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Supplemental Figures and Tables to augment (1) the mathematical modeling (providing details on specific therapeutics); (2) patient characteristics (treatment history, clinical and predicted response), (3) synergistic activities identified by EMMA; and (4) data processing guides

Multiple myeloma remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system, Ex vivo Mathematical Myeloma Advisor (EMMA), consisting of patient-specific mathematical models parameterized by an ex vivo assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from multiple myeloma patients, allowing us to predict clinical response to up to 31 drugs within 5 days after bone marrow biopsy. From a cohort of 52 multiple myeloma patients, EMMA correctly classified 96% as responders/nonresponders and correctly classified 79% according to International Myeloma Working Group stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson r = 0.5658, P < 0.0001). Preliminary estimates indicate that, among the patients enrolled in this study, 60% were treated with at least one ineffective agent from their therapy combination regimen, whereas 30% would have responded better if treated with another available drug or combination. Two in silico clinical trials with experimental agents ricolinostat and venetoclax, in a cohort of 19 multiple myeloma patient samples, yielded consistent results with recent phase I/II trials, suggesting that EMMA is a feasible platform for estimating clinical efficacy of drugs and inclusion criteria screening. This unique platform, specifically designed to predict therapeutic response in multiple myeloma patients within a clinically actionable time frame, has shown high predictive accuracy in patients treated with combinations of different classes of drugs. The accuracy, reproducibility, short turnaround time, and high-throughput potential of this platform demonstrate EMMA's promise as a decision support system for therapeutic management of multiple myeloma. Cancer Res; 77(12); 3336–51. ©2017 AACR.

Individual patient EMMA predicitons of actual responses to therapy (companion to Figure 3i).

Multiple myeloma is an incurable hematological malignancy that impacts tens of thousands of people every year in the United States. Treatment for eligible patients involves induction, consolidation with stem cell rescue, and maintenance. High-dose therapy with a DNA alkylating agent, melphalan, remains the primary drug for consolidation therapy in conjunction with autologous stem-cell transplantation; as such, melphalan resistance remains a relevant clinical challenge. Here, we describe a proteometabolomic approach to examine mechanisms of acquired melphalan resistance in two cell line models. Drug metabolism, steady-state metabolomics, activity-based protein profiling (ABPP, data available at PRIDE: PXD019725), acute-treatment metabolomics, and western blot analyses have allowed us to further elucidate metabolic processes associated with melphalan resistance. Proteometabolomic data indicate that drug-resistant cells have higher levels of pentose phosphate pathway metabolites. Purine, pyrimidine, and glutathione metabolisms were commonly altered, and cell-line-specific changes in metabolite levels were observed, which could be linked to the differences in steady-state metabolism of naïve cells. Inhibition of selected enzymes in purine synthesis and pentose phosphate pathways was evaluated to determine their potential to improve melphalan's efficacy. The clinical relevance of these proteometabolomic leads was confirmed by comparison of tumor cell transcriptomes from newly diagnosed MM patients and patients with relapsed disease after treatment with high-dose melphalan and autologous stem-cell transplantation. The observation of common and cell-line-specific changes in metabolite levels suggests that omic approaches will be needed to fully examine melphalan resistance in patient specimens and define personalized strategies to optimize the use of high-dose melphalan.

The clinical utility of histone/protein deacetylase (HDAC) inhibitors in combinatorial regimens with proteasome inhibitors for patients with relapsed and refractory multiple myeloma (MM) is often limited by excessive toxicity due to HDAC inhibitor promiscuity with multiple HDACs. Therefore, more selective inhibition minimizing off-target toxicity may increase the clinical effectiveness of HDAC inhibitors. We demonstrated that plasma cell development and survival are dependent upon HDAC11, suggesting this enzyme is a promising therapeutic target in MM. Mice lacking HDAC11 exhibited markedly decreased plasma cell numbers. Accordingly, in vitro plasma cell differentiation was arrested in B cells lacking functional HDAC11. Mechanistically, we showed that HDAC11 is involved in the deacetylation of IRF4 at lysine103. Further, targeting HDAC11 led to IRF4 hyperacetylation, resulting in impaired IRF4 nuclear localization and target promoter binding. Importantly, transient HDAC11 knockdown or treatment with elevenostat, an HDAC11-selective inhibitor, induced cell death in MM cell lines. Elevenostat produced similar anti-MM activity in vivo, improving survival among mice inoculated with 5TGM1 MM cells. Elevenostat demonstrated nanomolar ex vivo activity in 34 MM patient specimens and synergistic activity when combined with bortezomib. Collectively, our data indicated that HDAC11 regulates an essential pathway in plasma cell biology establishing its potential as an emerging theraputic vulnerability in MM.

Simple Summary Multiple myeloma (MM) is a cancer of antibody-producing plasma cells that remains incurable. These cells heavily depend on protein disulfide isomerase, PDIA1, for folding and structural integrity of antibodies and other secretory proteins to avoid unresolvable stress caused if they remain unfolded. High PDIA1 expression confers resistance to proteasome inhibitors and other stressors due to the gain in endoplasmic reticulum (ER) function, while maintaining or increasing vulnerability to PDIA1 inhibition. Here we report the identification and characterization of an orally bioavailable novel PDIA1 inhibitor CCF642-34 that is effective against multiple myeloma in pre-clinical models. PDIA1, the ER resident enzyme essential for the folding of disulfide bond-containing proteins, is upregulated in relapse and refractory myeloma. This increase in PDIA1 confers its sensitivity to CCF642-34, a structurally optimized PDIA1 inhibitor that induces apoptosis in myeloma cells but not in normal bone-marrow-derived CD34+ hematopoietic stem and progenitor cells. Abstract Multiple myeloma is a genetically complex hematologic neoplasia in which malignant plasma cells constantly operate at the maximum limit of their unfolded protein response (UPR) due to a high secretory burden of immunoglobulins and cytokines. The endoplasmic reticulum (ER) resident protein disulfide isomerase, PDIA1 is indispensable for maintaining structural integrity of cysteine-rich antibodies and cytokines that require accurate intramolecular disulfide bond arrangement. PDIA1 expression analysis from RNA-seq of multiple myeloma patients demonstrated an inverse relationship with survival in relapsed or refractory disease, supporting its critical role in myeloma persistence. Using a structure-guided medicinal chemistry approach, we developed a potent, orally bioavailable small molecule PDIA1 inhibitor CCF642-34. The inhibition of PDIA1 overwhelms the UPR in myeloma cells, resulting in their apoptotic cell death at doses that do not affect the normal CD34+ hematopoietic stem and progenitor cells. Bortezomib resistance leads to increased PDIA1 expression and thus CCF642-34 sensitivity, suggesting that proteasome inhibitor resistance leads to PDIA1 dependence for proteostasis and survival. CCF642-34 induces acute unresolvable UPR in myeloma cells, and oral treatment increased survival of mice in the syngeneic 5TGM1 model of myeloma. Results support development of CCF642-34 to selectively target the plasma cell program and overcome the treatment-refractory state in myeloma.

The plasma cell malignancy multiple myeloma (MM) evolves from a pre-malignant state and remains all but incurable due to emergence of therapy resistance. Despite intensive analyses, mechanisms driving MM progression and refractory disease are poorly understood. Integrating topologic, expression and epigenetic analyses of 1,016 patient specimens, we report super-enhancer core regulatory circuits (SECRCs) that drive and sustain MM epigenetic states. Reprogramming of cell identity and tumor microenvironment genes drive malignant conversion, while alterations in cell cycle and metabolic control genes cause refractory disease. Thus, select epigenetic states drive progression and therapy resistance, providing strategies to prevent and effectively treat MM.

Interactions between the inhibitor of apoptosis protein antagonist LCL161 and the histone deacetylase inhibitor panobinostat (LBH589) were examined in human multiple myeloma (MM) cells. LCL161 and panobinostat interacted synergistically to induce apoptosis in diverse MM cell lines, including those resistant to bortezomib (PS-R). Similar interactions were observed with other histone deacetylase inhibitors (MS-275) or inhibitors of apoptosis protein antagonists (birinapant). These events were associated with downregulation of the noncanonical (but not the canonical) NF-κB pathway and activation of the extrinsic, caspase-8–related apoptotic cascade. Coexposure of MM cells to LCL161/LBH589 induced TRAF3 upregulation and led to TRAF2 and NIK downregulation, diminished expression of BCL-XL, and induction of γH2A.X. Ectopic expression of TRAF2, NIK, or BCL-XL, or short hairpin RNA TRAF3 knock-down, significantly reduced LCL161/LBH589 lethality, as did ectopic expression of dominant-negative FADD. Stromal/microenvironmental factors failed to diminish LCL161/LBH589–induced cell death. The LCL161/LBH589 regimen significantly increased cell killing in primary CD138+ cells (N = 31) and was particularly effective in diminishing the primitive progenitor cell–enriched CD138–/19+/20+/27+ population (N = 23) but was nontoxic to normal CD34+ cells. Finally, combined LCL161/LBH589 treatment significantly increased survival compared with single-agent treatment in an immunocompetent 5TGM1 murine MM model. Together, these findings argue that LCL161 interacts synergistically with LBH589 in MM cells through a process involving inactivation of the noncanonical NF-κB pathway and activation of the extrinsic apoptotic pathway, upregulation of TRAF3, and downregulation of TRAF2/BCL-XL. Notably, this regimen overcomes various forms of resistance, is active against primary MM cells, and displays significant in vivo activity. This strategy warrants further consideration in MM.

Time-lapse microscopy is a powerful technique that generates large volumes of image-based information to quantify the behaviors of cell populations. This method has been applied to cancer studies to estimate the drug response for precision medicine and has great potential to address inter-patient (or intertumoral) heterogeneity. A couple of algorithms exist to analyze time-lapse microscopy images; however, most deal with very high-resolution images involving few cells (typically cell lines). There are currently no advanced and efficient computational frameworks available to process large-scale time-lapse microscopy imaging data to estimate patient-specific response to therapy based on a large population of primary cells. In this paper, we propose a robust and user-friendly pipeline to preprocess the images and track the behaviors of thousands of cancer cells simultaneously for a better drug response prediction of cancer patients.Availability and ImplementationSource code is available at: https://github.com/CompbioLabUCF/CellTrackACM Reference FormatQibing Jiang, Praneeth Sudalagunta, Mark B. Meads, Khandakar Tanvir Ahmed, Tara Rutkowski, Ken Shain, Ariosto S. Silva, and Wei Zhang. 2020. An Advanced Framework for Time-lapse Microscopy Image Analysis. In Proceedings of BioKDD: 19th International Workshop on Data Mining In Bioinformatics (BioKDD). ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

Introduction: Despite some long-term remissions, eventual drug resistance in most patients remains a critical obstacle in the treatment of multiple myeloma (MM). The development of new drugs/drug combinations with novel mechanisms of action are needed for continued improvement in patient outcomes. Initiation of tumor cell death via activation of the intrinsic (mitochondrial) and/or extrinsic (death receptor) apoptotic signaling pathways has been shown to be an effective therapeutic strategy in MM. Venetoclax (Ven) is a selective, small-molecule inhibitor of BCL-2 that exhibits clinical activity in MM cells, particularly in patients harboring the t(11;14) translocation. Navitoclax (Nav) is a small-molecule that targets multiple antiapoptotic BCL-2 family proteins, including BCL-XL, BCL-2, and BCL-W to initiate the intrinsic apoptotic pathway. Eftozanermin alfa (Eftoza) is a novel, second generation TRAIL receptor agonist that induces cell death via death receptor pathways and is under investigation in multiple solid and heme malignancies. In addition, the pan-BET inhibitor mivebresib (Miv) and the BDII selective BET inhibitor ABBV-744 have shown synergistic activity with Ven in cell line models of multiple heme malignancies. Results reported here describe ex vivo drug sensitivities and functional genomic analyses of Ven, Nav, Eftoza, Miv, and ABBV-744 alone or in combination with standard-of-care agents, including bortezomib, carfilzomib, panobinostat, daratumumab, or pomalidomide. Methods: A high-throughput ex vivo drug screening assay using a coculture system of bone marrow (BM)-derived MM and stromal cells was used to assess the sensitivity of MM patient tumor cells (Figure 1A). Paired whole exome sequencing (WES) and RNA sequencing (RNA-seq) analyses were performed. Results: Primary MM patient specimens (n=52) were evaluated in the ex vivo platform, including treatment-naïve, early relapse (1-3 prior lines), and late relapse (4-8 prior lines) patients treated with proteasome inhibitors, immunomodulatory drugs, and monoclonal antibodies. As expected, t(11;14)-positive MM patient specimens were more sensitive than wildtype to Ven ex vivo (D AUC, -18.6, P=0.002), however MM cells harboring amp(1q) were more resistant than wildtype (D AUC, +5.07, P=0.032), suggesting MCL1 (1q21 gene locus) is a key resistance factor to Ven single-agent activity in MM. Gene set enrichment analysis identified B-cell receptor signaling (normalized enrichment score (NES), 1.96, adjusted P=0.010) and MYC pathway (NES, 1.95, adjusted P=0.010) overexpression as predictors of increased sensitivity to Ven ex vivo. A t(11;14) gene expression signature was also generated using a penalized regression model approach in an additional MMWG/ORIEN MM patient cohort (n=155). The t(11;14) predictive gene expression signature was confirmed by correlation with Ven AUC in the ex vivo model. Additional pathway analyses were performed to identify potential predictive markers of sensitivity/resistance for each single agent and drug combination. Although ex vivo activity of Nav was higher in t(11;14) specimens compared to non-t(11;14) (D AUC, -17.8, P=0.011), ex vivo activity in non-t(11;14) specimens was also observed, indicating additional anti-MM activity by cotargeting of BCL-XL and BCL-2. Both Miv and ABBV-744 showed single-agent activity ex vivo, however Miv demonstrated higher activity (median LD50=88.4nM), suggesting that pan-BET inhibition is more effective than BDII-specific BET inhibition in MM. Finally, a novel drug-combination effect analysis was used that identified novel synergistic ex vivo combinations including Ven and panobinostat (P=0.0013) and Eftoza with bortezomib (P=1.8E-7) or carfilzomib (P=7E-4). Additionally, single-agent induction of macrophage-mediated phagocytosis was observed in both Ven and daratumumab, which was synergistic when the 2 drugs were combined (Figure 1B). Conclusion: An ex vivo functional genomic screen of MM patient specimens demonstrated the usefulness of this approach to identify candidate drugs and potential predictive biomarkers for continued evaluation in clinical trials. This approach confirmed known mechanisms of drug sensitivity and identified new ones, including a novel characterized immune-mediated synergy between Ven and daratumumab, and potential combination strategy for Eftoza and proteasome inhibitors. Figure 1 Disclosures Siqueira Silva: Karyopharm: Research Funding; NIH/NCI: Research Funding; AbbVie: Research Funding. Kulkarni:M2GEN: Current Employment. Mitchell:AbbVie: Other: payment for bioinformatics analysis, Research Funding; M2GEN: Current Employment, Research Funding. Dai:Cygnal Therapeutics: Current Employment; M2GEN: Ended employment in the past 24 months. Hampton:M2GEN: Current Employment. Lu:AbbVie: Current Employment, Current equity holder in publicly-traded company. Modi:AbbVie: Current Employment, Other: may own stock or stock options. Motwani:AbbVie: Current Employment, Current equity holder in publicly-traded company. Harb:AbbVie: Current Employment, Other: may hold stock or stock options. Ross:AbbVie: Current Employment, Current equity holder in publicly-traded company. Shain:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; GlaxoSmithKline: Speakers Bureau; Sanofi/Genzyme: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Karyopharm: Research Funding, Speakers Bureau; AbbVie: Research Funding; Takeda: Honoraria, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Amgen: Speakers Bureau; Adaptive: Consultancy, Honoraria; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. OffLabel Disclosure: While this is a preclinical study, venetoclax for treatment of multiple myeloma is not an approved indication

Introduction. Multiple myeloma (MM) is an incurable plasma cell malignancy with a growing list of anti-MM therapeutics. However, the development of predictive biomarkers has yet to be achieved for nearly all MM therapeutics. Selinexor (SELI), a nuclear export inhibitor targeting exportin 1 (XPO1), has been approved with dexamethasone (DEX) in penta-refractory MM. Clinical studies investigating promising SELI- 3 drug combinations are ongoing. Here, we have investigated potential synergistic combinations of SELI and anti-MM agents in terms of ex vivo sensitivity, as well as paired RNAseq and WES to identify companion biomarkers. Methods. MM cells isolated from fresh bone marrow aspirates were tested for drug sensitivity in an organotypic ex vivo drug sensitivity assay, consisting of co-culture with stroma, collagen matrix and patient-derived serum. Single agents were tested at 5 concentrations, while two-drug combinations were tested at fixed ratio of concentrations. LD50 and area under the curve (AUC) were assessed during 96h-exposure as metrics for drug resistance. Drug synergy was calculated as a modified BLISS model. Matching aliquots of MM cells had RNAseq and WES performed through ORIEN/AVATAR project. Geneset enrichment analysis (GSEA) was conducted using both AUC and LD50 as phenotypes for single agents and combinations. Both curated pathways (KEGG and cancer hallmarks) and unsupervised gene clustering were used as genesets. Student t-tests with multiple test correction were used to identify non-synonymous mutations in protein coding genes associated with single agent or combination AUC. Results. For this analysis, a cohort of specimens from 103 patients (48% female, 4% Hispanic, 11% African American) was tested with SELI and/or DEX. with a median of 2 lines of therapy (0-12). A smaller cohort of 37 have been examined with SELI, pomalidomide (POM), elotuzumab (ELO) and daratumumab (DARA). Within this cohort we observed synergy between SELI and DEX, POM and ELO as shown in Figure 1. The volcano plot illustrates the number of samples, maximum drug concentration, as well as magnitude (x- axis) and significance (y- axis) of synergy. Although the SELI-DARA combination trended toward synergy, statistical significance was not achieved. To identify molecular mechanisms and biomarkers associated with sensitivity to SELI and SELI- combinations, we investigated paired RNAseq and WES with ex vivo sensitivity. Initially, we conducted GSEA on two cohorts of primary MM samples tested with SELI alone at 5µM (n=53) and 10µM (n=50). Cell adhesion (KEGG CAMS), inflammatory cytokines (KEGG ASTHMA), and epithelial mesenchymal transition (HALLMARK EMT) were associated with resistance in both cohorts, while the HALLMARK MYC TARGETS was associated with sensitivity (FWER p Conclusions. We observed ex vivo synergy between SELI and DEX, POM and ELO. Molecular analysis of matched ex vivo drug sensitivity, transcriptome and mutational profile identified environment-mediated drug resistance pathways positively correlated with SELI single agent resistance, as well as MYC regulated genes associated with ex vivo sensitivity. We also identified a list of mutations associated with SELI drug resistance and sensitivity, with special emphasis on two novel NES-containing genes, CEP290 and BCL7A. The next step of this project is to analyze transcriptional and mutational patterns associated with ex vivo synergy in the combinations here described, as putative biomarkers for future clinical investigation. Disclosures Shain: Amgen: Speakers Bureau; Adaptive: Consultancy, Honoraria; Karyopharm: Research Funding, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; GlaxoSmithKline: Speakers Bureau; Janssen: Honoraria, Speakers Bureau; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi/Genzyme: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Honoraria, Speakers Bureau; AbbVie: Research Funding. Kulkarni:M2GEN: Current Employment. Zhang:M2GEN: Current Employment. Hampton:M2GEN: Current Employment. Argueta:Karyopharm: Current Employment. Landesman:Karyopharm Therapeutics Inc: Current Employment, Current equity holder in publicly-traded company. Siqueira Silva:AbbVie: Research Funding; NIH/NCI: Research Funding; Karyopharm: Research Funding.

Multiple myeloma (MM) is an incurable cancer of bone marrow-resident plasma cells, which evolves from a premalignant state, MGUS, to a form of active disease characterized by an initial response to therapy, followed by cycles of therapeutic successes and failures, culminating in a fatal multi-drug resistant cancer. The molecular mechanisms leading to disease progression and refractory disease in MM remain poorly understood. To address this question, we have generated a new database, consisting of 1,123 MM biopsies from patients treated at the H. Lee Moffitt Cancer Center. These samples ranged from MGUS to late relapsed/refractory (LR) disease, and were comprehensively characterized genetically (844 RNAseq, 870 WES, 7 scRNAseq), epigenetically (10 single-cell chromatin accessibility, scATAC-seq) and phenotypically (537 samples assessed for ex vivo drug resistance). Mutational analysis identified putative driver genes (e.g. NRAS, KRAS) among the highest frequent mutations, as well as a steady increase in mutational load across progression from MGUS to LR samples. However, with the exception of KRAS, these genes did not reach statistical significance according to FISHER's exact test between different disease stages, suggesting that no single mutation is necessary or sufficient to drive MM progression or refractory disease, but rather a common "driver" biology is critical. Pathway analysis of differentially expressed genes identified cell adhesion, inflammatory cytokines and hematopoietic cell identify as under-expressed in active MM vs. MGUS, while cell cycle, metabolism, DNA repair, protein/RNA synthesis and degradation were over-expressed in LR. Using an unsupervised systems biology approach, we reconstructed a gene expression map to identify transcriptomic reprogramming events associated with disease progression and evolution of drug resistance. At an epigenetic regulatory level, these genes were enriched for histone modifications (e.g. H3k27me3 and H3k27ac). Furthermore, scATAC-seq confirmed genome-wide alterations in chromatin accessibility across MM progression, involving shifts in chromatin accessibility of the binding motifs of epigenetic regulator complexes, known to mediate formation of 3D structures (CTCF/YY1) of super enhancers (SE) and cell identity reprograming (POU5F1/SOX2). Additionally, we have identified SE-regulated genes under- (EBF1, RB1, SPI1, KLF6) and over-expressed (PRDM1, IRF4) in MM progression, as well as over-expressed in LR (RFX5, YY1, NBN, CTCF, BCOR). We have found a correlation between cytogenetic abnormalities and mutations with differential gene expression observed in MM progression, suggesting groups of genetic events with equivalent transcriptomic effect: e.g. NRAS, KRAS, DIS3 and del13q are associated with transcriptomic changes observed during MGUS/SMOL=>active MM transition (Figure 1). Taken together, our preliminary data suggests that multiple independent combinations of genetic and epigenetic events (e.g. mutations, cytogenetics, SE dysregulation) alter the balance of master epigenetic regulatory circuitry, leading to genome-wide transcriptional reprogramming, facilitating disease progression and emergence of drug resistance. Figure 1: Topology of transcriptional regulation in MM depicts 16,738 genes whose expression is increased (red) or decreased (green) in presence of genetic abnormality. Differential expression associated with (A) hotspot mutations and (B) cytogenetic abnormalities confirms equivalence of expected pairs (e.g. NRAS and KRAS, BRAF and RAF1), but also proposes novel transcriptomic dysregulation effect of clinically relevant cytogenetic abnormalities, with yet uncharacterized molecular role in MM. Figure 1 Disclosures Kulkarni: M2GEN: Current Employment. Zhang:M2GEN: Current Employment. Hampton:M2GEN: Current Employment. Shain:GlaxoSmithKline: Speakers Bureau; Amgen: Speakers Bureau; Karyopharm: Research Funding, Speakers Bureau; AbbVie: Research Funding; Takeda: Honoraria, Speakers Bureau; Sanofi/Genzyme: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Adaptive: Consultancy, Honoraria; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Siqueira Silva:AbbVie: Research Funding; Karyopharm: Research Funding; NIH/NCI: Research Funding.

Introduction. Multiple myeloma (MM) is an all but incurable plasma cell malignancy without predictive biomarkers for approved therapies. Selinexor (SELI), a nuclear export inhibitor targeting exportin 1 (XPO1), is approved with dexamethasone (DEX) with promising SELI-combination studies ongoing. We investigated SELI combinations ex vivo to identify synergistic combinations and companion biomarkers. Methods. We established a platform to perform parallel RNA/exome sequencing and ex vivo drug sensitivity assessment on CD138+ cells from MM patient bone marrow aspirates. At the time of this analysis, 844 different samples with clinical, WES and RNA sequencing data were treated ex vivo with the following agents: SELI (n=75), DEX (192), pomalidomide (POM, 268), elotuzumab (ELO, 21), daratumumab (DARA, 117), SELI+DEX (22), SELI+POM (20), SELI+ELO (21), SELI+DARA (27). Cells were cultured with autologous macrophages, stroma, collagen matrix and patient-derived serum. Cell death (LD50 and AUC) was assessed through digital image analysis. Sequencing was performed through ORIEN/AVATAR. Links between non-synonymous mutations in coding genes and cell death were calculated using T-tests with multiple test correction. Results. Our analysis identified SELI+DEX (number of samples=60, p Conclusions. We observed ex vivo synergy between SELI and DEX, POM, ELO and DARA, and identified expression signatures and mutations associated with response to these agents. Ongoing analysis of additional samples is being performed to validate these results. Citation Format: Praneeth Reddy Sudalagunta, Mark B. Meads, Rafael Renatino Canevarolo, Maria Coelho Silva, Christopher Cubitt, Gabriel DeAvila, Raghunandan Reddy Alugubelli, Constantine N. Logothetis, Amit Kulkarni, Qi Zhang, Oliver Hampton, Christopher J. Walker, Yosef Landesman, Kenneth Shain, Ariosto Siqueira Silva. Characterization of synergistic selinexor combinations with dexamethasone, pomalidomide, elotuzumab, and daratumumab in primary MM cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1061.

Introduction Certain cancers have been shown to express a specific type of drug-resistance involving the export of drug target proteins from the nucleus of the cell to the cytoplasm. This mechanism of drug-resistance has been shown to be important in multiple myeloma (MM) and recently a nuclear export inhibitor, selinexor, has received approval by the FDA in MM. However, general nuclear export signal (NES) inhibitors like selinexor exhibit off-target toxicities when used in patients. We have begun to develop novel drugs that specifically prevent the export of the drug-target topoisomerase IIα (TOP2A). Methods In this study, we 1) generated an atomic homology model of human TOP2A to identify lead compounds targeting the NES of TOP2A; NES inhibitors (NESi), 2) examined binding specifically to TOP2A and inhibit nuclear export by immunofluorescence microscopy (IF) , nuclear-cytoplasmic fractionation, immunoprecipitation (IP), proximity ligation assay (PLA), and Biacore kinetic/affinity assay, and 3) tested the anticancer activity of these compounds in MM in vitro, ex vivo and in vivo MM. Results The atomic homology model of human TOP2A provided the basis for in silico molecular docking screenings of 139,735 small molecules (NCI) (MW < 500) to identify lead compounds targeting the NES of TOP2A. NESi compounds with the highest docking scores were assayed for their ability to induce apoptosis in human MM cells when used with the TOP2 inhibitor doxorubicin (DOX). We isolated four lead compounds that induced apoptosis when used with DOX and that inhibited nuclear export of TOP2A, as shown by IF and nuclear-cytoplasmic fractionation, IP, a PLA and Biacore kinetic/affinity assays. Inhibition by the lead compound, NCI-9138, was specific to TOP2A because p53 trafficking was unaffected and the inhibitor did not affect TOP2A protein expression or enzymatic function (decatenation). This NESi was found to sensitize human MM cells in vitro, in vivo mouse models, and patient MM cells exposed ex vivo to DOX but did not affect normal cell lines. These TOP2A-specific nuclear export inhibitors may potentially lead to a new approach in circumventing drug resistance. Conclusions Our previous studies have shown that TOP2A is exported from the nucleus of MM cells and that this is a significant cause of resistance to DOX therapy. In this study, we have shown that blocking export of TOP2A can sensitize human MM cells to DOX both in vitro, ex vivo in MM cells from patients, and in in vivo mouse models of MM. A drug that specifically inhibits the export of a single cancer-related protein has not been published. We intend to show that this unique approach to cancer-treatment may significantly advance the treatment of MM. This paper also demonstrates a proof of concept for the development of highly specific small molecule inhibitors that could be used as cancer therapeutics. Citation Format: Joel G. Turner, David Ostrov, Nicholas Lawrence, Jana Dawson, Juan Gomez, Alexis Bauer, Mark Meads, Alex Achille, Kenneth Shain. Novel small molecule inhibitors that target the exportin binding pocket of TOP2A for the treatment of multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1228.

Multiple myeloma remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system, Ex vivo Mathematical Myeloma Advisor (EMMA), consisting of patient-specific mathematical models parameterized by an ex vivo assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from multiple myeloma patients, allowing us to predict clinical response to up to 31 drugs within 5 days after bone marrow biopsy. From a cohort of 52 multiple myeloma patients, EMMA correctly classified 96% as responders/nonresponders and correctly classified 79% according to International Myeloma Working Group stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson r = 0.5658, P < 0.0001). Preliminary estimates indicate that, among the patients enrolled in this study, 60% were treated with at least one ineffective agent from their therapy combination regimen, whereas 30% would have responded better if treated with another available drug or combination. Two in silico clinical trials with experimental agents ricolinostat and venetoclax, in a cohort of 19 multiple myeloma patient samples, yielded consistent results with recent phase I/II trials, suggesting that EMMA is a feasible platform for estimating clinical efficacy of drugs and inclusion criteria screening. This unique platform, specifically designed to predict therapeutic response in multiple myeloma patients within a clinically actionable time frame, has shown high predictive accuracy in patients treated with combinations of different classes of drugs. The accuracy, reproducibility, short turnaround time, and high-throughput potential of this platform demonstrate EMMA's promise as a decision support system for therapeutic management of multiple myeloma. Cancer Res; 77(12); 3336–51. ©2017 AACR.

Drug resistance remains a critical problem for the treatment of multiple myeloma (MM), which can serve as a specific example for a highly prevalent unmet medical need across almost all cancer types. In MM, the therapeutic arsenal has expanded and diversified, yet we still lack in-depth molecular understanding of drug mechanisms of action and cellular pathways to therapeutic escape. For those reasons, preclinical models of drug resistance are developed and characterized using different approaches to gain insights into tumor biology and elucidate mechanisms of drug resistance. For MM, numerous drugs are used for treatment, including conventional chemotherapies (e.g., melphalan or L-phenylalanine nitrogen mustard), proteasome inhibitors (e.g., Bortezomib), and immunomodulators (e.g., Lenalidomide). These agents have diverse effects on the myeloma cells, and several mechanisms of drug resistance have been previously described. The disparity of these mechanisms and the complexity of these biological processes lead to the formation of complicated hypotheses that require omics approaches for efficient and effective analysis of model systems that can then be interpreted for patient benefit. Here, we describe the combination of metabolomics and proteomics to assess melphalan resistance in MM by examining three specific areas: drug metabolism, modulation of endogenous metabolites to assist in therapeutic escape, and changes in protein activity gauged by ATP probe uptake.

SUMMARY Ibrutinib, a bruton’s tyrosine kinase (BTK) inhibitor, provokes robust clinical responses in aggressive mantle cell lymphoma (MCL), yet many patients relapse with lethal Ibrutinib-resistant (IR) disease. Here, using genomic, chemical proteomic, and drug screen profiling, we report that enhancer remodeling-mediated transcriptional activation and adaptive signaling changes drive the aggressive phenotypes of IR. Accordingly, IR MCL cells are vulnerable to inhibitors of the transcriptional machinery and especially so to inhibitors of cyclin-dependent kinase 9 (CDK9), the catalytic subunit of the positive transcription elongation factor b (P-TEFb) of RNA polymerase II (RNAPII). Further, CDK9 inhibition disables reprogrammed signaling circuits and prevents the emergence of IR in MCL. Finally, and importantly, we find that a robust and facile ex vivo image-based functional drug screening platform can predict clinical therapeutic responses of IR MCL and identify vulnerabilities that can be targeted to disable the evolution of IR., In brief Zhao et al. conduct unbiased proteomic, enhancer, and transcriptional profiling in combination with high-throughput drug screening to identify super-enhancer and kinome remodeling as an Ibrutinib resistance mechanism. CDK9 and BRD4 are vulnerabilities for Ibrutinib resistance necessary to sustain super-enhancers, and inhibition of CDK9 or BRD4 prevents the emergence of resistance., Graphical Abstract

// Joel G. Turner 1 , Trinayan Kashyap 2 , Jana L. Dawson 1 , Juan Gomez 1 , Alexis A. Bauer 1 , Steven Grant 3 , Yun Dai 3 , Kenneth H. Shain 1, 4 , Mark Meads 1 , Yosef Landesman 2 , Daniel M. Sullivan 1, 5 1 Chemical Biology and Molecular Medicine Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA 2 Karyopharm Therapeutics, Natick, MA, USA 3 Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA 4 Department of Malignant Hematology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA 5 Department of Blood & Marrow Transplantation, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA Correspondence to: Daniel M. Sullivan, email: dan.sullivan@moffitt.org Keywords: XPO1, bortezomib, carfilzomib, multiple myeloma, acquired drug resistance Received: July 26, 2016 Accepted: October 12, 2016 Published: October 28, 2016 ABSTRACT Acquired proteasome-inhibitor (PI) resistance is a major obstacle in the treatment of multiple myeloma (MM). We investigated whether the clinical XPO1-inhibitor selinexor, when combined with bortezomib or carfilzomib, could overcome acquired resistance in MM. PI-resistant myeloma cell lines both in vitro and in vivo and refractory myeloma patient biopsies were treated with selinexor/bortezomib or carfilzomib and assayed for apoptosis. Mechanistic studies included NFκB pathway protein expression assays, immunofluorescence microscopy, ImageStream flow-cytometry, and proximity-ligation assays. IκBα knockdown and NFκB activity were measured in selinexor/bortezomib-treated MM cells. We found that selinexor restored sensitivity of PI-resistant MM to bortezomib and carfilzomib. Selinexor/bortezomib treatment inhibited PI-resistant MM tumor growth and increased survival in mice. Myeloma cells from PI-refractory MM patients were sensitized by selinexor to bortezomib and carfilzomib without affecting non-myeloma cells. Immunofluorescence microscopy, Western blot, and ImageStream analyses of MM cells showed increases in total and nuclear IκBα by selinexor/bortezomib. Proximity ligation found increased IκBα-NFκB complexes in treated MM cells. IκBα knockdown abrogated selinexor/bortezomib-induced cytotoxicity in MM cells. Selinexor/bortezomib treatment decreased NFκB transcriptional activity. Selinexor, when used with bortezomib or carfilzomib, has the potential to overcome PI drug resistance in MM. Sensitization may be due to inactivation of the NFκB pathway by IκBα.

Background: Multiagent therapies, due to their ability to delay or overcome resistance, are a hallmark of treatment in multiple myeloma (MM). The growing number of therapeutic options in MM requires high-throughput combination screening tools to better allocate treatment, and facilitate personalized therapy. Methods: A second-order drug response model was employed to fit patient-specific ex vivo responses of 203 MM patients to single-agent models. A novel pharmacodynamic model, developed to account for two-way combination effects, was tested with 130 two-drug combinations. We have demonstrated that this model is sufficiently parameterized by single-agent and fixed-ratio combination responses, by validating model estimates with ex vivo combination responses for different concentration ratios, using a checkerboard assay. This new model reconciles ex vivo observations from both Loewe and BLISS synergy models, by accounting for the dimension of time, as opposed to focusing on arbitrary time-points or drug effect. Clinical outcomes of patients were simulated by coupling patient-specific drug combination models with pharmacokinetic data. Findings: Combination screening showed 1 in 5 combinations (21.43% by LD50, 18.42% by AUC) were synergistic ex vivo with statistical significance (P < 0.05), but clinical synergy was predicted for only 1 in 10 combinations (8.69%), which was attributed to the role of pharmacokinetics and dosing schedules. Interpretation: The proposed framework can inform clinical decisions from ex vivo observations, thus providing a path toward personalized therapy using combination regimens. Funding: This research was funded by the H. Lee Moffitt Cancer Center Physical Sciences in Oncology (PSOC) Grant (1U54CA193489-01A1) and by H. Lee Moffitt Cancer Center's Team Science Grant. This work has been supported in part by the PSOC Pilot Project Award (5U54CA193489-04), the Translational Research Core Facility at the H. Lee Moffitt Cancer Center & Research Institute, an NCI-designated Comprehensive Cancer Center (P30-CA076292), the Pentecost Family Foundation, and Miles for Moffitt Foundation. Keywords: Clinical synergy, Multiple myeloma, Ex vivo, Pharmacodynamic model, High-throughput combination screening

Multiple Myeloma (MM) remains an incurable malignancy, despite the advent of several new therapeutic agents, including immunomodulatory drugs (IMiDs, e.g., Lenalidomide (Len)) and proteasome inhibitors (PIs, e.g., Bortezomib (Btz)). Accordingly, there is an urgent need to identify new targetable vulnerabilities for MM patients. We developed an ex vivo 384-well platform that allows one to define drug sensitivities of primary patient CD138+ MM cells in the context of a reconstructed tumor microenvironment (TME), including allogeneic bone marrow stromal cells, extracellular matrix and MM patient serum. Using this platform and activity-based proteomic profiling (ABPP), we identified shared signaling pathways induced by the interactions of MM with stromal cells and integrated these data with screens performed using a bank of protein kinase inhibitors (PKI) and current anti-MM therapeutics. These analyses revealed that the serine/threonine kinases casein kinase-1δ (CK1δ) and CK1ε as high priority targets for MM. Indeed, a highly selective and potent dual inhibitor of CK1δ/CK1ε coined SR-3029 is the most potent PKI versus MM. Further, our studies revealed SR-3029 has potent activity in 138/153 primary patient MM specimens tested thus far, including quad and penta-refractory MM. Analysis of RNAseq data of over 600 Moffitt Cancer Center (MCC) MM patients revealed that patients with high expression of CK1ε had worse survival outcomes while no survival difference was seen with CK1δ expression. Importantly, using the established 5TGM1/Kal-Ridge (C57B6/KaLwRijHsd) syngeneic mouse model of multiple myeloma, we show that tumors derived from 5TGM1 MM cells, which rapidly die following exposure to SR-3029 ex vivo, are also sensitive to CK1δ/CK1ε inhibition in vivo, where SR-3029 treatment reduced tumor burden and significantly improved survival. Similar results were observed using NSG immune compromised animals inoculated with human MM1.S multiple myeloma cells (both flank and tail vein models), where SR-3029 treated animals had reduced tumor burden and extended survival. Analysis of RNAseq on patients' samples (on stroma) treated ex vivo with SR-3029 revealed CK1δ/CK1ε inhibition suppressed multiple metabolic pathways (oxidative phosphorylation, glycolysis, xenobiotic metabolism). Interestingly, analyses of MCC MM patient RNAseq data revealed upregulation of the genes identified in these metabolic pathways as patients progress from pre-treatment to relapse, and that patient MM samples that were resistant to CK1δ/CK1ε inhibition had an upregulation of some of these metabolic genes. Functional studies are being performed to define the mechanism(s) by which CK1δ/CK1ε inhibition disables MM metabolism. Collectively, these findings establish CK1ε and/or CK1δ as attractive targets for anti-myeloma therapy that are required to sustain MM metabolism. Disclosures Dai: M2Gen: Employment. Shain:Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy; Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees.

Problem: Multiple myeloma (MM) is a treatable yet incurable hematologic cancer that lacks predictive biomarkers. Approach: Here we apply a systems biology approach to determine patient-specific mechanisms, as well as signatures of drug resistance in MM. To achieve this goal, we have combined ex vivo drug sensitivity data from 307 MM fresh primary samples tested with 162 drugs and combinations, with paired molecular data (RNAseq and mutational profiling) from a larger overlapping cohort of 606 MM samples from Moffitt's Multiple Myeloma Working Group (MMWG) repository in collaboration with M2Gen/Oncology Research Information Exchange Network (ORIEN). With the purpose of decoupling biological function from intracellular control mechanisms, we have re-constructed a MM-specific transcriptional regulatory network composed of clusters of co-expressing genes. We demonstrate how this gene cluster network regulates biology, and how different biological functions (e.g. Proteasome, Ribosome, Oxidative Phosphorylation) share common regulatory circuits. We have used gene set enrichment analysis (GSEA) to identify gene clusters with transcriptional profiles, and investigated mutations associated with drug resistance. Results: As a preliminary validation of this approach, we have confirmed established mechanisms of resistance (MOR) to targeted therapies, as well as proposed novel MOR to clinically relevant and experimental drugs in MM, as well as putative synergistic drug combinations. In addition, we have identified a list of low frequency mutations ( We have also explored evolution of drug resistance in sequential samples. Consistent with altered transcriptional programming in therapeutic escape, single sample GSEA demonstrated cumulative dysregulation of cancer-related genes with increasing lines of therapy. We have identified 60 MM-specific transcriptional core auto-regulatory circuits (CRC) correlated with ex vivo drug resistance, suggesting that characterization of transcriptional regulatory circuits is a critical approach to infer mechanisms of MM resistance, and providing a novel rationale for combination therapy. We hypothesized that identifying and targeting these transcriptional CRCs could facilitate patient-specific rational combination therapies, with the goal to overcome therapy resistance in the clinic. As proof-of-principle, we have identified a novel transcriptional network consisting of 3 of these CRCs (FOXP1, JUNB and JUN) associated with BCL2 inhibitor (BCL2i) response in MM. Our preliminary data suggests that this transcriptional regulatory circuit is associated to t(11;14) MM through CCND1 up-regulation, but is also present in non-t(11;14) BCL2i-sensitive primary samples, and can be modulated to induce BCL2i sensitivity in non-t(11;14) MM through HDAC inhibitors. Conclusion and next steps: Preliminary results confirm the potential of this combination of unsupervised and supervised, yet functionally testable approach, to infer novel, and patient-specific MOR for MM drugs. Disclosures Dai: M2Gen: Employment. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria. Shain:Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Adaptive Biotechnologies: Consultancy; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees.

Immune dysfunction is an important feature of multiple myeloma (MM). Characterization of lymphocytic infiltrates, comparing precursor, early & later disease stages, indicate that the anti-myeloma immune response evolves in conjunction with the progression of disease, while distinctive patterns identified in the infiltrating immune population have prognostic relevance (Pessoa de Magalhaes, Vidriales et al. 2013, Paiva, Mateos et al. 2016).The primary objective of this project is to characterize the marrow-infiltrating lymphocytic and myeloid populations to identify specific patterns of immune dysregulation & a biomarker signature that will guide predictions of efficacy of different immunotherapeutic modalities.(Willenbacher et al. 2016, Kini Bailur, Mehta et al. 2017). Patient samples are identified for inclusion based on clinical assessment parameters: detection of a monoclonal gammopathy, the presence or absence of end-organ damage indicative of active/symptomatic MM, and Classical flow analysis of the myeloid panel clearly reveals significant changes occurring in the G-MDSC population with disease progression. tSNE-based unsupervised analysis show increases in CD4 T effector cells in NDMM patients, but the differences in the CD4 memory compartment in MGUS suggest that these variations in T cells may originate during premalignancy. The expression of CD95 on CD4 cells is downregulated with disease progression. In addition to changes in T cells, we observe changes in innate cells- NDMM samples show an increase in a CD194+NK cell population which may represent a transitional population expanded during pathological tumor growth. With recent studies suggesting that innate cells could be targets for immune-modulatory drugs, this opens a new avenue. Taken together, this data illustrates complex alterations in the immune landscape from a premalignant to malignant stage, implicating that engaging the immune system early in the evolution of clinical MM may improve the potential of durable remissions. Kini Bailur, J., et al. (2017). "Changes in bone marrow innate lymphoid cell subsets in monoclonal gammopathy: target for IMiD therapy." Blood Adv1(25): 2343-2347. Paiva, B., et al. (2016). "Immune status of high-risk smoldering multiple myeloma patients and its therapeutic modulation under LenDex: a longitudinal analysis." Blood127(9): 1151-1162. Pessoa de Magalhaes, R. J., et al. (2013). "Analysis of the immune system of multiple myeloma patients achieving long-term disease control by multidimensional flow cytometry." Haematologica98(1): 79-86. Willenbacher, W., et al. (2016). "Bone marrow microenvironmental CD4 + and CD8 + lymphocyte infiltration patterns define overall- and progression free survival in standard risk multiple myeloma--an analysis from the Austrian Myeloma Registry." Leuk Lymphoma57(6): 1478-1481. Disclosures Shain: AbbVie: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy; Amgen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees. Brayer:Janssen: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau.

Multiple myeloma (MM) remains an incurable malignancy due, in part, to the influence of the bone marrow microenvironment on survival and drug response. Identification of microenvironment-specific survival signaling determinants is critical for the rational design of therapy and elimination of MM. Previously, we have shown that collaborative signaling between β1 integrin-mediated adhesion to fibronectin and interleukin-6 confers a more malignant phenotype via amplification of signal transducer and activator of transcription 3 (STAT3) activation. Further characterization of the events modulated under these conditions with quantitative phosphotyrosine profiling identified 193 differentially phosphorylated peptides. Seventy-seven phosphorylations were upregulated upon adhesion, including PYK2/FAK2, Paxillin, CASL and p130CAS consistent with focal adhesion (FA) formation. We hypothesized that the collaborative signaling between β1 integrin and gp130 (IL-6 beta receptor, IL-6 signal transducer) was mediated by FA formation and proline-rich tyrosine kinase 2 (PYK2) activity. Both pharmacological and molecular targeting of PYK2 attenuated the amplification of STAT3 phosphorylation under co-stimulatory conditions. Co-culture of MM cells with patient bone marrow stromal cells (BMSC) showed similar β1 integrin-specific enhancement of PYK2 and STAT3 signaling. Molecular and pharmacological targeting of PYK2 specifically induced cell death and reduced clonogenic growth in BMSC-adherent myeloma cell lines, aldehyde dehydrogenase-positive MM cancer stem cells and patient specimens. Finally, PYK2 inhibition similarly attenuated MM progression in vivo. These data identify a novel PYK2-mediated survival pathway in MM cells and MM cancer stem cells within the context of microenvironmental cues, providing preclinical support for the use of the clinical stage FAK/PYK2 inhibitors for treatment of MM, especially in a minimal residual disease setting.

The novel Bruton's tyrosine kinase inhibitor ibrutinib has demonstrated high response rates in B-cell lymphomas; however, a growing number of ibrutinib-treated patients relapse with resistance and fulminant progression. Using chemical proteomics and an organotypic cell-based drug screening assay, we determine the functional role of the tumour microenvironment (TME) in ibrutinib activity and acquired ibrutinib resistance. We demonstrate that MCL cells develop ibrutinib resistance through evolutionary processes driven by dynamic feedback between MCL cells and TME, leading to kinome adaptive reprogramming, bypassing the effect of ibrutinib and reciprocal activation of PI3K-AKT-mTOR and integrin-β1 signalling. Combinatorial disruption of B-cell receptor signalling and PI3K-AKT-mTOR axis leads to release of MCL cells from TME, reversal of drug resistance and enhanced anti-MCL activity in MCL patient samples and patient-derived xenograft models. This study unifies TME-mediated de novo and acquired drug resistance mechanisms and provides a novel combination therapeutic strategy against MCL and other B-cell malignancies., Ibrutinib has demonstrated high response rates in B-cell lymphomas but a lot of ibrutinib-treated patients relapse with resistance. This study unified TME-mediated de novo and acquired drug resistance through B-cell receptor signalling and PI3K-AKT-mTOR axis and provides a combination therapeutic strategy against B-cell malignancies.

Multiple myeloma (MM) remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system, EMMA (Ex vivo Mathematical Myeloma Advisor), consisting of patient-specific mathematical models parameterized by an ex vivo assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from MM patients, allowing us to predict clinical response to up to 31 drugs within 5 days post-bone marrow biopsy. From a cohort of 52 MM patients, EMMA correctly classified 96% as responders/non-responders and correctly classified 79% according to IMWG stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson r=0.5658, P

Summary Drug-tolerant "persister" tumor cells underlie emergence of drug-resistant clones and contribute to relapse and disease progression. Here we report that resistance to the BCL-2 targeting drug ABT-199 in models of mantle cell lymphoma and double-hit lymphoma evolves from outgrowth of persister clones displaying loss of 18q21 amplicons that harbor BCL2. Further, persister status is generated via adaptive super-enhancer remodeling that reprograms transcription and offers opportunities for overcoming ABT-199 resistance. Notably, pharmacoproteomic and pharmacogenomic screens revealed that persisters are vulnerable to inhibition of the transcriptional machinery and especially to inhibition of cyclin-dependent kinase 7 (CDK7), which is essential for the transcriptional reprogramming that drives and sustains ABT-199 resistance. Thus, transcription-targeting agents offer new approaches to disable drug resistance in B-cell lymphomas.

Background: Histone deacetylases (HDACs) are potential novel therapeutic targets for multiple myeloma (MM) treatment. A pan-HDAC inhibitor (HDI) panobinostat was approved by the FDA in 2015 to treat relapsed/refractory MM patients, and several other HDIs are currently in different phases of clinical trials. However, unfavorable side-effects of the non-selective HDIs necessitate further dissection of the roles of individual HDAC isoforms to best target plasma cell malignancies with minimal toxicity. HDAC11 was recently found to regulate function in key immune cell populations including regulatory T cells, effector T cells, neutrophils, and myeloid-derived suppressor cells (MDSC). Though HDAC11 expression is confirmed in B cells and plasma cells, its functions in these cells remain largely unknown. In this study, we attempted a functional analysis of HDAC11 in plasma cell development along with its pro-tumorigenic function in MM cells. Methods: Mouse models, including a transgenic mouse strain expressing eGFP under the regulation of the HDAC11 promoter (Tg-HDAC11-eGFP), and also an HDA11-deficient mouse (B6.HDAC11-/-) were studied to establish the importance of HDAC11 in plasma cell biology. Pharmacologic inhibition of HDAC11 in MM cell lines was accomplished by using elevenostat, a new HDAC11-selective inhibitor in comparison with pan-inhibitors quisinostat and panobinostat. Impact on viability in human-derived MM cell lines was assessed using the CCK-8 assay, while induction of cell death was measured via detection of activated Caspase-3 and annexin/propidium iodide staining by flow cytometry. Synergy studies were performed by following the Chou-Talalay method for drug combinations. Post-translational modifications and subcellular localization changes induced by HDIs exposure were assessed by western blotting of fractionated cell lysates, while immunoprecipitation and proximity ligation assays (in situ PLA) were used to identify a binding partner for HDAC11. Results: Studies in Tg-HDAC11-eGFP mice reveal that HDAC11 expression in B cell lymphopoiesis is minimally detectable prior to B cell activation but demonstrates strong induction upon maturation into a plasma cell. Consistent with this, plasma cell development is markedly impaired in the absence of HDAC11. The HDAC11-selective inhibitor elevenostat showed significant cytotoxic potential in different MM cell lines that express moderate to high level of HDAC11, with IC50 values ranging 0.6-2.0 µM. Consistently, MM cell lines expressing null/very low level of HDAC11 were insensitive to elevenostat. Moreover, combining elevenostat with proteasome inhibitors bortezomib (BTZ) and carfilzomib resulted in significant synergistic effects evident from combination index (CI) and dose-reduction index (DRI) values measured by CompuSyn software. Elevenostat was also able to re-sensitize BTZ-resistant sub-clones (e.g., RPMI-8226-B25, KAS-6-V10R, and ANBL6-V10R) to BTZ and exhibited superior synergistic effects. Furthermore, elevenostat-treated cells showed a time-dependent alteration in the subcellular localization of HDAC11. HDAC11 gradually disappeared from the nuclear fractions with simultaneous upregulation in cytoplasmic fractions; similar observations were made from pan HDIs (quisinostat and panobinostat) treatment. However, unlike pan HDIs, the elevenostat treatment caused global downregulation of HDAC11 in some MM cell lines at the later time points (72 or 96 hrs), suggesting differential effects of various HDIs. Inhibition of HDAC11 also caused downstream suppression of several pro-tumorigenic factors of MM cells including IRF4 and c-Myc. Additionally, a novel interaction between HDAC11 and IRF4, an essential regulator of PC differentiation and MM survival, was identified by using PLA. HDAC11 dynamically interacts with IRF4 which can be induced by LPS stimulation and inhibited by HDIs, indicating the involvement of HDAC11 in the IRF4-mediated regulatory circuit. Conclusions: We observe that targeted inhibition of HDAC11 can impair MM cell survival and overcome acquired resistance to proteasome inhibitors. Furthermore, we identify IRF4 as a nuclear binding partner of HDAC11 and propose this interaction as a candidate mechanism regulating PC maturation and survival. Disclosures No relevant conflicts of interest to declare.

Although advancements in therapeutic regimens for treating multiple myeloma (MM) have prolonged patient survival, the disease remains incurable. Several classes of drugs have contributed to these improvements, such as proteasome inhibitors, immunomodulators, deacetylase inhibitors, monoclonal antibodies, and alkylating agents including melphalan. An expanded arsenal of diverse chemotherapy targets has improved patient care significantly, yet we still lack sufficient knowledge of how cellular metabolism and drug processing can contribute to drug resistance. To address this issue, we utilize cell line models to simulate naïve and drug resistant states, which identify drug modifications, endogenous metabolites, proteins, and acute metabolic profile alterations associated with therapeutic escape. Here, we specifically focus on melphalan; an alkylating agent that forms DNA interstrand crosslinks, inhibits cell division, and leads to cell death through apoptosis (Povirk & Shuker. Mutat. Res. 1994, 318, 205). Melphalan remains a critical component of high dose therapy in the context of stem cell transplant and induction therapy in transplant ineligible patients outside the US. Ineffectiveness of alkylating agents remains a critical problem and serves as an excellent model for investigation of cellular metabolism and its contribution to drug resistance. Two parental MM cell lines (8226 & U266) were obtained from ATCC and resistant derivatives of each cell line (8226-LR5 & U266-LR6) were selected after chronic drug exposure. To assess mechanisms of melphalan resistance, we use liquid chromatography-mass spectrometry-based metabolomics and proteomics approaches, including studies of drug metabolism, untargeted metabolomics, and activity based protein profiling (ABPP). Drug metabolism monitors the intracellular and extracellular drug modifications over a 24-hour period after acute treatment. Untargeted metabolomics is used to compare the steady state endogenous intracellular metabolites of naïve and drug resistant cells. Differences in endogenous metabolites between naïve and drug resistant cell lines are also examined in the acute treatment dataset. ABPP utilizes desthiobiotinylating probes to enrich for ATP-utilizing enzymes, which are identified and quantified to enable comparison. We initially compared acute melphalan treatment in drug naive and resistant isogenic cell line pairs. Predictably, melphalan was converted into monohydroxylated and dihydroxylated metabolites more quickly in cells than in media controls. Differences in the formation of these metabolites between the naïve and resistant cell lines were not observed. The untargeted metabolomics data indicated in the 8226-LR5 model, glutathione and xanthine levels are elevated, while guanine is suppressed relative to naive cells. ABPP demonstrated changes in several enzymes related to purine and glutathione metabolism (Figure 1). Interestingly, the U266/U266-LR6 cell line models exhibit higher baseline levels of glutathione when compared with 8226/8226-LR5, indicating heterogeneous means of drug resistance. Alterations in arginine biosynthesis and nicotinate/nicotinamide metabolism are observed in the untargeted metabolomics and ABPP of U266/U266-LR6. Common pathways (e.g. purine biosynthesis) are altered in both models, although the changes involve different molecules. In examining two models of acquired melphalan resistance, we demonstrate frank differences in metabolic pathways associated with steady state and acute drug response. These data demonstrate the potential heterogeneity in drug resistance mechanisms and the need for more biomarkers to personalize treatment. Ongoing studies involve introduction of enzyme inhibitors in targeted pathways and supplementation of metabolites to validate their role in resistance. Furthermore, we will examine expression of these metabolic pathways associated with ex vivo melphalan resistance in a cohort of over 100 patient samples with paired RNA sequencing. The long term goals are to elucidate mechanisms of therapeutic response, identify biomarkers of metabolism in melphalan resistance, enhance drug efficacy, predict personalized patient treatment, and improve overall MM patient care. Disclosures No relevant conflicts of interest to declare.

Introduction: Innate and acquired resistance to anti-cancer therapies poses a major hurdle in effectively treating many cancers, especially an incurable cancer like multiple myeloma (MM). Rational combination therapies have shown improved efficacy and reduced toxicity in MM. Patient variability in response to single agents leads to variability in combination effects, which require quantification on a patient-to-patient basis. Conventional combination effect quantification methods rely on dose - response curves obtained from experiments involving cell lines. Such studies don't account for intratumoral and intertumoral heterogeneity that play an important factor in driving a patient's clinical response. Materials and Methods: We propose a framework that captures tumor-specific two-way combination effect in an ex vivo reconstruction of the tumor microenvironment using patient-derived primary multiple myeloma cells. The framework translates the data obtained from an ex vivo drug sensitivity assay to patient-specific combination therapy response predictions using mathematical modeling. MM cells (CD138+) extracted from fresh bone marrow aspirates are seeded in an ex vivo co-culture model with human stroma in multi-well plates, and tested with various drugs/combinations at several concentrations. Each well is imaged for at least 96 hours, once every 30 minutes to estimate percent viable cells. Such a platform facilitates measuring response with respect to dose and time, making this an ideal paradigm to capture pharmacodynamical interactions between drugs. An empirical mathematical model is used to measure the combination effects between two drugs, and when combined with their pharmacokinetic data obtained from Phase-I clinical trials the model predicts patient-specific response over a 90 day treatment period within five days post biopsy. Results: A total of 58 multiple myeloma patient samples were tested ex vivo with 19 two-drug combinations. The resulting ex vivo response data is fit to single agent (EMMA - Ex vivo Mathematical Myeloma Advisor) and combination (SAM - Synergy Augmented Model) mathematical models to estimate patient-drug/combination-specific LD50s and area under the curves (AUCs) from the dose-time-response curves (shown in Figs. 1a-f). The 96 hour single agent, additive (in the sense of Bliss), and combination LD50s for 19 patients tested with the combination Carfilzomib and Dexamethasone (CFZ+DEX) are presented as a box plot in Fig. 2a . A red dashed line signifies a patient who would see a benefit over additive LD50 (synergism), while a blue dashed line implies the opposite (antagonism). Similarly, Fig. 2b presents the AUCs as a box plot, where the "area" in AUC is in fact the volume under the dose-time-response curve. Inclusion of the time axis accounts for exposure-response effect in addition to the dose-response effect captured in LD50. The effect of accounting for exposure via AUC suggests greater synergy than LD50 as seen in Figs. 2a-b. In spite of being insightful, a decrease in LD50 and/or AUC doesn't always translate to a synergistic effect in patients. In order to predict the response observed in patients, the ex vivo models are integrated with pharmacokinetic data from Phase-I clinical trials to simulate patients' response over a 90 day treatment period (shown in Figs. 1j-l). The best response over a 90 day period for the single agents, additive, and the combination are presented in Fig. 2c as a box plot and the right y-axis classifies the response. However, additive effect is a theoretically computed quantity that may have pharmacological relevance but isn't significant clinically. A more clinically relevant reference model would be to compare the combination response with the better of the two single agents. Figure 2d presents the box plot comparing the predicted best single agent and combination responses. The model predictions indicate all of the 19 patients would benefit from the combination, although the extent of benefit varies from patient-to-patient. Conclusion: The proposed framework captures patient-specific combination effects using a pharmacodynamic model that can be used to screen for the most efficacious combination for a patient and across a cohort. Disclosures No relevant conflicts of interest to declare.

Proteasome inhibitors (PI) such as bortezomib and carfilzomib are critical components of anti-multiple myeloma (MM) therapy, yet all MM patients eventually develop refractory disease. We developed a non-biased method to identify and validate dysregulated pathways associated with PI-resistance in myeloma by combining RNAseq data from 522 MM patient specimens obtained from our Total Cancer Care/M2Gen/ORIEN network at Moffitt Cancer Center with paired ex vivo sensitivity to PIs and kinase inhibitors (KI). Dimensionality reduction analysis (t-SNE) and Fuzzy C-means was used to identify 422 clusters of genes that co-express in individual patients, and Gene Set Enrichment Analysis (GSEA) was used to identify clusters with gene expression patterns that correlated with PI sensitivity. Using publicly curated databases and in silico integrative analyses, we built protein-protein interaction networks to identify putative transcription factors, corresponding master regulators (kinases), and candidate KIs to promote PI sensitization. This systems biology approach identified a Chk1-Cdk1-Plk1 circuit associated with PI-resistance and also found 21 additional kinases (of 501 expressed in our cohort's kinome) that could be targeted to re-sensitize PI-resistant MM, which we confirmed in cell lines, specimens from relapsed patients, and two in vivo models. A panel of paired isogenic PI-resistant and sensitive MM cell lines were differentially screened to find kinases associated with PI-resistance using activity-based protein profiling (ABPP) and KI activity measured by high-throughput viability assay. The MM cell lines 8226 and U266, along with their drug resistant counterparts 8226-B25 and U266-PR, were grown in mono-culture for 24h and lysates were enriched for ATP binding proteins by affinity purification versus a chemical probe. Tryptic peptides were measured using discovery proteomics (nano-UPLC and QExactive Plus mass spectrometer) to identify 85 kinases out of a total of 715 proteins in 8226-B25 MM cells and 35 kinases out of a total of 688 proteins in U266-PR MM cells that were preferentially enriched by 2-fold change compared to parental cell lines. Twenty-four kinases were commonly activated among PI-resistant cell line pairs and were screened in PI-resistant myeloma lines using a label-free, high throughput viability assay that simulates the tumor microenvironment. Three KIs targeting Plk1 (volasertib and GSK461364) and Cdk1/5 (dinaciclib) consistently maintained LD50s in the low-nanomolar range and induced caspase-3 activation in four PI-resistant MM cell lines: 8226-B25, U266-PR, ANBL-6-V10R, and Kas6-V10R. Twenty-four kinases each were identified by RNAseq/ex vivo PI sensitivity of MM specimens and ABPP of PI-resistant/sensitive MM cell line pairs. Of these, 7 kinases were identified by both methods: Cdk1, Chk1, Plk1, ILK, Syk, PKA, and p70S6K. Several KIs targeting Cdk1, Plk1, ILK, DNAPK, Syk, MKK7, Nek2, and mTOR identified in patient specimen or cell-line screens showed single agent activity in MM patient bone marrow specimens purified by a CD138 affinity column. Among these, inhibitors to Cdk1, ILK, mTOR, and Plk1 showed the most activity in patient specimens with an average 96h LD50 of 25 nM (n=56), 2.4 uM (n=42), 2.7 uM (n=57) and 3.8 uM (n=53), respectively. Six KIs targeting Plk1, ILK, Syk, MKK7, Nek2 and MARK3 were synergistic with carfilzomib in 20 patient specimens and maintained or improved ex vivo activity in relapsed refractory MM (RRMM) specimens. Volasertib, which targets Plk1, was the most synergistic with carfilzomib of all KIs tested in patient specimens and was further validated in two in vivo models: a NSG/U266 xenograft model of PI resistance and the syngeneic C57BL/6-KaLwRij/5TGM1 immunocompetent model. Volasertib significantly increased survival and reduced tumor burden in both models as a single agent, and was more effective versus PI-resistant tumors compared to PI-sensitive counterparts. Our pharmaco-proteomic screen, coupled with rich gene expression data from patients identified Plk1 as a target critical to MM survival in the context of acquired PI resistance and represents a unique workflow to find tumor vulnerabilities that arise during therapy. We anticipate that these data will also produce a critical path for the personalized allocation of therapy to maximize efficacy and minimize the use of ineffective therapies in RRMM. Disclosures No relevant conflicts of interest to declare.

We describe an approach to identify patient-specific mechanisms of drug resistance in multiple myeloma (MM) patients through a combination of ex vivo chemosensitivity assay using fresh primary samples and gene set enrichment analysis from RNA-Seq and microarray gene expression profiles. Methods: We have performed RNA-Seq on 522 primary MM samples and used a combination of dimensionality reduction analysis (t-SNE) and clustering (fuzzy c-means) to group co-expressing genes in clusters, putatively under control of shared regulatory mechanisms. A data set of microarray gene expression from a second cohort of 762 primary MM samples was used to validate the topology of co-expressing clusters obtained from RNA-Seq. In addition, we have tested drug sensitivity of primary MM samples in an ex vivo reconstruction of the bone marrow tumor microenvironment, including primary human stroma, extra-cellular matrix, and patient-derived soluble factors. 312 of the aforementioned samples were screened against a panel of 95 drugs relevant to MM biology, including PIs -bortezomib (V), carfilzomib (K) and ixazomib (I)-, IMIDs -pomalidomide (P), lenalidomide (R)-, and other standard of care drugs -melphalan (M), dexamethasone (D), doxorubicin (Do), panobinostat (Pa), quisinostat (Q)- and experimental agents -e.g. kinase inhibitors (PKIs), CRM1 inhibitor KPT-330 (Kp) and BCL2 inhibitor ABT-199. Geneset enrichment analysis was performed using GSEA both agnostically, using the clusters of co-expressing genes, and in a knowledge-driven fashion, using pre-established genesets (KEGG, BIOCARTA, HALLMARKS and REACTOME) using LD50 (@96h) or area under the curve (AUC, 0h-96h) as measures of ex vivo drug resistance, and Spearman correlation as ranking metric. Results: We have identified: (a) MM-specific gene expression regulatory architecture, consisting of multiple clusters of co-expressing genes, "gravitating" around a cloud of more loosely correlated genes enriched for super enhancers and the mediator family of genes (Figure 1a); (b) clusters of genes differentially-expressed in ex vivo drug resistant primary samples, confirming that similar mechanisms of resistance were observed for drugs with similar mechanism of action (e.g. Figure 1b); (c) patient and drug-specific mechanisms of resistance (MOR) to therapy, and thus putative means of re-sensitization; and (d) offered candidate synergistic drug combinations based on mutually exclusive mechanisms of resistance. As proof of principle, here we discuss MOR observed in two drugs: ABT-199 and MK2206 (Akt inhibitor). The analysis conducted in MM samples tested with ABT-199 agreed with previous clinical studies in MM, pointing to over-expression of Bcl-xl and Mcl1, as well as under-expression of Bim, Bcl-2, and NOXA in resistant samples. A particular gene cluster, significantly underexpressed in ABT-199-resistant samples, was enriched for ribosomal subunits, regulated by, and contained, MYC, suggesting that MYC and ribogenesis may be linked to Bcl-2 inhibition resistance (Figure 1c). Ex vivo resistance to MK2206 led to ~2/3 of genome under-expression, with the most significant clusters linked to cell cycle (e.g. PLK1, CDK1, CHEK1, etc.) and histone subunits, suggesting a quiescence-mediated mechanism of resistance to Akt inhibition (Figure 1d), in addition to under-expression of AKT, BAD, FOXO and BIM. Importantly, our analysis has observed significant inter-patient heterogeneity, confirming that multiple MOR can be observed within a patient cohort, reinforcing the need for patient-specific molecular analysis of the disease for choice of therapy. Figure 1 legend. (a) tSNE clustering of ~22,000 genes according to co-expression within a cohort of 522 primary MM samples, as per RNA-Seq (one black dot per gene). Blue disks represent genes from the mediator complex family, red disks represent super-enhancer regulated genes, and green disks are genes coding for transcription factors. (b) Clustergram representing Spearman correlation between expression of ~22,000 and ex vivo resistance to 43 different drugs. Red stands for direct correlation (high expression in resistance), green for inverse (low expression in resistance). (c and d) tSNE clustering of genes colored according to individual Spearman correlation between gene expression and resistance to ABT-199 and MK2206, respectively. Arrows point to clusters highest related to resistance. Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.

Molecular biological techniques have an increasing role in the laboratory diagnosis of viral infections as a consequence of their being more sensitive and specific than methods developed in the past. This chapter discusses the principle, describes the methodology, and provides some practical clinical applications of Western blotting. Enzyme immunoassays (EIA) often are used for diagnosis of viral diseases as well as for screening of blood and blood products for viruses. The basic approach to the use of Western blotting for the diagnosis of viral infections begins with purified virions that are disrupted by ionic detergent treatment, releasing viral proteins. Chemiluminescence has two main advantages compared to chromogenic techniques. First, it enables a >10-fold increase in sensitivity without the use of isotopes, and second, exposure times can be varied to increase or decrease sensitivity. A potential competitor for the Western blot assay has been reported for the diagnosis of HIV infection. This new technique, called “recombinant-antigen immunoblot assay” (RIBA-HIV216), utilizes a set of purified antigens produced by recombinant technology. It remains to be determined whether this recombinant-protein assay is more specific and/or more sensitive than the standard Western blot assay. Nitrocellulose strips with blotted viral proteins or complete kits with all necessary reagents are available from commercial sources for human immunodeficiency virus (HIV-1 ), human T-cell leukemia virus type 1 (HTLV-l), and hepatitis C virus Western blot assays.

The bone marrow microenvironmental components interleukin (IL)-6 and fibronectin (FN) individually influence the proliferation and survival of multiple myeloma (MM) cells; however, in vivo, these effectors most likely work together. We examined signaling events, cell cycle progression, and levels of drug response in MM cells either adhered to FN via β1 integrins, stimulated with IL-6, or treated with the two combined. Although G1-S cell cycle arrest associated with FN adhesion was overcome when IL-6 was added, the cell adhesion–mediated drug resistance (CAM-DR) was maintained in the presence of IL-6. Concomitant exposure of MM cells to IL-6 and FN adhesion revealed a dramatic increase in signal transducers and activators of transcription 3 (STAT3) phosphorylation, nuclear translocation, and DNA binding, compared with either IL-6 or FN adhesion alone in four MM cell lines. Importantly, this increase in STAT3 activation correlated with a novel association between STAT3 and gp130 in cells adhered to FN before stimulation with IL-6, relative to nonadherent cells. Taken together, these results suggest a mechanism by which collaborative signaling by β1 integrin and gp130 confers an increased survival advantage to MM cells. [Cancer Res 2009;69(3):1009–15]

The hypothesis that the adhesion of whole cancer cells to glycosaminoglycan (GAG) substrates is a function of polysaccharide radius of gyration is presented. We use a worm-like chain (WLC) polymer model describing the global structure of the GAGs that will take into account the charge distribution and contour length of the polysaccharide implicitly in its radius of gyration to relate these parameters to cell adhesion. Specifically, we present measurements of the in vitro adhesion of cancer cells to isolated and individualized GAG substrates. We find that adhesion of the cells has a linear response with the radius of gyration and is essentially controlled by the charge per dimer. This dominating mechanism is not eliminated when the cells are subjected to resuspension in media with heparin. We then propose how these physical properties could be used to predict the preferred molecular structures of compounds for use as antimetastatic or antiinflammatory agents by comparing our results with known effective molecules. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2009

In this work we describe a novel approach that combines ex vivo drug sensitivity assays and digital image analysis to estimate chemosensitivity and heterogeneity of patient-derived multiple myeloma (MM) cells. This approach consists in seeding primary MM cells freshly extracted from bone marrow aspirates into microfluidic chambers implemented in multi-well plates, each consisting of a reconstruction of the bone marrow microenvironment, including extracellular matrix (collagen or basement membrane matrix) and stroma (patient-derived mesenchymal stem cells) or human-derived endothelial cells (HUVECs). The chambers are drugged with different agents and concentrations, and are imaged sequentially for 96 hr through bright field microscopy, in a motorized microscope equipped with a digital camera. Digital image analysis software detects live and dead cells from presence or absence of membrane motion, and generates curves of change in viability as a function of drug concentration and exposure time. We use a computational model to determine the parameters of chemosensitivity of the tumor population to each drug, as well as the number of sub-populations present as a measure of tumor heterogeneity. These patient-tailored models can then be used to simulate therapeutic regimens and estimate clinical response.

Viral interleukin-6 (vIL-6) is a homolog of cellular IL-6 that is encoded by the Kaposi's sarcoma-associated herpesvirus (KSHV) genome. vIL-6 binds to the IL-6 signal transducer gp130 without the cooperation of the IL-6 high affinity receptor to induce STAT3 DNA binding and cell proliferation. Although vIL-6 is believed to be important in the pathogenesis of KSHV-induced diseases, its secretion and post-translational modifications have not previously been characterized. Pulse-chase analysis revealed that the half-time of vIL-6 secretion is approximately 8-fold longer than that of human IL-6. Yet, the vIL-6 signal sequence targets human IL-6 secretion to nearly wild-type levels. Surprisingly, vIL-6 was not secreted from a cell line that does not express gp130 but expression of human gp130 in these cells enabled the secretion of vIL-6. Consistent with this observation, complete maturation of gp130 N-glycans is inhibited by vIL-6 coexpression, suggesting that the binding of the receptor to vIL-6 occurs intracellularly in early or pre-Golgi compartments. Furthermore, a vIL-6 mutant containing an endoplasmic reticulum retention signal is not secreted but does still induce receptor activation and signaling. Secreted vIL-6 is completely glycosylated at both possible N-glycosylaton sites and contains a large proportion of immature high-mannose glycans that is not typical of cytokines. These findings suggest that vIL-6 may induce gp130 signaling by an exclusively autocrine mechanism that relies on intracellular binding to its receptor. During KSHV infection, vIL-6 may only induce signaling in KSHV-infected cells to benefit the viral life cycle and promote oncogenic transformation.

8029 Background: Histone deacetylase (HDAC) inhibitors (HDI) have a therapeutic niche in multiple myeloma (MM) due to their ability to salvage proteasome inhibitor and immunomodulatory drug responsiveness in refractory patients, thus raising interest in this therapeutic class. Selective HDI may further improve therapeutic efficacy. Methods: B cell lymphopoiesis was evaluated using Tg-HDAC11-eGFP mice expressing eGFP regulated by the HDAC11 promoter and congenic mouse strains deficient in HDAC11 expression globally (B6.HDAC11-/-) or targeted to the B cell lineage (CD19Cre.HDAC11-/-). Molecular and pharmacologic means were used to impair HDAC11 in established MM cell lines. Viability was measured by activated caspase-3, Annexin/PI (A/PI) staining, and CCK-8 viability assay. Subcellular localization changes induced by HDI and identification of the novel binding partner IRF4 were assessed by proximity ligation assay (PLA). Results: Profound eGFP increases in PC of Tg-HDAC11-eGFP mice suggest HDAC11 influences late stage B cell development. In addition, HDAC11 deficiency results in dramatically reduced PC in the bone marrow and periphery. PC depletion in CD19Cre.HDAC11-/-mice suggests activity inherent in B cells rather than via externally derived signals. Quisinostat (QS), an HDI with enhanced HDAC11 selectivity, showed dose-dependent cytotoxicity in 10 MM cell lines (EC50 1-10nM). This activity was synergistic with bortezomib (BTZ) and carfilzomib (CFZ) in RPMI-8226 cells, while synergism was amplified in the BTZ-resistant RPMI-8226-B25 cell line. Exposure of RPMI-8226 cells to QS decreased detection of nuclear, but not cytosolic, HDAC11. Targeted siRNA–mediated silencing of HDAC11 in RPMI-8226 cells activated caspase-3 and reduced viability by A/PI staining. PLA of MM cell lines showed a novel interaction between HDAC11 and IRF4, an essential regulator of PC differentiation and MM survival, unmasking a potential mechanism for HDAC11-induced cytotoxicity in MM. This interaction was disrupted by QS. Conclusions: We show that HDAC11 inhibition reduces MM cell survival in vitro. Furthermore, we identify IRF4 as a binding partner for HDAC11 and propose this interaction as a candidate mechanism regulating PC maturation and MM survival.

Introduction: Multiple myeloma is a heterogeneous plasma cell neoplasm that remains all but incurable despite significant advances in treatment. We anticipate that the ability to overcome this hurdle resides in personalized strategies designed to specifically recognize, target, and anticipate dynamic tumor subpopulations with variable drug response profiles within an individual. To this end, we have developed a novel multi-disciplinary approach using organotypic drug screening and mathematical modeling to assess drug sensitivity of the different subpopulations within the tumor burden of individual patients and, in turn, provide accurate predictions of clinical outcome to anti-myeloma therapy. Material and methods: We have used a novel combination of ex vivo drug sensitivity assay and mathematical models to predict clinical response of 48 MM patients (11 newly diagnosed and 37 relapsed, 18 females and 30 males, median age 64.5, range 45-77) treated with a combination of proteasome inhibitors and IMIDs (37), nuclear export and topo2 isomerase inhibitors (10), and high dose melphalan (1). MM cells (CD138+) were extracted from fresh bone marrow aspirates and seeded in an ex vivo co-culture model with human stroma in 384-well plates. These cells were exposed to a number of chemotherapeutic and experimental agents (up to 31) for a period of 4 days, during which viability was assessed continuously using bright field imaging and digital image analysis. A mathematical model was used to interpolate the dose response dynamics to each drug, and combined with drug and regimen-specific pharmacokinetic data, generate predictions of clinical response to each individual drug. We have then validated ex vivo-based predictions with actual outcome 90 days post-biopsy. In patients treated with combinations, the mathematical model combined the effect of each single drug assuming additivity. Results: To examine the accuracy of the predicted in silico responses, we have assessed the model according to three increasingly strict standards of accuracy: (A) The model correctly predicted 32 out of 32 responders (100%) and 14 out of 16 non-responders (88%), with an overall accuracy of 96%; (B) According to IMWG stratification, the model correctly stratified 14 out of 16 patients as stable or progressive disease (PD/SD, 88%, the remaining 2 incorrectly predicted as MR/PR), 15 our of 18 as minimal or partial response (MR/PR, 83%, the remaining 3 incorrectly predicted as VGPR/CR), and 10 out of 14 patients as very good partial response or complete response (71%, the remaining 4 incorrectly classified as MR/PR), with an overall accuracy of 81%; (C) The 48 patients from this study provided a total of 120 measures of tumor burden (M-spike or SFLC) within the 90-day post-biopsy period. The direct correlation between tumor burden measures and model predictions led to a Pearson r=0.5547 (P Conclusion: We observed an excellent correlation between in silicopredicted and clinically observed responses in 48 MM patient specimens. Our data suggest that this model may provide critical insight in the selection the appropriate therapeutic agents and number of agents to combine for a given individual. Further validation is required to better define the role of this approach as a clinical decision support tool. Figure Figure. Disclosures Baz: Novartis: Research Funding; Signal Genetics: Research Funding; Karyopharm: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda/Millennium: Research Funding; Bristol-Myers Squibb: Research Funding; Merck: Research Funding. Shain:Signal Genetics: Research Funding; Takeda/Millennium: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen/Onyx: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Speakers Bureau.

Aberrant canonical Wnt/β-catenin signaling has long been known to play a role in cancer development and progression, where Wnt binding provokes nuclear localization of β-catenin, which functions as a coactivator for the TCF/LEF family of transcription factors that induce an oncogenic transcriptional program. Indeed, hallmarks of several tumor types are gain-of-function somatic mutations in β-catenin, and loss-of-function mutations in components of the β-catenin destruction complex, including the scaffold proteins APC and Axin1 and the serine/threonine kinase casein kinase1-α (CK1α) that phosphorylates β-catenin, priming it for destruction by the βTrCP E3 ubiquitin ligase. Interestingly, the CK1α-related kinase CK1δ antagonizes CK1α in Wnt signaling, where CK1δ triggers disassembly of the β-catenin destruction complex by phosphorylation of disheveled-1 (Dvl1). Notably, we have shown that CK1δ is amplified, and is necessary and sufficient, for β-catenin activation and Wnt signaling in some tumor types. Interestingly, lenalidomide resistance in multiple myeloma (MM) cell lines and patients has been shown to correlate with increased expression and activity of Wnt/β-catenin signaling. Here, we report that roughly 40% of MM patients have mutations in the Wnt/β-catenin pathway and that this pathway is active in a large panel of human MM cell lines. Notably, a highly potent and selective in-house CK1δ/CK1ε kinase inhibitor coined SR-3029 rapidly compromises the growth and survival of MM cell lines. Importantly, the anti-MM activity of SR-3029 is augmented in MM cell lines with selected resistance to bortezomib and lenalidomide relative to paired naïve myeloma cells. In concordance with the anti-MM activity of SR-3029, treatment of MM cells with this kinase inhibitor leads to marked reductions in the levels of β-catenin target genes (e.g., MYC, CCND1 and WNT3) and also with the suppression of CK1δ and CK1ε. Further, using an ex vivo platform that accurately quantifies the sensitivity of primary MM samples to agents in a reconstructed tumor microenvironment, we examined CD138-selected patient specimens from 29 patients against a panel of 31 drugs simultaneously. Notably, MM patient samples, including those that are quad-resistant, were highly sensitive to SR-3029 with a mean LD50 of 300nM and a range between 30nM and 990nM. Indeed, SR-3029 was by far the most potent kinase inhibitor assessed in this platform, where its mean LD50 is an order of magnitude more potent than all other kinase inhibitors included in the screen. Finally, using the well-established 5TGM1/Kal-Ridge (C57B6/KaLwRijHsd) syngeneic mouse model of multiple myeloma, we show that tumors derived from 5TGM1 cells, which are highly sensitive to SR-3029 ex vivo, are also sensitive to CK1δ/CK1ε inhibition in vivo, where the SR-3029 treated cohort of animals demonstrated decreased tumor burden as assessed by IgG2b levels and imaging, and by significantly improved survival relative tovehicle treated recipients. Collectively, these findings demonstrate that SR-3029 has potent activity in both naïve and therapy resistant multiple myeloma and establish CK1δ and/or CK1ε as attractive targets for anti-MM therapy. Disclosures Shain: Novartis: Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Signal Genetics: Research Funding; Takeda/Millennium: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen/Onyx: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.

Introduction: Acquired proteasome-inhibitor (PI) resistance is a major obstacle in the treatment of multiple myeloma (MM). We investigated whether the clinical XPO1-inhibitor selinexor, when combined with bortezomib or carfilzomib, could overcome acquired-resistance in MM. Materials and Methods: PI-resistant myeloma cell lines, RPMI8226-B25 and U226 PSR, and their respective parental cell lines RPMI8226 and U266, were treated both in vitro with selinexor/bortezomib or selinexor/carfilzomib and assayed for apoptosis. In vivo studies using U266 and U266PSR tumors were performed in NOD/SCID-gamma (NSG) mice. Mice were treated with selinexor/bortezomib and single agents. Bone marrow biopsies from refractory myeloma patients were treated ex vivo with selinexor/bortezomib or selinexor/carfilzomib and assayed for apoptosis. Mechanistic studies included NFkB pathway protein expression assays, immunofluorescence microscopy, ImageStream flow-cytometry and proximity-ligation assay. IkBα knockdown and NFkB transcriptional activity were measured in selinexor/bortezomib treated MM cells. Results: We found that selinexor restored sensitivity of PI-resistant RPMI8226-B25 and U266PSR MM cells to bortezomib (P = 0.00055) and carfilzomib (P = 0.0017). Bortezomib, when combined with selinexor reduced U266 MM tumor growth versus single-agent bortezomib (P = 0.022) in NSG mice. NSG mice challenged with PI-resistant U266PSR MM tumors also had reduced tumor growth with selinexor/bortezomib as compared to single agent bortezomib (P = 0.0006). Combining bortezomib and selinexor improved survival in mice with U266 MM tumors (P = 0.0072) and PI-resistant U266PSR when compared to single-agent bortezomib (P = 0.0072). Myeloma cells from PI-refractory MM patients (n=14) were sensitized by selinexor to bortezomib (P = 0.002) and carfilzomib (P = 0.001) without affecting non-myeloma cells. Immunofluorescence microscopy of PI-resistant human MM cell lines found a greater than 212% increase in IkBα when compared to untreated cells (confirmed by Western blot). A similar increase in IkBα immunofluorescence was found in newly diagnosed, relapsed and refractory patient MM cells. ImageStream analyses of MM cells showed an increase in total and nuclear IkBα from selinexor/bortezomib exposure. Proximity-ligation assays showed that IkBα-NFkB-complexes were increased 12-fold in bortezomib/selinexor treated MM cells. IkBα knockdown abrogated selinexor/bortezomib induced cytotoxicity in MM cells. Selinexor/bortezomib treatment decreased NFkB transcriptional activity in addition to a reduction of NFkB induced IAP-1, IAP-2, BCL-2, cyclin D2 and c-myc protein expression.. Conclusions: Selinexor, when used with bortezomib or carfilzomib has the potential to overcome proteasome-inhibitor drug-resistance in MM. Sensitization may be due to inactivation of the NFkB pathway by IkBα. Selinexor, an orally active selective inhibitor of XPO1-mediated nuclear export (SINE), is currently undergoing phase I/II studies in a variety of indications, including a combination with carfilzomib, in both relapsed and refractory MM patients (NCT02199665). The results presented in this study support combinatorial clinical trials in relapsed and refractory MM that utilize PI therapies. Disclosures Kashyap: Karyopharm Therapeutics: Employment, Equity Ownership. Shain:Takeda/Millennium: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Signal Genetics: Research Funding; Novartis: Speakers Bureau; Amgen/Onyx: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Landesman:Karyopharm Therapeutics Inc: Employment, Other: stockholder.

Multiple myeloma (MM) is a heterogeneous plasma cell neoplasm that remains all but incurable despite recent advances in treatment. Indeed, nearly all patients eventually experience disease progression or relapse due to a reservoir of residual myeloma cells that appear to persist through pro-survival signaling from interactions with the tumor microenvironment (TME), leading to eventual clonal expansion. Thus, identifying targets that are induced in MM by the TME may reveal new and important targets amenable to therapeutic intervention. To develop a non-biased method to screen bone marrow specimens from myeloma patients for activated targets throughout the course of disease, we used a combination of activity-based protein profiling (ABPP) and a high-throughput protein kinase inhibitor (PKI) screen using a platform that recapitulates the TME. Target validation was then performed using ex vivo functional screens of pathways using MM patient specimens. The MM cell lines MM1.S, H929, and OPM2 were grown in mono-culture or co-culture with HS5 bone marrow stroma cells for 24h and lysates were enriched for ATP binding proteins by affinity purification versus a chemical probe (ActivX, Thermo). Tryptic peptides were measured using discovery proteomics (nano-UPLC and QExactive Plus mass spectrometer). Using this method, 176, 136, and 85 kinases out of a total of 1511, 1409, and 1281 proteins were preferentially enriched by 2-fold change from MM1.S, H929, and OPM2 myeloma cells grown in co-culture conditions with HS5 bone marrow stroma, respectively. Of these, 42 kinases were common to all three and 87 were common to two of three MM cell lines. Kinases were chosen for target validation after pathway analysis using the Kyoto Encyclopedia of Genes and Genomes database to identify signaling networks. To identify functionally relevant signaling networks identified via ABPP experiments, the same MM cell lines were simultaneously screened with 30 protein kinase inhibitors (PKIs) in a novel high throughput viability assay. This label-free method measures the viability of MM cells grown in a collagen matrix with bone marrow stroma cells in 384-well plates to simulate the TME by capturing brightfield images every 30 minutes for 96h using a motorized microscope equipped with an incubation chamber. Digital image analysis software measures live cell numbers by detecting membrane motion and generates viability curves as a function of drug concentration and exposure time (Khin et al. Cancer Research 2014). This functional screen confirming known MM survival networks, validated 12 kinases/PKIs in the context of the TME and highlighted novel targetable pathways. To provide an additional level of screening, the same PKIs were tested in CD138-MACS-selected cells from 15 MM patient specimens in a high-throughput viability assay. Eight PKIs targeting IGFR, PLK1, Abl, mTOR, FAK/Pyk2, ALK, Akt, and Casein Kinase-1δ (CK1δ)/CK1ε also showed significant activity in the 15 primary MM specimens. Our three-tiered pharmaco-proteomic screen identified eight kinases critical to MM survival in the context of the TME. Notably, a highly specific in-house inhibitor of Casein Kinase 1δ/CK1ε, SR-3029, which targets the Wnt/β-catnenin pathway, was identified as the most effective compound assessed as a single agent in our ex vivo viability assay in all patients with an average 36h LD50 of 290nM. This compound is under further investigation in MM (Submitted Abstract: Burger, et al, ASH 2016). Additional studies are underway to functionally interrogate the pathways identified in this screen, including ErbB1/EGFR, EphA1 and AMPK. Future work will optimize this method for evaluation of primary bone marrow specimens with ABPP followed by functional validation to better predict potential clinical response at different disease stages. We anticipate that this iterative "at the moment of care" approach is critical because drug resistant tumor phenotypes fluctuate with therapy, and this strategy can track and define clinically relevant changes in tumor cells in situ after the selection pressures applied by exposure to therapy. Disclosures Shain: Novartis: Speakers Bureau; Amgen/Onyx: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda/Millennium: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Signal Genetics: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau.

Introduction:Multiple myeloma (MM) is an incurable cancer of plasma cells. It accounts for approximately 10% of all hematologic malignancies. In the US, it is estimated that there will be approximately 30,330 new cases and 12,650 deaths in 2016. In the past decade, responses/survivals have been significantly increased by newer therapies. However, almost all of the patients will eventually die from multi-drug resistant disease. Materials and Methods:Weused XPO1 inhibitors (XPO1i) selinexor (300nM) or KPT-8602 (300nM) +/- melphalan (15 μM) to treat human MM parental RPMI8226 and U266 cells, and melphalan resistant LR5 and LR6 cell lines for 20 hours and then assayed for apoptosis and viability by flow cytometry. DNA damage was assayed by the comet assay and phospho-H2AX protein expression in H929 human myeloma cells. p53, NFkB, IKKα, FANCF, and FANCL were assayed by Western blot in H929 MM cells. We also treated cells from patients with newly diagnosed or relapsed/refractory MM with the XPO1i (300nM)/ melphalan (10μM) combination and assayed for apoptosis. In addition, selinexor/melphalan treated NOD/SCID-gamma mice with U226 MM tumors were assayed for tumor growth, survival, and toxicity. Results:Cell viability of all tested MM cell lines was decreased synergistically and apoptosis increased by XPO1i/melphalan treatment (selinexor/melphalan, P = 2.2x10E-6 to 0.0032, KPT-8602/ melphalan, P = 1.2X10E-7 to 0.0031). Comet assays showed that the XPO1i/ melphalan drug combination increased DNA damage more than single agent melphalan or XPO1i alone. Phospho-H2AX expression also was increased (selinexor/ melphalan, P = 0.005 and KPT-8602/melphalan, P = 0.001). Western blot analysis showed that XPO1i treatment can increase p53 and decrease NFkB, IKKα, FANCF, and FANCL in MM cells. Apoptosis assays showed that both melphalan-resistant and parental MM cell lines were sensitized to melphalan by XPO1i. In addition, CD138+/light chain+ MM cells from newly diagnosed and relapsed/refractory MM patients were sensitized (20-fold and 5 to10-fold respectively) by XPO1i to melphalan. XPO1i/melphalan combination treatment demonstrated a strong synergistic anti-tumor effect when compared to single-agent melphalan (selinexor, P = 0.0024 and KPT-8602, P = 0.0030) in NOD/SCID-gamma mice challenged with U266 MM tumors. XPO1i/ melphalan treated mice had increased survival and no significant toxicity. Conclusions:XPO1i's can improve the response of human MM cell lines and patient MM cells to melphalan both in vitro and ex vivo. The mechanism of this synergy reversing melphalan resistance may be due to increased nuclear p53, in combination with decreased NFkB and IKKα, and decreased DNA repair proteins FANCL and FANCF of the Fanconi Anemia/BRCA pathway. Our preliminary data suggest that the synergistic cell kill may be because XPO1i's increase melphalan-induced DNA damage and block the repair of the DNA damage. Thus using combination therapies of XPO1i, especially the clinical compounds selinexor and KPT-8602 +/- melphalan may have potential to improve the treatment outcomes of MM. Based on these promising pre-clinical data, we designed a phase 1/2 clinical trial evaluating the combination of selinexor and high-dose melphalan as a conditioning regimen for autologous hematopoietic cell transplantation in patients with multiple myeloma (NCT02780609). Disclosures Shain: Novartis: Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Signal Genetics: Research Funding; Takeda/Millennium: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen/Onyx: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Baloglu:Karyopharm Therapeutics Inc: Employment, Other: stockholder. Nishihori:Signal Genetics: Research Funding; Novartis: Research Funding.

The future of cancer treatment lies in personalized strategies designed to specifically recognize, target, and anticipate dynamic tumor subpopulations within an individual in response to drug. Multiple myeloma (MM) is at present an incurable malignancy of bone marrow resident plasma cells with highly variable survival as a consequence of both disease- and host-specific factors. 20% of MM patients, deemed high-risk (HRMM), have shown little benefit in the era of novel agents, with an OS of less than 2 years. Intuitive treatment strategies fail to account for the complexities and evolutionary dynamics of human tumors in the face of drugs. Intuitive treatment fails to adequately account for MM evolutionary dynamics and remains a critical barrier to successful cure or, at least, long-term disease control. Reasons for therapy failure include, but are not limited to, alternation of dominant clones with each line of therapy as a consequence of Darwinian dynamics, genomic instability leading to of tumor heterogeneity, and tumor microenvironment(TME)- mediated drug resistance. We have developed an integrated computational method accounting for phenotypic tumor heterogeneity. This novel ex vivo drug screen approach, termed EMMA (evolutionary mathematical myeloma advisor), predicts patient-specific drug response in silico from fresh bone marrow biopsies within 5 days. This method utilizes longitudinal non-destructive quantification of rate and dose responses of patient-derived MM cells to drugs in an ex vivo 3D reconstruction of the bone marrow microenvironment to provide real-time personalized predictions of treatment success (percent decrease in disease burden at 90 days). The current automated 384-well plate format allows testing of 31 different drugs or combinations against a single patient sample in 5 days. An evolutionary-based computational model uses the drug sensitivity profile obtained ex vivo to detect sub-populations and their contribution to overall clinical drug response. Each drug dose is imaged once every 30 minutes for 96h. This generates 1,920 data points per drug (or combination). From these data we characterize clonal architecture as it relates to drug sensitivity as phenotypic/functional biomarker for each drug or drug combination in each MM patient sample simultaneously. We have examined the predictive accuracy of EMMA in 26 patients to date. The Pearson correlation between ex vivo model predictions and actual tumor burden changes for the 26 patients examined generated the correlation coefficient r=0.87 (P Disclosures Baz: Karyopharm: Research Funding; Celgene Corporation: Research Funding; Millennium: Research Funding; Sanofi: Research Funding.

GM-CSF, IL-3, and IL-5 are proinflammatory cytokines that control the production and function of myeloid and lymphoid cells. Their receptors are composed of a ligand-specific α subunit and a shared common signal-transducing β subunit (β common receptor or GM-CSFR β [βc]). The pleiotropic nature of biologic outcomes mediated by βc and the presence of large, uncharacterized regions of its cytoplasmic domain suggest that much remains to be learned about its downstream signaling pathways. Although some previous work has attempted to link βc with NF-κB activation, a definitive mechanism that mediates this pathway has not been described and, to date, it has not been clear whether the receptor can directly activate NF-κB. We demonstrate that NF-κB activation by βc is dependent on TNFR-associated factor 6 (TRAF6) and that association of TRAF6 with βc requires a consensus-binding motif found in other molecules known to interact with TRAF6. Furthermore, point mutation of this motif abrogated the ability of βc to mediate NF-κB activation and reduced the viability of an IL-3–dependent hematopoietic cell line. Because this receptor plays a key role in hematopoiesis and the βc cytoplasmic domain identified in this work mediates hematopoietic cell viability, this new pathway is likely to contribute to immune cell biology. This work is significant because it is the first description of a TRAF6-dependent signaling pathway associated with a type I cytokine receptor. It also suggests that TRAF6, a mediator of TNFR and TLR signaling, may be a common signaling intermediate in diverse cytokine receptor systems.