Ana Bojorquez-Gomez

Combinatorial CRISPR–Cas9 screens for de novo mapping of genetic interactions

We developed a systematic approach to map human genetic networks by combinatorial CRISPR–Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision. From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies.

A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy

An emerging therapeutic strategy for cancer is to induce selective lethality in a tumor by exploiting interactions between its driving mutations and specific drug targets. Here we use a multi-species approach to develop a resource of synthetic lethal interactions relevant to cancer therapy. First, we screen in yeast ∼169,000 potential interactions among orthologs of human tumor suppressor genes (TSG) and genes encoding drug targets across multiple genotoxic environments. Guided by the strongest signal, we evaluate thousands of TSG-drug combinations in HeLa cells, resulting in networks of conserved synthetic lethal interactions. Analysis of these networks reveals that interaction stability across environments and shared gene function increase the likelihood of observing an interaction in human cancer cells. Using these rules, we prioritize ∼10(5) human TSG-drug combinations for future follow-up. We validate interactions based on cell and/or patient survival, including topoisomerases with RAD17 and checkpoint kinases with BLM.

Chemogenetic profiling identifies RAD17 as synthetically lethal with checkpoint kinase inhibition

Chemical inhibitors of the checkpoint kinases have shown promise in the treatment of cancer, yet their clinical utility may be limited by a lack of molecular biomarkers to identify specific patients most likely to respond to therapy. To this end, we screened 112 known tumor suppressor genes for synthetic lethal interactions with inhibitors of the CHEK1 and CHEK2 checkpoint kinases. We identified eight interactions, including the Replication Factor C (RFC)-related protein RAD17. Clonogenic assays in RAD17 knockdown cell lines identified a substantial shift in sensitivity to checkpoint kinase inhibition (3.5-fold) as compared to RAD17 wild-type. Additional evidence for this interaction was found in a large-scale functional shRNA screen of over 100 genotyped cancer cell lines, in which CHEK1/2 mutant cell lines were unexpectedly sensitive to RAD17 knockdown. This interaction was widely conserved, as we found that RAD17 interacts strongly with checkpoint kinases in the budding yeast Saccharomyces cerevisiae. In the setting of RAD17 knockdown, CHEK1/2 inhibition was found to be synergistic with inhibition of WEE1, another pharmacologically relevant checkpoint kinase. Accumulation of the DNA damage marker γH2AX following chemical inhibition or transient knockdown of CHEK1, CHEK2 or WEE1 was magnified by knockdown of RAD17. Taken together, our data suggest that CHEK1 or WEE1 inhibitors are likely to have greater clinical efficacy in tumors with RAD17 loss-of-function.

A global transcriptional network connecting noncoding mutations to changes in tumor gene expression

Although cancer genomes are replete with noncoding mutations, the effects of these mutations remain poorly characterized. Here we perform an integrative analysis of 930 tumor whole genomes and matched transcriptomes, identifying a network of 193 noncoding loci in which mutations disrupt target gene expression. These ‘somatic eQTLs’ (expression quantitative trait loci) are frequently mutated in specific cancer tissues, and the majority can be validated in an independent cohort of 3,382 tumors. Among these, we find that the effects of noncoding mutations on DAAM1, MTG2 and HYI transcription are recapitulated in multiple cancer cell lines and that increasing DAAM1 expression leads to invasive cell migration. Collectively, the noncoding loci converge on a set of core pathways, permitting a classification of tumors into pathway-based subtypes. The somatic eQTL network is disrupted in 88% of tumors, suggesting widespread impact of noncoding mutations in cancer.Analysis of whole-genome sequences and transcription data from tumors identifies noncoding loci in which mutations affect target gene expression. These somatic eQTLs can classify tumors into pathway-based subtypes and are disrupted in 88% of tumors.

Disruption of NSD1 in head and neck cancer promotes favorable chemotherapeutic responses linked to hypomethylation

Human papillomavirus (HPV)–negative head and neck squamous cell carcinoma (HNSCC) represents a distinct classification of cancer with worse expected outcomes. Of the 11 genes recurrently mutated in HNSCC, we identify a singular and substantial survival advantage for mutations in the gene encoding Nuclear Set Domain Containing Protein 1 (NSD1), a histone methyltransferase altered in approximately 10% of patients. This effect, a 55% decrease in risk of death in NSD1-mutated versus non-mutated patients, can be validated in an independent cohort. NSD1 alterations are strongly associated with widespread genome hypomethylation in the same tumors, to a degree not observed for any other mutated gene. To address whether NSD1 plays a causal role in these associations, we use CRISPR-Cas9 to disrupt NSD1 in HNSCC cell lines and find that this leads to substantial CpG hypomethylation and sensitivity to cisplatin, a standard chemotherapy in head and neck cancer, with a 40% to 50% decrease in the IC50 value. Such results are reinforced by a survey of 1,001 cancer cell lines, in which loss-of-function NSD1 mutations have an average 23% decrease in cisplatin IC50 value compared with cell lines with wild-type NSD1. Significance: This study identifies a favorable subtype of HPV–negative HNSCC linked to NSD1 mutation, hypomethylation, and cisplatin sensitivity. Mol Cancer Ther; 17(7); 1585–94. ©2018 AACR.

Abstract 129: RAD17 loss of function is synthetically lethal with the checkpoint kinase inhibitors AZD7762 or MK-1775

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA Synthetic lethal interactions are a type of genetic interaction in which the simultaneous loss of function of two genes in combination results in cell death. Recently, there has been much interest in the discovery of drugs that are selectively toxic to cancer cells by targeting proteins that form synthetic lethal interactions with tumor specific mutations. He we have identified that RAD17 loss of function is synthetically lethal with AZD7762, an inhibitor of Chk1 and Chk2, and with MK-1775, an inhibitor of Wee1. HeLa or LN428 glioblastoma cells were selectively responsive to AZD7762 or MK-1775 following shRNA-mediated depletion of RAD17. Cells with dual knockdown of CHEK1 and CHEK2 or knockdown of WEE1 were also sensitized to RAD17 knockdown. At baseline, cells with RAD17 knockdown had greater expression of the DNA damage marker H2AX, an effect that was magnified by either chemical inhibition or siRNA-mediated knockdown of CHEK1/2 or WEE1. Accumulation of H2AX was observed primarily in S and G2 phases, RAD17 loss did not have a significant effect on cell cycle progression. The combination of AZD7762 and MK-1775 resulted in synergistic toxicity in RAD17 knockdown but not control cells. Collectively, these results demonstrate that RAD17 loss of function sensitizes cells to inhibition of S and G2/M checkpoint kinases, resulting in greater accumulation of DNA damage and ultimately cell death. We suggest that AZD7762 and MK-1775 may have greater clinical efficacy in tumors with RAD17 mutation or deletion. Citation Format: John Paul Shen, Rohith Srivas, Ana Bojorquez-Gomez, Katherine Licon, Vignesh Sivaganesh, Jia L. Xu, Huwate Yeerna, Andrew Gross, Jian Feng Li, Robert Sobol, Trey Ideker. RAD17 loss of function is synthetically lethal with the checkpoint kinase inhibitors AZD7762 or MK-1775. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 129. doi:10.1158/1538-7445.AM2015-129

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

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.

Abstract AP23: A PLATINUM–RESISTANT SUBTYPE OF HIGH–GRADE SEROUS OVARIAN CANCER IDENTIFIED BY A NETWORK OF SOMATIC MUTATIONS

PURPOSE: Like many solid tumors, high grade serous ovarian carcinoma (HGSOC) is a heterogeneous entity with a widely variable clinical course even among patients of the same stage and histological subtype. Although most patients will achieve remission with platinum-based chemotherapy, approximately 20% will display primary platinum resistance. If it were possible to platinum resistance from the molecular profile of a tumor, patients with platinum resistant tumor could be identified for alternative therapy, however, currently no such biomarker exists. METHODS: We have recently developed an analysis method called Network-based Stratification (NBS), which combines genome-scale somatic mutation profiles with genetic interaction networks. Briefly, somatic mutations for each patient are mapped onto a network, and then the influence of each mutation is propagated over its network neighborhood to give each gene a propagated mutation score (PM score). Unsupervised clustering is then used to assign the propagated networks into subtypes. RESULTS: Using NBS a high risk subtype consisting of approximately 20% of the patients was identified in both TCGA (n=330) and the ICGC (n=92) HGSOC cohorts. The median overall survival (OS) for the high risk (HR) subtype was a year less than standard risk (SR) subtype (36 vs. 48 mo, Log rank p = 1.6x 10-5) in TCGA cohort and similarly shorter (22 mo vs. not-yet-reached, p = 3.6x10-4), see figure 1. These survival differences were independent of age, tumor stage and residual tumor after surgical resection. The PM scores of the HR tumors from TCGA and ICGC cohorts we remarkably correlated (Pearson r2 = 0.94, p We then built a classifier to identify molecularly matched cell line models for in vitro study. The three cell lines with mutation profiles match the HR subtype (Kuramochi,Ovkate, OAW28) were significantly more resistant to cisplatin relative to cell lines characteristic of SR tumors (COV318, TYK-NU, OVCAR4), (IC50 14.4 vs. 3.3 µM, p < 0.0001). There was no significant difference in sensitivity to paclitaxel. CONCLUSIONS: NBS can be used to identify a molecular distinct subtype of HGSOC characterized by poor patient survival and primary platinum resistance. Citation Format: John Paul Shen, Ana Bojorquez-Gomez, Justin Huang, Matan Hofree, Kristin Klepper, Alex Beckett, Cheryl Saenz, Jason Kreisberg, Trey Ideker. A PLATINUM–RESISTANT SUBTYPE OF HIGH–GRADE SEROUS OVARIAN CANCER IDENTIFIED BY A NETWORK OF SOMATIC MUTATIONS [abstract]. In: Proceedings of the 11th Biennial Ovarian Cancer Research Symposium; Sep 12-13, 2016; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(11 Suppl):Abstract nr AP23.