ALBERT

Description: Abstract 2385 The t(4;14) group in multiple myeloma (MM) is associated with a significantly impaired prognosis based on the overexpression of MMSET, a histone methyltransferase and epigenetic modifier that is thought to play a major role in myeloma genesis. We have previously shown that t(4;14) myelomas are associated with a specific pattern of DNA hypermethylation using Illumina HumanMethylation27 BeadChip arrays. These results were interpreted as being consistent with that MMSET overexpression leads to specific changes in the epigenetic architecture of MM. However, the detailed mechanism underlying this remains unclear partly due to the low resolution of methylation array technology used. In order to address this we have performed high-resolution genome-wide analyses of DNA methylation for t(4;14) and t(11;14) samples from patients with myeloma, plasma cell leukemia (PCL) and myeloma cell lines (HMCL) using methyl binding domain next generation sequencing (MBD-seq) in order to define DNA methylation patterns specific for t(4;14) MM, as well as to analyse methylation changes accompanying the progression from MM to PCL at a high-resolution, genome-wide scale. DNA from 6 myeloma patients at diagnosis [3 MM with t(4;14), 3 MM with t(11;14)], 6 patients with PCL [3 PCL with t(4;14), 3 PCL with t(11;14)] and 6 HMCL [3 with t(4;14), 3 non-t(4;14)] was fragmented. Methylated DNA fractions were captured using biotin-labelled methyl-binding domain 2 (MBD2) protein. Captured sequences bound to MBD were washed and eluted using elution buffers with increasing salt concentrations. Eluted fragments were purified and sequenced on an Illumina GAIIx, using 1.5 lanes per patient sample, generating 36 bp single-end reads. On average, 1.4 Gbases of reads were generated per sample. Sequences were aligned and de-duplicated using stampy and bwa algorithms. The reference genome (build hg19) was divided into overlapping bins of 200 bp (termed probes) and short read coverage per bin was normalised to per million reads aligned. Differentially methylated regions were defined by comparing normalised reads per probe between the t(4;14) and the t(11;14) groups for MM, PCL and HMCL groups. We first compared probe values that were higher in all three t(4;14) MM samples compared to the three t(11;14) samples. About 16500 probe values were higher in t(4,14) cases compared the t(11;14) group, whereas only 470 probes values were higher in all t(11;14) cases compared to t(4;14) cases. This confirms our previous observation that t(4;14) MM cases are characterised by pronounced hypermethylation. Of the 16500 probes values higher in t(4;14), about 9500 probes mapped to gene bodies and 600 to gene promoters, affecting in total about 1600 genes, indicating that gene or gene regulatory sequence hypermethylation is a common feature in t(4;14). Gene set enrichment analyses of these genes demonstrated highly significant enrichment of KEGG pathways ‘pathways in cancer’, ‘cell adhesion molecules’, the GO term ‘cell development’, among others, and an overrepresentation of probes mapping to chromosomal regions on chromosome 1q. When comparing the progression from MM to PCL, about 2600 genomic probe values were higher in all 3 t(11;14) PCL vs all 3 t(11;14) MM and 1600 probes in all 3 t(4;14) PCL vs MM, indicating that hypermethylation from MM to PCL is more pronounced in t(11;14) than in t(4;14). Very few differences in probe values were present when comparing all 6 MM (both t(4;14) and t(11;14)) with the 6 PCL samples, indicating that the epigenetic mechanisms involved in progression from MM to PCL might be different between the cytogenetic subgroups. Enrichment of methylated sequences was strong for both translocation groups when comparing PCLs with HMCLs, demonstrating that the epigenetic architecture of HMCLs differs significantly even from late-stage patient tumour material. This genome-wide methylation analysis provides us with candidate genes that are likely to be directly or indirectly epigenetically modified by MMSET. We are integrating this methylation data with gene expression data to identify expression-methylation correlations. Furthermore, additional experiments using MMSET knockout models will be used to further filter MMSET-specific effects on genome wide methylation. Finally, we go on to define epigenetic markers that could serve as biomarkers for future epigenetic therapies targeting epigenetic modifiers in t(4;14) myeloma. Disclosures: No relevant conflicts of interest to declare.

Description: Abstract 2907 In order to aid the pre-clinical development of novel therapeutics for multiple myeloma, an in vivo model which recapitulates the human condition in particular tumor growth patterns and response to treatment is required. An important feature of such a model is the interaction of the myeloma cells with the bone marrow microenvironment as this is known to modulate tumor activity and protect against drug-induced apoptosis. We have developed a model with myeloma restricted to the bone marrow, which proceeds rapidly from initial inoculation to disease progression, and possesses a range of chemo-sensitive markers with which to monitor anti-tumor response. Female NOD/SCID γcnull mice were injected inta-osseously with luciferase-tagged myeloma cell lines. Disease progression was monitored weekly by bioluminescent imaging (BLI) and measurement of paraprotein levels (ELISA). These methods were compared to histological assessment of tumor infiltration and MRI which provided a quantitative measurement of progression. On T2-weighted images tumor was identified as a hyperintense signal enclosed within cortical bone. Tumor burden was quantified from regions of interest drawn on the periphery of the hyperintense signal. Luciferase-tagged cells engrafted by 3 weeks at the injection site and progressed to the femurs, spine and pelvis from week 4. BLI showed a significant increase in radiance from 5.6×105 to 43.0×105p/s/cm2/sr between weeks 5 and 7 (p

Description: Abstract 4307 The von Willebrand factor-cleaving protease, also known as ADAMTS13, is synthesized, in part, by endothelial cells (EC). We previously reported that proliferating EC secreted ∼3-fold more ADAMTS13 (antigen and activity) than confluent EC, and that this synthesis was transcriptionally regulated (SJ Kling et al, Pathophysiol Haemost Thromb 2008; 36: 233.) Thrombospondin domains, a defining feature of the ADAMTS protease family, in other ADAMTS family members mediate inhibition of angiogenesis. In particular, ADAMTS1 inhibits angiogenesis by sequestering vascular endothelial growth factor (VEGF) (A Luque et al, J Biol Chem, 2003; 278: 23656). Since ADAMTS13 also has thrombospondin domains, we hypothesized that ADAMTS13 might also mediate anti-angiogenic activity. Human umbilical vein EC angiogenesis was quantified by Angioquant analysis of fluorescence microscopy tube images in a Matrigel assay. Treatment of EC with inhibitors of angiogenesis (anti-VEGF, vasostatin) did not alter EC production of ADAMTS13. However, exogenous recombinant ADAMTS13 inhibited angiogenesis in a dose-dependent manner (Figure 1), in media containing VEGF. In VEGF-free media, ADAMTS13 had no effect on EC tube formation. Preincubation of ADAMTS13 with an antibody to the ADAMTS13 thrombospondin domains 5–7 partially reversed the inhibition of tube formation, implicating these domains in the anti-angiogenic interaction (Figure 2). We also found that VEGF co-immunoprecipitates with ADAMTS13, providing strong evidence that these two proteins interact. When EC lysates were crosslinked with DTSSP prior to immunoprecipitation, anti-ADAMTS13 immunoprecipitated as much VEGF as did anti-ADAMTS1, while an antibody to ADAM17, a similar protein that lacks thrombospondin domains, failed to co-immunoprecipitate VEGF (Figure 3). These data indicate that as with other ADAMTS family members, ADAMTS13 inhibits tubule formation - a parameter of angiogenesis – through its interaction with VEGF, an effect likely mediated by its thrombospondin domains. Inhibition of angiogenesis adds to the expanding roles of ADAMTS13 in down-regulation of thrombosis and inflammation. Fig. 1. Fig. 1. Fig. 2. Fig. 2. Fig. 3. An antibody to ADAMTS13 immunoprecipitates VEGF in similar amounts as anti-ADAMTS1 when proteins from HUVEC lysates are crosslinked prior to co-immunoprecipitation. Nonspecific IgG fails to immunoprecipitate VEGF, as does anti-ADAM17. (A1 = ADAMTS1; A13 = ADAMTS13) Fig. 3. An antibody to ADAMTS13 immunoprecipitates VEGF in similar amounts as anti-ADAMTS1 when proteins from HUVEC lysates are crosslinked prior to co-immunoprecipitation. Nonspecific IgG fails to immunoprecipitate VEGF, as does anti-ADAM17. (A1 = ADAMTS1; A13 = ADAMTS13) Disclosures: Fryer: American Diagnostica Inc: Employment. Greenfield:American Diagnostica Inc: Employment.

Description: Multiple Myeloma (MM) is a proliferation of aberrant plasma cells in the bone marrow. Ig translocations or hyperdiploidy are MM initiating events present in all clonal cells in contrast to other secondary lesions involved in progression (e.g. RASmutations), which may be subclonal. We and others have recently described the presence of different tumour clonal populations—a phenomenon called intra-tumour heterogeneity—in MM, yet the phylogenetic relationships of the tumour subclones remains to be fully elucidated. To describe the intra-tumour heterogeneity and tumour phylogenies in a series of t(11;14) MM patients characterised by KRAS/NRAS/BRAF mutations, we combined whole-exome sequencing and single-cell genetic analysis. A novel approach for single-cell multiplex-qPCR analysis using nano-fluidic arrays (Fluidigm) was followed in 100-250 single-cells per sample. The t(11;14) breakpoint was defined using Ig regions-targeted massively-parallel sequencing. Taqman assays specific for detection of the t(11;14) breakpoints and for mutations in selected genes were custom-made designed. Copy number assays for chromosomal regions of interest were also used. This strategy allowed us: first, to report the presence of the t(11;14), mutations and copy number aberrations (gains/losses) at the single-cell level; second, to define subclonal populations; and third, to delineate the most plausible sequence of events for each case. An additional series of 14 MM patients with paired-samples at presentation/relapse was included for the study of the effect of treatment in clonal architecture. Lastly, to analyse the engraftment-ability of subclonal populations, we injected 1x106 CD138+ cells from one of the patient-samples at diagnosis into the tibia of NOD/SCIDyc(null)-mice and compared the engrafted-myeloma with the paired presentation-relapse samples. We demonstrate that MM is comprised of 2-6 clones, related through a linear (3/7 cases) or a branching (4/7) phylogeny. The t(11;14) was seen in 91-100% of tumour cells, supporting its aetiological role as a MM initiating event. For the first time in MM, we describe a parallel evolutionary pattern in two samples that carried double hits in KRAS or KRAS/NRAS. These mutations were acquired separately in two divergent clonal lineages, which were derived from the same ancestor but evolved independently. We suggest that RAS is a true driver mutation in MM as such alterations seem to provide clonal advantages for myeloma subclones in the bone marrow environment. We also report the concomitant acquisition of RAS mutations and IRF4p.K123R mutation in 2% (9/453) screened MM patients. This finding suggests a collaborative effect provided by the mutations in both pathways to trigger myeloma development. We examine the ability of subclonal populations to survive patient treatment by the analysis of paired presentation-relapse samples. Not only did the intra-tumour heterogeneity shift in the transition to relapse meaning that subclonal populations fluctuated during treatment, but also new clones emerged from early and late subclones formerly described at diagnosis. To note, some of these new subclones acquired mutations in KRAS/BRAF during transition, likely leading to relapse. In parallel, we tested the ability to recapitulate the disease in NOD/SCIDyc(null)-mice. Engrafted-myelomas also had clonal fluctuations with 1/3 clones shown at patient-diagnosis neither found at relapse, nor in the engrafted-MM. This missing clone was similarly outcompeted by the other clones during patient treatment and xeno-transplantation. Altogether, these results suggest that subclonal populations have different abilities to survive treatment or xeno-transplantation and hence, to lead to relapse or reconstitution of myeloma by the generation of new clonal subpopulations. We confirm the existence of distinct MM subclones that are related through a linear or a branching phylogeny, with examples of parallel evolution for alteration of the RAS/MAPK pathway. Myeloma subclones are subject of a selection process involving clonal extinction and clonal tides, similar to the theory of Natural Selection by Charles Darwin. In conclusion, intra-tumour heterogeneity is an elementary foundation for Darwinian selection underlying both disease progression and the development of treatment resistance in MM. Disclosures: No relevant conflicts of interest to declare.