Mukherjee, Ujjal Kumar

Multiple myeloma is the second-most common hematological malignancy in the US that remains a challenging disease to cure despite recent advancements in treatment strategies. Drug resistance is a major cause of concern in myeloma chemotherapy. We have created an optimization-regularization-based computational prediction method called secDrug that identified several novel secondary drug (‘secDrug‘) combinations against drug-resistant myeloma (relapsed/refractory multiple myeloma or RRMM). Top among our predicted secDrugs includes the survivin inhibitor, YM155. Survivin is a member of the inhibitor of apoptosis protein (IAP) family that has dual roles in mitosis and apoptosis. YM155 inhibits survivin at the transcriptional level by inhibiting the survivin gene promoter. Several studies have demonstrated the efficacy of YM155 in solid tumors and heme malignancies, including myeloma. However, the precise mechanism of action is not yet known. MM cell lines express high levels of IAPs. We observed high in vitro cytotoxicity of YM155 as a single agent (IC 50 range 0.41 to 12.85nM) as well as in combination with the primary drugs like proteasome inhibitors and Immunomodulatory drugs (IMiDs) against >15 human myeloma cell lines representing innate/refractory and acquired/emerging (relapse) resistance. Next, we explored the molecular mechanisms underlying the efficacy of YM155 using pre-vs-post-treatment tumor whole-transcriptome analysis (RNAseq). The top differentially regulated genes include DDIT3 (CHOP), ATF3, TXNDC5 SEC24D, and DHX15 ( Figure 1A). Ingenuity Pathway Analysis (IPA) predicted that, in addition to regulating apoptosis, survivin inhibition in myeloma significantly upregulated unfolded protein response and downregulated vehicle trafficking and genes associated with immune cell function. Further, we performed single-cell transcriptomics (scRNAseq; 10X Genomics) analysis to explore treatment-induced changes to the subclonal architecture. Our scRNAseq results revealed a distinct shift in clusters representing myeloma single-cell subpopulations following YM155 treatment ( Figure 1B). For example, Cluster 1 (53.11%), characterized by high expression of cell survival markers Ki67 and MYC, was pre-dominant in untreated myeloma cells, while Cluster 4 (52.19%) was the primary subclone in post-treatment tumors. Cluster 3 was shared between the untreated and treated groups. Detailed distinctions between each single-cell cluster will be presented. Furthermore, our scRNAseq data alluded to the benefits of combining survivin-inhibitors and Hsp90-inhibitors based on the expression of drug target genes. Hsp90 inhibitors indirectly target survivin by disrupting the interaction between survivin and Hsp90, resulting in a destabilized survivin protein that ultimately results in survivin degradation. In fact, we have earlier shown the effectiveness of Hsp90 inhibitors (17-AAG, AICAR, and CCT018159) as potent secDrugs against RRMM. Finally, high-dimensional mass cytometry or CyTOF analysis using heavy metal-conjugated proteins in patient bone marrow-derived primary myeloma cells ( ex vivo model system) showed that YM155 induced dose-dependent reduction of several protein markers implicated in cell survival and chemotherapeutic resistance in multiple myeloma, including myc, Ki-67, CD28, CD81, and CD274 (PD-L1). Previous studies have shown that CD28 and CD81 are high-risk immunophenotypes associated with significantly worse prognosis in patients with myeloma. PD-L1 (Programmed death-ligand 1) is a cell-surface glycoprotein expressed in a large number of malignancies that is involved in the termination of immune response and immune evasion. An earlier study has shown that YM155 enhances daratumumab-mediated cellular lysis of multiple myeloma cells. Our RNAseq and CyTOF results thus suggest a potential basis of synergy between YM155 with immunotherapies approved for myeloma, which we will test further. Our approach thus integrates in silico prediction with single-cell multi-ome analysis to identify molecular mechanisms potentially underlying subclonal response to novel combination therapy candidates (secDrugs) for the treatment of RRMM.

Abstract Multiple myeloma (MM) is the second-most common hematological malignancy in the US. MM is an incurable, age-dependent plasma cell neoplasm with a 5-year survival rate of less than 50%. Extensive inter-individual variation in response to standard-of-care drugs like proteasome inhibitors (PIs) and immunomodulatory drugs (IMiDs), drug resistance, and dose-limiting toxicities are critical problems for the treatment of MM. Clinical success in anti-myeloma treatment, therefore, warrants continuous development of novel combination therapy strategies with the explicit goal to improve the therapeutic efficacy by concomitantly targeting multiple signaling pathways. Previously, we have reported the development of an in-house computational pipeline called secDrug that applies greedy algorithm-based set-covering computational optimization method followed by a regularization technique to predict secondary drugs that can be repurposed as novel synergistic partners of standard-of-care drugs for the management of refractory/ resistant MM. Top among these secondary drugs (secDrugs) were the HSP90 inhibitor 17-AAG. In this study, we used 17-AAG as a proof of principle to establish a pipeline that integrates our in silico predictions with in vitro and ex vivo validation as well as multi-omics technologies to identify, validate, and characterize therapeutic agents that could be used either alone or in combination with standard-of-care drugs for the treatment of R/R MM patients (Figure 1). To screen and validate our in silico prediction results, we performed in vitro cytotoxicity assays using 17-AAG on a panel of human myeloma cell lines (HMCLs; in vitro model systems) that captures a wide range of biological and genetic heterogeneity representing the complexities encountered in clinical settings. These cell lines include HMCLs representing innate sensitive/resistance, >10 pairs of parental and clonally-derived PI- and IMiD-resistant pairs (P vs VR or LenR; representing acquired/emerging resistance/relapse), NRAS mutants which leads to the constitutive activation of oncogenic Ras signaling, and CRISPR-edited HSP90 knockdown cell line. Our results showed that 17-AAG has high synergistic activity in combination with PI in inducing apoptosis even in innate and acquired PI-resistant HMCLs and significantly reduces the effective dose of PI required to achieve IC 50 (Chou-Talalay's Dose Reduction Index or DRI 7±1.4). Moreover, 17-AAG+IMiD showed synergistic cell killing activity in clonally-derived IMiD resistant HMCL. Further, 17-AAG induced cell death was comparable with Hsp90 knockdown as evident from the cytotoxicity assay using PI and 17-AAG in combination in RPMI8226-wild type and RPMI-HSP90AA1 knocked down cell line. Notably, 17-AAG was strikingly effective against the NRAS-mutant cell line indicating an additional niche (NRas mutant myeloma) where 17-AAG could be most effective. Next, we performed RNA sequencing to elucidate the molecular mechanisms behind 17-AAG drug action, drug synergy, 17-AAG-induced cell death. Our gene expression profiling (GEP) followed by Ingenuity Pathway Analysis (IPA) analysis revealed protein ubiquitination, aryl hydrocarbon receptor signalling pathway as the top canonical pathways. 17-AAG induced apoptosis via mitochondrial mediated pathway in myeloma. 17-AAG exerts its cytotoxic effect by activating intrinsic pathway of apoptosis which we further confirmed through the increase in reactive oxygen species generation and decrease in mitochondrial membrane potential. 17-AAG was also effective in reducing the expression of hallmarks of MM such as p65/NF-kB, IRF4, c-Myc. Finally, we performed mass cytometry (CyTOF; Cytometry by time of flight) on primary bone-marrow cells (PMCs) from myeloma patients for further validation of proteomic signatures at the single-cell level. CyTOF analysis confirmed 17-AAG-induced cell death and key changes in MM-specific proteomic markers. 17-AAG treated PMCs showed elevated cleaved caspase levels and down-regulation of IRF4 and phospho-STAT3. GEP and CyTOF results were confirmed using immunoblotting assays. Together, our study demonstrates a unique pipeline for drug repositioning that has the potential to revolutionize clinical decision-making by minimizing the number of drugs required for discovering successful combination chemotherapy regimens against drug-resistant myeloma. Figure 1 Figure 1. Disclosures Kumar: BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Beigene: Consultancy; Oncopeptides: Consultancy; Antengene: Consultancy, Honoraria; Carsgen: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Roche-Genentech: Consultancy, Research Funding; Bluebird Bio: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that likely plays a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Such heterogeneity is a driving factor in the evolution of MM, from founder clones through outgrowth of subclonal fractions. DNA Sequencing studies on MM samples have indeed demonstrated such heterogeneity in subclonal architecture at diagnosis based on recurrent mutations in pathologically relevant genes that may ultimately to lead to relapse. However, no study so far has reported a predictive gene expression signature that can identify, distinguish and quantify drug sensitive and drug-resistant subpopulations within a bulk population of myeloma cells. In recent years, our laboratory has successfully developed a gene expression profile (GEP)-based signature that could not only distinguish drug response of MM cell lines, but also was effective in stratifying patient outcomes when applied to GEP profiles from MM clinical trials using proteasome inhibitors (PI) as chemotherapeutic agents. Further, we noted myeloma cell lines that responded to the drug often contained residual sub-population of cells that did not respond, and likely were selectively propagated during drug treatment in vitro, and in patients. In this study, we performed targeted qRT-PCR analysis of single cells using a gene panel that included PI sensitivity genes and gene signatures that could discriminate between low and high-risk myeloma followed by intensive bioinformatics and statistical analysis for the classification and prediction of PI response in individual cells within bulk multiple myeloma tumors. Fluidigm's C1 Single-Cell Auto Prep System was used to perform automated single-cell capture, processing and cDNA synthesis on 576 pre-treatment cells from 12 cell lines representing a wide range of PI-sensitivity and 370 cells from 7 patient samples undergoing PI treatment followed by targeted gene expression profiling of single cells using automated, high-throughput on-chip qRT-PCR analysis using 96.96 Dynamic Array IFCs on the BioMark HD System. Probability of resistance for each individual cell was predicted using a pipeline that employed the machine learning methods Random Forest, Support Vector Machine (radial and sigmoidal), LASSO and kNN (k Nearest Neighbor) for making single-cell GEP data-driven predictions/ decisions. The weighted probabilities from each of the algorithms were used to quantify resistance of each individual cell and plotted using Ensemble forecasting algorithm. Using our drug response GEP signature at the single cell level, we could successfully identify distinct subpopulations of tumor cells that were predicted to be sensitive or resistant to PIs. Subsequently, we developed a R Statistical analysis package (http://cran.r-project.org), SCATTome (Single Cell Analysis of Targeted Transcriptome), that can restructure data obtained from Fluidigm qPCR analysis run, filter missing data, perform scaling of filtered data, build classification models and successfully predict drug response of individual cells and classify each cell's probability of response based on the targeted transcriptome. We will present the program output as graphical displays of single cell response probabilities. This package provides a novel classification method that has the potential to predict subclonal response to a variety of therapeutic agents. Disclosures Kumar: Skyline: Consultancy, Honoraria; BMS: Consultancy; Onyx: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Novartis: Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.