Identification Of Synthetic Lethal Interactions Research Articles

Abstract B014: Unveiling the future: Exploring cutting-edge technologies for synthetic lethality discovery

Abstract Synthetic lethality discovery is an emerging field of research that aims to reshape the landscape of cancer treatment, offering new hope and possibilities for patients faced with challenging diagnoses. In this article, we explored the cutting-edge technologies shaping the future of synthetic lathality research. Artificial intelligence and machine learning are not just buzzwords but indispensable tools that are shaping the field of precision medicine and personalized cancer treatment. By leveraging the power of computational tools, scientists can accelerate the identification of synthetic lethal interactions and pave the way for the development of more effective targeted cancer therapies. Through case studies, researchers can uncover the functional genomics of complex diseases and identify novel drug targets for cancer therapy. The integration of multi-omics approaches not only enhances the depth of analysis but also offers a more nuanced understanding of the molecular mechanisms driving synthetic lethal interactions, paving the way to unlock novel therapeutics and personalized treatment strategies. In addition, the integration of high-throughput screening techniques, such as CRISPR-Cas9 gene editing, opens up new avenues for identifying new drug targets and therapeutic strategies, ultimately paving a new era where personalized and targeted therapies redefine the way we approach cancer care, ushering in a novel era of precision and efficacy in medicine. Citation Format: Peter Oloche David. Unveiling the future: Exploring cutting-edge technologies for synthetic lethality discovery [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B014.

Synthetic lethality prediction in DNA damage repair, chromatin remodeling and the cell cycle using multi-omics data from cell lines and patients.

Discovering synthetic lethal (SL) gene partners of cancer genes is an important step in developing cancer therapies. However, identification of SL interactions is challenging, due to a large number of possible gene pairs, inherent noise and confounding factors in the observed signal. To discover robust SL interactions, we devised SLIDE-VIP, a novel framework combining eight statistical tests, including a new patient data-based test iSurvLRT. SLIDE-VIP leverages multi-omics data from four different sources: gene inactivation cell line screens, cancer patient data, drug screens and gene pathways. We applied SLIDE-VIP to discover SL interactions between genes involved in DNA damage repair, chromatin remodeling and cell cycle, and their potentially druggable partners. The top 883 ranking SL candidates had strong evidence in cell line and patient data, 250-fold reducing the initial space of 200K pairs. Drug screen and pathway tests provided additional corroboration and insights into these interactions. We rediscovered well-known SL pairs such as RB1 and E2F3 or PRKDC and ATM, and in addition, proposed strong novel SL candidates such as PTEN and PIK3CB. In summary, SLIDE-VIP opens the door to the discovery of SL interactions with clinical potential. All analysis and visualizations are available via the online SLIDE-VIP WebApp.

Conditional Covalent Lethality Driven by Oncometabolite Accumulation.

Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) is a cancer predisposition syndrome driven by mutation of the tumor suppressor fumarate hydratase (FH). Inactivation of FH causes accumulation of the electrophilic oncometabolite fumarate. In the absence of methods for reactivation, tumor suppressors can be targeted via identification of synthetic lethal interactions using genetic screens. Inspired by recent advances in chemoproteomic target identification, here, we test the hypothesis that the electrophilicity of the HLRCC metabolome may produce unique susceptibilities to covalent small molecules, a phenomenon we term conditional covalent lethality. Screening a panel of chemically diverse electrophiles, we identified a covalent ligand, MP-1, that exhibits FH-dependent cytotoxicity. Synthesis and structure-activity profiling identified key molecular determinants underlying the molecule's effects. Chemoproteomic profiling of cysteine reactivity together with clickable probes validated the ability of MP-1 to engage an array of functional cysteines, including one lying in the Zn-finger domain of the tRNA methyltransferase enzyme TRMT1. TRMT1 overexpression rescues tRNA methylation from inhibition by MP-1 and partially attenuates the covalent ligand's cytotoxicity. Our studies highlight the potential for covalent metabolites and small molecules to synergistically produce novel synthetic lethal interactions and raise the possibility of applying phenotypic screening with chemoproteomic target identification to identify new functional oncometabolite targets.

TAMI-17. INDUCTION OF SYNTHETIC LETHALITY BY ACTIVATION OF MITOCHONDRIAL CLPP AND INHIBITION OF HDAC1/2 IN GLIOBLASTOMA

Abstract Activation of the mitochondrial ClpP protease is an innovative therapeutic concept and the identification of synthetic lethal interactions may foster the development of novel therapies for glioblastoma (GBM). By integration of a transcriptome, metabolite and U-13C-glucose tracing analyses, we showed that activation of the mitochondrial ClpP protease through constitutively active ClpP (Y118A) or utilization of second-generation imipridone compounds (ONC206 and ONC212) in combination with genetic interference of HDAC1 and HDAC2 as well as with global (Panobinostat) and selective (Romidepsin) HDAC inhibitors caused synergistic reduction of viability in established, neuro-sphere and patient-derived xenograft (PDX) cultures of human GBM, which was mediated by interference with tricarboxylic acid cycle activity and GBM cell respiration. Notably, human astrocytes were significantly less susceptible to the combination treatment of HDAC-inhibitors and ClpP activators. The reduction of GBM viability occurred independent of TP53 status and was accompanied by activation of cell death with apoptotic features along with cleavage of caspases regulated chiefly by Bcl-xL and Mcl-1. Importantly, knockdown of the ClpP protease or ectopic expression of a ClpP D190A mutant almost completely rescued from the inhibition of oxidative energy metabolism as well as from the reduction of cellular viability by ClpP activators and the combination treatment, suggesting critical involvement of this protein. Finally, utilizing GBM PDX models, we demonstrated that the combination treatment of HDAC-inhibitors and imipridones reduced tumor growth and prolonged host survival more potently than single treatments or vehicle in vivo. Collectively, these observations suggest that the efficacy of HDAC inhibitors might be significantly enhanced through ClpP activators in model systems of human GBM.

Abstract C018: Primary tumor data analysis reveals novel synthetic lethal dependencies between KRAS mutation and the spliceosome

Abstract Identification of mutation-specific targeted therapies is a critical challenge in precision medicine. Synthetic lethality provides the basis for an approach to identify new therapeutic targets for mutations that are difficult to target directly, since in synthetic lethal (SL) interactions, an alteration in one gene leads to dependency on a second gene. Neither alteration by itself is essential for survival, but together these alterations lead to cancer cell death. We previously developed a new computational method, Mining Synthetic Lethals (MiSL), that can infer relationships from primary human tumor genomic and transcriptomic data and enable the identification of SL interactions in the context of native human tumors. The underlying assumption of MiSL is that SL partners of a mutation will be selectively amplified or never deleted in primary tumor samples harboring the mutation. Here, we used MiSL to identify novel SL partners of KRAS mutations in lung cancer. KRAS activating mutations are highly prevalent in lung cancer and aside from early clinical data on a novel covalent inhibitor for KRAS G12C mutations, there is no reported treatment to date for patients with KRAS mutations. We applied MiSL to identify SL interactions with KRAS G12 mutations in non-small cell lung cancer (NSCLC) and identified 220 SL partner genes. Some of these genes were foreseeable dependencies, such as MAPK, growth factor and inflammatory signaling associated genes; while others were novel SL candidates and included genes associated with splicing. Since we have access to a splicing-modulating drug in preclinical development, sudemycin D6 (SD6) which targets SF3B1, a spliceosome subunit, we focused on testing the SF3B1 interactors identified by MiSL, SLU7 and SRRM2. To enable validation studies, we first verified KRAS growth dependency in a panel of NSCLC cell lines using siRNA knock-down (KD). Next, we tested siRNA KD of predicted MiSL candidates on growth of KRAS-dependent (H358 and H441 – KRAS mutant) and KRAS-independent (H2228 – KRAS WT) cell lines. We confirmed that the KD of SLU7 expression reduced viability of KRAS mutant (75 to 85% reduced) but not KRAS wild-type (WT) cells lines. Similar differential viability was also observed with siRNA KD of SF3B1. Pharmacological inhibition of SF3B1 using SD6 also demonstrated that KRAS mutant cell lines were more sensitive to SD6 compared to a KRAS WT line (>5-30x). Finally, we validated these findings in an in vivo tumor xenograft model by treating KRAS WT and KRAS mutant tumors with SD6 and showed that the KRAS mutant tumors were significantly more inhibited by SD6 than KRAS WT tumors. Studies are ongoing to further confirm the SL interaction in lung cancer cell lines that are isogenic except for the KRAS mutation. In conclusion, SL predictions based on primary KRAS G12 mutant tumors indicated that splicing modulation is a vulnerability of KRAS tumors. Finally, synthetic lethal targets identified by primary tumor data mining can provide new therapeutic targets for KRAS specific mutant cancers, which can lead to personalized treatment options for NSCLC cancer patients. Citation Format: Claire E Repellin, Puja Patel, Yihui Shi, Helena Gong, Leena Shewade, Thomas Webb, Lidia Sambucetti, Subarna Sinha. Primary tumor data analysis reveals novel synthetic lethal dependencies between KRAS mutation and the spliceosome [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr C018. doi:10.1158/1535-7163.TARG-19-C018

Abstract 4015: Synthetic lethality-based predictive biomarker identification of splicing modulators in lung cancer

Abstract Identification of predictive biomarkers for targeted drugs is an active research area in precision oncology. Usually, a genetic alteration in the drug target is used to identify responders, which can limit the use of the drug in other responsive cancers. Alternatively, cell line-based drug sensitivity data are used to build machine-learning models to identify predictive biomarkers. These methods require large amounts of data and the black-box models developed are often hard to interpret. Synthetic lethality provides an alternate approach for predictive biomarker identification of targeted drugs. In synthetic lethal (SL) interactions, a defect in one gene leads to dependency on a second gene. Neither defect by itself is essential for survival, but together they lead to cell death. Thus, the inhibition of the drug target in the presence of a cancer-specific genetic alteration would result in cancer cell death. The genetic alterations can be used as biomarkers for the drug. We developed a novel computational pipeline for predictive biomarker identification based on our tool, Mining Synthetic Lethals (MiSL), that mines SL interactions from pan-cancer primary tumor data (such as TCGA). Since MiSL utilizes primary human tumor data, it enables the identification of SL interactions in the native context of human tumors and is more likely to find relationships relevant to in vivo tumor biology than shRNA/CRISPR screens. To discover biomarkers of a targeted drug, we used MiSL to identify genetic alterations that are SL with targets of the drug in a specific cancer type. As proof of principle, we applied our method to identify biomarkers of sudemycin-D6 (SD6), a potent splicing modulator, in non-small cell lung cancer (NSCLC). First, we identified putative drug targets of SD6. Since SD6 is known to inhibit SF3B1, any gene belonging to the same protein complex as SF3B1 was termed a SD6 drug target. Using these SD6 drug targets, our MiSL-based pipeline identified KRAS mutations, present in ~30% of NSCLC patients, as a SD6 biomarker. To confirm our predictions, we treated a panel of KRAS-mutant-and-dependent NSCLC lines (H358, H23 and H441) and a KRAS-WT NSCLC line (H2228) with SD6 and measured cell viability. All KRAS-mutant lines tested were significantly more sensitive to SD6 (> 5-30X lower IC50) than the KRAS-WT line. Furthermore, the KRAS-mutant lines were also more sensitive (5-20X lower IC50), compared to the KRAS-WT line, to a splicing modulator of a different class – a kinase inhibitor that phosphorylates components of the spliceosome. In vivo studies are underway to further validate our in vitro findings. This study identifies KRAS mutation as a novel predictive biomarker for splicing modulators in NSCLC, which could lead to new therapeutic options for KRAS-mutated NSCLC. Furthermore, our work demonstrates the feasibility of a synthetic lethality-based pipeline to predict biomarkers for targeted drugs. Citation Format: Claire E. Repellin, Puja Patel, Yihui Shi, Helena Gong, Thomas R. Webb, Lidia Sambucetti, Subarna Sinha. Synthetic lethality-based predictive biomarker identification of splicing modulators in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4015.

Abstract 3206: Synthetic lethal interactions with APOBEC3A identified by functional genomic screening

Abstract The APOBEC3 (A3) family of cytosine deaminase enzymes is integral to innate defenses by editing viral genomes to limit infectivity. However, several members of the A3 enzyme family (A3A-A3H) have also been implicated in mutational signatures observed in human cancers. The A3 mutational signature has emerged as the most prevalent across all cancers, implicating A3 enzymes as powerful mutators of the human genome. While few prospective studies have addressed the potential for A3 enzymes to induce specific mutational patterns in human genomes, the possibility of exploiting the mutagenic activity of A3 for cancer therapy is beginning to be explored. We recently showed that A3A is highly expressed in human acute myeloid leukemia (AML), and that deamination activates the replication checkpoint via ATR kinase signaling. In an AML model, we showed that inhibition of ATR or downstream Chk1 resulted in accumulated DNA damage by A3A and ultimately death of leukemia cells. In search of additional cellular pathways that may be manipulated to take advantage of A3 activity for therapeutic effect, we performed a functional genomics screen using a genome-wide CRISPR knock-out approach in a model of human leukemia with inducible A3A expression. We introduced Cas9 and the Brunello guide RNA lentivirus library into the human AML cell line, THP1, which we engineered to express doxycycline-inducible A3A. The Brunello library consists of 4 guide RNAs targeting each human gene. Transduction was performed to enable one lentivirus integration per cell, thus resulting in a single gene disruption per cell. Cells were treated with doxycycline and genomic DNA was harvested from treated and untreated cells every week. Guide RNAs were amplified by PCR from genomic DNA and identified by next-generation sequencing. We applied the MaGECK algorithm to identify the quantity of each guide RNA remaining in treated samples and compared to untreated samples. In THP1 cells induced to express A3A, knockout of genes in cell cycle and DNA repair pathways resulted in synthetic lethality. Specifically, expression of A3A synergized with genes essential for cell cycle checkpoints and DNA damage responses to cause leukemia cell death. Clinical-grade inhibitors are available for protein products of many of the genes identified; thus these represent feasible targets for therapeutic intervention in AML that has elevated A3A activity. In addition to leukemia, A3A and the closely related enzyme A3B have been implicated in several malignant processes including breast, bladder, and ovarian cancers. We show that functional genomic screening is a powerful tool for identification of synthetic lethal interactions, which can provide therapeutic opportunities for additional cancers in which A3 enzymes are active. Citation Format: Abby M. Green, Katharina E. Hayer, Julia H. Szeto, Ophir Shalem, Matthew D. Weitzman. Synthetic lethal interactions with APOBEC3A identified by functional genomic screening [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 3206.

Abstract 2974: Primary tumor data mining identifies a novel synthetic lethal partner of the BRCA1 mutation in breast cancer

Abstract Identification of mutation-specific targeted therapies is a critical challenge in precision medicine, in part because many cancer mutations are loss-of-function and are not druggable. Even when a mutation-specific targeted therapy exists, resistance invariably develops necessitating alterative therapeutic options. Synthetic lethality can help identify new therapeutic targets for these mutations. In synthetic lethal (SL) interactions, an alteration in one gene leads to dependency on a second gene. Neither alteration by itself is essential for survival, but together these alterations lead to cancer cell death. We have recently developed a new computational method (Mining Synthetic Lethals, MiSL) that mines SL interactions from pan-cancer primary tumor data (such as TCGA). The underlying assumption of MiSL is that SL partners of a mutation will be selectively amplified or never deleted and overexpressed in primary tumor samples harbouring the mutation. Since MiSL mines relationships from primary human tumor data, it enables the identification of SL interactions in the native context of human tumors and is more likely to find relationships relevant to in vivo tumor biology than shRNA screens. Here, we applied MiSL to identify SL partners of the BRCA1 mutation in triple-negative breast cancer (TNBC). BRCA1 is mutated in 15-20% of TNBC and is a known cancer susceptibility gene in breast cancer. MiSL identified 22 SL partners of the BRCA1 mutation in TNBC: these were enriched for DNA repair genes consistent with the known SL relationship between BRCA1 and the DNA repair gene PARP. Notably, XRCC6, a gene involved in non-homologous end joining, was predicted to be a SL partner of the BRCA1 mutation in TNBC. MiSL selected XRCC6 because XRCC6 deletions (which were associated with lowered expression of XRCC6) were mutually exclusive with BRCA1 mutations in pan-cancer analysis (p = 0.01), and XRCC6 was upregulated in BRCA1-mutated TNBC compared to BRCA1-WT TNBC (p = 0.02). To test the prediction, we examined the effect of XRCC6 knockdown (KD) in the BRCA1-mutated TNBC cell line SUM149. Consistent with our prediction, we found that XRCC6 KD significantly increased apoptosis (37.3%) and reduced viability (50% reduction, p < 0.0001) in SUM149 cells compared to controls. XRCC6 KD in a BRCA1-WT breast cancer cell line, T47D, resulted in significantly lower reduction in cell viability compared to the BRCA1-mutated cell line (p<0.0001). These data indicated that XRCC6 KD more effectively induced cell death in the presence of BRCA1 mutation than in cells WT for the gene. We are currently testing XRCC6 KD effects in TNBC cell lines harbouring germline BRCA1 mutations. Thus, we have identified a novel SL partner of the BRCA1 mutation in TNBC that can lead to the development of new therapeutic options for BRCA1-mutated breast cancers and to new chemoprevention strategies for individuals carrying germline BRCA1 mutations. Citation Format: Yihui Shi, Xiaohe Liu, Jia Li, Lidia Sambucetti, Subarna Sinha. Primary tumor data mining identifies a novel synthetic lethal partner of the BRCA1 mutation in breast cancer [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 2974.

Abstract PR09: Harnessing synthetic lethality to predict clinical outcomes of cancer treatment

Abstract Significance: The identification of Synthetic Lethal interactions (SLi) have long been considered a foundation for the advancement of cancer treatment. The rapidly accumulating large-scale patient data now provides a golden opportunity to infer SLi directly from patient samples. Here we present a new data-driven approach termed ISLE for identifying SLi, which is then shown to be predictive of clinical outcomes of cancer treatment in an unsupervised manner, for the first time. Methods: ISLE consists of four inference steps, analyzing tumor, cell line and gene evolutionary data: It first identifies putative SL gene pairs whose co-inactivation is underrepresented in tumors, testifying that they are selected against. Second, it further prioritizes candidate SL pairs whose co-inactivation is associated with better prognosis in patients, testifying that they may hamper tumor progression. Finally, it eliminates false positive SLi using gene essentiality screens (testifying to causal SLi relations) and prioritizing SLi paired genes with similar evolutionary phylogenetic profiles. Results: We applied ISLE to analyze the TCGA tumor collection and generated the first clinically-derived pan-cancer SL-network, composed of SLi common across many cancer types. We validated that these SLi match the known, experimentally identified SLi (AUC=0.87), and show that the SL-network is predictive of patient survival in an independent breast cancer dataset (METABRIC). Based on the predicted SLi, we predicted drug response in a wide variety of in vitro, mouse xenograft and patient data, altogether encompassing >700 single drugs and >5,000 drug combinations in >1,000 cell lines, 375 xenograft models and >5,000 patient samples. Importantly, these predictions were performed in an unsupervised manner, reducing the known risk of over-fitting the data commonly associated with supervised prediction methods. SL-derived predictions are based on computing an SL-score that estimates the efficacy of a given drug in a given tumor based on the latter's omics data. The SL-score counts the number of inactive SL-partners of a given drug target(s) in the given tumor, reflecting the notion that a drug is likely to be more effective in tumors where many of its targets' SL-partners are inactive. The predicted SL-scores show significant correlations (R > 0.4) with large-scale in vitro and in vivo drug response screens for the majority of drugs tested. Based on the conjecture that synergism between drugs may be mediated by underlying SLi between their targets, we additionally provide accurate predictions of drug synergism for both in vitro and in vivo drug combination screens (AUC~0.8). Most importantly, we demonstrate for the first time that an SL-network can successfully predict the treatment outcome in cancer patients in multiple large-scale patient datasets including the TCGA, where SLis successfully predict patients' response for 75% of cancer drugs. Conclusions: ISLE is predictive of the patients' response for the majority of current cancer drugs. Of paramount importance, the predictions of ISLE are based on SLi between (potentially) all genes in the cancer genome, thus prioritizing treatments for patients whose tumors do not bear specific actionable mutations in cancer driver genes, offering a novel approach to precision-based cancer therapy. The predictive performance of ISLE is likely to further improve with the expected rapid accumulation of additional cancer omics and clinical phenotypic data. Citation Format: Joo Sang Lee, Avinash Das, Livnat Jerby-Arnon, Dikla Atias, Arnaud Amzallag, Cyril H. Benes, Talia Golan, Eytan Ruppin. Harnessing synthetic lethality to predict clinical outcomes of cancer treatment [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 PR09.

Identifying synthetic lethal targets using CRISPR/Cas9 system.

Synthetic lethality occurs when co-occurrence of two genetic events is unfavorable for the survival of the cell or organism. The conventional approach of high throughput screening of synthetic lethal targets using chemical compounds has been replaced by RNAi technology. CRISPR/Cas9, an RNA guided endonuclease system is the most recent technology for this work. Here, we have discussed the major considerations involved in designing a CRISPR/Cas9 based screening experiment for identification of synthetic lethal targets. It mainly includes CRISPR library to be used, cell types for conducting the experiment, the most appropriate screening strategy and ways of selecting the desired phenotypes from the complete cell population. The complete knockdown of genes can be achieved using CRISPR/Cas9 knockout libraries. For higher quality loss-of-function screens, haploid cells with defective homology-directed DNA repair mechanism could be used. Two widely used screening formats include arrayed and pooled screens followed by negative or positive selection of the cells with desired phenotype. However, pooled screening format with negative selection of cells serves the best. The advantages of using CRISPR/Cas9 system over the other RNAi approaches have also been discussed. Finally, some studies using CRISPR/Cas9 for genome-wide knockout screening in human cells and computational approaches for identification of synthetic lethal interactions have been discussed.

Abstract 543: Harnessing synthetic lethality to predict clinical outcomes of cancer treatment

Abstract Significance: The identification of Synthetic Lethal interactions (SLi) has long been considered a foundation for the advancement of cancer treatment. The rapidly accumulating large-scale patient data now provides a golden opportunity to infer SLi directly from patient samples. Here we present a new data-driven approach termed ISLE for identifying SLi, which is then shown to be predictive of clinical outcomes of cancer treatment in an unsupervised manner, for the first time. Methods: ISLE consists of four inference steps, analyzing tumor, cell line and gene evolutionary data: It first identifies putative SL gene pairs whose co-inactivation is underrepresented in tumors, testifying that they are selected against. Second, it further prioritizes candidate SL pairs whose co-inactivation is associated with better prognosis in patients, testifying that they may hamper tumor progression. Finally, it eliminates false positive SLi using gene essentiality screens (testifying to causal SLi relations) and prioritizing SLi paired genes with similar evolutionary phylogenetic profiles. Results: We applied ISLE to analyze the TCGA tumor collection and generated the first clinically-derived pan-cancer SL-network, composed of SLi common across many cancer types. We validated that these SLi match the known, experimentally identified SLi (AUC=0.87), and show that the SL-network is predictive of patient survival in an independent breast cancer dataset (METABRIC). Based on the predicted SLi, we predicted drug response of single agents and drug combinations in a wide variety of in vitro, mouse xenograft and patient data, altogether encompassing >700 single drugs and >5,000 drug combinations in >1,000 cell lines, 375 xenograft models and >5,000 patient samples. Of note, these predictions were performed in an unsupervised manner, reducing the known risk of over-fitting the data commonly associated with supervised prediction methods. Our prediction is based on the notion that a drug is likely to be more effective in tumors where many of its targets’ SL-partners are inactive, and drug synergism may be mediated by underlying SLi between their targets. Most importantly, we demonstrate for the first time that an SL-network can successfully predict the treatment outcome in cancer patients in multiple large-scale patient datasets including the TCGA, where SLis successfully predict patients’ response for 75% of cancer drugs. Conclusions: ISLE is predictive of the patients’ response for the majority of current cancer drugs. Of paramount importance, the predictions of ISLE are based on SLi between (potentially) all genes in the cancer genome, thus prioritizing treatments for patients whose tumors do not bear specific actionable mutations in cancer driver genes, offering a novel approach to precision-based cancer therapy. The predictive performance of ISLE is likely to further improve with the expected rapid accumulation of additional patient data. Citation Format: Joo Sang Lee, Avinash Das, Livnat Jerby-Arnon, Seung Gu Park, Matthew Davidson, Dikla Atias, Arnaud Amzallag, Chani Stossel, Ella Buzhor, Welles Robinson, Kuoyuan Cheng, Joshua J. Waterfall, Paul S. Meltzer, Sridhar Hannenhalli, Cyril H. Benes, Talia Golan, Emma Shanks, Eytan Ruppin. Harnessing synthetic lethality to predict clinical outcomes of cancer treatment [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 543. doi:10.1158/1538-7445.AM2017-543

Multi-omic measurement of mutually exclusive loss-of-function enriches for candidate synthetic lethal gene pairs.

BackgroundIdentification of synthetic lethal interactions in cancer cells could offer promising new therapeutic targets. Large-scale functional genomic screening presents an opportunity to test large numbers of cancer synthetic lethal hypotheses. Methods enriching for candidate synthetic lethal targets in molecularly defined cancer cell lines can steer effective design of screening efforts. Loss of one partner of a synthetic lethal gene pair creates a dependency on the other, thus synthetic lethal gene pairs should never show simultaneous loss-of-function. We have developed a computational approach to mine large multi-omic cancer data sets and identify gene pairs with mutually exclusive loss-of-function. Since loss-of-function may not always be genetic, we look for deleterious mutations, gene deletion and/or loss of mRNA expression by bimodality defined with a novel algorithm BiSEp.ResultsApplying this toolkit to both tumour cell line and patient data, we achieve statistically significant enrichment for experimentally validated tumour suppressor genes and synthetic lethal gene pairings. Notably non-reliance on genetic loss reveals a number of known synthetic lethal relationships otherwise missed, resulting in marked improvement over genetic-only predictions. We go on to establish biological rationale surrounding a number of novel candidate synthetic lethal gene pairs with demonstrated dependencies in published cancer cell line shRNA screens.ConclusionsThis work introduces a multi-omic approach to define gene loss-of-function, and enrich for candidate synthetic lethal gene pairs in cell lines testable through functional screens. In doing so, we offer an additional resource to generate new cancer drug target and combination hypotheses. Algorithms discussed are freely available in the BiSEp CRAN package at http://cran.r-project.org/web/packages/BiSEp/index.html.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2375-1) contains supplementary material, which is available to authorized users.

SWI/SNF proteins as targets in cancer therapy.

Recent identification of synthetic lethal interactions involving several proteins of the SWI/SNF complex discussed in this Research Highlight has opened the possibility of new cancer therapeutic approaches.

Whole genome RNAi screens reveal a critical role of REV3 in coping with replication stress

REV3, the catalytic subunit of translesion polymerase zeta (polζ), is commonly associated with DNA damage bypass and repair. Despite sharing accessory subunits with replicative polymerase δ, very little is known about the role of polζ in DNA replication. We previously demonstrated that inhibition of REV3 expression induces persistent DNA damage and growth arrest in cancer cells. To reveal determinants of this sensitivity and obtain insights into the cellular function of REV3, we performed whole human genome RNAi library screens aimed at identification of synthetic lethal interactions with REV3 in A549 lung cancer cells. The top confirmed hit was RRM1, the large subunit of ribonucleotide reductase (RNR), a critical enzyme of de novo nucleotide synthesis. Treatment with the RNR-inhibitor hydroxyurea (HU) synergistically increased the fraction of REV3-deficient cells containing single stranded DNA (ssDNA) as indicated by an increase in replication protein A (RPA). However, this increase was not accompanied by accumulation of the DNA damage marker γH2AX suggesting a role of REV3 in counteracting HU-induced replication stress (RS). Consistent with a role of REV3 in DNA replication, increased RPA staining was confined to HU-treated S-phase cells. Additionally, we found genes related to RS to be significantly enriched among the top hits of the synthetic sickness/lethality (SSL) screen further corroborating the importance of REV3 for DNA replication under conditions of RS.

Synthetic Lethal Screens as a Means to Understand and Treat MYC-Driven Cancers

Although therapeutics against MYC could potentially be used against a wide range of human cancers, MYC-targeted therapies have proven difficult to develop. The convergence of breakthroughs in human genomics and in gene silencing using RNA interference (RNAi) have recently allowed functional interrogation of the genome and systematic identification of synthetic lethal interactions with hyperactive MYC. Here, we focus on the pathways that have emerged through RNAi screens and present evidence that a subset of genes showing synthetic lethality with MYC are significantly interconnected and linked to chromatin and transcriptional processes, as well as to DNA repair and cell cycle checkpoints. Other synthetic lethal interactions with MYC point to novel pathways and potentially broaden the repertoire of targeted therapies. The elucidation of MYC synthetic lethal interactions is still in its infancy, and how these interactions may be influenced by tissue-specific programs and by concurrent genetic change will require further investigation. Nevertheless, we predict that these studies may lead the way to novel therapeutic approaches and new insights into the role of MYC in cancer.

Abstract B66: A novel cre-versible approach for identifying synthetic lethal interactions in the DNA damage response in vivo.

Abstract A fundamental limitation in the ability to devise new therapeutic strategies for manipulating the DNA damage response to treat cancer is the identification of synthetic lethal interactions that function in tumors in vivo. We recently identified the stress activated p38MAPK/MK2 pathway as a critical component of the DNA damage response network in tumor cells in vitro. When treated with genotoxic chemotherapy, MK2 is essential for the survival of tumor cell lines that lack functional p53, but is dispensable in p53-proficient cells. A critical function of MK2 in p53-deficient cells is the post-transcriptional regulation of gene expression, namely the stabilization of the mRNA of cell cycle regulators such as Gadd45α. Intriguingly, MK2 also plays an essential role in the biosynthesis of inflammatory cytokines, including IL-1, IL-6 and TNFα that are key signaling components in the tumor microenvironment. These MK2-regulated cytokines are likely to play important roles in promoting tumor development, as many cancers arise in setting of chronic inflammation, as well as in the creation of chemo-resistant niches where therapeutic resistance develops. The fact that p53 is commonly lost in tumors suggests that the therapeutic targeting of MK2 may be a useful strategy to enhance destruction of tumors by genotoxic chemotherapy. To explore the role of the p38MAPK/MK2 pathway in the integration of the DNA damage response and cytokine signaling during tumor development and chemotherapeutic response in vivo, we have generated a novel mouse model in which Cre-mediated recombination can be used to create MK2-proficient and deficient tumors within a single animal. This strategy allows direct comparison of MK2 synthetic lethality with genetic mutations that promote tumor development and with the tumor response to DNA damaging chemotherapy in a novel internally controlled setting in vivo. Furthermore, this approach potentially allows tumor autonomous signaling events after DNA damage to be distinguished from signaling events within the stromal microenvironment. Using this approach in a murine autochthonous model of non-small-cell lung cancer (NSCLC) we demonstrate that synthetic lethality between p53 and MK2 can successfully be exploited for enhanced chemo-sensitization of tumors to DNA damaging chemotherapy in vivo. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B66. Citation Format: Sandra Morandell, H.-Christian Reinhardt, Ian G. Cannell, Michael B. Yaffe. A novel cre-versible approach for identifying synthetic lethal interactions in the DNA damage response in vivo. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B66.

Abstract A32: Systematic reconstruction of the cancer synthetic lethal network and its application for the identification of selective cancer drug targets

Abstract Motivation: Synthetic Lethal (SL) interactions can provide a basis for the development of personalized anticancer drugs with higher therapeutic efficacy and reduced toxicity. Emerging screening technologies have enabled the detection of SL interactions in human cell lines in recent years. However, such screens are confined to find the SL-partners of a small set of genes or drugs, and yet cannot encompass the large spectrum of cancer types and genetic interactions. As these screens are mostly conducted in cancer cell lines or in animal models, they may fail to capture the SL interactions that occur in human cancers in-vivo. As a result, the promise of harnessing SL interactions to develop selective anticancer drugs is yet to be fully explored and exploited. Methods: Here we present a novel computational framework for the genome-scale identification of SL interactions in cancer, which spans different cancer types in a clinical setting. Our approach stems from the basic assertion that cancer cells that have lost two SL-paired genes are strongly selected against. This observation enables us to identify SL interactions by analyzing the genetic profiles of thousands of clinical samples and detecting events of gene-co-deletions that occur significantly less than expected. Based on converging evidence from such an analysis of different genome-scale cancer datasets we have assembled a core set of SL interactions that are supported by at least three different datasets. These SLs are shared by the wide range of cancer types in a statistically enriched manner, and constitute the Cancer Synthetic Lethal Network (CSLN). Results: Testing the veracity of the CSLN, we employed it to infer gene essentiality in 26 different cancer cell lines, based on their genetic profiles. The obtained cell line-specific gene essentiality predictions significantly match pertaining experimental essentiality measures (Marcotte et al., Cancer Discovery, 2011). Remarkably, the significant predictive power of the CSLN surpassed that obtained when using a cohort of SL-interactions detected experimentally. Following, we comprehensively tabulated the therapeutic potential of targeting the CSLN genes in different cancer types, to provide an array of novel cancer-specific drug target and repurposing predictions. Extending the concept of synthetic lethality to target oncogenes that are difficult to target directly, we identified synthetic lethality arising from gene amplification, rather than deletion, and constructed the Cancer Amplification SL Network (CASLN). We employed it to predict the efficacy of 30 different drugs across 593 cancer cell lines, which have been recently tested experimentally (Garnett et al., Nature, 2012). Indeed, drugs were significantly more effective in the predicted sensitive cell lines than in those predicted insensitive. Significance: This study paves the way for obtaining and exploiting a system-level understanding of SL interactions in cancer in three important ways: 1. It provides a novel computational pipeline for detecting SL interactions in cancer. An approach that can also be employed to detect context-dependent SL interactions that are specific to certain cancer types (as we demonstrate by generating and testing a breast cancer SL network). 2. It presents two large-scale networks of genetic interactions in cancer, whose utility is manifested by their ability to predict gene essentiality and drug efficacy in a cell-line-specific manner. 3. The broad set of novel cancer-specific drug target predictions obtained is likely to provide a basis for numerous follow-up experimental studies. In summary, our approach opens up exciting opportunities for rational drug design and for the development of personalized therapeutic strategies for cancer. Citation Format: Livnat Jerby Arnon, Adam Weinstock, Tamar Geiger, Eytan Ruppin. Systematic reconstruction of the cancer synthetic lethal network and its application for the identification of selective cancer drug targets. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr A32.

Abstract 1219: Identification of synthetic lethal interactions with the BRAF oncogene in colorectal cancer.

Abstract Background Approximately 8-15% of colorectal (CRC) patients carry an activating mutation in BRAF. This CRC subtype is associated with poor outcome and with resistance, both to chemotherapeutic treatments and to tailored drugs. We recently showed that BRAF (V600E) colon cancers (CCs) have a characteristic gene expression signature (1, 2) which is found also in subsets of KRAS mutant and KRAS-BRAF wild type (WT2) tumors. Tumors having this gene signature, referred as “BRAF-like," have a similar poor prognosis irrespective of the presence of the BRAF (V600E) mutation. By using a shRNA-based genetic screen in BRAF mutant CC cell lines we aimed to identify genes and pathways necessary for survival and growth of BRAF mutant CC. Such studies may reveal additional targets for therapy and potentially provide new biomarkers for patient stratification. Method We identified 363 genes that are selectively overexpressed in BRAF mutant tumors as compared to WT2 type tumors, based on gene expression profiles of the PETACC3 (1) and Agendia (1) datasets. The TRC human genome-wide shRNA collection (TRC-Hs1.0) was used to generate a 1815 hairpins sub-library targeting those identified genes (BRAF library). BRAF(V600E) CC cell lines were infected with the BRAF library and screened for shRNAs that cause lethality. LIM1215 CC cell line (WT2) was used as a control. Cells stably expressing the shRNA library were cultured for 13 days, after which shRNAs were recovered by PCR. Deep sequencing was applied to determine the specific depletion of shRNA in BRAF(V600E) cells as compared to LIM1215 cells. Results Candidate genes were identified by using following filtering criteria: depletion in BRAF(V600E) cells by at least 50% and depletion in BRAF(V600E) cells 1,5-fold higher than in control cells with the corresponding p-value to be ≤ 0.1. A total of 34 genes met our criteria of which 6 genes were presented with more than one hairpin and were concordant across the cell lines selected for validation. Conclusion We identified candidate synthetic lethal genes in BRAF mutant CC cell lines. Functional analysis is ongoing. Data will be presented. Citation Format: Loredana Vecchione, Valentina Gambino, Giovanni D'Ario, Tian Sun, Iris Simon, Vlad Popovici, Mauro Delorenzi, Rene’ Bernards, Sabine Tejpar. Identification of synthetic lethal interactions with the BRAF oncogene in colorectal cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1219. doi:10.1158/1538-7445.AM2013-1219

Identification of synthetic lethal interactions with the BRAF oncogene in colorectal cancer.

403 Background: Approximately 8-15% of colorectal (CRC) patients carry an activating mutation in BRAF. This CRC subtype is associated with poor outcome and with resistance, both to chemotherapeutic treatments and to tailored drugs. We recently showed that BRAF (V600E) colon cancers (CCs) have a characteristic gene expression signature (1, 2) which is found also in subsets of KRAS mutant and KRAS-BRAF wild type (WT2) tumors. Tumors having this gene signature, referred as “BRAF-like”, have a similar poor prognosis irrespective of the presence of the BRAF (V600E) mutation. By using a shRNA-based genetic screen in BRAF mutant CC cell lines we aimed to identify genes and pathways necessary for survival and growth of BRAFmutant CC. Such studies may reveal additional targets for therapy and potentially provide new biomarkers for patient stratification Methods: We identified 363 genes that are selectively overexpressed in BRAF mutant tumors as compared to WT2 type tumors, based on gene expression profiles of the PETACC3 (1) and Agendia (2) datasets. The TRC human genome-wide shRNA collection (TRC-Hs1.0) was used to generate a 1815 hairpins sub-library targeting those identified genes (BRAF library). BRAF(V600E) CC cell lines were infected with the BRAF library and screened for shRNAs that cause lethality. LIM1215 CC cell line (WT2) was used as a control. Cells stably expressing the shRNA library were cultured for 13 days, after which shRNAs were recovered by PCR. Deep sequencing was applied to determine the specific depletion of shRNA in BRAF(V600E) cells as compared to LIM1215 cells Results: Candidate genes were identified by using following filtering criteria: depletion in BRAF(V600E) cells by at least 50% and depletion in BRAF(V600E) cells 1, 5-fold higher than in control cells with the corresponding p-value to be ≤ 0.1. A total of 34 genes met our criteria of which 6 genes were presented with more than one hairpin and were concordant across the cell lines selected for validation. Conclusions: We identified candidate synthetic lethal genes in BRAF mutant CC cell lines. Functional analysis is ongoing. Data will be presented.

554 Identification of Synthetic Lethal Interactions with Oncogenic Ras Through Large Scale Functional Genomic Screening

554 Identification of Synthetic Lethal Interactions with Oncogenic Ras Through Large Scale Functional Genomic Screening