Combination drug therapy is a widely used paradigm for managing numerous human malignancies. In cancer treatment, additive and/or synergistic drug combinations can convert weakly efficacious monotherapies into regimens that produce robust antitumor activity. This can be explained in part through pathway interdependencies that are critical for cancer cell proliferation and survival. However, identification of the various interdependencies is difficult due to the complex molecular circuitry that underlies tumor development and progression. Here, we present a highthroughput platform that allows for an unbiased identification of synergistic and efficacious drug combinations. In a screen of 22,737 experiments of 583 doublet combinations in 39 diverse cancer cell lines using a 4 by 4 dosing regimen, both wellknown and novel synergistic and efficacious combinations were identified. Here, we present an example of one such novel combination, a Wee1 inhibitor (AZD1775) and an mTOR inhibitor (ridaforolimus), and demonstrate that the combination potently and synergistically inhibits cancer cell growth in vitro and in vivo. This approach has identified novel combinations that would be difficult to reliably predict based purely on our current understanding of cancer cell biology.

By analyzing, in parallel, large literature-derived and highthroughput experimental datasets we investigate genes harboring human inherited disease mutations in the context of molecular interaction networks. Our results demonstrate that network properties influence the likelihood and phenotypic consequences of disease mutations. Genes with intermediate connectivities have the highest probability of harboring germ-line disease mutations, suggesting that disease genes tend to occupy an intermediate niche in terms of their physiological and cellular importance. Our analysis of tissue expression profiles supports this view. We show that disease mutations are less likely to occur in essential genes compared with all human genes. Disease genes display significant functional clustering in the analyzed molecular network. For about one-third of known disorders with two or more associated genes we find physical clusters of genes with the same phenotype. These clusters are likely to represent disorder-specific functional modules and suggest a framework for identifying yet-undiscovered disease genes.computational biology ͉ disease genes ͉ systems biology

BackgroundHyperactivation of the Ras signaling pathway is a driver of many cancers, and RAS pathway activation can predict response to targeted therapies. Therefore, optimal methods for measuring Ras pathway activation are critical. The main focus of our work was to develop a gene expression signature that is predictive of RAS pathway dependence.MethodsWe used the coherent expression of RAS pathway-related genes across multiple datasets to derive a RAS pathway gene expression signature and generate RAS pathway activation scores in pre-clinical cancer models and human tumors. We then related this signature to KRAS mutation status and drug response data in pre-clinical and clinical datasets.ResultsThe RAS signature score is predictive of KRAS mutation status in lung tumors and cell lines with high (> 90%) sensitivity but relatively low (50%) specificity due to samples that have apparent RAS pathway activation in the absence of a KRAS mutation. In lung and breast cancer cell line panels, the RAS pathway signature score correlates with pMEK and pERK expression, and predicts resistance to AKT inhibition and sensitivity to MEK inhibition within both KRAS mutant and KRAS wild-type groups. The RAS pathway signature is upregulated in breast cancer cell lines that have acquired resistance to AKT inhibition, and is downregulated by inhibition of MEK. In lung cancer cell lines knockdown of KRAS using siRNA demonstrates that the RAS pathway signature is a better measure of dependence on RAS compared to KRAS mutation status. In human tumors, the RAS pathway signature is elevated in ER negative breast tumors and lung adenocarcinomas, and predicts resistance to cetuximab in metastatic colorectal cancer.ConclusionsThese data demonstrate that the RAS pathway signature is superior to KRAS mutation status for the prediction of dependence on RAS signaling, can predict response to PI3K and RAS pathway inhibitors, and is likely to have the most clinical utility in lung and breast tumors.

Inhibition of the DNA damage checkpoint kinase WEE1 potentiates genotoxic chemotherapies by abrogating cell-cycle arrest and proper DNA repair. However, WEE1 is also essential for unperturbed cell division in the absence of extrinsic insult. Here, we investigate the anticancer potential of a WEE1 inhibitor, independent of chemotherapy, and explore a possible cellular context underlying sensitivity to WEE1 inhibition. We show that MK-1775, a potent and selective ATP-competitive inhibitor of WEE1, is cytotoxic across a broad panel of tumor cell lines and induces DNA double-strand breaks. MK-1775-induced DNA damage occurs without added chemotherapy or radiation in S-phase cells and relies on active DNA replication. At tolerated doses, MK-1775 treatment leads to xenograft tumor growth inhibition or regression. To begin addressing potential response markers for MK-1775 monotherapy, we focused on PKMYT1, a kinase functionally related to WEE1. Knockdown of PKMYT1 lowers the EC 50 of MK-1775 by five-fold but has no effect on the cell-based response to other cytotoxic drugs. In addition, knockdown of PKMYT1 increases markers of DNA damage, gH2AX and pCHK1 S345 , induced by MK-1775. In a post hoc analysis of 305 cell lines treated with MK-1775, we found that expression of PKMYT1 was below average in 73% of the 33 most sensitive cell lines. Our findings provide rationale for WEE1 inhibition as a potent anticancer therapy independent of a genotoxic partner and suggest that low PKMYT1 expression could serve as an enrichment biomarker for MK-1775 sensitivity.

Although we have made great progress in understanding the complex genetic alterations that underlie human cancer, it has proven difficult to identify which molecularly targeted therapeutics will benefit which patients. Drug-specific modulation of oncogenic signaling pathways in specific patient subpopulations can predict responsiveness to targeted therapy. Here, we report a pathway-based phosphoprofiling approach to identify and quantify clinically relevant, drug-specific biomarkers for phosphatidylinositol 3-kinase (PI3K) pathway inhibitors that target AKT, phosphoinositide-dependent kinase 1 (PDK1), and PI3K-mammalian target of rapamycin (mTOR). We quantified 375 nonredundant PI3K pathway-relevant phosphopeptides, all containing AKT, PDK1, or mitogen-activated protein kinase substrate recognition motifs. Of these phosphopeptides, 71 were drug-regulated, 11 of them by all three inhibitors. Drug-modulated phosphoproteins were enriched for involvement in cytoskeletal reorganization (filamin, stathmin, dynamin, PAK4, and PTPN14), vesicle transport (LARP1, VPS13D, and SLC20A1), and protein translation (S6RP and PRAS40). We then generated phosphospecific antibodies against selected, drug-regulated phosphorylation sites that would be suitable as biomarker tools for PI3K pathway inhibitors. As proof of concept, we show clinical translation feasibility for an antibody against phospho-PRAS40(Thr246). Evaluation of binding of this antibody in human cancer cell lines, a PTEN (phosphatase and tensin homolog deleted from chromosome 10)-deficient mouse prostate tumor model, and triple-negative breast tumor tissues showed that phospho-PRAS40(Thr246) positively correlates with PI3K pathway activation and predicts AKT inhibitor sensitivity. In contrast to phosphorylation of AKT(Thr308), the phospho-PRAS40(Thr246) epitope is highly stable in tissue samples and thus is ideal for immunohistochemistry. In summary, our study illustrates a rational approach for discovery of drug-specific biomarkers toward development of patient-tailored treatments.

The SWI/SNF complex is a major regulator of gene expression and is increasingly thought to play an important role in human cancer, as evidenced by the high frequency of subunit mutations across virtually all cancer types. We previously reported that in preclinical models, malignant rhabdoid tumors, which are deficient in the SWI/SNF core component INI1 (SMARCB1), are selectively killed by inhibitors of the H3K27 histone methyltransferase EZH2. Given the demonstrated antagonistic activities of the SWI/SNF complex and the EZH2-containing PRC2 complex, we investigated whether additional cancers with SWI/SNF mutations are sensitive to selective EZH2 inhibition. It has been recently reported that ovarian cancers with dual loss of the redundant SWI/SNF components SMARCA4 and SMARCA2 are characteristic of a rare rhabdoid-like subtype known as small-cell carcinoma of the ovary hypercalcemic type (SCCOHT). Here, we provide evidence that a subset of commonly used ovarian carcinoma cell lines were misdiagnosed and instead were derived from a SCCOHT tumor. We also demonstrate that tazemetostat, a potent and selective EZH2 inhibitor currently in phase II clinical trials, induces potent antiproliferative and antitumor effects in SCCOHT cell lines and xenografts deficient in both SMARCA2 and SMARCA4. These results exemplify an additional class of rhabdoid-like tumors that are dependent on EZH2 activity for survival. Mol Cancer Ther; 16(5); 850-60. Ó2017 AACR.

BackgroundInhibition of kinases involved in the DNA damage response sensitizes cells to genotoxic agents by abrogating checkpoint-induced cell cycle arrest. CHK1 and WEE1 act in a pathway upstream of CDK1 to inhibit cell cycle progression in response to damaged DNA. Therapeutic targeting of either CHK1 or WEE1, in combination with chemotherapy, is under clinical evaluation. These studies examine the overlap and potential for synergy when CHK1 and WEE1 are inhibited in cancer cell models.MethodsSmall molecules MK-8776 and MK-1775 were used to selectively and potently inhibit CHK1 and WEE1, respectively.ResultsIn vitro, the combination of MK-8776 and MK-1775 induces up to 50-fold more DNA damage than either MK-8776 or MK-1775 alone at a fixed concentration. This requires aberrant cyclin-dependent kinase activity but does not appear to be dependent on p53 status alone. Furthermore, DNA damage takes place primarily in S-phase cells, implying disrupted DNA replication. When dosed together, the combination of MK-8776 and MK-1775 induced more intense and more durable DNA damage as well as anti-tumor efficacy than either MK-8776 or MK-1775 dosed alone. DNA damage induced by the combination was detected in up to 40% of cells in a treated xenograft tumor model.ConclusionsThese results highlight the roles of WEE1 and CHK1 in maintaining genomic integrity. Importantly, the strong synergy observed upon inhibition of both kinases suggests unique yet complimentary anti-tumor effects of WEE1 and CHK1 inhibition. This demonstration of DNA double strand breaks in the absence of a DNA damaging chemotherapeutic provides preclinical rationale for combining WEE1 and CHK1 inhibitors as a cancer treatment regimen.

Common genetic disorders are believed to arise from the combined effects of multiple inherited genetic variants acting in concert with environmental factors, such that any given DNA sequence variant may have only a marginal effect on disease outcome. As a consequence, the correlation between disease status and any given DNA marker allele in a genomewide linkage study tends to be relatively weak and the implicated regions typically encompass hundreds of positional candidate genes. Therefore, new strategies are needed to parse relatively large sets of 'positional' candidate genes in search of actual disease-related gene variants. Here we use biological databases to identify 383 positional candidate genes predicted by genomewide genetic linkage analysis of a large set of families, each with two or more members diagnosed with autism, or autism spectrum disorder (ASD). Next, we seek to identify a subset of biologically meaningful, high priority candidates. The strategy is to select autism candidate genes based on prior genetic evidence from the allelic association literature to query the known transcripts within the 1-LOD (logarithm of the odds) support interval for each region. We use recently developed bioinformatic programs that automatically search the biological literature to predict pathways of interacting genes (PATHWAYASSIST and GENEWAYS). To identify gene regulatory networks, we search for coexpression between candidate genes and positional candidates. The studies are intended both to inform studies of autism, and to illustrate and explore the increasing potential of bioinformatic approaches as a compliment to linkage analysis.