Genome-scale analysis identifies paralog lethality as a vulnerability of chromosome 1p loss in cancer.

Functional redundancy shared by paralog genes may afford protection against genetic perturbations, but it can also result in genetic vulnerabilities due to mutual interdependency1-5. Here, we surveyed genome-scale short hairpin RNA and CRISPR screening data on hundreds of cancer cell lines and identified MAGOH and MAGOHB, core members of the splicing-dependent exon junction complex, as top-ranked paralog dependencies6-8. MAGOHB is the top gene dependency in cells with hemizygous MAGOH deletion, a pervasive genetic event that frequently occurs due to chromosome 1p loss. Inhibition of MAGOHB in a MAGOH-deleted context compromises viability by globally perturbing alternative splicing and RNA surveillance. Dependency on IPO13, an importin-β receptor that mediates nuclear import of the MAGOH/B-Y14 heterodimer9, is highly correlated with dependency on both MAGOH and MAGOHB. Both MAGOHB and IPO13 represent dependencies in murine xenografts with hemizygous MAGOH deletion. Our results identify MAGOH and MAGOHB as reciprocal paralog dependencies across cancer types and suggest a rationale for targeting the MAGOHB-IPO13 axis in cancers with chromosome 1p deletion

High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines.

Hundreds of genetically characterized cell lines are available for the discovery of genotype-specific cancer vulnerabilities. However, screening large numbers of compounds against large numbers of cell lines is currently impractical, and such experiments are often difficult to control. Here we report a method called PRISM that allows pooled screening of mixtures of cancer cell lines by labeling each cell line with 24-nucleotide barcodes. PRISM revealed the expected patterns of cell killing seen in conventional (unpooled) assays. In a screen of 102 cell lines across 8,400 compounds, PRISM led to the identification of BRD-7880 as a potent and highly specific inhibitor of aurora kinases B and C. Cell line pools also efficiently formed tumors as xenografts, and PRISM recapitulated the expected pattern of erlotinib sensitivity in vivo

PIK3CA mutant tumors depend on oxoglutarate dehydrogenase

Oncogenic PIK3CA mutations are found in a significant fraction of human cancers, but therapeutic inhibition of PI3K has only shown limited success in clinical trials. To understand how mutant PIK3CA contributes to cancer cell proliferation, we used genome scale loss-of-function screening in a large number of genomically annotated cancer cell lines. As expected, we found that PIK3CA mutant cancer cells require PIK3CA but also require the expression of the TCA cycle enzyme 2-oxoglutarate dehydrogenase (OGDH). To understand the relationship between oncogenic PIK3CA and OGDH function, we interrogated metabolic requirements and found an increased reliance on glucose metabolism to sustain PIK3CA mutant cell proliferation. Functional metabolic studies revealed that OGDH suppression increased levels of the metabolite 2-oxoglutarate (2OG). We found that this increase in 2OG levels, either by OGDH suppression or exogenous 2OG treatment, resulted in aspartate depletion that was specifically manifested as auxotrophy within PIK3CA mutant cells. Reduced levels of aspartate deregulated the malate-aspartate shuttle, which is important for cytoplasmic NAD + regeneration that sustains rapid glucose breakdown through glycolysis. Consequently, because PIK3CA mutant cells exhibit a profound reliance on glucose metabolism, malate-aspartate shuttle deregulation leads to a specific proliferative block due to the inability to maintain NAD + /NADH homeostasis. Together these observations define a precise metabolic vulnerability imposed by a recurrently mutated oncogene. Keyword: PIK3CA; 2OG; OGDH; TCA cycle; glycolysisDamon Runyon Cancer Research Foundation (HHMI Fellowship

Characterizing genomic alterations in cancer by complementary functional associations.

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

RAC1(P29S) Induces a Mesenchymal Phenotypic Switch via Serum Response Factor to Promote Melanoma Development and Therapy Resistance

RAC1 P29 is the third most commonly mutated codon in human cutaneous melanoma, after BRAF V600 and NRAS Q61. Here, we study the role of RAC1P29S in melanoma development and reveal that RAC1P29S activates PAK, AKT, and a gene expression program initiated by the SRF/MRTF transcriptional pathway, which results in a melanocytic to mesenchymal phenotypic switch. Mice with ubiquitous expression of RAC1P29S from the endogenous locus develop lymphoma. When expressed only in melanocytes, RAC1P29S cooperates with oncogenic BRAF or with NF1-loss to promote tumorigenesis. RAC1P29S also drives resistance to BRAF inhibitors, which is reversed by SRF/MRTF inhibitors. These findings establish RAC1P29S as a promoter of melanoma initiation and mediator of therapy resistance, while identifying SRF/MRTF as a potential therapeutic target

Prognostically relevant gene signatures of high-grade serous ovarian carcinoma

Because of the high risk of recurrence in high-grade serous ovarian carcinoma (HGS-OvCa), the development of outcome predictors could be valuable for patient stratification. Using the catalog of The Cancer Genome Atlas (TCGA), we developed subtype and survival gene expression signatures, which, when combined, provide a prognostic model of HGS-OvCa classification, named “Classification of Ovarian Cancer” (CLOVAR). We validated CLOVAR on an independent dataset consisting of 879 HGS-OvCa expression profiles. The worst outcome group, accounting for 23% of all cases, was associated with a median survival of 23 months and a platinum resistance rate of 63%, versus a median survival of 46 months and platinum resistance rate of 23% in other cases. Associating the outcome prediction model with BRCA1/BRCA2 mutation status, residual disease after surgery, and disease stage further optimized outcome classification. Ovarian cancer is a disease in urgent need of more effective therapies. The spectrum of outcomes observed here and their association with CLOVAR signatures suggests variations in underlying tumor biology. Prospective validation of the CLOVAR model in the context of additional prognostic variables may provide a rationale for optimal combination of patient and treatment regimens

Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies

Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell proliferation and/or viability. Cell line dependencies on 11,000 genes were interrogated by 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,000 shRNAs after 16 population doublings using Next Generation Sequencing. All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines. When combined with genomic characterization of these cell lines, this dataset facilitates the linkage of genetic dependencies with specific cellular contexts (e.g., gene mutations or cell lineage). To enable such comparisons, we developed and provided a bioinformatics tool to identify linear and nonlinear correlations between these features

Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models

Abstract In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions

Identification of highly penetrant Rb-related synthetic lethal interactions in triple negative breast cancer.

Although defects in the RB1 tumour suppressor are one of the more common driver alterations found in triple-negative breast cancer (TNBC), therapeutic approaches that exploit this have not been identified. By integrating molecular profiling data with data from multiple genetic perturbation screens, we identified candidate synthetic lethal (SL) interactions associated with RB1 defects in TNBC. We refined this analysis by identifying the highly penetrant effects, reasoning that these would be more robust in the face of molecular heterogeneity and would represent more promising therapeutic targets. A significant proportion of the highly penetrant RB1 SL effects involved proteins closely associated with RB1 function, suggesting that this might be a defining characteristic. These included nuclear pore complex components associated with the MAD2 spindle checkpoint protein, the kinase and bromodomain containing transcription factor TAF1, and multiple components of the SCFSKP Cullin F box containing complex. Small-molecule inhibition of SCFSKP elicited an increase in p27Kip levels, providing a mechanistic rationale for RB1 SL. Transcript expression of SKP2, a SCFSKP component, was elevated in RB1-defective TNBCs, suggesting that in these tumours, SKP2 activity might buffer the effects of RB1 dysfunction

Synthetic lethal therapies for cancer: what's next after PARP inhibitors?

The genetic concept of synthetic lethality has now been validated clinically through the demonstrated efficacy of poly(ADP-ribose) polymerase (PARP) inhibitors for the treatment of cancers in individuals with germline loss-of-function mutations in either BRCA1 or BRCA2. Three different PARP inhibitors have now been approved for the treatment of patients with BRCA-mutant ovarian cancer and one for those with BRCA-mutant breast cancer; these agents have also shown promising results in patients with BRCA-mutant prostate cancer. Here, we describe a number of other synthetic lethal interactions that have been discovered in cancer. We discuss some of the underlying principles that might increase the likelihood of clinical efficacy and how new computational and experimental approaches are now facilitating the discovery and validation of synthetic lethal interactions. Finally, we make suggestions on possible future directions and challenges facing researchers in this field