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Multiple myeloma (MM) is an incurable plasma cell malignancy accounting for more than 10,000 deaths in the US each year. Hence the pursuit for novel therapeutic agents remains critically important. Myeloma pathogenesis is associated in part with aberrant cell cycle progression. Inhibition of cyclin dependent kinases CDK4/6 results in cell cycle arrest and sensitization to Bortezomib and other active agents in MM (Huang, Blood 2012). Here, we show that ARK5, a novel member of the human AMPKfamily, is overexpressed in 70% of MM and helps promote proliferation and cell cycle progression via G1/S phase activation in an mTOR dependent manner. We examined the role of ARK5 using loss of function studies by ARK5 siRNA transfection in MM1.S, NCI-H929 cells as well as treatment with ON 123300, a dual CDK4/ARK5 kinase inhibitor. ARK5 siRNA knockdown decreased MM cell viability and cell proliferation via G1/S arrest compared to control siRNA. ARK5 siRNA treatment significantly (~70%) induced apoptosis in MM cells as detected by Annexin V/PI staining. We observed that phosphorylation of Rb, a critical cell cycle protein was significantly reduced in ARK5 depleted cells. Moreover, mTOR pathway inhibition was confirmed by reduction of pS6K in ARK5depleted cells as compared to control siRNA treated cells. ON 123300 decreased viability in MM cell lines and patient cells but was not lethal to normal PBMCs. A single treatment of 50nM drug stratified MM cell lines into 2 groups, 5 resistant (MM.1R, KMS11, U266, RPMI-8226 and ARP1) and 4 sensitive cell lines (>80% cell kill-MM.1S, EJM, JJN3, NCI-H929). ARK5 protein expression by western blot analysis was much higher in sensitive cell lines. ON 123300 triggered G0/G1 cell cycle arrest and induced apoptosis similar to the effect of ARK5 siRNA (80% vs 70%). ON 123300 treatment also reduced phosphorylation of pRb and pS6K downstream of mTOR pathway. These results confirm that cell inhibitory effects of ON 123300 in MM are mediated in a large part via inhibition of ARK5. Co-culture experiments with BMSCs showed that ON 123300 not only targets MM cells but also overcomes the cytoprotective effects of the MM-host BM microenvironment. 4/5 ARK5 positive primary samples with adverse cytogenetics including 1q amplification and CyclinD1 translocation were sensitive to ON 123300 (>80% cytotoxicity) at 50nM. Further, IP injection of ON 123300 (100mg/kg) in tumor xenograft models (MM1.S, NCI-H929) showed that ON 123300 is well tolerated and significantly inhibits tumor growth in vivo(p To study the mechanism of action for ON 123300, we performed geneset enrichment analysis (GSEA) on drug induced gene expression signature of RNA-Seq data from pre-post treated cell lines. We interrogated a wide array of geneset libraries, including MSigDB (Subramanian, PNAS 2005), drug induced transcriptional modules (Iskar, Mol Sys Bio. 2013) and disease signatures (Sirota, Sci Transl Med 2011). GSEA showed significant representation of genes that are enriched in normal plasma cells and rapamycin sensitive geneset. Next, we developed a weighted co-expression network (WGCNA, Langfelder BMC Bioinformatics 2008) based classifier using 304 MM samples from MMRC collection and RNA-Seq from 28 MM patients from Mt. Sinai Hospital. WGCNA defines a network that continuously links all genes and then clusters the most highly co-expressed genes in defined modules. These network modules were associated with clinical traits and UAMS GEP classification (Zhan, Blood 2006) of each sample. There was significant overlap between highly proliferative “PR” and “Cyclin D1/2” patient subsets based on classification and sensitivity to CDK4/ARK5 inhibition. Our classifier accurately discriminated 4 sensitive primary samples from one resistant sample, all tested in vitro. All sensitive samples were either Cyclin D1/2 or PR as per UAMS classification. Our preclinical studies provide the basis for clinical evaluation of CDK4/ARK5 inhibitor ON 123300 due to its selective cytotoxicity on MM cells in vitro and in vivo. Using WGCNA we establish a systematic framework by constructing for the first time, MM-associated gene co-expression networks contributing to tumorigenesis and progression. Thus, WGCNA modeling is a novel approach for identification of MM patient subgroups that have a higher likelihood of response in clinical trials with CDK4/ARK5 inhibitors. Disclosures Chari: Array BioPharma: Scientific Advisory Board Other; Celgene Corporation: Scientific Advisory Board, Scientific Advisory Board Other; Millennium Pharmaceuticals, Inc: Scientific Advisory Board, Scientific Advisory Board Other; Onyx Pharmaceuticals, Inc.: Scientific Advisory Board, Scientific Advisory Board Other. Reddy:Onconova Therapeutics, Inc.: Consultancy, Patents & Royalties. Reddy:Onconova Therapeutics, Inc.: Consultancy, Patents & Royalties. Dudley:Ecoeos, Inc: Consultancy, Equity Ownership; GNS Healthcare: Consultancy; GlaxoSmithKline: Consultancy; Ayasdi, Inc: Equity Ownership; NuMedii, Inc: Equity Ownership; Ubalo, Inc.: Equity Ownership. Jagannath:Onyx Pharmaceuticals, Inc.: Consultancy; Celgene Corporation: Scientific Advisory Board, Scientific Advisory Board Other; Millennium Pharmaceuticals, Inc. : Scientific Advisory Board, Scientific Advisory Board Other.

Novel methods are needed to leverage the complex information generated by recent large-scale genomic profiling efforts in Multiple Myeloma (MM) to inform therapeutic discovery and personalized medicine strategies. We propose that network modeling can be used to organize patient specific gene signatures in a way that is consistent with and builds on known molecular classifications of multiple myeloma, can facilitate identification of putatively novel sub-types, and can inform prioritization of therapies. In this study, we applied weighted gene co-expression analysis WGCNA (Langfelder, 2008) to 116 relapsed MM samples in the MMRC reference collection, producing 18 modules of co-expression genes. We identified significant correlations between several co-expression modules and clinical traits relevant to multiple myeloma (e.g. serum LDH, albumin). To compare these modules with existing methods of disease classification, we used gene expression profiles to assign patients into one of the seven categories of molecular classification for multiple myeloma (Zhan, 2006). The classifier was built using a PAM approach (Hastie, 2006), achieving an accuracy of >95% in test datasets. Each of the samples used to build the co-expression network was classified, building up a map of associations between sample traits, co-expression modules, and molecular classifications. With the exception of Cyclin-D group-2 (CD-2), which was not associated with any modules, each of the molecular classification groups has a unique set of module associations. We observed consistent agreement between functional molecular and clinical annotations of network modules. For example, we identified a network module associating with the Proliferative (PR) subtype (Zhan, 2006) of MM that was also positively enriched for genes located on the 1q locus of chromosome 1. Another module associated strongly with the c-MAF/MAFB subtype (MAF), was enriched for known targets of MAF, including ITGB7, CX3R1, NUAK1/ARK5, NTRK2, ARID5A, SMARCA1, TLR4, SPP1 and G6MB6. To explore how the co-expression network could be used to identify therapeutic options, we projected each module on to a large library of drug induced transcriptional profiles (Lamb, 2006), identifying compounds predicted to perturb the module in a specific manner. To characterize the chemogenomic features that may underpin the relevance of these drugs to each module, we performed target set enrichment analysis for each module’s drug-list, building a map of the most relevant drug targets for each module. We found that drugs predicted to modulate the MAF associated module are known to target Adenosine receptor 2A (ADORA2A), which is not associated with any other module. Reports of ADORA2A agonists synergizing with dexamethasone and PDE inhibitors induce apoptosis and reduce proliferation in myeloma samples (Rickles, 2010), suggesting ADORA2A modulators might be relevant to the MAF subtype. We assessed the ability of the network to organize individual patient transcriptional profiles using an independent set of RNA-seq profiles of CD138+ cells from twenty-eight multiple relapsed MM patients treated at Mount Sinai. Patient-specific differential gene expression was estimated by calculating the fraction of samples with a lower FPKM than a given sample. This vector of gene scores was used to perform a rank based measure of gene set enrichment against each of the modules using GAGE (Luo, 2009). Clustering the resulting sample-module scores using multiscale bootstrap resampling (Suzuki, 2006) uncovered 5 stable clusters. We applied the Zhan classifier to these samples and looked for enrichment within each stable clusters for each class. There were four novel clusters in addition to one cluster positively enriched (P < 0.003) for MS, a poor prognosis class, defined by MMSET-spiking, and overexpression of genes localizing to chr1q. Clustering these samples according to gene expression did not reproduce the MS (or any other) cluster enrichment associations, suggesting the potential of this network approach for organizing patient samples in a way that is clinically and biologically relevant. Disclosures Chari: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millenium : Consultancy, Membership on an entity's Board of Directors or advisory committees; Array Biopharma: Membership on an entity's Board of Directors or advisory committees. Jagannath:Celgene; Bristol-Myers Squibb; Sanofi-Aventis: Honoraria.