Abstract PR08: Combinatorial CRISPR-Cas9 reveals many cancer synthetic lethal interactions are private to cell type

Abstract

Abstract We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbation coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with multiplexed guide RNAs in two cell lines, testing 23,652 combinations. Numerous therapeutically relevant interactions were identified, most private to one cell line. These patterns replicated with combinatorial drugs at 80% precision. Thus, cellular context will be critical to synthetic-lethal therapies. Here, we combined multiplex targeting with array-based oligonucleotide synthesis to create dual-gRNA libraries covering up to 105 defined gene pairs. We conducted genetic interaction screens by transducing the dual-gRNA lentiviral library into a population of cells stably expressing Cas9, maintaining these cells in exponential growth over the course of four weeks, then sampling the relative changes in gRNAs at days 3, 14, 21 and 28 post-transduction. To robustly quantify gene fitness and genetic interactions, we developed a computational analysis framework that integrates all samples across the multiple days of the experiment. Using this method we evaluated all pairwise gene knockout combinations among a panel of 73 genes divided between tumor-suppressor genes (TSG) and cancer-relevant drug targets (DT), a subset of which were also verified oncogenes. Experiments were performed in two cancer cell lines: HeLa, a cervical cancer cell line driven by Human Papilloma Virus (HPV); and A549, a lung cancer cell line driven by KRAS G12S mutation. With nine gRNA pairs per combination, the library comprised 23,652 double gene knockout constructs and 657 single gene constructs; testing two replicates in each cell line yielded a total of 94,608 unique tests of interaction. Measurements of gene fitness (fg) were well correlated between biological replicates in the same cell line; Pearson r=0.96, p = 4.2×(10)^(-40), as were the π scores for significant genetic interactions; r = 0.75 p = 7.7×(10)^(-12). Moreover, we observed a significant correlation between the number of genetic interactions identified for a gene and its single gene fitness (HeLa: r = 0.77, p = 3.4×(10)^(-10); A549: r = 0.45, p = 0.0018), suggesting that network hubs may have increased functional importance to cancer cells relative to genes with fewer interactions; such a relationship has been previously observed in model organisms but not before in humans5. Interestingly, we found that the genetic interactions identified from these data were remarkably different between cell lines. A total of 97 synthetic-lethal (negative) genetic interactions were identified in either HeLa or A549 cells. Of these, only 12 were identified in both cell lines, while the remaining 85 interactions were private to a cell line (HeLa: 40 of 52, A549: 45 of 57). We next sought to validate these findings, and in particular the discrepancies across cell lines. We selected ten pairs of DT genes for which a synthetic-lethal genetic interaction had been identified in only one of the two cell lines. Rather than simply reproduce the dual CRISPR knockout experiment (gene-gene interaction), our goal was to examine the viability of cells exposed to drugs inhibiting the corresponding gene products (drug-drug interaction). In total, drug-drug assays validated eight of ten interactions when tested in the cell line for which the interaction had been first observed by dual CRISPR (80% precision or positive predictive value). In contrast, when the same ten gene pairs were tested in the other cell line that had not been implicated by dual CRISPR knockout, only two showed a negative genetic interaction by drug-drug assay (80% negative predictive value). Thus, the differences in genetic interaction across cell lines seen by systematic CRISPR could be largely reproduced as drug-drug interactions in small-scale assays. In summary, we have introduced a combinatorial CRISPR-Cas9 genetic interaction mapping technology that successfully identifies many therapeutically-relevant genetic interactions in cancer and shows the great importance of cellular context on the architecture of the genetic interaction network. Recognizing that there will be great diversity in genetic interaction between different tumors it will be important to perform these studies across a large number of samples, which is enabled by the high-throughput method we have developed. Citation Format: John Paul Shen, Dongxin Zhao, Roman Sasik, Jens Luebeck, Amanda Birmingham, Ana Bojorquez-Gomez, Jason Kreisberg, Trey Ideker, Prashant Mali. Combinatorial CRISPR-Cas9 reveals many cancer synthetic lethal interactions are private to cell type [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr PR08.