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Supplemental Table 1. Descriptions of the CpG-associated genes reveal a predominance of genes that function in MHC class or skin/connective tissue structure. Supplemental Table 2. Consistently hypomethylated genes in NSD1 disrupted cell lines. Supplemental Figure 1. Effect of various factors on NSD1 mutation survival effect in TCGA and validation of NSD1 survival effect in a second independent cohort from the University of Chicago (12). A, Distribution of smokers and non-smokers and B, Kaplan-Meier curve for overall survival of current and former smokers from the HPV(-) HNSCC cohort in TCGA (13). C, Kaplan-Meier curve for progression free survival and D, for overall survival for the HPV(-) HNSCC cohort from the University of Chicago. E, Kaplan-Meier curve for overall survival of patients with truncating (blue) or missense (orange) NSD1 mutations versus those with wild type NSD1 (green). Analysis is for the TCGA HPV(-) HNSCC cohort. All p-values for Kaplan-Meier curves in this figure were derived from the Log-Rank Test. Supplemental Figure 2. Regional analysis of differentially methylated CpG sites in TCGA patients. A strong cluster of differentially methylated CpG sites appear around chromosome 6 from 31 MB - 33 MB. Supplemental Figure 3. NSD1 protein expression levels in parental and monoclonal cell lines with NSD1 disrupted by CRISPR-Cas9. A, Immunoblots of NSD1 and TBP (TATA Binding Protein) for NSD1 wild type CAL33, two monoclonal and one polyclonal NSD1 disrupted cell lines. B, Relative NSD1 expression levels were calculated by taking the ratio of intensity of the NSD1 band relative to the TBP band and setting the wild type expression level to 100%. C, Immunoblots of NSD1 and TBP (TATA Binding Protein) for NSD1 wild type UM-SCC47 and one monoclonal NSD1 disrupted cell line. D, Same as B except with the NSD1 expression levels from the UM-SCC47 cell lines. NSD1 alleles from monoclonal populations are characterized as follows: wt, wild type; trunc, contains a truncating mutation. Supplemental Figure 4. Effect of NSD1 mutations on CpG site methylation and gene expression in HNSCC cell lines. A, Clustered bar chart describing the distribution of all differentially methylated CpG sites (DMRs) for Sotos Syndrome patients (14) (red), TCGA HNSCC NSD1 mutant tumors (gold), and the top 10,000 most differentially methylated probes from CAL33 (blue, green) and UM-SCC47 (purple) NSD1 knockout cell lines (Methods) compared to the standard distribution of CpG sites in the Illumina 450k chip for each site feature (light blue). B and C, Differential mRNA expression by RT-qPCR of selected genes in CAL33 (B) and UM-SCC47 (C) cell lines, with and without NSD1 disruption. Supplemental Figure 5. Effect of NSD1 mutations on cell line sensitivity to radiation and DNA damage repair. A, Relative gain in percentage of γH2AX positive CAL33 cells when treated with cisplatin compared to cells without cisplatin treatment for CAL33 wild-type and pooled NSD1-knockout cells. γH2AX gain is significantly increased at 2µM cisplatin (Holm-Sidak corrected p-value < 0.005, Student''s T-Test), consistent with the significant difference in cisplatin IC50 between the two groups. B, Clonogenic plating efficiency differences between CAL33 wild-type and pooled NSD1-knockout cells. C, Clonogenic survival assay of CAL33 wild-type and pooled NSD1-knockout cells with increasing doses of radiation. Two independent curves do not fit the data significantly better than a single curve (extra-sum-of-squares F-Test). Supplemental Figure 6. Distribution of cell line types in the Sanger dataset (blue) with number of NSD1 mutations (red) and truncating NSD1 mutations (orange).

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.

Genetic screens in Saccharomyces cerevisiae have allowed for the identification of many genes as sensors or effectors of DNA damage, typically by comparing the fitness of genetic mutants in the presence or absence of DNA-damaging treatments. However, these static screens overlook the dynamic nature of DNA damage response pathways, missing time-dependent or transient effects. Here, we examine gene dependencies in the dynamic response to ultraviolet radiation-induced DNA damage by integrating ultra-high-density arrays of 6144 diploid gene deletion mutants with high-frequency time-lapse imaging. We identify 494 ultraviolet radiation response genes which, in addition to recovering molecular pathways and protein complexes previously annotated to DNA damage repair, include components of the CCR4-NOT complex, tRNA wobble modification, autophagy, and, most unexpectedly, 153 nuclear-encoded mitochondrial genes. Notably, mitochondria-deficient strains present time-dependent insensitivity to ultraviolet radiation, posing impaired mitochondrial function as a protective factor in the ultraviolet radiation response.

The cell is a multi-scale structure with modular organization across at least four orders of magnitude(1). Two central approaches for mapping this structure—protein fluorescent imaging and protein biophysical association—each generate extensive datasets, but of distinct qualities and resolutions that are typically treated separately(2,3). Here we integrate immunofluorescence images in the Human Protein Atlas(4) with affinity purifications in BioPlex(5) to create a unified hierarchical map of human cell architecture. Integration is achieved by configuring each approach as a general measure of protein distance, then calibrating the two measures using machine learning. The map, known as the multi-scale integrated cell (MuSIC 1.0), resolves 69 subcellular systems, of which approximately half are to our knowledge undocumented. Accordingly, we perform 134 additional affinity purifications and validate subunit associations for the majority of systems. The map reveals a pre-ribosomal RNA processing assembly and accessory factors, which we show govern rRNA maturation, and functional roles for SRRM1 and FAM120C in chromatin and RPS3A in splicing. By integration across scales, MuSIC increases the resolution of imaging while giving protein interactions a spatial dimension, paving the way to incorporate diverse types of data in proteome-wide cell maps.

Mapping protein interactions driving cancer Cancer is a genetic disease, and much cancer research is focused on identifying carcinogenic mutations and determining how they relate to disease progression. Three papers demonstrate how mutations are processed through networks of protein interactions to promote cancer (see the Perspective by Cheng and Jackson). Swaney et al . focus on head and neck cancer and identify cancer-enriched interactions, demonstrating how point mutant–dependent interactions of PIK3CA, a kinase frequently mutated in human cancers, are predictive of drug response. Kim et al . focus on breast cancer and identify two proteins functionally connected to the tumor-suppressor gene BRCA1 and two proteins that regulate PIK3CA. Zheng et al . developed a statistical model that identifies protein networks that are under mutation pressure across different cancer types, including a complex bringing together PIK3CA with actomyosin proteins. These papers provide a resource that will be helpful in interpreting cancer genomic data. —VV

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.

Genetic interaction screens have long been used to map gene functions and cellular pathways in model organisms and more recently in human cells. However, due to the technical challenges of these maps, most studies in human cells have focused on a single cell line, limiting the ability to determine how different genetic and cellular contexts influence genetic interactions. Here, we engineered a 110,728-element combinatorial CRISPR-Cas9 library to systematically map 12,282 genetic interactions among tumor suppressor genes and druggable targets. We mapped the landscape of genetic interactions in cell lines from breast cancer, head and neck squamous carcinoma, and non-small cell lung cancer as well as in non-cancerous, epithelial cells. The resulting genetic interaction maps emphasize the context dependency of these interactions. Of the interactions that are conserved across multiple cell lines, many include frequently essential genes, suggesting that these interactions may be critical for fundamental cellular functions. We found that genetic interaction maps from cancer cell lines are more similar to one another than non-cancer interaction maps, even between cancer and normal cells from the same tissue. Subsequent targeted chemogenetic screens confirmed interaction hubs across multiple cell lines while highlighting the robustness of some of the strongest interactions. Many of the identified genetic interactions suggest therapeutic interventions, such as the combination of CDK4 and MAPK inhibitors. Using the tissue-specific genetic interaction networks, we also built a deep learning model that accurately predicts patient outcomes, outperforming models that use accepted biomarkers such as MammaPrint. In summary, these genetic interaction maps provide a broad resource to investigate context dependency in cancer signaling pathways and in cancer drug response. Citation Format: Samson H. Fong, Brent M. Kuenzi, John Lee, Kyle Sanchez, Ana Bojorquez-Gomez, Kyle Ford, Brenton P. Munson, Katherine Licon, Jeff Hager, John Paul Shen, Jason F. Kreisberg, Prashant Mali, Trey Ideker. Systematic mapping of genetic interactions in human cancer cells reveals context dependent cancer signaling pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2133.

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, altogether comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified and these patterns replicated with combinatorial drugs at 75% precision. Based on these results we anticipate cellular context will be critical to synthetic-lethal therapies.

HIV-infected individuals are living longer on antiretroviral therapy, but many patients display signs that in some ways resemble premature aging. To investigate and quantify the impact of chronic HIV infection on aging, we report a global analysis of the whole-blood DNA methylomes of 137 HIV+ individuals under sustained therapy along with 44 matched HIV− individuals. First, we develop and validate epigenetic models of aging that are independent of blood cell composition. Using these models, we find that both chronic and recent HIV infection lead to an average aging advancement of 4.9 years, increasing expected mortality risk by 19%. In addition, sustained infection results in global deregulation of the methylome across >80,000 CpGs and specific hypomethylation of the region encoding the human leukocyte antigen locus (HLA). We find that decreased HLA methylation is predictive of lower CD4 / CD8 T cell ratio, linking molecular aging, epigenetic regulation, and disease progression.

Summary Accurately translating genotype to phenotype requires accounting for the functional impact of genetic variation at many biological scales. Here we present a strategy for genotype-phenotype reasoning based on existing knowledge of cellular subsystems. These subsystems and their hierarchical organization are defined by the Gene Ontology or a complementary ontology inferred directly from previously published datasets. Guided by the ontology’s hierarchical structure, we organize genotype data into an “ontotype,” that is, a hierarchy of perturbations representing the effects of genetic variation at multiple cellular scales. The ontotype is then interpreted using logical rules generated by machine learning to predict phenotype. This approach substantially outperforms previous, non-hierarchical methods for translating yeast genotype to cell growth phenotype, and it accurately predicts the growth outcomes of two new screens of 2,503 double gene knockouts impacting DNA repair or nuclear lumen. Ontotypes also generalize to larger knockout combinations, setting the stage for interpreting the complex genetics of disease.

All mammals progress through similar physiological stages throughout life, from early development to puberty, aging, and death. Yet, the extent to which this conserved physiology reflects underlying genomic events is unclear. Here, we map the common methylation changes experienced by mammalian genomes as they age, focusing on comparison of humans with dogs, an emerging model of aging. Using oligo-capture sequencing, we characterize methylomes of 104 Labrador retrievers spanning a 16-year age range, achieving >150× coverage within mammalian syntenic blocks. Comparison with human methylomes reveals a nonlinear relationship that translates dog-to-human years and aligns the timing of major physiological milestones between the two species, with extension to mice. Conserved changes center on developmental gene networks, which are sufficient to translate age and the effects of anti-aging interventions across multiple mammals. These results establish methylation not only as a diagnostic age readout but also as a cross-species translator of physiological aging milestones.

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.

We have developed a general progressive procedure, Active Interaction Mapping, to guide assembly of the hierarchy of functions encoding any biologicalsystem. Using this process, we assemble an ontology of functions comprising autophagy, a central recycling process implicated in numerous diseases. A first-generation model, built from existing gene networks in Saccharomyces, captures most known autophagy components in broad relation to vesicle transport, cell cycle, and stress response. Systematic analysis identifies synthetic-lethal interactions as most informative for further experiments; consequently, we saturate the model with 156,364 such measurements across autophagy-activating conditions. These targeted interactions provide more information about autophagy than all previous datasets, producing a second-generation ontology of 220 functions. Approximately half are previously unknown; we confirm roles for Gyp1 at the phagophore-assembly site, Atg24 in cargo engulfment, Atg26 in cytoplasm-to-vacuole targeting, and Ssd1, Did4, and others in selective and non-selective autophagy. The procedure and autophagy hierarchy are at http://atgo.ucsd.edu/.

Genetic interactions, in particular negative or "synthetic-lethal" interactions for which simultaneous disruption of two genes causes cell killing, have implications for therapeutic development as has been demonstrated by the clinical success of PARP inhibitors specifically for tumors with loss-of-function mutations in BRCA1/2. However, further applications of synthetic-lethal cancer therapy have been limited by poor understanding of the important genetic interactions in a cancer cell, and how these vary from one cancer type to another or from patient to patient. To enable systematic mapping of these genetic interaction networks, we recently developed a CRISPR-Cas9 screening methodology for knocking out single and pairs of genes in high throughput. Critical to this method is the precise determination of single-gene knockout effects, which is accomplished by serial measurement of 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 novel computational analysis framework that integrates all samples across the multiple days of the experiment; with said method we achieve Pearson correlation of 0.95 or greater between biologic replicates in the same cell line (p < 1x10-30). Additionally we demonstrate that our analysis method is robust to the compositional effects inherent in a pooled knockout experiment. To facilitate reproducibility of analyses and distribution to the scientific community, the code has been packaged into a modular series of python notebooks freely available on github. Evaluating all pairwise gene knockout combinations among a panel of 73 genes divided between tumor-suppressor genes (TSG) and cancer-relevant drug targets (DT) in a total of 5 cancer cell lines from diverse lineages (HeLa, A549, 293T, U2OS, LN229), we identified 226 synthetic lethal and 14 epistatic interactions at a Z-score cut-off of -3 (FDR ~0.3). Of the synthetic lethal interactions 203 (89.8%) were private to a single cell line, and no interaction was seen in more than 3 of 5 five cell lines. Thus far 10 (out of 16 tested) therapeutically relevant interactions have been replicated in low-throughput assays using either combinatorial drugs or CRISPR knockout of a TSG paired with a drug (71% precision or positive predictive value). The cell line specificity of interactions was also confirmed in low-throughput assays (75% negative predictive value). In summary, we have discovered many therapeutically relevant genetic interactions in cancer and identified 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 future 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, Brenton Munson, Amanda Birmingham, Roman Sasik, Ana Bojorquez-Gomez, Katherine Licon, Kristin Klepper, Alex Beckett, Kyle Salinas Sanchez, Prashant Mali, Trey Ideker. High-throughput combinatorial CRISPR-Cas9 gene knockout reveals most genetic interactions are context dependent [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3299.

Genetic interactions, in particular negative or ‘synthetic-lethal’ interactions for which simultaneous disruption of two genes causes cell killing, have implications for therapeutic development. The feasibility of this approach has been demonstrated with the recent approval of the drug olaparib, an inhibitor of PARP1, specifically for tumors with loss-of-function mutations in BRCA1/2. However, beyond olaparib, further applications of synthetic-lethal cancer therapy have been limited by poor understanding of the important genetic interactions in a cancer cell, and how these vary from one cancer type to another or from patient to patient. To enable systematic mapping of these genetic interaction networks, we developed a CRISPR-Cas9 screening methodology for knocking out single and pairs of genes in high-throughput. Here, we combined multiplex targeting with array-based oligonucleotide synthesis to create dual-gRNA libraries covering up to 10^5 defined gene pairs. In these libraries, each construct bears two gRNAs, with each gRNA designed to target either a gene or a scrambled non-targeting sequence absent from the genome. 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); including negative controls this amounted to a total of 23,652 combinations. Experiments were performed in three cancer cell lines: HeLa, A549 and 293T. In total 162 therapeutically relevant interactions were identified, of which 146 (90.1%) were private to one cell line. None of the interactions were observed in all three cell lines. These patterns replicated in low throughput assays with combinatorial drugs at 80% precision. 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 future 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, Ana Bojorquez-Gomez, Katherine Licon, Trey Ideker, Prashant Mali. Combinatorial CRISPR-Cas9 gene knockout to enable genetic interaction mapping in human cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3039. doi:10.1158/1538-7445.AM2017-3039

Transcriptional networks have been shown to evolve very rapidly, prompting questions as to how such changes arise and are tolerated. Recent comparisons of transcriptional networks across species have implicated variations in the cis-acting DNA sequences near genes as the main cause of divergence. What is less clear is how these changes interact with trans-acting changes occurring elsewhere in the genetic circuit. Here, we report the discovery of a system of compensatory trans and cis mutations in the yeast AP-1 transcriptional network that allows for conserved transcriptional regulation despite continued genetic change. We pinpoint a single species, the fungal pathogen Candida glabrata, in which a trans mutation has occurred very recently in a single AP-1 family member, distinguishing it from its Saccharomyces ortholog. Comparison of chromatin immunoprecipitation profiles between Candida and Saccharomyces shows that, despite their different DNA-binding domains, the AP-1 orthologs regulate a conserved block of genes. This conservation is enabled by concomitant changes in the cis-regulatory motifs upstream of each gene. Thus, both trans and cis mutations have perturbed the yeast AP-1 regulatory system in such a way as to compensate for one another. This demonstrates an example of “coevolution” between a DNA-binding transcription factor and its cis-regulatory site, reminiscent of the coevolution of protein binding partners.

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

Loss of tumor suppressor activity is a near universal feature of cancer. Synthetic lethality, in which combined perturbation of two genes results in cell death, has been proposed as a strategy to identify drug targets that specifically attack cancers deficient in a tumor suppressor (TS) gene. To develop a catalog of such combinations, we screened for synthetic-lethal interactions among all yeast orthologs of known TS and druggable genes, resulting in quantitative growth rates for 130,000 pairwise genetic mutants. Similar interaction profiles were highly predictive of TS genes in the same pathway, identifying a novel role for NDNL2/NSE3 in the G1/S checkpoint. TS/drug combinations likely to induce cancer cell death were prioritized using multiple criteria, including conservation across divergent species. Among the top hits were putative interactions involving either BRCA1 or XRCC3 with histone deacetylases, as well as Rad17 with CHK1/2. These were confirmed using small molecule inhibitors and short-pin RNA silencing in human cancer cell lines. This work provides a resource of >300 deeply conserved interactions among tumor suppressors and matched drug targets. Citation Format: Rohith Srivas, John Paul Shen, Jian Feng Li, Katherine Licon, Ze Zhong Wang, Ana Bojoquez-Gomez, Lucy Xu, Andrew Gross, Gordon Bean, Robert Sobol, Trey Ideker. High-throughput synthetic lethal interaction screening in model organisms as a strategy for the identification of novel therapeutic targets in cancer. [abstract]. In: Proceedings of the AACR Special Conference: The Translational Impact of Model Organisms in Cancer; Nov 5-8, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2014;12(11 Suppl):Abstract nr B34.

11105 Background: Mutation, deletion, or epigenetic silencing of tumor suppressor genes is a near universal feature of malignant cells. However, therapeutic strategies for restoring the function of mutated or deleted genes have proven difficult. Synthetic lethality, an event in which the simultaneous perturbation of two genes results in cellular death, has been proposed as a method to selectively target cancer cells. Identifying and pharmacologically inhibiting proteins encoded by genes that are synthetic lethal with known tumor suppressor mutations should result in selective toxicity to tumor cells. Methods: To identify candidate target proteins we measured all pair-wise genetic interactions between all known orthologs of human tumor suppressor genes (162 genes) and all orthologs of druggable human proteins (~400 genes) in the model organism S. Cerevisiae. Analysis of the data uncovered 2,087 distinct synthetic lethal interactions between a tumor suppressor and druggable gene. A computational algorithm was then developed to identify those interactions which were likely to be conserved in humans based on conservation of the synthetic lethal relationship in the distant fission yeast S. pombe. Results: Our bioinformatic analysis suggested a high probability of conservation of the synthetic lethal interactions between the yeast RAD51 (ortholog of BRCA1) and RAD57 (ortholog of XRCC3) with HDA1 (a histone deacetylase; HDAC). We confirmed this by treating LN428 cells with stable lentiviral knockdown of BRCA1 or XRCC3 with the HDAC inhibitors vorinostat (SAHA) and entinostat (MS-275). Both the BRCA1 and XRCC3 knockdown cell lines were significantly more sensitive to HDAC inhibition relative to wild-type (non-silencing lentiviral control) cell line (Table). Conclusions: These results demonstrate that high-throughput approaches for screening synthetic lethal interactions in model organisms such as S. cerevisiae and S. pombecan serve as a valuable resource in helping to identify novel therapeutic targets in human cancer. [Table: see text]