Synthetic Lethal Pairs Research Articles

Integrated multi‐omics data analyses for exploring the co‐occurring and mutually exclusive gene alteration events in colorectal cancer

Co-occurring and mutually exclusive gene alteration events are helpful for understanding carcinogenesis but systematic screening for such events is quite limited. We conducted pairwise screening tests to identify "hit pairs" in colorectal cancer (CRC) by utilizing the cross-omics data from The Cancer Genome Atlas (TCGA). Numerous hit pairs involving somatic mutations, copy number variations, and DNA methylation were found to occur nonrandomly in CRC, such as KRAS and HOXB6, SMAD4 and PMEPA1. Based on these hit pairs, we identified 32 synthetic lethal pairs and 7,527 co-occurring pairs relating to drug response. Our further biological experiments showed that the co-occurrence of mutant FCGBP and NUDT12 silencing (or mutant TMC3 and RPS6KA6 silencing) with small interfering RNA reduced cell viability. Moreover, novel hit pairs could influence prognosis. The patients who carried concurrent mutations of IRF5 and NEFH, SYNE1 and TTN, or MUC16 and NEFH had worse survival outcomes. Particularly, the presence of mutant SYNE1 and TTN pair not only affects prognosis, but also is related to CRC patients' response to drug treatment. Our "hit pair" genes may provide insights into colorectal carcinogenesis and help open new avenues for CRC therapy.

Abstract A141: Computational models of synergy contribute to efficient combination screening

Abstract Introduction: Combination drug treatment in cancer therapy aims to improve response rate and decrease the development of drug resistance. The discovery of novel drug combinations is constrained by the cost and effort of carrying out large unbiased screens and is hampered by poor translation towards the clinic. We investigate if computational models combined with screening can more efficiently reveal synergistic combinations and improve translation towards the clinic. The models are based on three different biological views of synergy. First, compounds that exploit similar vulnerabilities in cancer cells frequently show synergy in a particular genetic context (targeted synergy, model 1) [1,2]. Secondly, drugs with non-overlapping resistance mechanisms often yield clinically relevant drug combinations (model 2) [3]. Finally, drugs that mimic synthetic lethal interactions will often act synergistically (model 3) [4]. Experimental procedures Oncolines™ is a panel of 102 genetically characterized cell lines from diverse tumor origins, on which proliferation assays are run in parallel in nine-point dose-response curves. In this cell panel we profiled 162 different cancer therapeutics, including many standard of care, chemotherapeutic agents, epigenetic modulators, and approved and pre-clinical drugs such as CDK4/6, ALK and PI3K-inhibitors [5]. Cancer hotspot mutations and gene expression data were downloaded from the DepMap and CCLE databases. ANOVA and Pearson correlations were used to analyze the single agent response and genetic data in the statistical software R, to determine gene mutation (model 1) and resistance markers (model 2). For model 3, clinically relevant synthetic lethal pairs were combined with gene expression data [4]. The methods were used to predict results from published synergy screens, e.g. DREAM [6] and results from an independent experiment (NTRC SynergyScreen™) in which fixed concentrations of the poly-ADP ribose polymerase (PARP) inhibitor niraparib and the BET bromodomain inhibitor JQ1, were combined with nine-point dose-response curves of 150 anticancer agents. Synergies were quantified using curve-shift and CI-index. Results: Using a cancer cell line IC50 profile of a compound (such as Oncolines™), all three computational models of synergy prediction can be applied. Method 1 is successful in predicting combinations that augment a targeted effect, such as combining BRAF and MEK inhibitors in BRAF-mutant cancer [1]. Method 2 does not distinguish between additive or synergistic combinations but can identify clinically relevant combinations [3]. For method 3, a tool was developed that computationally assesses if drug pairs can pharmacologically mimic clinically validated synthetic lethal interactions [4]. This can predict DREAM data with a high AUC of 0.67 in a ROC curve. The tool successfully predicts synergy between niraparib and the SUMO inhibitor 2-D08, and niraparib and BCL2 inhibitors, which we observed in our screen. Conclusions: Computational tools for synergy have predictive value and can be useful to prioritize libraries for empirical combination screening.

Predicting synthetic lethal interactions using heterogeneous data sources.

A synthetic lethal (SL) interaction is a relationship between two functional entities where the loss of either one of the entities is viable but the loss of both entities is lethal to the cell. Such pairs can be used as drug targets in targeted anticancer therapies, and so, many methods have been developed to identify potential candidate SL pairs. However, these methods use only a subset of available data from multiple platforms, at genomic, epigenomic and transcriptomic levels; and hence are limited in their ability to learn from complex associations in heterogeneous data sources. In this article, we develop techniques that can seamlessly integrate multiple heterogeneous data sources to predict SL interactions. Our approach obtains latent representations by collective matrix factorization-based techniques, which in turn are used for prediction through matrix completion. Our experiments, on a variety of biological datasets, illustrate the efficacy and versatility of our approach, that outperforms state-of-the-art methods for predicting SL interactions and can be used with heterogeneous data sources with minimal feature engineering. Software available at https://github.com/lianyh. Supplementary data are available at Bioinformatics online.

Systematical Analysis of the Protein Targets of Lactoferricin B and Histatin-5 Using Yeast Proteome Microarrays.

Antimicrobial peptides (AMPs) have potential antifungal activities; however, their intracellular protein targets are poorly reported. Proteome microarray is an effective tool with high-throughput and rapid platform that systematically identifies the protein targets. In this study, we have used yeast proteome microarrays for systematical identification of the yeast protein targets of Lactoferricin B (Lfcin B) and Histatin-5. A total of 140 and 137 protein targets were identified from the triplicate yeast proteome microarray assays for Lfcin B and Histatin-5, respectively. The Gene Ontology (GO) enrichment analysis showed that Lfcin B targeted more enrichment categories than Histatin-5 did in all GO biological processes, molecular functions, and cellular components. This might be one of the reasons that Lfcin B has a lower minimum inhibitory concentration (MIC) than Histatin-5. Moreover, pairwise essential proteins that have lethal effects on yeast were analyzed through synthetic lethality. A total of 11 synthetic lethal pairs were identified within the protein targets of Lfcin B. However, only three synthetic lethal pairs were identified within the protein targets of Histatin-5. The higher number of synthetic lethal pairs identified within the protein targets of Lfcin B might also be the reason for Lfcin B to have lower MIC than Histatin-5. Furthermore, two synthetic lethal pairs were identified between the unique protein targets of Lfcin B and Histatin-5. Both the identified synthetic lethal pairs proteins are part of the Spt-Ada-Gcn5 acetyltransferase (SAGA) protein complex that regulates gene expression via histone modification. Identification of synthetic lethal pairs between Lfcin B and Histatin-5 and their involvement in the same protein complex indicated synergistic combination between Lfcin B and Histatin-5. This hypothesis was experimentally confirmed by growth inhibition assay.

Application of artificial intelligence to predict a new class of novel synthetic lethal targets.

2598 Background: Synthetic lethal targets are proteins that are contextually vulnerable. Inhibitors of PARP1, for example, selectively produce a lethal phenotype in the context of cancer cells which have lost BRCA1 or BRCA2 function. As a high mutation rate is a hallmark of many cancers, targeting synthetic lethal interactions to selectively inhibit cancer cells with altered genetic backgrounds may increase the specificity and efficacy of therapeutics. Recently, clinical trials have targeted synthetic lethal pairs such as EGFR and BRAF, TP53 and BCL2, and PTEN and CHD1. Previous attempts to identify synthetic lethal targets have relied on empirical results from published studies of biological pathways perturbed in cancer cells. Developing strategies to rapidly identify synthetic lethals by combining multiple experimental and computational approaches would result in a new class of potential cancer drug targets beyond the existing efforts that rely on single experimental or computational methods alone. Methods: Here we present Expansive AI, an artificial intelligence augmented knowledge network that enables rapid hypothesis generation for accelerated discovery research. Using a purpose-built, hypergraph database of massive, integrated genomic and biomedical data, we can query all synthetic lethals and their component genes, as well as a wealth of data related to these genes. The database of biological data includes 11,000+ cancer genomes from TCGA, prior knowledge resources such as gene ontology and pathway resources, and experimental data including chemical and protein interaction and patent data. The hypergraph’s architecture allows for linking and nesting data, enabling efficient extraction of biologically-relevant features. Results: Using these features, a neural network classified 540 new candidate pairs that have previously not been reported. The candidate pairs were filtered to include only known oncogenes and least-studied genes. This produced a list of gene pairs which may represent the most novel class of synthetic lethal target candidates identified to date. Conclusions: We highlight the results of this AI-based approach and discuss validation efforts of the predicted interactions in specific cancer contexts.

SL2MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization.

Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, such as high cost, low consistency across platforms, or cell lines. Therefore, computational prediction methods are needed to address these issues. This paper proposes a novel SL prediction method, named SL2 MF, which employs logistic matrix factorization to learn latent representations of genes from the observed SL data. The probability that two genes are likely to form SL is modeled by the linear combination of gene latent vectors. As known SL pairs are more trustworthy than unknown pairs, we design importance weighting schemes to assign higher importance weights for known SL pairs and lower importance weights for unknown pairs in SL2 MF. Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO). In particular, we calculate the similarity between genes based on their GO annotations and topological properties in the PPI network. Extensive experiments on the SL interaction data from SynLethDB database have been conducted to demonstrate the effectiveness of SL2 MF.

Abstract LB-022: Synthetic lethal pairs for cancer treatment by agnostic exploration of mutually exclusive genomics

Abstract Human cancers are highly heterogeneous diseases that rely on the accumulation of genetic and epigenetic alterations for initiation and maintenance. The increasing knowledge emerging from large sequencing efforts, such as The Cancer Genome Atlas (TCGA) or the International Cancer Genome Consortium (ICGC) can be used to identify new targets for treatment by applying the concept of synthetic lethality (SL), with new synthetic lethal pairs identified through mutual exclusivity analysis of patient-derived tumor data. However several confounding factors, such as sample purity or sequencing platform, prevent the systematic explorations of the full spectrum of synthetic lethal (SL) pairs. Here, we present a two-step approach to handle large SL studies: i) SPICE (Synthetic Lethal Phenotype Identification through Cancer Evolution analysis) pipeline to process in a uniform environment thousands of samples; (ii) FaMe (Fast Mutual Exclusivity algorithm) to explore millions of aberration pairs and then nominate mutual exclusive pairs, which will be validated in vitro. The SPICE pipeline handles DNA sequencing data (whole genome, whole exome, and targeted sequencing) and generates purity- and ploidy-adjusted somatic copy number profiles. Then, it assesses allele specific copy number, a strong confounding factor in mutual exclusivity analysis. For instance, a copy number neutral LOH gene is interpreted as “wild type” in classic log2 ratio-based copy number analysis, potentially impairing SL analysis. Moreover, SPICE pipeline distinguishes between clonal and subclonal copy number events and processes single nucleotide variants (SNVs) and structural rearrangements. Afterwards, FaMe models each type of aberration in a dataset as a binary matrix reporting whether a gene in a sample is aberrant or not. The approach is quite general and searches for any combination of aberration types (SNVs, deletions and amplifications). Fast matrix multiplication, logarithm-based implementation of Fisher's exact test and massive parallelization allow testing for mutual exclusivity of hundreds of millions of aberration pairs in few minutes. Filters based on expression data, interaction databases, and drug responses resources are integrated to shortlist nominated pairs for in vitro validation.We processed 8,397 samples belonging to 29 tumor types from TCGA. Preliminary results identify known SL pairs as for example the mutual exclusivity of SNVs and combination of homozygous deletion and SNVs of the EGFR and K-RAS genes in lung adenocarcinoma (LUAD) (p= 0.005 and p= 0.0029, respectively). Details on the general features of these analyses and on the newly identified mutually exclusive pairs will be presented. Citation Format: Francesca Lorenzin, Davide Prandi, Paola Gasperini, Tarcisio Fedrizzi, Francesca Demichelis. Synthetic lethal pairs for cancer treatment by agnostic exploration of mutually exclusive genomics [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 LB-022.

Abstract 2344: Identification and validation of synthetic lethal cancer pairs from genome-wide CRISPR screens

Abstract Although most canonical somatic cancer mutations have now been cataloged, only a fraction are amenable to drug discovery efforts. These are limited to targets with gain of function mutations and druggable protein domains. Examples include kinases such as FLT3 and metabolic enzymes such as IDH1/2. However, excluded from this list are undruggable protein targets with gain of function mutations, such as KRAS, or tumor suppressors which acquire loss of function mutations, such as APC. The concept of synthetic lethality is being used to address this gap in cancer discovery. Cells which carry these directly undruggable somatic mutations become susceptible to inhibition of secondary targets. This susceptibility is specific in context to cells carrying the cancer associated mutation, offering both a novel approach for establishing efficacy as well as a potential therapeutic window for normal cells. The successful development of PARP inhibitors for BRCA mutant ovarian and breast cancers has provided the first clinical validation of this idea. Pooled, genome-wide knock out experiments are being pursued to systematically discover more of these types of synthetic lethalities. Typically, a sgRNA library is used to transfect a CAS9 expressing cancer cell line, and sequencing of the guides over time identifies lethalities specific to the cell line. Once this experiment is repeated over hundreds of cell lines, one can use the genomic information of the cell lines, such as mutations or copy number aberrations, to identify potential new combinations of molecular lethality. We have analyzed pooled CRISPR knockout data in a large panel of cancer cell lines generated from two independent sources (Broad Institute and Open Targets) to identify these types of lethality. Besides identifying targets for prevalent cancer mutations, we have also identified lethal combinations based on genes aberrantly deleted/silenced in cancers, termed “collateral lethality.” These include pairs of paralogs, many of which code enzymes with redundant functions. In this presentation we will outline our analysis strategies and methods, and describe the development of a downstream experimental validation pipeline, using a specific target/predictor pair to exemplify our approach. Citation Format: Benjamin Schwartz, Junping Jing, Euan Stronach, David Cooper, Boris Wilson, Sarah Middleton, Mugdha Khaladkar, Coco Dong, Usman Shabon, Yanhua Rao, Stefanie Riesenberg. Identification and validation of synthetic lethal cancer pairs from genome-wide CRISPR screens [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 2344.

Conceptual frameworks of synthetic lethality in clear cell carcinoma of the ovary.

Targeting non-oncogenes may result in the selective death of cancer cells. Clear cell carcinoma of the ovary (CCC) may exhibit resistance against conventional chemotherapy and is associated with poor prognosis. The aim of the present report was to review synthetic lethality-based therapies for CCC. Previous English-language studies were reviewed to accumulate preclinical and clinical data on targeting synthetic lethal partners. Synthetic lethal interactions have a variety of types, involving components of a backup or parallel pathway with overlapping functions, components encoded by paralogous pairs, subunit components that form heteromeric complexes and components that are arranged in a single linear pathway. A set of candidate gene targets potentially resulting in synthetic lethality have been previously identified. HNF class homeobox, AT-rich interaction domain 1A, ATR serine/threonine kinase, ATM serine/threonine kinase, checkpoint kinase 1 and phosphatase and tensin homolog may be the key partner genes. A variety of loss of function genes in CCC are driver or passenger events and may function as synthetic lethal pairs under replication stress conditions. Further clinical studies will be required to investigate the safety and therapeutic effect of synthetic lethality pairs in CCC tumor types with replication stress.

MinGenome: An In Silico Top-Down Approach for the Synthesis of Minimized Genomes.

Genome minimized strains offer advantages as production chassis by reducing transcriptional cost, eliminating competing functions and limiting unwanted regulatory interactions. Existing approaches for identifying stretches of DNA to remove are largely ad hoc based on information on presumably dispensable regions through experimentally determined nonessential genes and comparative genomics. Here we introduce a versatile genome reduction algorithm MinGenome that implements a mixed-integer linear programming (MILP) algorithm to identify in size descending order all dispensable contiguous sequences without affecting the organism's growth or other desirable traits. Known essential genes or genes that cause significant fitness or performance loss can be flagged and their deletion can be prohibited. MinGenome also preserves needed transcription factors and promoter regions ensuring that retained genes will be properly transcribed while also avoiding the simultaneous deletion of synthetic lethal pairs. The potential benefit of removing even larger contiguous stretches of DNA if only one or two essential genes (to be reinserted elsewhere) are within the deleted sequence is explored. We applied the algorithm to design a minimized E.coli strain and found that we were able to recapitulate the long deletions identified in previous experimental studies and discover alternative combinations of deletions that have not yet been explored in vivo.

Abstract A03: Several rational combination kinase inhibitor treatments identified by synthetic lethality screens are efficacious in intracranial triple negative breast cancer models

Abstract Introduction: Nearly half of metastatic triple negative breast cancer (TNBC) patients develop brain metastases (BMs) and face a poor prognosis. There are no FDA-approved systemic therapies to treat TNBC BM, due in part to the blood-brain barrier. TNBC and breast cancer BMs exhibit both activation of the PI3K and MEK pathways, but attempts to target them in preclinical models have shown limited efficacy due to innate and acquired resistance to kinase inhibition in TNBC. In this study, we identify several clinically relevant rational combination therapies based on synthetic lethality and evaluate the efficacy of combined brain-penetrant, clinically-available kinase inhibitors in intracranial TNBC models. Methods: An siRNA screen against 720 kinase genes was used to identify synthetic enhancers of lethality with pan-PI3K inhibitor (BKM120) or MEK1/2 inhibitor (AZD6244) treatment in vitro using TNBC models capable of growing in mouse brain (SUM149, MDA-MB-231Br). The efficacy of these and other brain-penetrant drugs of interest based on the screen were assessed for efficacy in vitro, alone (IC50s) and combined (synergy). Some combinations were evaluated in vivo in mice bearing intracranial TNBC tumors for their effects on survival and tumor burden. Tumor burden was monitored via bioluminescence. IC tumors from treated mice were extracted, fresh frozen, and analyzed for the activation state of the kinome using multiplexed kinase inhibitor bead (MIB) enriched mass spectrometry (MS). Results: The screen identified the following combinations as synthetic lethal pairs: PI3K+MEK, MEK+PDGFR, PI3K+AURKA, MEK+BRAF. Pharmacological synergy of combined treatments was confirmed in vitro between PI3K(/mTOR)+MEK, MEK+PDGFR, and PI3K+AURKA, in TNBC cells (SUM149, 231Br) using brain-penetrant drugs in clinical development (mTOR inhibitor Everolimus, dual PI3K/mTOR inhibitor GNE317, PDGFR inhibitor Pazopanib, AURKA inhibitor MLN8237, BRAF inhibitor Dabrafenib). For some combinations, particularly PI3K+AURKA inhibition, sequencing of the drugs significantly altered the combined effects and synergy. Combinations which were synthetically lethal and synergistic at physiologically relevant doses in vitro demonstrated enhanced efficacy in vivo, including PI3K+MEK, MEK+PDGFR, and PI3K+AURKA. In contrast, other combinations (i.e. PI3K+mTOR) did not significantly improve survival or tumor burden in vivo. Despite improved survival with some combination treatments, mice eventually succumbed to tumor burden as tumors eventually grew. Kinome analysis of IC tumors treated with PI3K (BKM120) and/or MEK1/2 (AZD6244) inhibitors for 2 weeks identified several potential resistance markers, including INSR, IGF1R, and FGFR2, which may be targetable clinically. Conclusions: Synthetic lethality screens identified multiple rational combination therapies based on PI3K and/or MEK inhibition in TNBC cells, particularly PI3K+MEK, MEK+PDGFR, and PI3K+AURKA. Combined use of brain-penetrant, clinically available inhibitors against these targets showed promising efficacy in intracranial TNBC mouse models. Rational combinations of brain-penetrant kinase inhibitors are promising strategies for a patient population with few options. In vivo studies assessing the efficacy of other identified combinations, as well as more extensive characterization of potential resistance mechanisms, in intracranial TNBC mouse models are warranted to provide the translational foundation for future clinical studies. Citation Format: Amanda E.D. Van Swearingen, Maria J. Sambade, Marni B. Siegel, Shivani Sud, Samantha M. Bevill, Brian T. Golitz, Ryan E. Bash, Charlene M. Santos, David B. Darr, Joel S. Parker, C. Ryan Miller, Gary L. Johnson, Carey K. Anders. Several rational combination kinase inhibitor treatments identified by synthetic lethality screens are efficacious in intracranial triple negative breast cancer models [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 A03.

Abstract PR04: A CRISPR-based genetic interaction map identifies synergistic drug combinations for cancer

Abstract Identification of effective combination therapies is critical to address the emergence of drug-resistant cancers. Although millions of drug combinations might be created by repurposing existing drugs, direct screening of these combinations is infeasible. Here, we designed a scalable CRISPR-based double knockout (CDKO) system to generate a mammalian genetic interaction (GI) map at unprecedented scale, comprised of 490,000 double-sgRNAs directed against 21,321 pairs of drug targets. We first developed an efficient strategy for cloning and sequencing the libraries, as well as a robust statistical scoring method for calculating GIs from CRISPR-deleted gene pairs. We then extensively validated this system by identifying known and novel genetic interactions in the ricin pathway, and compared the GIs to known protein-protein interactions (PPIs). Using this validated system, we searched for rare synthetic lethal drug target pairs in K562 leukemia cells and identified a number of potent combinations for which corresponding drugs exhibit synergistic killing. Together, this work demonstrates an effective strategy to screen synergistic drug combinations in high throughput, and a powerful CRISPR-based tool to dissect functional genetic interaction networks. Citation Format: Kyuho Han, Edwin Jeng, Gaelen Hess, David Morgens, Amy Li, Michael Bassik. A CRISPR-based genetic interaction map identifies synergistic drug combinations for cancer [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 PR04.

Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions.

Identification of effective combination therapies is critical to address the emergence of drug-resistant cancers, but direct screening of all possible drug combinations is infeasible. Here we introduce a CRISPR-based double knockout (CDKO) system that improves the efficiency of combinatorial genetic screening using an effective strategy for cloning and sequencing paired single-guide RNA libraries and a robust statistical scoring method for calculating genetic interactions (GIs) from CRISPR-deleted gene pairs. We applied CDKO to generate a large-scale human GI map, comprising 490,000 double-sgRNAs directed against 21,321 pairs of drug targets in K562 leukemia cells and identified synthetic lethal drug target pairs for which corresponding drugs exhibit synergistic killing. These included the BCL2L1 and MCL1 combination, which was also effective in imatinib-resistant cells. We further validated this system by identifying known and previously unidentified GIs between modifiers of ricin toxicity. This work provides an effective strategy to screen synergistic drug combinations at high-throughput and a CRISPR-based tool to dissect functional GI networks.

Uncovering synthetic lethal interactions for therapeutic targets and predictive markers in lung adenocarcinoma.

Two genes are called synthetic lethal (SL) if their simultaneous mutation leads to cell death, but mutation of either individual does not. Targeting SL partners of mutated cancer genes can selectively kill cancer cells, but leave normal cells intact. We present an integrated approach to uncover SL gene pairs as novel therapeutic targets of lung adenocarcinoma (LADC). Of 24 predicted SL pairs, PARP1-TP53 was validated by RNAi knockdown to have synergistic toxicity in H1975 and invasive CL1-5 LADC cells; additionally FEN1-RAD54B, BRCA1-TP53, BRCA2-TP53 and RB1-TP53 were consistent with the literature. While metastasis remains a bottleneck in cancer treatment and inhibitors of PARP1 have been developed, this result may have therapeutic potential for LADC, in which TP53 is commonly mutated. We also demonstrated that silencing PARP1 enhanced the cell death induced by the platinum-based chemotherapy drug carboplatin in lung cancer cells (CL1-5 and H1975). IHC of RAD54B↑, BRCA1↓-RAD54B↑, FEN1(N)↑-RAD54B↑ and PARP1↑-RAD54B↑ were shown to be prognostic markers for 131 Asian LADC patients, and all markers except BRCA1↓-RAD54B↑ were further confirmed by three independent gene expression data sets (a total of 426 patients) including The Cancer Genome Atlas (TCGA) cohort of LADC. Importantly, we identified POLB-TP53 and POLB as predictive markers for the TCGA cohort (230 subjects), independent of age and stage. Thus, POLB and POLB-TP53 may be used to stratify future non-Asian LADC patients for therapeutic strategies.

Abstract 1246: Development of STAT3 dual-targeting strategies for the treatment of pancreatic cancer

Abstract Pancreatic cancer remains a largely incurable disease, with patients facing the worst 5-year survival rate of any cancer. The challenge is to identify the molecular effectors that regulate the survival of pancreatic ductal adenocarcinoma (PDAC) cells, to devise molecular-targeted strategies that are effective in the metastatic setting, and overcome the protective role of the tumor-associated fibrosis and stroma. Strategies targeting multiple molecular effectors in PDAC are likely going to make a bigger impact. Thus, we are identifying molecular targets or synthetic lethal pairs that regulate critical pro-survival or pro-invasive pathways in PDAC. Constitutively activated Signal Transducer and Activator of Transcription 3 (STAT3) protein has been found to be a key regulator of pancreatic cancer and a target for molecular therapeutic intervention. To better model the tumor and its microenvironment, we utilized ex vivo 3-Dimensional (3D) cultures of patient-derived pancreatic cancer cells in the absence and presence of cancer-associated fibroblasts (CAFs). We can quantitate the inhibitory effect on both the tumor and CAFs as they are labeled with different fluorescent markers. In this co-culture model, inhibition of tumor growth is maintained following STAT3 inhibition in the presence of CAFs. We screened several STAT3 small molecule inhibitors, derived from the SH-4-54 class of STAT3 inhibitors, and found inhibition of pancreatic cancer cell proliferation in the low μM range. Our inhibitors bind the STAT3 protein potently, as shown by SPR, and demonstrate no effect in a kinome screen. In vitro studies demonstrated potent cell killing as well as inhibition of STAT3 activation in the 3D co-culture model. We have previously reported that Ref-1 (redox factor-1) regulates STAT3 activity through its redox function and blocking STAT3 through phosphorylation and redox inhibition synergizes for PDAC cell killing. In our 3D co-culture system, Ref-1 inhibitor, APX3330 decreases tumor area and intensity in a dose-dependent manner. The addition of APX3330 to STAT3 pathway inhibition via Ruxolitinib (Rux, Jak 2 inhibitor) or direct STAT3 inhibitor potentiated the killing effect in the tumor. However, the combination treatment did not appear to sensitize CAF cells, suggesting that targeting of Ref-1 and the STAT3 pathway is more specifically targeting tumor cells. Utilizing APX3330, Rux, and our lead STAT3 inhibitors, we evaluated the effects of Ref-1/STAT3 inhibition in PDAC low passage patient-derived cell lines. The activity of STAT3 and specificity of lead compounds was assessed by immunoblotting for levels of phosphorylated proteins including STAT3 (Y705) and STAT5 (Y694) in 3D culture. These studies establish the rationale for the development of STAT3 dual-targeting strategies for the treatment of pancreatic cancer and suggest that Ref-1 and STAT3 may be a synthetic lethal pair. Citation Format: Melissa L. Fishel, Michelle L. Grimard, Mark R. Kelley, David A. Rosa, Andrew Shouksmith, Gary Tin, Ji Park, Patrick T. Gunning. Development of STAT3 dual-targeting strategies for the treatment of pancreatic cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1246.

Ranking novel cancer driving synthetic lethal gene pairs using TCGA data.

Synthetic lethality (SL) has emerged as a promising approach to cancer therapy. In contrast to the costly and labour-intensive genome-wide siRNA or CRISPR-based human cell line screening approaches, computational approaches to prioritize potential synthetic lethality pairs for further experimental validation represent an attractive alternative. In this study, we propose an efficient and comprehensive in-silico pipeline to rank novel SL gene pairs by mining vast amounts of accumulated tumor high-throughput sequencing data in The Cancer Genome Atlas (TCGA), coupled with other protein interaction networks and cell line information. Our pipeline integrates three significant features, including mutation coverage in TCGA, driver mutation probability and the quantified cancer network information centrality, into a ranking model for SL gene pair identification, which is presented as the first learning-based method for SL identification. As a result, 107 potential SL gene pairs were obtained from the top 10 results covering 11 cancers. Functional analysis of these genes indicated that several promising pathways were identified, including the DNA repair related Fanconi Anemia pathway and HIF-1 signaling pathway. In addition, 4 SL pairs, mTOR-TP53, VEGFR2-TP53, EGFR-TP53, ATM-PRKCA, were validated using drug sensitivity information in the cancer cell line databases CCLE or NCI60. Interestingly, significant differences in the cell growth of mTOR siRNA or EGFR siRNA knock-down were detected between cancer cells with wild type TP53 and mutant TP53. Our study indicates that the pre-screening of potential SL gene pairs based on the large genomics data repertoire of tumor tissues and cancer cell lines could substantially expedite the identification of synthetic lethal gene pairs for cancer therapy.

Current situation and future usage of anticancer drug databases.

Cancer is a deadly disease with increasing incidence and mortality rates and affects the life quality of millions of people per year. The past 15years have witnessed the rapid development of targeted therapy for cancer treatment, with numerous anticancer drugs, drug targets and related gene mutations been identified. The demand for better anticancer drugs and the advances in database technologies have propelled the development of databases related to anticancer drugs. These databases provide systematic collections of integrative information either directly on anticancer drugs or on a specific type of anticancer drugs with their own emphases on different aspects, such as drug-target interactions, the relationship between mutations in drug targets and drug resistance/sensitivity, drug-drug interactions, natural products with anticancer activity, anticancer peptides, synthetic lethality pairs and histone deacetylase inhibitors. We focus on a holistic view of the current situation and future usage of databases related to anticancer drugs and further discuss their strengths and weaknesses, in the hope of facilitating the discovery of new anticancer drugs with better clinical outcomes.

Synthetic lethal approaches for assessing combinatorial efficacy of chemotherapeutic drugs.

The recent advances in pharmacogenomics have made personalized medicine no longer a pipedream but a precise and powerful way to tailor individualized cancer treatment strategies. Cancer is a devastating disease, and contemporary chemotherapeutic strategies now integrate several agents in the treatment of some types of cancer, with the intent to block more than one target simultaneously. This constitutes the premise of synthetic lethality, an attractive therapeutic strategy already demonstrating clinical success in patients with breast and ovarian cancers. Synthetic lethal combinations offer the potential to also target the hitherto "undruggable" mutations that have challenged the cancer field for decades. However, synthetic lethality in clinical cancer therapy is very much still in its infancy, and selecting the most appropriate combinations-or synthetic lethal pairs-is not always an intuitive process. Here, we review some of the recent progress in identifying synthetic lethal combinations and their potential for therapy and highlight some of the tools through which synthetic lethal pairs are identified.

Abstract A2-12: Opposing roles of the 19S regulatory and 20S core proteasomal subunits in controlling sensitivity to proteasome inhibitors

Abstract Targeted therapies for cancer are often hindered by the variable genetic makeup within and between tumors. This hurdle makes the identification of therapeutic targets challenging. Genetic susceptibilities found within networks encoded in the genome of a cancer cell can provide such therapeutic opportunities. Essential cellular networks overseeing homeostasis are often rewired in tumors, providing a landscape where synthetic lethal pairs may exist. A prominent example is the susceptibility of multiple myeloma (MM) cells to proteasome inhibitors. Because of their highly secretory nature, the proteasomes of MM cells can be burdened in a load-versus-capacity tug-of-war. This renders MM cells sensitive to proteasome inhibitors. Unfortunately, single agent therapies against the proteasome do not provide a cure, as tumor cells often acquire resistance. Because of this, we sought to understand how MM cancer cells respond and adapt to proteasome inhibition. Utilizing ultra-complex shRNA libraries, we screened several MM cell lines exposed to the proteasome inhibitor bortezomib. In these screens, we identified several genes that either sensitize or protect MM cells towards bortezomib intoxication. Unexpectedly we found that, contrary to knockdown of genes encoding subunits of the 20S core particle of the proteasome, knockdown of genes encoding subunits of the 19S regulatory particle of the proteasome lead to bortezomib desensitization. To understand the nature of this phenotype, we queried proteome changes in bortezomib treated cells where we ablated a single 19S subunit. By performing ubiquitin-remnant profiling and total proteome SILAC experiments, we identified several proteins involved in autophagy and NF-κB signaling as potential mediators of the protective phenotype. We are currently investigating the roles of components of these pathways in tailoring the response towards bortezomib. We surmise that some of these will be amenable for pharmacological targeting in developing novel combination therapies. Citation Format: Min Y. Cho, Diego Acosta-Alvear, Martin Kampmann, Thomas Wild, Chuna Ram Choudhary, Jonathan S. Weissman, Peter Walter. Opposing roles of the 19S regulatory and 20S core proteasomal subunits in controlling sensitivity to proteasome inhibitors. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-12.

Abstract A2-57: Identification of genetic vulnerabilities within the proteostasis network of multiple myeloma

Abstract Multiple myeloma (MM) remains an incurable disease afflicting more than 20,000 patients yearly in the US alone. Because of their secretory nature, MM cells rely on the proteostasis network and re-wire it to their advantage. We focused on two interconnected essential pathways within the proteostasis network to query genetic vulnerabilities yielding synthetic lethality. These pathways are the ubiquitin-proteasome system (UPS) and the unfolded protein response (UPR). Standard-of-care proteasome inhibitors, like bortezomib, remain non-curative in MM patients, suggesting the selection of genetic escape routes. The same rationale is applicable to the UPR, a conserved regulatory network overseeing the processing capacity of the endoplasmic reticulum (ER). Since proteasomal degradation of unfolded ER client proteins is accomplished after their dislocation and ubiquitination, we reasoned that common genetic escape routes might exist within the UPS and the UPR. Because single agents provide the driving force for acquired drug resistance and do not lead to long-term remission, we employed a systems-level shRNA screening method that systematically identified synthetic-lethal interactions that can enhance the therapeutic benefit of proteasome inhibition or modulation of the UPR. In the latter, we focused on the UPR branch overseen by the sensor kinase/nuclease IRE1 because it is thought to confer a survival advantage to MM. Applying chemical-genetics approaches using bortezomib or novel chemical inhibitors of IRE1, and an ultra-complex shRNA library we developed, we performed pooled shRNA screens in RPMI-8226 and MM1-S MM cells and identified genes whose diminished function impact the response to proteasome or IRE1 inhibition. Unlike proteasome inhibition, blocking IRE1 did not lead to considerable MM cell death, suggesting that IRE1 alone is not a fate determinant but a weakness that can be exploited through the identification of synthetic-lethal combinations. We applied the same rationale to proteasome inhibition and found synthetic-lethal pairs. Because some of these genes can be targeted pharmacologically, we explored the susceptibility of a panel of MM cells to combinations of bortezomib and drug-like molecules, including novel Hsp70 inhibitors. Retrospective analyses on publicly available gene expression datasets of MM patients treated with bortezomib indicated that several of the genes we found in our screen also predicted clinical outcomes. Together, our preliminary results indicate our approach is a powerful tool for the discovery of synthetic-lethal pairs that can be exploited in combination therapies. Citation Format: Diego Acosta-Alvear, Martin Kampmann, Min Y. Cho, Blake T. Aftab, Xiaokai Li, Jason E. Gestwicki, Marc A. Shuman, Jonathan S. Weissman, Peter Walter. Identification of genetic vulnerabilities within the proteostasis network of multiple myeloma. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-57.