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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.

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.

Over the last decade we have witnessed an explosion in the number of therapeutic options available to patients with multiple myeloma (MM). In spite of the marked improvements in patient outcomes paralleling these approvals, MM remains an incurable malignancy for the vast majority of patients following a course of therapeutic successes and failures. As such, there remains a dire need to develop new tools to improve the management of MM patients. A number of groups are leading efforts to combine big data and artificial intelligence to better inform patient care via precision medicine. At Moffitt, in collaboration with the M2Gen/ORIEN (Oncology Research Information Exchange Network), we have begun to accumulate big data in MM. Patients opt in to (consent) for collection of rich clinical data (demographics, staging, risk, complete disease course treatment data) and in the setting of bone marrow biopsy the allocation of CD138-selected cells for molecular analysis (whole exome sequencing (WES) and RNA sequencing as well as peripheral blood mononuclear cells for WES). To date, we have collected over 1000 samples for over 800 individual patients with plasma cell disorders. In the setting of oncology, the ultimate goal of model will be selection of ideal treatments. We expect that AI analysis may validate of patient response to treatments and enable cohort selection, as real patient cohorts can be selected from those predicted by the model. One approach is to utilize reinforcement learning (RL). In RL, the algorithm attempts to learn actions to optimize a type action a defined state and weight any tradeoffs for maximal reward. Our initial utilization of RL involved a relatively small cohort of 402 patients with treatment medication data. This encompassed 1692 lines of treatment with a mean of 4.21 lines of therapy per patient (Median of 4 lines per patient). This included 132 combinations of 22 myeloma therapeutics. The heterogeneity in treatment is highlighted by the fact that no pathways overlap after line 4. Each Q-value in Q-table is the current reward for an action in a state plus the discounted anticipated future reward for taking that action. Iteration helps you converge on the actual values for the future reward (can be model-free). The end result is a policy, P(s), that tells you what the ideal action is at state. There are a near infinite number of possible states, considering treatment history, age, GEP, cytogenetics, comorbidities, staging and others. We presume that action makes intuitive sense as medication (treatment) only and that reward should be some form of treatment response. We have begun the iterative process of trying different state and reward functions. Median imputation shows 5% improvement in response accuracy over listwise, but median imputation throws off practical accuracy in a binary reward case. While we found that the exercise has great potential. We found that there are possible improvements (multiple imputation). We will need to expand covariate analysis. Combinatorics need to be considered in machine learning in medium-sized data sets. Model-free machine learning is limited on medium-sized data. As such, combined resources and/or utilization of large networks such as ORIEN will be critical for the successful integration of RL or other AI tools in MM. We also learned that adding variables to the model doesn't necessarily increase accuracy. Future work will involve continued application of alternate state/reward functions. Loosen iQ-learning framework to allow for better covariate selection for state/reward functions. Improve imputation techniques to include more covariates and have more certainty in model accuracy. We may also refine accuracy metric to allow for prediction of bucketed response and temporal disease burden (M-spike vs. time). Updated data on a larger cohort will be presented at the annual meeting. Disclosures Shain: Adaptive Biotechnologies: Consultancy; Celgene: 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; 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; Janssen: Membership on an entity's Board of Directors or advisory committees. Dai:M2Gen: Employment. Nishihori:Novartis: Research Funding; Karyopharm: Research Funding. Brayer:Janssen: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau. Alsina:Bristol-Myers Squibb: Research Funding; Janssen: Speakers Bureau; Amgen: Speakers Bureau. Baz:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Research Funding; Merck: Research Funding; Sanofi: Research Funding; Bristol-Myers Squibb: Research Funding. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria.

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.

Background: c-MYC is a transcription factor that promotes oncogenesis by activating and repressing its target genes that control cell growth, metabolism, and proliferation. MYC is deregulated in a large proportion of aggressive B-cell lymphomas. A typical example is the Double-Hit Lymphoma (DHL) and Double-Expression Lymphoma (DEL) which present with a rapidly progressing clinical course, refractory to treatment, poor clinical outcome, and currently considered incurable. Nevertheless, MYC is considered as an "undruggable" target since it has no "active site" amenable to binding by conventional small molecule inhibitors. Moreover, MYC has a broad spectrum of functions in cell proliferation, survival, metabolism, and others, so direct inhibition would likely cause severe side effects. Besides direct inhibition, another practical strategy is to target druggable proteins that are essential for the viability of MYC-driven tumors, inducing MYC-dependent "synthetic lethality". The advantage of such approach is a capability of killing tumor cells discriminately, while leaving non-tumor cells intact or less influenced. This study is designed to identify such targets and explore practical novel strategies to treat MYC-driven lymphomas, especially DHL/DEL. Methods and Results: By integrating activity-based proteomic profiling and drug screens in isogenic MYC on/off lymphoma cells, we identified polo-like kinase-1 (PLK1) as an essential regulator of the MYC-dependent kinome in DHL/DEL. Notably, PLK1 was expressed at high levels in DHL, correlated with MYC expression and connoted poor outcome. Further, PLK1 is directly activated by MYC on transcriptional level and in turn, PLK1 signaling augmented MYC protein stability by promoting its phosphorylation and suppressing its degradation. Thus, MYC and PLK1 form a feed-forward circuit in lymphoma cells. Finally, both in vitro and in vivo studies demonstrated that inhibition of PLK1 triggered degradation of MYC and of the anti-apoptotic protein MCL1, and PLK1 inhibitors showed synergy with BCL-2 antagonists in blocking DHL/DEL cell growth, survival, and tumorigenicity. These data support that PLK1 is a promising therapeutic target in MYC-driven lymphomas. Brief summary: Functional pharmacoproteomics identified PLK1 as a therapeutic vulnerability for MYC-driven lymphoma, which was a synthetic lethal for DHL/DEL when targeted with BCL-2 inhibitors. Disclosures Vose: Roche: Honoraria; Merck Sharp & Dohme Corp.: Research Funding; Acerta Pharma: Research Funding; Seattle Genetics, Inc.: Research Funding; Novartis: Honoraria, Research Funding; Kite Pharma: Research Funding; Bristol Myers Squibb: Research Funding; Epizyme: Honoraria; Legend Pharmaceuticals: Honoraria; Abbvie: Honoraria; Celgene: Research Funding; Incyte Corp.: Research Funding.

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.