Abstract: Introduction: Umbilical cord blood (UCB) grafts are the only option for a significant minority of patients who require hematopoietic stem cell transplantation (HSCT) but lack a suitable related or unrelated donor. While UCB can serve as a suitable 'off the shelf' graft for many patients, units with the best HLA match often contain low and sometimes insufficient numbers of CD34+ cells for use in transplantation, particularly for adult patients. Furthermore, UCB grafts contain lower CD34+ cells number compared to BM or PBSC grafts, which leads to longer engraftment times and a higher risk of graft failure. BM and PBSC grafts are usually injected intravenously, homing to the bone marrow after several hours in the circulation. During this time, CD34+ cells are lost in the lungs, liver and spleen with typically 〈 20% making it to the bone marrow.1 Investigators have sought to overcome the limitation of low cell dose in UCB grafts and loss of CD34+ cells in the circulation by injecting CD34+ cells directly into the bone marrow space. However, we have recently shown that conventional intrabone delivery methods used in investigational trials to transplant UCB, result in low-level retention of hematopoietic progenitor cells in the intrabone space. Recently, we developed an optimized intrabone (OIB) transplant method using computer controlled low pressure and low volume injection (controlled infusion rate 〈 0.2ml/min, total volume 〈 5ml) under CT guidance. Utilizing porcine and rhesus macaque models, we have shown that OIB delivery of CD34+ cells improves intrabone retention, preventing circulation of CD34+ cells through the lungs, which is observed with conventional non-optimized intrabone methods.2 Here we conducted experiments using an autologous myeloablative transplant model in rhesus macaques comparing engraftment of gene-marked CD34+ cells transplanted intravenously, with cells transplanted using OIB delivery. Methods: Rhesus macaques received GCSF and plerixafor mobilization prior to apheresis. Products were CD34+ selected using Miltenyi beads. CD34+ cells were split equally for transduction with lentiviral vectors encoding the reporters GFP and YFP. After myeloablative conditioning with 10Gy total body irradiation, half the graft was injected directly intra-bone using the OIB method, with the other half of the autograft simultaneously being injected intravenously via slow iv push. Peripheral blood samples were measured daily by flow cytometry to assess the proportion of engrafting cells deriving from each source. To address whether OIB transplantation could allow engraftment of low CD34+ cell numbers, as found in UCB units, the cell doses transplanted in one rhesus macaque recipient were reduced to only 0.5 x 106CD34+ cells/kg. Results: CD34+ cells injected intrabone utilizing the OIB method engrafted in all 3 animals. However, flow cytometric analysis gating on GFP vs YFP positive neutrophils showed CD34+ cells injected utilizing OIB delivery did not engraft quicker than IV transplanted cells. Sequential monitoring of neutrophils over 30 days showed the contribution to hematopoiesis of OIB delivered cells was significantly lower than the cells injected IV (Table 1.) Conclusions: We developed a novel intrabone delivery system that optimizes the retention of CD34+ cells into the bone marrow space. Although autologous CD34+ cells injected using this OIB transplant method were capable of engrafting in rhesus macaques that had undergone myeloablative conditioning, they did not engraft faster and contributed less to hematopoiesis than CD34+ cells simultaneously transplanted using conventional IV infusion. These data raise questions over whether intrabone delivery, even when using techniques to optimize intrabone retention, has utility in improving CD34+ cell engraftment. References: 1. Van der Loo JC et al. Marrow and spleen seeding efficiencies of all murine hematopoietic stem cell subsets are de- creased by preincubation with hematopoietic growth factors. Blood. 1995; 85:2598-2606 2. Pantin et. al. Optimization of intrabone delivery of hematopoietic progenitor cells in a swine model using cell radiolabeling with 89zirconium. American J Transplant. 2015 Mar;15(3):606-17. Disclosures Davidson: Macrogenics: Employment. Pantin:NIH: Patents & Royalties: a patent application for an intrabone delivery device. Dunbar:National Institute of Health: Research Funding.

Abstract: Lymphoplasmacytic lymphoma (LPL) is characterized by t(9;14)(p13;q32) in 50% of patients who lack paraproteinemia. Waldenström macroglobulinemia (WM), which has an immunoglobulin M (IgM) paraproteinemia, is classified as an LPL. Rare reports have suggested that WM sometimes is associated with 14q23 translocations, deletions of 6q, and t(11;18)(q21;q21). We tested for these abnormalities in the clonal cells of WM patients. We selected patients with clinicopathologic diagnosis of WM (all had IgM levels greater than 1.5 g/dL). Southern blot assay was used to detect legitimate and illegitimate IgH switch rearrangements. In addition to conventional cytogenetic (CC) and multicolor metaphase fluorescence in situ hybridization (M-FISH) analyses, we used interphase FISH to screen for t(9;14)(p13;q32) and other IgH translocations, t(11;18)(q21;q21), and 6q21 deletions. Genomic stability was also assessed using chromosome enumeration probes for chromosomes 7, 9, 11, 12, 15, and 17 in 15 patients. There was no evidence of either legitimate or illegitimate IgH rearrangements by Southern blot assay (n = 12). CC (n = 37), M-FISH (n = 5), and interphase FISH (n = 42) failed to identify IgH or t(11;18) translocations. Although tumor cells from most patients were diploid for the chromosomes studied, deletions of 6q21 were observed in 42% of patients. In contrast to LPL tumors that are not associated with paraproteinemia and that have frequent t(9;14)(p13;q32) translocations, IgH translocations are not found in WM, a form of LPL tumor distinguished by IgM paraproteinemia. However, WM tumor cells, which appear to be diploid or near diploid, often have deletions of 6q21.

Abstract: Multiple myeloma (MM) is a genetically heterogeneous cancer of bone marrow plasma cells with variable outcome. To assess the prognostic relevance of clonal heterogeneity of TP53 copy number, we profiled tumors from 1777 newly diagnosed Myeloma XI trial patients with multiplex ligation-dependent probe amplification (MLPA). Subclonal TP53 deletions were independently associated with shorter overall survival, with a hazard ratio of 1.8 (95% confidence interval, 1.2-2.8; P = .01). Clonal, but not subclonal, TP53 deletions were associated with clinical markers of advanced disease, specifically lower platelet counts (P & lt; .001) and increased lactate dehydrogenase (P & lt; .001), as well as a higher frequency of features indicative of genomic instability, del(13q) (P = .002) or del(1p) (P = .006). Biallelic TP53 loss-of-function by mutation and deletion was rare (2.4%) and associated with advanced disease. We present a framework for identifying subclonal TP53 deletions by MLPA, to improve patient stratification in MM and tailor therapy, enabling management strategies.

Abstract: The angiopoietin-like proteins (angptls) are novel growth factors that are capable of stimulating expansion of mouse and human hematopoietic stem and progenitor cells (HSPCs) ex vivo. Although the receptor for some of the angptl family members is known, their molecular mechanism of action is undefined. Here, we show that overexpression of angptl2 in a stable heatshock inducible transgenic zebrafish line, Tg(hsp70:zangptl2), is sufficient to increase cmyb- and runx1-positive HSPCs in the aorta-gonad-mesonephros (AGM) region, the site of definitive hematopoiesis. Anti-sense morpholino knockdown of angptl1 and 2 resulted in a significant decrease in cmyb- and runx1-positive HSPCs in the AGM, suggesting that angptls are required for definitive hematopoiesis. These double morphants also displayed severe disruption in vascular development and differentiation prior to the defects observed in the AGM, indicating that angptl regulation of HSPC development occurs through an early specification of the hemogenic endothelium. The loss of function phenotypes in the developing aorta is reminiscent of mutant fish with defective notch signaling (mindbomb) and a tight genetic interaction occurs between angptl and notch signaling. Knocking down angptl1 and 2 decreased notch signaling in a transgenic notch reporter line while overexpression of a constitutively active intracellular notch rescues the angptl double morphant phenotype. These data imply that angptls function upstream of notch signaling. Interestingly, the absence of HSPCs and notch signaling in the mindbomb mutants are rescued by overexpression of angptl2 in Tg(hsp70:zangptl2), suggesting that angptls can regulate notch signaling. To examine the molecular mechanism of Angptl-mediated Notch activation, we stimulated cultured human CD34+ cells or endothelial cells with purified Angptl2 and observed a rapid increase in Notch receptor cleavage indicating that Angptl2 can induce Notch activation. Furthermore, we found through endogenous co-immunoprecipitation experiments that the Angptl receptor, LILRB2, interacted with Notch receptor. This strongly points to a direct regulation of Notch activation/signaling by Angptls through physical interactions between Notch and Angptl receptors. Previously, we found that angptl-mediated akt activation is important for HSPC formation in the AGM. To examine the role of Akt during Notch activation, we immunoprecipitated all Akt-phosphorylated substrates in Angptl-stimulated cells and found the presence of ADAM17/TACE, one of the cleavage enzymes that is crucial to Notch receptor activation. Together these results suggest a model in which Angptl-binding of the LILRB2 receptor enables recruitment of downstream molecules such as Akt proximal to Notch, allowing for subsequent cleavage and activation of Notch receptor. Finally, to examine downstream signaling of Angptl-mediated Notch activation, we performed chromatin immunoprecipitation for Notch followed by sequencing in Angptl2-stimulated CD34+ cells and found enrichment for Myc binding elements. Independent microarrays also revealed a strong Myc signature through gene set enrichment analysis. Thus, to confirm results from our bioinformatics analyses, we overexpressed zebrafish myc in angptl1 and 2 double morphants or mindbomb mutants, and found a significant rescue in HSPCs formation in the AGM. Collectively with these results, we propose that angptl can regulate notch signaling through receptor interaction, leading to activation of myc target genes during definitive hematopoiesis. Our data provide new insights to the previously uncharacterized Angptl signaling during HSPC development and present a novel mechanism of action for Notch activation. Disclosures: Aster: Cell Signaling Technology: Consultancy; Merck, Inc.: Research Funding; Pfizer, Inc.: Research Funding; Genentech, Inc.: Honoraria. White:N-of-One: Consultancy, Equity Ownership. Zon:FATE Therapeutics, Inc: Consultancy, Equity Ownership, Founder Other, Membership on an entity’s Board of Directors or advisory committees, Patents & Royalties; Stemgent, Inc: Consultancy, Membership on an entity’s Board of Directors or advisory committees, Stocks, Stocks Other; Scholar Rock: Consultancy, Equity Ownership, Founder, Founder Other, Membership on an entity’s Board of Directors or advisory committees, Patents & Royalties.

Abstract: Introduction Epigenetic dysregulation is a hallmark of cancer and has significant impact on disease biology. The epigenetic structure of myeloma is heterogeneous and we previously demonstrated that gene specific DNA methylation changes are associated with outcome, using low-resolution arrays. We now performed a high-resolution genome wide DNA methylation analysis of a larger group of patients from a UK national phase III study to further define the role of epigenetic modifications in disease behaviour and outcome. Patients and Methods Highly purified ( 〉 95%) CD138+ myeloma bone marrow cells from 465 newly diagnosed patients enrolled in the UK NCRI Myeloma XI study were analysed. The extracted DNA was bisulfite-converted using the EZ DNA methylation kit (Zymo) and hybridized to Infinium HumanMethylation450 BeadChip arrays. Raw data was processed using the R Bioconductor package "minfi". SNP containing probes and probes on the sex chromosomes were removed. 464 samples and 441293 probes were retained following inspection of quality control metrics. Beta values were summarized across functional genomic units or differentially methylated regions (DMRs) that included: gene bodies, promoters, insulators, CpG-islands and enhancers. K-means was applied to each DMR to cluster patients into 2 groups (high or low methylation) per region. Filters were applied to define a clinically meaningful minimum group size and methylation differences between the groups. Overall survival (OS) and progression free survival (PFS) were assessed by a Cox proportional hazards regression model fitted to each DMR with a time-dependent covariate of the trial pathway. Pathway analyses were performed using GREAT (Stanford University) and GSEA (Broad Institute). Results We identified 589 differentially methylated regions that were significantly associated with PFS and OS when using a cut-off of P 〈 0.01 (log-rank). Of these, 114 DMRs were located within 10kb of a gene transcription start site (TSS). Among these, several genes implicated in myeloma disease biology, such as immune cell-cell interaction genes (e.g. CD226) or stemness-associated transcription factors (e.g. PAX4) were identified to be differentially methylated. Using pathway analysis on all 589 DMRs, Gene Ontology biologic groups were enriched for positive regulation of proliferation, cell migration and cytoskeleton organisation (FDR P 〈 0.05). This was further supported by enrichment of proliferative E2F1 transcription factor target structures (FDR P 〈 0.05). Matched gene expression profiles have been generated and integrated analyses correlating epigenetic with GEP and genetic risk data and individual gene level methylation-expression associations will be presented at the meeting. This data is also being integrated with drug resistance profiles from the Cancer Cell Line Encyclopedia (CCLE; Barretina, et al, 2016). Conclusion Epigenetic mechanisms play a significant role in influencing tumour cell behaviour. We have identified here differentially methylated regions that are significantly associated with patient outcome. Pathway analyses suggest an epigenetic regulation of biologic mechanisms involved in high risk disease, such as proliferation and migration. Integration of epigenetic data with matched gene expression profiles is currently ongoing to delineate independent epigenetic biomarkers associated with high risk disease behaviour. Disclosures Jones: Celgene: Honoraria, Research Funding. Pawlyn:Takeda Oncology: Consultancy; Celgene: Consultancy, Honoraria, Other: Travel Support. Jenner:Janssen: Consultancy, Honoraria, Other: Travel support, Research Funding; Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Other: Travel support; Takeda: Consultancy, Honoraria, Other: Travel support; Celgene: Consultancy, Honoraria, Research Funding. Cook:Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Glycomimetics: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau. Drayson:Abingdon Health: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Davies:Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Morgan:Univ of AR for Medical Sciences: Employment; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria. Jackson:MSD: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau. Kaiser:Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Other: Travel Support; BMS: Consultancy, Other: Travel Support; Chugai: Consultancy.

Abstract: Introduction A significant proportion of myeloma patients relapse early and show short survival with current therapies. Molecular diagnostic tools are needed to identify these high risk patients at diagnosis to stratify treatment and offer the prospect of improving outcomes. Two validated molecular approaches for risk prediction are widely used: 1) molecular genetic risk profiling [e.g. del(17p), t(4;14)] 2) gene expression (GEP) risk profiling, [e.g. EMC92 (Kuiper et al., Leukemia 2012)] . We profiled patients from a large multicentric UK National trial using both approaches for integrated risk stratification. Methods A representative group of 221 newly diagnosed, transplant eligible patients (median age 64 years) treated on the UK NCRI Myeloma XI trial were molecularly profiled. DNA and RNA were extracted from immunomagnetically CD138-sorted bone marrow plasma cells. Molecular genetic profiles, including t(4;14), t(14;16), Del(17p), Gain(1q) were generated using MLPA (MRC Holland) and a TC-classification based qRT-PCR assay (Boyle EM, et al., Gen Chrom Canc 2015, Kaiser MF, et al., Leukemia 2013). GEP risk status as per EMC92 was profiled on a diagnostic Affymetrix platform using the U133plus2.0-based, CE-marked MMprofiler (SkylineDx) which generates a standardised EMC92 risk score, called 'SKY92'. Progression-free (PFS) and overall survival (OS) were measured from initial randomization and median follow-up for the analysed group was 36 months. Statistical analyses were performed using R 3.3.0 and the 'survival' package. Results were confirmed in an independent dataset, MRC Myeloma IX, for which median follow-up was 82.7 months. Results Of the 221 analysed patients, 116 were found to carry an established genetic high risk lesion [t(4;14), t(14;16), del(17p) or gain(1q)]. We and others have recently demonstrated that adverse lesions have an additive effect and that co-occurrence of ≥2 high risk lesions is specifically associated with adverse outcome (Boyd KD et al, Leukemia 2011). 39/221 patients (17.6%) were identified as genetic high risk with ≥2 risk lesions (termed HR2). By GEP, 53/221 patients (24.0%) were identified as SKY92 high risk. Genetic and GEP high risk co-occurred in 22 patients (10.0%), 31 patients (14.0%) were high risk only by GEP and 17 patients (7.7%) by genetics only. SKY92 high risk status was associated with significantly shorter PFS (median 17.1 vs. 34.3 months; P 〈 0.0001; Hazard ratio [HR] 3.2 [95%CI: 2.2-4.7] ) and OS (median 36.0 vs. not reached; P 〈 0.0001; HR 3.9 [2.3-6.9]). Genetic risk by HR2 was similarly associated with adverse outcome: median PFS 17.0 vs. 33.6 months; P 〈 0.0001; HR 2.9 [1.9-4.4]), median OS 33.5 vs. not reached; P 〈 0.0001; HR 4.1 [2.3-7.2]). Importantly, by multivariate analysis GEP and genetic high risk status were independently associated with shorter PFS (P 〈 0.001) and OS (P 〈 0.005). We next investigated interactions between genetic and gene expression high risk status. Three groups were defined: 1) Patients with both SKY92 and genetic (HR2) high risk status (n=22), 2) either GEP or genetic high risk (n=48) or 3) absence of GEP or genetic (HR2) high risk status (n=151). Co-occurring GEP and genetic high risk status was associated with very short PFS (median 12.5 vs. 20.0 vs. 38.3 months; P 〈 0.0001) and OS (median 25.6 vs. 47.3 vs. not reached; P 〈 0.0001) [Figure]. When comparing this ultra-high risk group against the remainder of cases (n=199), their risk of progressing and dying early was significantly elevated (PFS HR 4.4 [2.5-6.7] ; OS HR 5.9 [3.1-11.0]). We confirmed this finding in 116 transplant-eligible patients from the MRC Myeloma IX trial. Patients carrying both EMC92 and genetic high risk status had a median PFS of 7.8 vs. 25.5 months and median OS of 9.5 vs. 62.1 months (both P 〈 0.0001). Moreover, all patients in this ultra-high risk group progressed within 24 months and died within 48 months. Conclusion We demonstrate, for the first time, that combined genetic and gene expression risk profiling identifies a group of patients with ultra-high risk disease behaviour with high fidelity, using molecular features of the disease. Our results indicate that GEP and genetic high risk profiling identify independently relevant, but inter-related features of high risk disease biology. Integrated genetic and gene expression risk profiling could serve as a valuable tool for risk stratified, innovative treatment approaches in myeloma. Figure Figure. Disclosures Jones: Celgene: Honoraria, Research Funding. Pawlyn:Takeda Oncology: Consultancy; Celgene: Consultancy, Honoraria, Other: Travel Support. Jenner:Amgen: Consultancy, Honoraria, Other: Travel support; Janssen: Consultancy, Honoraria, Other: Travel support, Research Funding; Novartis: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Other: Travel support; Celgene: Consultancy, Honoraria, Research Funding. Cook:Glycomimetics: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau. Drayson:Abingdon Health: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Davies:Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Morgan:Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Univ of AR for Medical Sciences: Employment; Bristol Meyers: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Jackson:Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; MSD: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau. Kaiser:Takeda: Consultancy, Other: Travel Support; Amgen: Consultancy, Honoraria; BMS: Consultancy, Other: Travel Support; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Chugai: Consultancy.