Genome and Epigenome Editing: A Revolution in Science and Medicine
Abstract Targeted genome and epigenome editing technologies have recently emerged as important tools for biomedical research and as potential reagents for therapies of gene-based diseases. This talk will focus on our recent work on both improving and defining the genome wide specificities of clustered regularly interspaced short palindromic repeat (CRISPR)-Cas nucleases, a robust platform for introducing targeted genome sequence alterations. The talk will also briefly describe the creation and validation of new technologies for modifying specific epigenomic marks on histones and DNA that can be used to induce targeted alterations in endogenous human gene expression. Taken together, these methodologies provide transformative tools for understanding human biology and offer promising pathways forward for developing new classes of therapeutics based on targeted alterations of gene sequence and/or gene expression. Disclosures Joung: Editas Medicine: Consultancy, Equity Ownership, Other, Patents & Royalties; Transposagen Biopharmaceuticals: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties; Horizon Discovery: Consultancy, Membership on an entity's Board of Directors or advisory committees.
Dynamic Epigenetic Landscapes Define Multiple Myeloma Progression and Drug Resistance
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.
Abstract Acute myeloid leukemia (AML) stem cells (LSC) are an extremely rare fraction of the overall disease (likely <0.3%), largely quiescent, and capable of both long-term self-renewal and production of more differentiated leukemic blasts. Besides their role in disease initiation, they are also hypothesized as the likely source of deadly, relapsed leukemia. Due to the quiescent nature of the LSCs, they are capable of evading the majority of chemotherapeutic agents that rely on active cell-cycling for cytotoxicity. Therefore, novel therapeutic approaches specifically engineered to eradicate LSCs are critical for curing AML. We previously introduced a novel bioinformatics approach that harnessed publically available AML gene expression data to identify genes significantly over-expressed in LSCs when compared to their normal hematopoietic stem cell (HSC) counterparts (Herbrich et al Blood 2017 130:3962). These datasets contain gene expression arrays on human AML patient samples sorted by leukemia stem, progenitor, and blast cells (with normal hematopoietic cell subsets for comparison). We have since expanded our statistical model to identify targets that are both significantly overexpressed in AML LSCs when compared to HSC as well as LSCs compared to their corresponding, more differentiated blast cells. Instead of traditional methods for multiple testing corrections, we looked at the intersection of genes that met the above criteria in 3 independently generated datasets. This resulted in a list of 30 genes, 28 of which appear to be novel markers of AML LSCs. From this list, we first chose to focus on CD200, a type-1 transmembrane glycoprotein. CD200 is broadly expressed on myeloid, lymphoid, and epithelial cells, while the CD200 receptor (CD200R) expression is strictly confined to myeloid and a subset of T cells. CD200 has been shown to have an immunosuppressive effect on macrophages and NK cells and correlates with a high prevalence FOXP3+ regulatory T cells (Coles et al Leukemia 2012; 26:2146-2148). Additionally, CD200 has been implicated as a poor prognostic marker in AML (Damiani et al Oncotarget 2015; 6:30212-30221). To date, we have screened 20 primary AML patient samples by flow cytometry, 90% of which are positive for CD200. Expression is significantly enriched in the CD34+/CD123+ stem cell compartment. To examine the role of CD200 in AML, we established two in vitro model systems. First, we used CRISPR/Cas9 to knockout the endogenous CD200 protein in Kasumi-1. Further, we induced CD200 in the OCI-AML3 cell line that had no expression at baseline. Both cell lines did not express the CD200 receptor before or after manipulation, negating any autocrine signaling. In both systems, CD200 manipulation did not affect the proliferation rate or viability of the cells. To examine the immune function of CD200 in AML, we performed a series of mixed lymphocyte reactions. We cultured normal human peripheral blood mononuclear cells (PBMCs) with the CD200+ or CD200- cells from each line both. Cells were incubated in the culture media for 4-48 hours before being harvested and measured by flow cytometry for apoptosis or intracellular cytokine production. The presence of CD200 on the cell surface reduced the rate of immune-specific apoptosis among these leukemia cells. The difference in cell killing was most likely attributable to a CD200-specific suppression of CD107a, a surrogate marker or cytotoxic activity. In the OCI-AML3 model, PBMCs co-cultured with CD200+ cells produced approximately 40% less CD107a when compared to the CD200- co-culture. Additionally, we characterized our new cell lines using RNA sequencing. By comparing the CD200+ to the CD200- cells within each line, we observed that CD200+ cells significantly downregulate genes involved in defining an inflammatory response as well as genes regulated by NF-κB in response to TNFα. This indicates that CD200 may have an undiscovered intrinsic role in suppressing the immune microenvironment of AML LSCs. In conclusion, we have expanded our novel bioinformatics approach for robustly identifying AML LSC-specific targets. Additionally, we have shown that one of these markers, CD200, has a potential role as a stem cell-specific immunosuppressive target by reducing immune-mediated apoptosis and transcriptionally suppressing inflammatory cell processes. We are extending our study to explore CD200 in primary patient samples using a CD200-blocking antibody. Disclosures Andreeff: SentiBio: Equity Ownership; Amgen: Consultancy, Research Funding; Oncolyze: Equity Ownership; Reata: Equity Ownership; United Therapeutics: Patents & Royalties: GD2 inhibition in breast cancer ; Jazz Pharma: Consultancy; Astra Zeneca: Research Funding; Aptose: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Consultancy, Patents & Royalties: MDM2 inhibitor activity patent, Research Funding; Eutropics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Oncoceutics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy. Konopleva:Stemline Therapeutics: Research Funding.
Abstract Introduction: Therapeutic options for patients with AML were recently expanded with FDA approval of four drugs in 2017. As their efficacy is limited in some patient subpopulations and relapse ultimately ensues, there remains an urgent need for additional treatment options tailored to well-defined patient subpopulations to achieve durable responses. Two comprehensive profiling efforts were launched to address this need-the multi-center Beat AML initiative, led by the Oregon Health & Science University (OHSU) and the AML Individualized Systems Medicine program at the Institute for Molecular Medicine Finland (FIMM). Methods: We performed a comparative analysis of the two large-scale data sets in which patient samples were subjected to whole-exome sequencing, RNA-seq, and ex vivo functional drug sensitivity screens: OHSU (121 patients and 160 drugs) and FIMM (39 patients and 480 drugs). We predicted ex vivo drug response [quantified as area under the dose-response curve (AUC)] using gene expression signatures selected with standard regression and a novel Bayesian model designed to analyze multiple data sets simultaneously. We restricted analysis to the 95 drugs in common between the two data sets. Results: The ex vivo responses (AUCs) of most drugs were positively correlated (OHSU: median Pearson correlation r across all pairwise drug comparisons=0.27; FIMM: median r=0.33). Consistently, a samples's ex vivo response to an individual drug was often correlated with the patient's Average ex vivo Drug Sensitivity (ADS), i.e., the average response across the 95 drugs (OHSU: median r across 95 drugs=0.41; FIMM: median r=0.58). Patients with a complete response to standard induction therapy had a higher ADS than those that were refractory (p=0.01). Further, patients whose ADS was in the top quartile had improved overall survival relative to those having an ADS in the bottom quartile (p<0.05). Standard regression models (LASSO and Ridge) trained on ADS and gene expression in the OHSU data set had improved ex vivo response prediction performance as assessed in the independent FIMM validation data set relative to those trained on gene expression alone (LASSO: p=2.9x10-4; Ridge: p=4.4x10-3). Overall, ex vivo drug response was relatively well predicted (LASSO: mean r across 95 drugs=0.62; Ridge: mean r=0.62). The BCL-2 inhibitor venetoclax was the only drug whose response was negatively correlated with ADS in both data sets. We hypothesized that, whereas the predictive performance of many other drugs was likely dependent on ADS, the predictive performance of venetoclax (LASSO: r=0.53, p=0.01; Ridge: r=0.63, p=1.3x10-3) reflected specific gene expression biomarkers. To identify biomarkers associated with venetoclax sensitivity, we developed an integrative Bayesian machine learning method that jointly modeled both data sets, revealing several candidate biomarkers positively (BCL2 and FLT3) or negatively (CD14, MAFB, and LRP1) correlated with venetoclax response. We assessed these biomarkers in an independent data set that profiled ex vivo response to the BCL-2/BCL-XL inhibitor navitoclax in 29 AML patients (Lee et al.). All five biomarkers were validated in the Lee data set (Fig 1). Conclusions: The two independent ex vivo functional screens were highly concordant, demonstrating the reproducibility of the assays and the opportunity for their use in the clinic. Joint analysis of the two data sets robustly identified biomarkers of drug response for BCL-2 inhibitors. Two of these biomarkers, BCL2 and the previously-reported CD14, serve as positive controls credentialing our approach. CD14, MAFB, and LRP1 are involved in monocyte differentiation. The inverse correlation of their expression with venetoclax and navitoclax response is consistent with prior reports showing that monocytic cells are resistant to BCL-2 inhibition (Kuusanmäki et al.). These biomarker panels may enable better selection of patient populations likely to respond to BCL-2 inhibition than would any one biomarker in isolation. References: Kuusanmäki et al. (2017) Single-Cell Drug Profiling Reveals Maturation Stage-Dependent Drug Responses in AML, Blood 130:3821 Lee et al. (2018) A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia, Nat Commun 9:42 Disclosures Druker: Cepheid: Consultancy, Membership on an entity's Board of Directors or advisory committees; ALLCRON: Consultancy, Membership on an entity's Board of Directors or advisory committees; Fred Hutchinson Cancer Research Center: Research Funding; Celgene: Consultancy; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees; Aileron Therapeutics: Consultancy; Third Coast Therapeutics: Membership on an entity's Board of Directors or advisory committees; Oregon Health & Science University: Patents & Royalties; Patient True Talk: Consultancy; Millipore: Patents & Royalties; Monojul: Consultancy; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Leukemia & Lymphoma Society: Membership on an entity's Board of Directors or advisory committees, Research Funding; GRAIL: Consultancy, Membership on an entity's Board of Directors or advisory committees; Beta Cat: Membership on an entity's Board of Directors or advisory committees; MolecularMD: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Henry Stewart Talks: Patents & Royalties; Bristol-Meyers Squibb: Research Funding; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Aptose Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; McGraw Hill: Patents & Royalties; ARIAD: Research Funding; Novartis Pharmaceuticals: Research Funding. Heckman:Orion Pharma: Research Funding; Novartis: Research Funding; Celgene: Research Funding. Porkka:Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Tyner:AstraZeneca: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Leap Oncology: Equity Ownership; Seattle Genetics: Research Funding; Syros: Research Funding; Takeda: Research Funding; Gilead: Research Funding; Genentech: Research Funding; Aptose: Research Funding; Agios: Research Funding. Aittokallio:Novartis: Research Funding. Wennerberg:Novartis: Research Funding.
Abstract Background: In a previously reported Phase 2 randomized study of patients with acute myeloid leukemia (AML), addition of the investigational agent glasdegib (PF-04449913) to low-dose cytarabine (LDAC) improved overall survival (OS) when compared with LDAC alone. In a non-randomized study arm, glasdegib together with 7+3 chemotherapy was well tolerated and associated with clinical activity. We used a comprehensive biomarker analysis, evaluating gene expression, circulating cytokine levels, and gene mutations, to identify molecular drivers that predict overall response (OR) and OS. Methods: In this Phase 2 multicenter study (NCT01546038), patients with AML who were suitable for non-intensive therapy were randomized (2:1) to LDAC + glasdegib 100 mg QD or LDAC alone, and patients suitable for intensive therapy were assigned 7+3 plus glasdegib 100 mg QD. Whole blood, serum, and bone marrow aspirate samples were collected at baseline, and used to assess 19 genes for expression analysis, 38 analytes for circulating cytokine levels, and 109 genes for mutation analysis. Gene expression was analyzed using TaqMan Low Density Array Cards (TLDCs), cytokine levels were analyzed using quantitative, multiplexed immunoassays (Myriad RBM), and mutation analysis was performed using the Illumina® MiSeq instrument (San Diego, CA). All correlations were performed either for OS or for OR. For gene expression and cytokine analysis, a cut-off value above or below the median expression level for each treatment arm was used to separate samples into two subgroups (< or ≥ the median value) to explore the relationship of expression levels with OS data. Criteria for significance in the non-intensive cohort required one subgroup to have a p-value of <0.05 in the between-treatment arms comparison and the HR difference between the two subgroups to be ≥2 fold. Responses were defined as patients with a complete remission (CR), CR with incomplete blood count recovery (CRi), morphologic leukemia-free state, partial remission (PR), or PRi. For response correlations, genes or cytokines were considered to be differentially expressed if they had a p-value <0.05 and were differentially expressed by ≥2-fold. Results: Within the non-intensive arm (LDAC + glasdegib, n=68; LDAC alone, n=30), expression levels of several genes correlated with improved OS with glasdegib plus LDAC. Lower levels of expression of FOXM1 and MSI2, and higher expression levels of BCL2 and CCND2 correlated with improved OS with the combination. Additionally, lower levels of the cytokines 6CKINE (CCL21), ICAM-1, MIP-1α, and MMP-3 correlated with improved OS. An analysis of correlations of gene expression and cytokine levels with OR could not be completed due to the low number of responders in the LDAC only group (n=2). In the intensive treatment arm (glasdegib and 7+3, n=59), higher PTCH1 expression correlated with improved OS (p=0.0219, median OS 10.8 versus 39.5 months). In this cohort, lower levels of IL-8 (p=0.0225) and MIP-3β (p=0.0403) correlated with lower OS. Expression levels of no genes or cytokines significantly correlated with OR in this arm. We also examined correlations between gene mutation status and OS in both study arms. In the non-intensive arm (LDAC + glasdegib, n=58; LDAC alone, n=25), no genes mutated in at least 5 patients correlated with OS. In the intensive treatment arm (n=47), mutations in FLT3, TP53, CEP170, NPM1, and ANKRD26 correlated with OS (all p<0.05). Patients in this arm with FLT3 mutations responded better than patients with wild type FLT3 (p=0.0336, median OS of 13.1 months versus unreached for FLT3 mutant). Conclusions: In this biomarker analysis, we found that expression levels of a select number of genes and circulating cytokines implicated in AML correlated with OS in the non-intensive and the intensive arms. The improved response for patients with FLT3 mutations and high PTCH1 expression levels in the intensive arm deserves further investigation. These findings need to be verified in larger controlled studies, which are ongoing. Disclosures Cortes: Novartis: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Astellas Pharma: Consultancy, Research Funding; Arog: Research Funding. Pollyea:Argenx: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Consultancy; Celyad: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Research Funding; Curis: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Heuser:Astellas: Research Funding; Daiichi Sankyo: Research Funding; Sunesis: Research Funding; Tetralogic: Research Funding; Bayer Pharma AG: Consultancy, Research Funding; StemLine Therapeutics: Consultancy; Janssen: Consultancy; Pfizer: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; BergenBio: Research Funding; Karyopharm: Research Funding. Chan:Pfizer: Employment, Equity Ownership. Wang:Pfizer: Employment, Equity Ownership. Ching:Pfizer Inc: Employment, Equity Ownership. Johnson:Pfizer Inc: Employment, Equity Ownership. O'Brien:Pfizer Inc: Employment, Equity Ownership.
Abstract Introduction The multiple myeloma (MM) tumor microenvironment (TME) strongly influences patient outcomes as evidenced by the success of immunomodulatory therapies. To develop precision immunotherapeutic approaches, it is essential to identify and enumerate TME cell types and understand their dynamics. Methods We estimated the population of immune and other non-tumor cell types during the course of MM treatment at a single institution using gene expression of paired CD138-selected bone marrow aspirates and whole bone marrow (WBM) core biopsies from 867 samples of 436 newly diagnosed MM patients collected at 5 time points: pre-treatment (N=354), post-induction (N=245), post-transplant (N=83), post-consolidation (N=51), and post-maintenance (N=134). Expression profiles from the aspirates were used to infer the transcriptome contribution of immune and stromal cells in the WBM array data. Unsupervised clustering of these non-tumor gene expression profiles across all time points was performed using the R package ConsensusClusterPlus with Bayesian Information Criterion (BIC) to select the number of clusters. Individual cell types in these TMEs were estimated using the DCQ algorithm and a gene expression signature matrix based on the published LM22 leukocyte matrix (Newman et al., 2015) augmented with 5 bone marrow- and myeloma-specific cell types. Results Our deconvolution approach accurately estimated percent tumor cells in the paired samples compared to estimates from microscopy and flow cytometry (PCC = 0.63, RMSE = 9.99%). TME clusters built on gene expression data from all 867 samples resulted in 5 unsupervised clusters covering 91% of samples. While the fraction of patients in each cluster changed during treatment, no new TME clusters emerged as treatment progressed. These clusters were associated with progression free survival (PFS) (p-Val = 0.020) and overall survival (OS) (p-Val = 0.067) when measured in pre-transplant samples. The most striking outcomes were represented by Cluster 5 (N = 106) characterized by a low innate to adaptive cell ratio and shortened patient survival (Figure 1, 2). This cluster had worse outcomes than others (estimated mean PFS = 58 months compared to 71+ months for other clusters, p-Val = 0.002; estimate mean OS = 105 months compared with 113+ months for other clusters, p-Val = 0.040). Compared to other immune clusters, the adaptive-skewed TME of Cluster 5 is characterized by low granulocyte populations and high antigen-presenting, CD8 T, and B cell populations. As might be expected, this cluster was also significantly enriched for ISS3 and GEP70 high risk patients, as well as Del1p, Del1q, t12;14, and t14:16. Importantly, this TME persisted even when the induction therapy significantly reduced the tumor load (Table 1). At post-induction, outcomes for the 69 / 245 patients in Cluster 5 remain significantly worse (estimate mean PFS = 56 months compared to 71+ months for other clusters, p-Val = 0.004; estimate mean OS = 100 months compared to 121+ months for other clusters, p-Val = 0.002). The analysis of on-treatment samples showed that the number of patients in Cluster 5 decreases from 30% before treatment to 12% after transplant, and of the 63 patients for whom we have both pre-treatment and post-transplant samples, 18/20 of the Cluster 5 patients moved into other immune clusters; 13 into Cluster 4. The non-5 clusters (with better PFS and OS overall) had higher amounts of granulocytes and lower amounts of CD8 T cells. Some clusters (1 and 4) had increased natural killer (NK) cells and decreased dendritic cells, while other clusters (2 and 3) had increased adipocytes and increases in M2 macrophages (Cluster 2) or NK cells (Cluster 3). Taken together, the gain of granulocytes and adipocytes was associated with improved outcome, while increases in the adaptive immune compartment was associated with poorer outcome. Conclusions We identified distinct clusters of patient TMEs from bulk transcriptome profiles by computationally estimating the CD138- fraction of TMEs. Our findings identified differential immune and stromal compositions in patient clusters with opposing clinical outcomes and tracked membership in those clusters during treatment. Adding this layer of TME to the analysis of myeloma patient baseline and on-treatment samples enables us to formulate biological hypotheses and may eventually guide therapeutic interventions to improve outcomes for patients. Disclosures Danziger: Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment. Gockley:Celgene Corporation: Employment. Young:Celgene Corporation: Employment, Equity Ownership. Schmitz:Celgene Corporation: Employment, Equity Ownership. Reiss:Celgene Corporation: Employment, Equity Ownership. Davies:MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; TRM Oncology: Honoraria; Abbvie: Consultancy; ASH: Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria. Copeland:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Fitch:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment, Equity Ownership. Barlogie:Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: Other: travel stipend; Multiple Myeloma Research Foundation: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Millenium: Consultancy, Research Funding; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC. Trotter:Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Hershberg:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Dervan:Celgene Corporation: Employment, Equity Ownership. Ratushny:Celgene Corporation: Employment, Equity Ownership. Morgan:Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding.
Abstract Uncontrolled proliferation is a hallmark of tumorigenesis and is associated with perturbed transcriptional profiles. The proliferative program in Multiple myeloma (MM), a complex disease with heterogeneous genetic changes, is controlled by transcription factors (TFs) and chromatin-associated factors. The dependency on these transcriptional regulators, leading to the dysregulated proliferation, is not predicted by genetic changes, making the tumor cells more sensitive to inhibition of these regulators than normal cells.The relationship between promoter proximal transcription factor-associated gene expression and super-enhancer-driven transcriptional programs is not well-defined. However, their distinct genomic occupancy suggests a mechanism for specific and separable gene control. Our genome-wide epigenomic profile in myeloma has identified the existence of two non-overlapping regulatory axes controlled by promoter and enhancer-driven processes, governing distinct biological functions. We have utilized E2F1 as promoter proximal transcription factor, and evaluated its transcriptional and functional interrelationship with enhancer-associated factors, such as BET bromodomain transcriptional co-activators. We identified that the transcription factor E2F1 and its heterodimerization partner DP1 represent a dependency in MM cells. Global chromatin analysis revealed two distinct regulatory axes, with E2F and MYC predominantly localized to active gene promoters of growth/proliferation genes and CDK9 and BETs disproportionately at enhancer-regulated tissue-specific genes. This divergent BRD4 enhancer and E2F promoter axes is also observed in diffuse large B-cell lymphoma, suggesting a more broader transcriptome control process. Dual inhibition of E2F and BETs displays a superior activity against MM cell growth and viability, both in vitro and in vivo, compared to single perturbation alone providing an important molecular mechanism for combination therapy. Moreover, at low doses of BRD4 inhibitor JQ1, the addition of E2F1 depletion down-regulates the promoter controlled proliferation gene expression axis. As for many TFs, direct pharmacologic inhibition of E2F remains a difficult challenge in drug discovery. However, E2F is not entirely "undruggable" as inhibitors of upstream regulators of the pRB-E2F axis are available. For example, a number of Cyclin dependent kinases (CDK) 4/6 inhibitors, including Palbociclib are now being investigated in clinical trials in in fact approved by the FDA in select malignancies. CDKs are serine threonine kinases that modulate cell cycle progression. CDK4 and CDK6 together with D-type cyclins and cyclin E/CDK2 complexes control the commitment to cell cycle entry from quiescence and the G1 phase. These kinase complexes can phosphorylate RB, releasing E2F to modulate the expression of E2F target genes that are required for S phase entry. We investigated combination of low doses of JQ1 and Palbociclib and observed a profound effect on E2F promoter driven transcriptional activity, and was highly synergistic with JQ1 in a large panel of MM cell lines and primary MM cells from newly diagnosed and relapsed patients. Cell cycle analysis revealed complete G1 arrest after treatment. Importantly, the combination regimen was not effective in healthy donor PBMCs activated with PHA, suggesting a favorable therapeutic index. Transcriptomic changes to assess the impact on promoter and SE-driven processes are ongoing and will be presented. In conclusion, these data implicates the existence of a sequestered cellular functional control that may be perturbed in cancer to maintain the tumor cell state. Simultaneous targeting of non-overlapping promoter and enhancer vulnerabilities impairs the myeloma proliferative program, with potential for development of a promising therapeutic strategy in MM and other malignancies. Disclosures Young: Omega Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Syros Pharmaceuticals: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Camp4 Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Anderson:Bristol Myers Squibb: Consultancy; C4 Therapeutics: Equity Ownership, Other: Scientific founder; Celgene: Consultancy; Millennium Takeda: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; OncoPep: Equity Ownership, Other: Scientific founder. Munshi:OncoPep: Other: Board of director.
Abstract INTRODUCTION Evolving techniques have made possible the direct detection, physical isolation, and study of AML minimal residual disease (MRD) after treatment. This could allow for better identification of therapeutic vulnerabilities in AML. Prior studies have focused on cells that initiate leukemia in mouse models, known as leukemia-initiating cells (LIC), generally with a foundational CD34+CD38- immunophenotype. LIC are typically derived from diagnostic samples of untreated patients. Such stem-like cells do not necessarily represent the residual fraction of AML after treatment. Relapse may originate from non-LIC, and the presence of phenotypically and molecularly defined MRD is now firmly established as a critical prognostic factor for patients. High-risk AML is characterized by relapse, despite morphologic complete remission with initial therapy in most cases. RNA-sequencing was performed on pre- and post-treatment AML subpopulations, including MRD, from high-risk patients, to determine differences in gene expression. METHODS Matched primary AML samples were collected from marrow and peripheral blood of patients with high-risk AML (including patients with unfavorable karyotype and/or TP53 mutation) at diagnosis and after treatment. Mononuclear cells were flow-sorted for bulk (CD45dim) and LIC (Lin-CD34+CD38-CD123+) from diagnostic samples. Post-treatment samples were sorted for bulk mononuclear cells (MNC) and MRD, based on difference-from-normal/MRD immunophenotype specific for each patient as determined from established 20-marker clinical flow cytometry analysis. RNA was isolated using low-input methodology, and RNA-sequencing was performed using Illumina HiSeq 2000. Gene expression was assessed using GO-Elite, and differences between patients and subpopulations were assessed using rank product method. RESULTS Gene expression in MRD was analyzed by RNA-sequencing in comparison to diagnostic samples in eight patients with high-risk AML. Four patients had unfavorable karyotype, including two with TP53 mutations. Patients had additional high-risk features, such as FLT3-ITD or RUNX1 mutations, or secondary/therapy-related AML. Treatment consisted of chemotherapy (6/8) or hypomethylating agents (2/8), with or without other targeted drugs. Residual leukemia was detected in post-treatment samples in all study patients. Significant differences in gene expression were detected between MRD and other sorted populations, including diagnostic bulk AML and LIC. Relevant MRD pathways included those with strong interactions with the microenvironment. Anti-apoptotic mechanisms, cytoskeletal, and cell adhesion related genes, WNT/beta-catenin signaling, and TGFbeta signaling ranked among the most relevant processes in AML MRD subpopulations (Figure 1A, GO-Elite interactome of highly expressed genes in AML MRD). To identify potentially critical and unique MRD-specific genes, rank product method was applied using 1) the most highly expressed genes in AML MRD, 2) the most differential expressed genes between MRD and bulk AML at diagnosis, and 3) the most differentially expressed genes between MRD and bulk MNC after treatment. Among the top 50 scoring genes using this approach (Figure 1B), 16 genes were among the top 5% of genes expressed in MRD among all patients and 20 genes have cell surface gene-products (shown in yellow). Several potential leukemia- and cancer-related genes of interest were identified (shown in bold). CONCLUSIONS Key differences exist between the gene expression profiles of post-treatment MRD from high-risk AML patients, in comparison to other populations and subpopulations of sorted cells before and after treatment. The highlighted differences suggest that MRD relies on specific intrinsic gene expression changes and microenvironmental interactions, and therefore may be targetable after elimination of bulk AML with initial therapy. Accessible surfacesome targets are among top hits. Disclosures Konopleva: cellectis: Research Funding; Immunogen: Research Funding; abbvie: Research Funding; Stemline Therapeutics: Research Funding. Andreeff:Astra Zeneca: Research Funding; Amgen: Consultancy, Research Funding; Jazz Pharma: Consultancy; Celgene: Consultancy; Reata: Equity Ownership; SentiBio: Equity Ownership; Aptose: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; United Therapeutics: Patents & Royalties: GD2 inhibition in breast cancer ; Eutropics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Oncolyze: Equity Ownership; Daiichi-Sankyo: Consultancy, Patents & Royalties: MDM2 inhibitor activity patent, Research Funding; Oncoceutics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees.
Multiple myeloma (MM) is a hematological malignancy of terminally differentiated plasma cells residing within the bone marrow with 25,000-30,000 patients diagnosed in the United States each year. The disease's clinical course depends on a complex interplay chromosomal abnormalities and mutations within plasma cells and patient socio-demographic factors. Novel treatments extended the time to disease progression and overall survival for the majority of patients. However, a subset of 15%-20% of MM patients exhibit an aggressive disease course with rapid disease progression and poor overall survival regardless of treatment. Accurately predicting which patients are at high-risk is critical to designing studies with a better understanding of myeloma progression and enabling the discovery of novel therapeutics that extend the progression free period of these patients. To date, most MM risk models use patient demographic data, clinical laboratory results and cytogenetic assays to predict clinical outcome. High-risk associated cytogenetic alterations include deletion of 17p or gain of 1q as well as t(14;16), t(14;20), and most commonly t(4,14), which leads to juxtaposition of MMSET with the immunoglobulin heavy chain locus promoter, resulting in overexpression of the MMSET oncogene. While cytogenetic assays, in particular fluorescence in situ hybridization (FISH), are widely available, their risk prediction is sub-optimal and recently developed gene expression based classifiers predict more accurately rapid progression. To investigate possible improvements to models of myeloma risk, we organized the Multiple Myeloma DREAM Challenge, focusing on predicting high-risk, defined as disease progression or death prior to 18 months from diagnosis. This effort combined 4 discovery datasets providing participants with clinical, cytogenetic, demographic and gene expression data to facilitate model development while retaining 4 additional datasets, whose clinical outcome was not publicly available, in order to benchmark submitted models. This crowd-sourced effort resulted in the unbiased assessment of 171 predictive algorithms on the validation dataset (N = 823 unique patient samples). Analysis of top performing methods identified high expression of PHF19, a histone methyltransferase, as the gene most strongly associated with disease progression, showing greater predictive power than the expression level of the putative high-risk gene MMSET. We show that a simple 4 feature model composed of age, stage and the gene expression of PHF19 and MMSET is as accurate as much larger published models composed of over 50 genes combined with ISS and age. Results from this work suggest that combination of gene expression and clinical data increases accuracy of high risk models which would improve patient selection in the clinic. Disclosures Towfic: Celgene Corporation: Employment, Equity Ownership. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria. Goldschmidt:Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding; Amgen: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Molecular Partners: Research Funding; MSD: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Dietmar-Hopp-Stiftung: Research Funding; John-Hopkins University: Research Funding. Avet-Loiseau:takeda: Consultancy, Other: travel fees, lecture fees, Research Funding; celgene: Consultancy, Other: travel fees, lecture fees, Research Funding. Ortiz:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Dervan:Celgene: Employment. Flynt:Celgene Corporation: Employment, Equity Ownership. Dai:M2Gen: Employment. Bassett:Celgene: Employment, Equity Ownership. Sonneveld:SkylineDx: Research Funding; Takeda: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Honoraria; Amgen: Honoraria, Research Funding. Shain:Amgen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; 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; Adaptive Biotechnologies: Consultancy. Munshi:Abbvie: Consultancy; Takeda: Consultancy; Oncopep: Consultancy; Celgene: Consultancy; Adaptive: Consultancy; Amgen: Consultancy; Janssen: Consultancy. Morgan:Bristol-Myers Squibb, Celgene Corporation, Takeda: Consultancy, Honoraria; Celgene Corporation, Janssen: Research Funding; Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses. Walker:Celgene: Research Funding. Thakurta:Celgene: Employment, Equity Ownership.
Genetic mutations in FLT3 (fms-like tyrosine kinase-3) play an important role in the pathogenesis of acute myeloid leukemia (AML). FLT3 internal tandem duplications (FLT3-ITD) occur in approximately 25% of all AML cases and various tyrosine kinase inhibitors (TKIs) targeting FLT3-ITD such as quizartinib, crenolanib, and gilteritinib have been developed. Although these selective FLT3 inhibitors were thought to be promising, their effects turned out to be temporary due to the rapid development of resistance associated with clonal switching. Acquired FLT3 point mutations at D835 in the activation loop of tyrosine kinase domain are often accountable for clonal switching at least for Type II TKIs. In addition, adjunct mutations in other genes are also found to be associated with TKI resistance. To investigate the underlying molecular mechanism of this secondary, mutation-driven acquired resistance, we first analyzed co-occurring mutations in the leukemia cells obtained from 26 AML patients with FLT3-ITD (n=14) or FLT3-ITD/D835 dual (n=12) mutations, and performed cap analysis of gene expression (CAGE) sequencing, which identifies and quantifies the 5' ends of capped mRNA transcripts (transcription start sites) and allows investigating promoter structures necessary for gene expression. Patients with FLT3-ITD/D835 harbored a higher number of co-mutations such as ASXL1 and RUNX1 compared to AML with FLT3-ITD (FLT3-ITD/D835: 2.83 ± 0.52, FLT3-ITD: 0.49 ± 0.13, p<0.0001). Intriguingly, CAGE detected significantly higher expression of the anti-apoptotic Bcl-2 family genes BCL2 and BCL2A1 in FLT3-ITD/D835 compared to FLT3-ITD mutant primary samples. Specifically, the CAGE peak of BCL2 was highest in samples with FLT3-ITD/D835 alone (p<0.01), while the CAGE peak of BCLA1 was highest in samples with FLT3-ITD/D835 and co-mutations compared with the other samples (p=0.01). To recapitulate the observations obtained with primary human AML samples, we generated MV4;11 cells with acquired FLT3-ITD/D835 mutations (MV4;11-QR cells) by culturing FLT3-ITD MV4;11 leukemia cells in the presence of quizartinib (1.5 nM), a selective FLT3 inhibitor, for 6 months. While quizartinib (0.2 nM) suppressed the proliferation of 50% of the parental MV4;11 at 72 hours, much higher concentrations of quizartinib (10 nM) was required to suppress the proliferation of MV4;11-QR cells. Quantitative RT-PCR and immunoblot analysis revealed that MV4;11-QR cells expressed higher transcript and protein levels of BCL2A1 than MV4;11 parental cells, while BCL2 levels were similar in both cells and MCL1 and BCLxL expression were lower in the MV4;11-QR than in the parental cells. Next, to investigate the molecular properties of AML cells bearing FLT3-ITD or FLT3-ITD/D835 without other co-mutations, we created Ba/F3 cells stably expressing FLT3-ITD or FLT3-ITD/D835. Of notes, the FLT3-ITD/D835 Ba/F3 cells expressed markedly higher BCL2 transcript and protein levels with lower expression of BCLxL than in FLT3-ITD Ba/F3 cells. No significant difference of MCL1 expression was observed. The sensitivity to quizartinib was massively decreased in the FLT3-ITD/D835 Ba/F3 cells (IC50: FLT3-ITD/D835 >1000nM vs. FLT3-ITD, 0.8nM, at 48h). Finally, we examined the efficacy of the BCL-2 specific inhibitor venetoclax in FLT3-ITD/D835 dual mutated cells with or without upregulation of BCL2 or BCL2A1, the latter shown to confer resistance to venetoclax by sequestering released BIM (Esteve-Arenys, Oncogene. 2018). As expected, venetoclax caused more profound cell growth inhibition and apoptosis induction in BCL2 upregulated FLT3-ITD/D835 Ba/F3 compared to FLT3-ITD Ba/F3 cells (IC50: FLT3-ITD/D835 301nM vs. FLT3-ITD >1000 nM, 96 h). However, FLT3-ITD/D835 bearing MV4;11-QR cells with upregulated BCL2A1 were less sensitive to venetoclax than MV4;11 parental cells (IC50: MV4;11-QR, 149nM vs. MV4;11, 33 nM, 72 h). In conclusion, these results demonstrate that acquisition of D835 mutation in FLT3-ITD mutated AML is often accompanied with multiple co-occurring genetic mutations, and depends on anti-apoptotic BCL-2 associated pro-survival mechanisms. BCL2A1 upregulation might be involved in pathogenesis of acquired drug resistance of FLT3-ITD/D835 dual mutant AML cells, and is a promising new target in FLT3-ITD/D835 refractory AML with complex mutations. Disclosures Carter: Amgen: Research Funding; AstraZeneca: Research Funding; Ascentage: Research Funding. Shah:Bristol-Myers Squibb: Research Funding. Konopleva:Genentech: Honoraria, Research Funding; Ascentage: Research Funding; Kisoji: Consultancy, Honoraria; Reata Pharmaceuticals: Equity Ownership, Patents & Royalties; Ablynx: Research Funding; Astra Zeneca: Research Funding; Agios: Research Funding; Calithera: Research Funding; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; Forty-Seven: Consultancy, Honoraria; Eli Lilly: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Cellectis: Research Funding; Amgen: Consultancy, Honoraria; F. Hoffman La-Roche: Consultancy, Honoraria, Research Funding. Andreeff:Daiichi Sankyo, Inc.: Consultancy, Patents & Royalties: Patents licensed, royalty bearing, Research Funding; Jazz Pharmaceuticals: Consultancy; Celgene: Consultancy; Amgen: Consultancy; AstaZeneca: Consultancy; 6 Dimensions Capital: Consultancy; Reata: Equity Ownership; Aptose: Equity Ownership; Eutropics: Equity Ownership; Senti Bio: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Oncoceutics: Equity Ownership; Oncolyze: Equity Ownership; Breast Cancer Research Foundation: Research Funding; CPRIT: Research Funding; NIH/NCI: Research Funding; Center for Drug Research & Development: Membership on an entity's Board of Directors or advisory committees; Cancer UK: Membership on an entity's Board of Directors or advisory committees; NCI-CTEP: Membership on an entity's Board of Directors or advisory committees; German Research Council: Membership on an entity's Board of Directors or advisory committees; Leukemia Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; NCI-RDCRN (Rare Disease Cliln Network): Membership on an entity's Board of Directors or advisory committees; CLL Foundation: Membership on an entity's Board of Directors or advisory committees; BiolineRx: Membership on an entity's Board of Directors or advisory committees.
Abstract Introduction: The proto-oncogene MYC (locus 8q24.21) is a key transcription factor in multiple myeloma (MM) resulting in significant gene deregulation and impacting on many biological functions, including cell growth, proliferation, apoptosis, differentiation, and transformation. Chromosomal rearrangement and copy number change at the MYC locus are secondary events involved in MM progression, which are thought to lead to aggressive disease. Current analyses of the MYC locus have not been large and have reported rearrangements in 15% of new-diagnosed MM. However, more recent studies using advanced genomic techniques suggest that the frequency of MYC rearrangements may be much higher, and that a full reassessment of the role of MYC in MM pathogenesis may be critical. In this study, we analyzed 1280 MM patients to provide a better understanding of the role of this important genomic driver in MM pathogenesis. Methods: In total, 1280 tumor normal pairs of CD138 sorted bone marrow plasma cells and their germline control samples were analyzed by: 1. Targeted sequencing of 131 genes and 27 chromosome regions (n=100) with 4.5 Mb captured region surrounding MYC ; 2. Exome sequencing (n=461) with 2.3 Mb captured region surrounding MYC ; 3. Whole genome sequencing (n=719). Normalized tumor/germline depth ratio in targeted-sequencing cases and MANTA were used for detection of somatic copy number and structural variants. Expression analysis was performed using RNA-seq or microarrays. Results: MYC translocations were found in 25% (323/1280) of patients and occurred most frequently as inter-chromosomal translocations involving 2-5 chromosomes (90%, 291/323). Of the remaining cases, 5% (17/323) of the translocations involved inversion of chromosome 8 and 5% (15/323) were complex, affecting more than 5 chromosomal loci. The proportion of MYC translocations involving 2, 3, 4, and 5 loci was 62% (200/323), 23% (74/323), 8% (26/323) and 3% (8/323), respectively. Using abnormal rearranged cases (29/100), we found copy number imbalances >14.2 kb in size associated with a MYC translocation in 76% (22/29). Another 7% (2/29) of cases with translocations showed complex intra-chromosomal rearrangement. A region of 2.0 Mb surrounding MYC was identified as a translocation breakpoint hot-spot incorporating 96% of breakpoints. This region also contained two hotspots for chromosomal gain and tandem duplications. MYC rearrangements were not randomly distributed across the spectrum of MM with an excess being seen in hyperdiploidy (76% of rearranged samples, P <0.0001). Importantly, 67% (207/308) of cases with a MYC translocation involving 5 or less chromosomes had one of the commonly known super-enhancers involved in the translocation. Gene expression analysis was used to explore the impact of these events on downstream gene expression patterns. The results showed that inter- and/or intra-chromosomal rearrangements were associated with a significantly (P <0.0001) higher MYC expression (4.1-fold). In patients where rearrangements were associated with additional copies of MYC there was higher expression of MYC in comparison to cases with a translocation but lacking copy number gain (P=0.04). To identify downstream genes deregulated by MYC rearrangements we compared gene expression between those with and without a translocation, independently of hyperdiploidy. Genes that showed >2-fold change in expression (P <0.01) included MYC and the non-protein coding oncogene PVT1 that is located next to MYC . Genes with significantly lower levels of expression were involved in B-cell biology including CD79A and AHR, or were associated with cell proliferation, migration, adhesion, apoptosis and/or angiogenesis (FGF16, ADAMTS1, FBXL7, HRK, PDGFD, and PRKD1) . Conclusions: This study confirms the central role of MYC in the pathogenesis of clinical cases of MM, and as such defining it as a critical therapeutic target. We will be able to target MYC better if we understand how it is deregulated and in this respect we show that the MYC locus rearrangements are complex and it is a hot-spot for heterogeneous inter- as well intra-chromosomal rearrangements, including complex rearrangements involving >5 chromosomes. These events lead to increased MYC expression consistent with it being a driver of disease progression, particularly in the hyperdiploid subset of MM. Disclosures Mavrommatis: Celgene Corporation: Employment. Trotter: Celgene Corporation: Equity Ownership; Celgene Institute for Translational Research Europe: Employment. Davies: Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria. Thakurta: Celgene Corporation: Employment, Equity Ownership. Morgan: Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Myers: Consultancy, Honoraria.
