies presents a significant clinical challenge. Recent studies have revealed intratumoral heterogeneity as a source of therapeutic resistance. However, it is unclear whether resistance is driven predominantly by pre-existing or de novo alterations, in part because of the resolution limits of next-generation sequencing. To address this, we developed a high-complexity barcode library, ClonTracer, which enables the high-resolution tracking of more than 1 million cancer cells under drug treatment. In two clinically relevant models, ClonTracer studies showed that the majority of resistant clones were part of small, pre-existing subpopulations that selectively escaped under therapeutic challenge. Moreover, the ClonTracer approach enabled quantitative assessment of the ability of combination treatments to suppress resistant clones. These findings suggest that resistant clones are present before treatment, which would make up-front therapeutic combinations that target non-overlapping resistance a preferred approach. Thus, ClonTracer barcoding may be a valuable tool for optimizing therapeutic regimens with the goal of curative combination therapies for cancer.