ALBERT

Description: 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

Description: 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.

Description: 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.

Description: 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.

Description: 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 (

Description: 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

Description: 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.