Combinatorial CRISPR/Cas9 Screening Reveals Epistatic Networks of Interacting Tumor Suppressor Genes and Therapeutic Targets in Human Breast Cancer.

Abstract

The majority of cancers are driven by multiple genetic alterations, but how these changes collaborate during tumorigenesis remains largely unknown. To gain mechanistic insights into tumor-promoting genetic interactions among tumor suppressor genes (TSG), we conducted combinatorial CRISPR screening coupled with single-cell transcriptomic profiling in human mammary epithelial cells. As expected, different driver gene alterations in mammary epithelial cells influenced the repertoire of tumor suppressor alterations capable of inducing tumor formation. More surprisingly, TSG interaction networks were comprised of numerous cliques-sets of three or four genes such that each TSG within the clique showed oncogenic cooperation with all other genes in the clique. Genetic interaction profiling indicated that the predominant cooperating TSGs shared overlapping functions rather than distinct or complementary functions. Single-cell transcriptomic profiling of CRISPR double knockouts revealed that cooperating TSGs that synergized in promoting tumorigenesis and growth factor independence showed transcriptional epistasis, whereas noncooperating TSGs did not. These epistatic transcriptional changes, both buffering and synergistic, affected expression of oncogenic mediators and therapeutic targets, including CDK4, SRPK1, and DNMT1. Importantly, the epistatic expression alterations caused by dual inactivation of TSGs in this system, such as PTEN and TP53, were also observed in patient tumors, establishing the relevance of these findings to human breast cancer. An estimated 50% of differentially expressed genes in breast cancer are controlled by epistatic interactions. Overall, our study indicates that transcriptional epistasis is a central aspect of multigenic breast cancer progression and outlines methodologies to uncover driver gene epistatic networks in other human cancers. SIGNIFICANCE: This study provides a roadmap for moving beyond discovery and development of therapeutic strategies based on single driver gene analysis to discovery based on interactions between multiple driver genes.See related commentary by Fong et al., p. 6078.