Ness, Brian G. Van

Abstract Multiple myeloma (MM) is the second-most common hematological malignancy in the United States. Myeloma tumors have significant genetic heterogeneity that impacts disease progression and promotes resistance to treatment. RAS is a key oncogenic driver that activates the mitogen-activated protein kinase (MAPK) and the phosphatidylinositol 3-kinase (PI3K)/AKT pathways..Mutations in genes that code for RAS isozymes, KRAS, HRAS, or NRAS, lead to increased levels of activated GTP-RAS and are associated with the most aggressive forms of cancer. Mutations in the RAS family of genes are most common (40-50%) in myeloma. We and others have reported that RAS mutations may not be disease-initiating but rather arise during disease progression and relapse or as subclonal events that impart selective growth advantage and emerging drug resistance. In addition, non-mutant RAS can also be hyperactivated from upstream signaling (like receptor tyrosine kinases) or loss-of-function mutations in GTPase-activating proteins (GAPs). Given the importance of RAS mutations, allele-specific RAS inhibitors have long been considered an attractive strategy for myeloma treatment. However, multiple studies have reported various mechanisms of resistance to allele-specific RAS inhibitors as well as isoform-specific pan-RAS inhibitors, including newly acquired activating RAS mutations, RAS amplification, unchecked activation of co-expressed wild-type RAS (WT-RAS) isozymes, or acquired bypass mechanisms like new oncogenic fusions. Since MM remains incurable, we sought to improve upon currently available treatments by developing small-molecule inhibitors that preferentially inhibit myeloma cells with activated RAS isozymes. We have created NRasQ61X and KRasG12X mutations by CRISPR-Cas directed edits in a single endogenous RAS allele in human myeloma cell lines (HMCLs) that initially had two wild-type alleles. We found that these mutant-NRAS and mutant-KRAS edited cells have increased proliferation compared to cells with WT-RAS, as well as acquired resistance to several commonly used therapeutics. In this study, we used a triangulation method that combines our drug combination candidate prediction algorithm called ‘secDrug’ and the ‘Direct to Drug’ screening platform that evaluated 84 FDA-approved oncology drugs and emerging therapeutics in these RAS mutant myeloma lines. We identified BIRC5 as a potential target for RAS-mutant myeloma. BIRC5 expression is a key mechanism for the evasion of apoptosis. Notably, oncogenic RAS has been shown to promote MYC stabilization via upregulation of BIRC5. Earlier studies suggested that BIRC5 inhibitors are potent in targeting drug-resistant subclones in hematological malignancies. We performed in vitro cytotoxicity studies and cell-based assays using an in-house panel of HMCLs (n>50) representing WT- and mutant-RAS, as well as innate and acquired response/resistance to the standard of care drugs, proteasome inhibitors (PIs) and immunomodulatory drugs (IMiDs), to confirm the potency of novel selective BIRC5 inhibitors, as single agent (IC50 range 0.22-15.30 nM; Mean 4.431±1.041 nM) and in combination with PIs and IMiDs (Chou-Talalay's Combination Index or CI <0.3, indicating strong synergism). Further, we used bulk and single-cell whole-transcriptomics, siRNA-mediated gene knockdown studies, and functional validation to characterize the treatment-induced genes and molecular pathways underpinning the mechanism of action and drug synergy at the subclone level. Finally, we compared the efficacy of these BIRC5 inhibitors against several RAS inhibitors, including cyclorasin B4-27, a bicyclic peptidyl pan-RAS inhibitor, RMC-6236, a multi-selective noncovalent RAS(on) inhibitor, and ADT-007, a novel, highly potent, and selective pan-RAS inhibitor that we have synthesized, and observed comparable effects on cell death and apoptosis. Ex vivo analysis in bone marrow-derived CD138+ cells from myeloma patients using high-throughput Mass Cytometry (CyTOF or Cytometry by Time-Of-Flight) confirmed the dose-dependent reduction of MCL1, Ki-67, CCND1, c-KIT, and MYC. We will present these results that demonstrate the clinical potential of BIRC5 inhibitors as novel candidates in curbing progression and drug resistance in RAS-mutant myeloma.

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.

Myeloma is well known to have significant genetic heterogeneity among patients' tumors, affecting both progression as well as therapeutic responses. Moreover, subclonal diversity can lead to selective outgrowths that are influenced by new mutations, as well as the bone marrow microenvironment and therapeutic exposures. The ras family of genes (N -and K-) are the most common mutations (40-50% ) in myeloma. Notably, ras mutations occur among a variety of other co-existing chromosomal abnormalities. Ras mutations were found to be more commonly associated with cyclin D1 high expression (e.g., resulting from 11;14 translocations). We and others have also shown that rasmutations may not be disease-initiating but may arise during disease progression and relapse; or are subclonal events that impart selective growth advantage and emerging drug resistance. There is evidence that activating mutations (codons 12,13, 61) in K- ras may have different clinical impact than activating mutations in N- ras yet there are conflicting clinical outcomes that may be due to therapies analyzed or differences in other co-existing genetic mutations. Previous studies using transfection of genes with targeted mutations show random integration and often in multiple copies, thus, limiting interpretations. We chose to create N ras61 and K ras12 mutations by CRISPR-cas directed edits in a single endogenous ras allele in a myeloma cell line (FLAM76) that initially had two wild type alleles (and an 11;14 translocation). We then compared the impact of these edits on growth, signaling, gene expression, and drug responses. We find both the mutN ras and mutK ras edited cells have increased proliferation compared to wild type. However, we find that the N ras mutation increases MAPK signaling and downstream gene activation to a greater extent than the K rasmutation. Based on RNAseq analysis, a MAPK gene activation score was higher in mutN ras edited cells than mutK ras edited cells. Further, we see distinct gene expression patterns between mutN ras versus mutK ras edited cells (See heat map Fig) impacting distinct pathways. Notably, mutNras cells have much higher NOXA expression, increasing sensitivity to apoptosis-inducing drugs (e.g., proteasome inhibitors); and Kras induces anti-apoptotic Bcl-xL, reducing sensitivity. We have conducted a high throughput drug screen with 84 drugs, and while some drugs are active against both mutN ras and mutK ras edits (Proteasome inhibitors), we have identified drugs with differential response to K versus N ras mutations. For example, mutK ras FLAM76 cells are more resistant to proteasome inhibitors; and particularly more resistant to HSP inhibitors and HDAC inhibitors compared to wild type or mutN ras cells. In contrast, mutK ras cells are more sensitive to PikFyve and IL-12 inhibitors than mutNras or wild type cells. Gene expression analysis shows that mutKras cells result in more downregulation of apoptotic genes and higher upregulation of cell survival genes, as well as ER stress pathway genes. Our results suggest that treating myelomas containing K versus N ras activating mutations may benefit from understanding distinct pathway activations and targeting selective drug therapies. We are currently testing new pan-Ras inhibitors to determine if there are distinct responses between our N- and K- ras edited cells, and we are expanding testing to a wider panel of myeloma cell lines containing ras mutations in the context of other genetic abnormalities. Details of gene expression distinctions, pathways, and drug responses that distinguish mutN ras and mutK ras in myeloma will be presented.

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.

Background: Multiple myeloma (MM) remains an incurable plasma cell (PC) malignancy. Despite improvements in clinical outcomes, variability in treatment response exists. Some patients experience innate resistance, while others develop resistance over the course of treatment. Identifying signatures of treatment response and resistance is essential to predicting effective treatment strategies. Given the phenotypic clonal heterogeneity observed in MM, we hypothesize that resistant and sensitive subpopulations have unique phenotypic signatures. We used mass cytometry (CyTOF) to characterize primary MM samples and correlated their phenotypic signatures with treatment response. Methods: We characterized 102 primary MM samples by CyTOF using 37 antibodies (see figure). Frozen whole BM specimens were thawed, stained with antibodies, barcoded, and analyzed on a Helios mass cytometer. Data was processed using Cytobank or Pathsetter and analyzed using t-distributed stochastic neighbor embedding (t-SNE) algorithm. T-SNE plots for CD45+ cells were created using lineage defining markers. T-SNE plots of neoplastic PCs (CD138+,CD3-,CD16-,CD19-,cytoplasmic kappa or lambda+) were created using all remaining markers. A differential analysis of each sample's clusters was performed against all other events. Z-score values, representing marker changes from the differential analysis, for each cluster were used to group similar clusters across all samples, allowing us to compare sub populations of PCs between patients (see figure). From these groups of clusters with similar changes in markers, we calculated the proportion of cells with increases or decreases in markers for each sample. Results were compared to IMWG 90-day response criteria using a random forest model. Results: We performed mass cytometry on 102 samples from 68 unique MM patients. Median age was 66 years (range 34-95) with a 1.5:1 male:female ratio. 37 (36%) had NDMM, 55 (54%) had RRMM, 2 (2%) had SMM and 8 cases (8%) had unknown status. FISH studies revealed 47 (46%) had hyperdiploidy without a recurrent primary IgH rearrangement, 31 (30%) had t(11;14), 13 (13%) had t(4;14), 5 (5%) had 14;16 or 14;20, 3 (3%) had hyperhaploidy and 3 (3%) had an undefined primary. 54 (53%) had 1q gain/amp, 22 (22%) had TP53 deletion, 22 (22%) had a MYC rearrangement and 71% had high mSMART risk. Nearly all patients received an immunomodulatory compound, a proteasome inhibitor and dexamethasone. 2 (2%) had complete response (CR), 10 (9.8%) very good partial response (VGPR), 36 (35%) had partial response (PR), 14 (14%) had minimal response (MR), 13 (13%) had stable disease (SD) and 17 (17%) had progressive disease (PD). Characterization of CD45+ cells identified a relatively conserved and reproducible map from each patient. Notable differences included an ~2-fold reduction in the percentage of total B cells (CD45+,CD3-,CD19+)/total CD45+ cells in cases with high risk mSMART, RRMM and TP53 deletion compared to cases with standard risk, NDMM or normal TP53 status. Similar B-cell reductions were found in RRMM vs. NDMM samples from matched-patients. In contrast to the conserved t-SNE structure of the CD45+ population, the neoplastic PC population displayed dramatically reduced t-SNE structural consistency. Heterogeneity between patient samples and between the same patient samples over time was observed. We focused our analysis on 22 patients with serial samples. The proportion of each t-SNE group present in each patient sample was calculated and then correlated to the 90-day response metric. We identified a phenotypic group whose proportion increased in relation to disease progression (R2=0.85). This group had elevated BCL-2, pSTAT3, CD56 levels and reduced CD45 and CD81 levels. Evaluating individual markers, we found an increase in the proportion of cells with elevated BCL-2, pS6, Ki67, and IKZF1 levels in relation to disease progression. We also observed an increase in the proportion of cells with reduced pCREB levels and a reduction in the proportion of cells with reduced CD147 levels in relation to disease progression. Conclusions: We have utilized CyTOF to characterize phenotypic and functional markers in MM cells on a single cell level and can identify unique subgroups that change in relation to disease progression. Future analysis will correlate phenotypic signatures with ISS stage, mSMART risk, cytogenetic abnormality and type of treatment. Figure 1 Disclosures Kumar: Kite Pharma: Consultancy, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Sanofi: Research Funding; MedImmune: Research Funding; Karyopharm: Consultancy; Merck: Consultancy, Research Funding; Carsgen: Other, Research Funding; Cellectar: Other; Novartis: Research Funding; Adaptive Biotechnologies: Consultancy; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genecentrix: Consultancy; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Tenebio: Other, Research Funding; BMS: Consultancy, Research Funding; Dr. Reddy's Laboratories: Honoraria.

Abstract Patients with multiple myeloma show significant heterogeneity with respect to progression, therapeutic response and toxicities. Part of the Bank On A Cure project is to examine potential genetic polymorphisms that may be associated with common toxicities monitored in clinical trials. The SWOG/ECOG intergroup trial S9321 therapy includes VAD induction followed by randomization to high dose melphalan with autologous transplant support. Paticularly important toxicities with VAD include infection, thrombosis and hyperglycemia; and with melphalan toxicities in GI function are noted. Toxicities and genotypes were assessable during VAD induction on 569 patients; and during high dose melphalan on 161 patients. Toxicities examined included an array of symptoms: cardiovascular, flu-like, GI, neurologic, infection, lung, metabolic, and pain. Grade 3 toxicities found in at least 20 patients were compared to SNP genotypes. For VAD induced toxicities,TNF-alpha SNP genotypes showed association with fatigue, dyspnea and thrombosis; CYP3A4 SNP genotypes showed association with fatigue; DNA repair SNP genotypes in XRCC1 showed associations with infection, hyperglycemia and dyspnea; LT-alpha SNP genotypes showed association with dyspnea; and IL-6 and IL-10 SNP genotypes were marginally associated with neuropathy. After high dose melphalan, diarrhea was associated with SNP genotypes in TGF-beta, CYP3A4 and ERCC2. Interestingly, IL-6 polymorphisms were significantly associated with infection. The p values on the above associations ranged from .05 to .004. In addition, we noted ethnic distributions of some SNP genotypes were significantly different; and significant ethnic disparities in therapy induced hyperglycemia, fatigue, dyspnea and thrombosis were noted (p values range from .04 to .009). Of particular note is that the high producer IL-6 genotype is present in over 90% African Americans, compared to 35% Caucasians. Details of each association will be presented. Not all associations can easily be explained by known biologic function of the genotype, and it is important to qualify these results, as the likelihood of seeing some genotype associations by chance is quite high given the number of comparisons made. Further, we are continuing to examine additional prognostic factors that may, together with genotypes, provide collective predictors of toxicities. We consider these associations as targets for further study through the increasing sample acquisition in the Bank On A Cure project.

Abstract There is a high background risk of venous thrombotic events (VTE) in patients with Multiple Myeloma (MM) and this is significantly increased following treatment with Thalidomide. The risk may be mediated by the production of a prothrombotic state, via an angiopathic mechanism, or alternatively it may arise from the release of prothrombotic factors from dying myeloma cells targeted by the treatment. No unifying evidence of a prothrombotic state has been demonstrated to date and it remains important to understand the mechanism underlying VTEs in order to prevent them effectively. Inherited genetic variants in the form of SNPs can affect the behaviour of clotting factors. These SNPs are functional and well characterised in the coagulation system and we have used them to analyse the mechanism underlying thalidomide induced VTE. As part of the Bank On A Cure TM project we have carried out an analysis of SNPs in 11 genes: GPIIA, ITGA, MTHFR, MTR, PPARG2, TFP1, VCAM, factor V, HABP2, prothrombin and THDB in a case series of 90 patients treated with thalidomide, with and without DVTs. This study showed a significant association with MTHFR and factor V polymorphisms. In order to validate and extend these initial observations we carried out an analysis of a second nested case control study using 131 Myeloma patients from within the MRC Myeloma IX trial, comprising 47 cases with a VTE and 94 age and sex matched controls exposed to the same treatment. We used a SNPlex panel containing 16 functional venous thrombosis associated SNPs, all of these functional polymorphisms have reported mechanisms and biology. We looked at polymorphisms in the genes: fibrinogen A (Ala331Thr), B (-455G>A, Lys478Arg) and G polypeptides (His140Tyr); Coagulation factors V(Arg534Gln), VIII (Glu1260Asp), IX(Ala194Thr), XI (Phe339Cys) and XII(-4G>T); PROCR(Gly219Ser); prothrombin(19911A>G ,20210G>A); MTHFR(Glu429Ala, Ala222Val); Serpine1(-844G>A) and TFPI(Met292Val). We have typed our selected SNPs using the SNPlex™ Genotyping System, which is based on the oligonucleotide ligation/PCR assay with a universal fluorescently labeled ZipChute™ probe detection. The probes are hybridized to complementary ZipCode™ sequences that are part of genotype-specific amplicons. Probes are eluted and detected on a Applied Biosystems 3130x DNA Analyser. This approach is an attractive alternative to existing genotyping methodologies, as it requires only three unlabeled probes per SNP, and consumes very little gDNA. The data analysis on the polymorphisms frequencies is ongoing to examine associations of thalidomide treatment with VTE events and will be presented. These datasets will allow a better definition of the pathogenesis of thalidomide related VTE, so that we can intervene therapeutically in a rational fashion.

Abstract While there are certain common clinical features in myeloma, the disease shows significant heterogeneity with regard to disease progression, and responses to therapy, affecting both survival and toxicities. Heritable variations in a wide variety of genes and pathways affecting cellular functions and drug responses likely impact patient outcomes. In the Bank On A Cure (BOAC) program we have developed a custom chip that assesses 3,404 SNPs representing variations in cellular functions and pathways that may be involved in myeloma progression and response. The chip has gone through rigorous quality controls checks for high call rates, accuracy, and reproducibility that will be presented. Using the BOAC chip, we have conducted studies to look for SNPs that may identify biologic variations that are associated with good or poor response across a variety of treatments. In this study we looked for SNPs that may distinguish short term and long term survivors in two phase III clinical trials: ECOG E9486 and intergroup trial S9321. E9487 patients were treated with VBMCP followed by randomization to no further treatment, IFN-alpha, or cylcophosphamide; and, although there was variation in survival, no significant differences in survival were noted among the 3 arms of the trial. Patients included in this SNP study from S9321 received VAD induction followed by randomization to VBMCP or high dose melphalan + TBI. SNP profiles were obtained for patients with less than 1 year EFS (n=20 in E9487; n=50 in S9321) and patients showing greater than 3 years EFS (n=32 in E9486; n=41 in S9321). Statistical approaches were performed to identify single and groups of SNPs that best discriminated the survival groups. Previous studies have suggested genetic variations in drug metabolism genes, p-glycoprotein transport, and DNA repair genes may influence survival outcomes. Our results show significant survival associations of genetic variations in genes within these functional categories (eg. GST, XRCC, ABCB, and CYP genes). Although genetic variations were found that were uniquely associated with each clinical trial, several of these genetic variations show survival associations that increase in significance when the two trials were examined as a conglomerate data set. Grouping genetic variations through common pathway approaches using gene set enrichment analysis, as well as clustering or partitioning algorithms, further improve the value of the SNPs as potential prognostic markers of survival outcomes. These results and statistical approaches will be presented, and represent steps toward identifying patient variations in biologic mechanisms important in predicting therapeutic outcomes.

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.