Katherine Licon

Rewiring of Genetic Networks in Response to DNA Damage

DNA Damage Pathways Revealed Despite the dynamic nature of cellular responses, the genetic networks that govern these responses have been mapped primarily as static snapshots. Bandyopadhyay et al. (p. 1385; see the Perspective by Friedman and Schuldiner) report a comparison of large genetic interactomes measured among all yeast kinases, phosphatases, and transcription factors, as the cell responded to DNA damage. The interactomes revealed were highly dynamic structures that changed dramatically with changing conditions. These dynamic interactions reveal genetic relationships that can be more effective than classical “static” interactions (for example, synthetic lethals and epistasis maps) in identifying pathways of interest. A network comparison of genetic interactions mapped at two conditions reveals genetic responses to DNA damage in yeast. Although cellular behaviors are dynamic, the networks that govern these behaviors have been mapped primarily as static snapshots. Using an approach called differential epistasis mapping, we have discovered widespread changes in genetic interaction among yeast kinases, phosphatases, and transcription factors as the cell responds to DNA damage. Differential interactions uncover many gene functions that go undetected in static conditions. They are very effective at identifying DNA repair pathways, highlighting new damage-dependent roles for the Slt2 kinase, Pph3 phosphatase, and histone variant Htz1. The data also reveal that protein complexes are generally stable in response to perturbation, but the functional relations between these complexes are substantially reorganized. Differential networks chart a new type of genetic landscape that is invaluable for mapping cellular responses to stimuli.

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.

A UV-Induced Genetic Network Links the RSC Complex to Nucleotide Excision Repair and Shows Dose-Dependent Rewiring

Efficient repair of UV-induced DNA damage requires the precise coordination of nucleotide excision repair (NER) with numerous other biological processes. To map this crosstalk, we generated a differential genetic interaction map centered on quantitative growth measurements of >45,000 double mutants before and after different doses of UV radiation. Integration of genetic data with physical interaction networks identified a global map of 89 UV-induced functional interactions among 62 protein complexes, including a number of links between the RSC complex and several NER factors. We show that RSC is recruited to both silenced and transcribed loci following UV damage where it facilitates efficient repair by promoting nucleosome remodeling. Finally, a comparison of the response to high versus low levels of UV shows that the degree of genetic rewiring correlates with dose of UV and reveals a network of dose-specific interactions. This study makes available a large resource of UV-induced interactions, and it illustrates a methodology for identifying dose-dependent interactions based on quantitative shifts in genetic networks.

Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems

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.

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 B34: High-throughput synthetic lethal interaction screening in model organisms as a strategy for the identification of novel therapeutic targets in cancer

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.