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Abstract Introduction: Increased bone marrow angiogenesis is a characteristic feature of multiple myeloma (MM) and correlates with disease progression. Increased bone marrow angiogenesis at the time of diagnosis, measured in terms of microvessel density (MVD), is a powerful adverse prognostic factor for patients with MM. To better understand the biological basis of this phenomenon and to understand the mechanisms responsible for increased MVD in MM we compared the gene expression profiles of plasma cells from newly diagnosed MM patients with low and high MVD. Methods: Bone marrow biopsy sections from pts with newly diagnosed MM were studied using CD34 immunostaining and graded as low, intermediate, or high by MVD as previously described. 19 pts each, with low or high MVD were included for this study. RNA was isolated from CD138+ plasma cells of MM patients and analyzed using Affymetrix HG-U133A arrays. To examine differential expression, GC-RMA normalized data was analyzed using GeneSpring 7.2 software and genes with ≥ 2-fold differential expression between high and low MVD samples were identified. The Welch T-test was used to determine the significance of differential expression. Results and Conclusion: Expression of 42 transcripts was increased ≥ 2-fold in samples with high MVD in comparison to samples with low MVD. Of these transcripts, 14 were found to be significantly increased (P<0.05) using the Welch T-test (see Table 1). Expression of 16 transcripts was decreased ≥ 2-fold in samples with high MVD in comparison to samples with low MVD. Of these transcripts, 6 were found to be significantly decreased (P<0.05) using the Welch T-test (see Table 1). Genes differentially expressed include those involved in inhibiting apoptosis, facilitating IL-6 signaling, ion transport, extracellular matrix interaction, and regulating gene expression. Classical angiogenesis genes including VEGF, FGF, and IGF were not found to be differentially expressed (> 2-fold). In conclusion, the list of differentially expressed genes reveals many functions relevant to MM disease pathology including proliferation, apoptosis, regulation of gene expression, cytokine signaling, and adhesion. Current studies evaluating the expression and function of these genes in relation to MM may identify factors critical for angiogenesis and MM progression and provide insight for therapeutic intervention. Gene Description Fold Change COL1A2 collagen, type I, alpha 2 3.008 MCL1 myeloid cell leukemia sequence 1 (BCL2-related) 2.793 209183_s_at chromosome 10 open reading frame 10 2.541 VIL2 villin 2 2.478 IL6ST interleukin 6 signal transducer (gp130) 2.404 CLIC2 chloride intracellular channel 2 2.364 DEPDC6 DEP domain containing 6 2.35 PBXIP1 pre-B-cell leukemia transcription factor interacting protein 1 2.337 CLIC4 chloride intracellular channel 4 2.334 CYP51A1 cytochrome P450, family 51, subfamily A, polypeptide 1 2.095 LIMS1 LIM and senescent cell antigen-like domains 1 2.091 YWHAE tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, epsilon polypeptide 2.042 OAS1 2′,5′-oligoadenylate synthetase 1, 40/46kDa 2.031 S100A11 S100 calcium binding protein A11 2.018 222378_at unknown transcribed sequences 0.496 SLC5A3 mitochondrial ribosomal protein S6 0.463 213089_at unknown transcribed sequences 0.406 PDE4B phosphodiesterase 4B, cAMP-specific 0.454 SVIL supervillin 0.386 RIPX rap2 interacting protein x 0.383

Abstract Background: Thalidomide and dexamethasone combination has been shown to be an effective therapy for multiple myeloma (MM). In newly diagnosed patients with MM, the combination results in overall response rates of over 70% of patients. However, a considerable number of patients do not respond and will need to proceed to other therapies. The ability to identify patients who are unlikely to respond to particular therapies will allow tailoring of therapeutic approaches and in turn will reduce unnecessary toxicity and morbidity from lack of rapid disease control. With the availability of new technology such as gene expression profiling (GEP), this has become a possibility. We employed this approach to identify genes that can reliably predict lack of response to the thalidomide dexamethasone combination. Methods: Patient samples from the Eastern Co-operative Oncology Group clinical trial (E1A00), that compared thalidomide-dexamethasone combination with dexamethasone, and patient samples from the phase II Mayo Clinic study of thalidomide and dexamethasone, both for newly diagnosed MM, were used for this study. Thirty newly diagnosed patients who were evaluable for response were included in the analysis. GEP was performed using the Affymetrix U133A microarray platform as per manufacturer’s recommendation. The Affymetrix output (CEL files) was imported into Genespring 7.2 (Agilent Technologies) microarray analysis software, normalized across chips using GCRMA followed by per gene normalization to median. For the purposes of this study, responses were defined as reduction in the serum paraprotein (or the urinary M protein in the absence of a serum M protein) of >=50% (PR), 25 to 49% (MR), increase of >=50% (PD) and stable disease for the remaining. Results: Five of the 30 patients had no response to the thalidomide Dexamethasone therapy. Using the class prediction tool available in Genespring (Support Vector Machines), we identified 25 genes that reliable predicted non responders (PD, NC) from the responders (MR, PR, and CR). See table for a list of predictive genes with identified genes. Conclusion: Using a combination of two datasets we have identified a set of genes that can be used to reliably predict lack of response to thalidomide and dexamethasone in patients with newly diagnosed MM. We think that this represents a step towards creation of custom microarrays spotted with genes that are capable of predicting lack of response (or response) for the purpose of tailoring therapy to the patient. Gene Predictive strength hypothetical protein FLJ20719 1.655 Tubulin alpha 6 1.653 SEC3-like 1 (S. cerevisiae) 1.649 Ribosomal protein L18 1.546 Ribosomal protein L4 1.432 Tubulin alpha 6 1.409 cleavage stimulation factor 1.393 Tubulin, alpha, ubiquitous 1.393 CD99 antigen 1.38 hmel2 1.346 Leptin receptor 1.291 Integrin-linked kinaseSMC4 structural maintenance of chromosomes 4-like 1 1.269 Cornichon homolog 1.255 Williams-Beuren syndrome chromosome region 5 1.217 Ubiquitin specific protease 1 1.2 Sarcoma antigen NY-SAR-91 1.2

Abstract Waldenström’s macroglobulinemia (WM) is an incurable low-grade lymphoproliferative disorder characterized by bone marrow (BM) infiltration of a clonal population of small B-lymphocytes, plasmacytoid lymphocytes and plasma cells that secrete monoclonal IgM antibody. Because it is difficult to obtain tumor metaphases for karyotype studies, few recurrent chromosomal abnormalities have been reported and the genetic basis of the disease remains poorly defined. Therefore, we performed a comprehensive high-resolution study to identify genomic abnormalities involved in WM pathogenesis. Fifty-seven WM patients were analyzed using a combination of array-based comparative genomic hybridization (aCGH)(N=42); gene expression profiling (GEP)(N=22), DNA sequencing (N=24) and cIgM-FISH (N=57). Agilent 244A and Affymetrix U133 platforms were used in the aCGH and GEP experiments, respectively. An NF-κB index was calculated from GEP data, as a surrogate marker of NF-κB transcription activity. NFKB2 sub cellular localization was determined by immunofluorescence staining. Overall, 83% of samples have aCGH defined chromosomal abnormalities, with a median of 3 abnormalities per patient (range 0 to 27). We identified 16 recurrent regions of copy number change found in >5% (3 or more patients); 10 deleted and 6 amplified regions. The most common abnormality was the entire or partial deletion of 6q, identified in 40% of cases. Four nonoverlapped minimal deleted regions were identified on 6q (MDR1 to MDR4), each of them being present in at least 14 of 17 patients with the abnormality. Gain of 6p was the second most common abnormality (17%) and its presence was always concomitant with 6q loss. Other recurrent deletions were 13q14 (10%) and 7q22, 8p, 11q22-q23, 11q23-q24 and 17p11-p13 (7% each). Copy gains were identified as partial or entire gains of chromosomes 18 (17%), 4 (12%) and 3 (10%), 8q (10%) and Xq27.1-q28 (10%). Three patients had either homozygous deletions or LOH with mutations affecting both alleles of TRAF3 on 14q32. TRAF3 inactivation was correlated with an increased NF-kB transcriptional signature and it was associated with activation of the non-canonical NFkB pathway. Additionally, we identified the inactivation of TNFAIP3, another negative regulator of the NF-kB pathways, in one of 24 patients. Finally, interstitial deletions identified in 4 patients at 13q14 identified a 1.1 Mb MDR, including MIRN15A and MIRN16-1. These findings confirm that focal 13q14 deletion is not restrict to B-CLL and that the MIRN15A/MIRN16-1 abnormalities might be common to several indolent B-cell diseases. Overall, we identified TRAF3 and TNFAIP3 inactivation in 5.3% and 2.4%, respectively. To our knowledge, this is the first study showing the inactivation of bona fide tumor suppressor genes in WM patients. Furthermore, these genes are negative regulators of NF-kB signaling pathway, highlighting its biologic importance in sustaining and limiting growth in WM. Mutational activation of this pathway, which is normally activated by ligand-receptor interactions within the BM microenvironment, suggest a therapeutic role for inhibitors of NF-KB pathway activation in the treatment of WM.

Abstract The objective of this study was to analyze protein changes that occur a the molecular level with novel therapeutic agents used in MM in order to identify potential combinations for clinical trials. We employed antibody protein microarrays (BD Clonetech, CA) to measure changes in the patterns of protein expression in Kas 6/1 MM cell line after treatment with bortezomib, dexamethasone, CC-5013, and 17-AAG. The antibody array is a new technique that assays protein differences directly by hybridizing fluorescently labeled protein mixtures from cell extracts onto glass slides spotted with 512 different monoclonal antibodies specific for human proteins. Induction of apoptosis was assessed by Annexin/Propidium iodide FACS analysis at 24 and 48 hours using different concentrations of the inhibitors. Protein arrays were performed at concentrations and treatment times that did not induce more than 25% apoptosis to ensure adequate analysis of early changes in signaling pathways. Bortezomib 5nM at 10 hours, dexamethasone 100mM, CC-5013 250nM, and 17-AAG 1mM at 24 hours were used. Control cells were treated with vehicles ETOH or DMSO at the same concentrations as the inhibitors. To assess differential protein expression, the mean of the Cy5/Cy3 ratios for each sample and its control were analyzed using the Clontech software and an internally normalized ratio (INR) was calculated. INR values >1.3 or <0.77 were considered as valid changes in protein abundance for this study. All inhibitors induced the upregulation of apoptosis proteins including BAK, BIK, SMAC/DIABLO, and caspase 14. Several proteins were inhibited by all 4 inhibitors indicating common pathways affected by these agents. These included members of the PI3K pathway (PI3K-p85a, p70S6kinase, and cyclin dependent kinases), MAPKinases (ERK1 and 2), antiapoptotic protein Bclx, heat shock proteins Hsp70 and HSP60, and ubiqutin/NFkB pathway (Ubch6, IKKalpha, and IKBe). These results indicate that inhibitors of the PI3K pathway such as mTOR inhibitors, IKK inhibitors or MAPkinase inhibitors may be used in the future in combination with bortezomib, dexamethasone, CC-5013 or 17-AAG. Fatty acid synthetase was downregulated by bortezomib and dexamethasone indicating potential use of fatty acid synthase inhibitors in combination with bortezomib or dexamethasone in future clinical trials. These results suggest that antibody protein arrays may be used in the rational design of combinations of targeted therapies in MM.

Abstract Our understanding of the molecular pathogenesis of myeloma is largely based on observations made from the available human myeloma cell lines. Cell lines have proved invaluable to the characterization of the known immunoglobulin translocations, recurrent deletions and amplifications, and the development of novel therapies. However, it has long been perceived that the cell lines are not representative of patient samples as they often have complex genomes and not even a single cell line has been described with a typical hyperdiploid karyotype. The absence of a hyperdiploid model system is significant as conventional cytogenetic studies of myeloma patient samples has established that patients can be almost evenly split into two broad categories of hyperdiploid and non-hyperdiploid based on their chromosome counts, with the hyperdiploid patients having recurrent trisomies of chromosomes 3, 5, 7, 9, 11, 15, 19, and 21 plus periodic gains of 1q, 18, 20 and loss of 13. Though all of the cell lines have abnormal genomes none of the previously tested cell lines had the characteristic pattern of chromosomal gains seen in hyperdiploid patients and the majority have immunoglobulin translocations, which are more commonly associated with non-hyperdiploid patients. These observations have provided the basis for the assumption that the cell lines are only representative of the non-hyperdiploid patient population. Furthermore, even though very few cell lines have been directly compared to the patient sample from which they were derived it is widely believed that the cell line genomes are unstable and have diverged significantly from the patient samples from which they were derived. Therefore, we decided to compare patient-cell line pairs available from the Mayo Clinic using Agilent 244k array-based comparative genomic hybridization (aCGH) and Affymetrix U133Plus2.0 gene expression profiling and repeated the analysis of our previously studied 46 cell lines on the higher resolution Agilent 244k platform. To date we have completed the analysis of four patient cell line pairs (ALMC-1; ALMC-2; KP-6; KAS-6/1, KAS-6/2) and have repeated our entire cell line panel on the higher resolution array. Four additional patient-cell line pairs (DP-6, JMW-1, SPMC, and VPC-6) are currently being completed. The first compelling observation from this study was that the patient sample from which KP-6 was derived is a perfect match to the characteristic hyperdiploid karyotype with a CGH defined karyotype (51, XY, −1p, +1q, +3, +7, +11, −13, +15, −16q, −17p, +19, +21). The KP-6 cell line showed a similar aCGH defined karyotype (50, X, −Y, −1p, +1q, +3q, +7, −8p, +9p, +11, +15, −16q, −17p, +17q, +19, +21) with the most significant changes occurring on the trisomic chromosomes as many transitioned from trisomies to single arm gains. Therefore, KP-6 appears to be the first myeloma cell line derived from a patient with a characteristic hyperdiploid phenotype. The ALMC patient cell line series is comprised of patient samples at diagnosis and relapse and cell lines derived from both stages. The two patient-cell line pairs only show five and two aCGH definable changes, respectively. In comparison the two matched patient sample pairs have diverged at six regions in the genome. The KAS patient-cell line series is actually two cell lines derived from pre- (KAS-6/1) and post-transplant (KAS-6/2) samples and the KAS-6/2 matching post-transplant patient sample. The comparison of the paired patient sample and derived cell line shows only four aCGH detectable changes. Similarly, there are only three changes between the KAS-6/1 cell line and post-transplant patient sample suggesting that the evolution of this tumor in both the patient and tissue culture is very limited. In conclusion, this study has identified the first bona fide hyperdiploid cell line, thereby providing a valuable investigative tool to better understand the biological differences between the two broad genetic categories of myeloma. Moreover, we demonstrate that the genomes of myeloma cell lines are typically stable and highly representative of the patient sample that was placed into culture to establish the cell line thereby validating their use in the study of this disease.

Abstract Marginal zone B-cell lymphomas (MZL) represent a sub-group of indolent B-cell lymphomas, normally localized in the “marginal zone” of the secondary lymphoid follicles. Depending on the site of involvement, three distinct sub-types have been identified: extra nodal MZL of MALT type, splenic MZL (SMZL), and nodal MZL (NMZL). MALT lymphoma is the most frequent of the MZL subtypes, representing 70% of MZL and 7% to 8% of non-Hodgkin lymphomas. SMZL and NMZL comprise the remaining 20% and 10% of MZL, respectively, and account for less than 1% of NHL. The relative rarity of these lymphomas and difficulty in distinguishing them from other low-grade B-cell lymphoma subtypes like lymphoplasmacytic lymphomas (LPL), making therapeutic decisions difficult. The aim of this study was to perform a comprehensive high-resolution study to identify distinctive molecular markers in MZL and LPL, which would help to better define these lymphomas at genomic level and distinguish them from other types of low-grade B-cell lymphoma. We analyzed 119 patients, including 22 pulmonary MALT (pMALT), 20 non-pulmonary MALT (npMALT), 34 SMZL, 21 NMZL and 22 LPL (8 Waldenström’s Macroglobulinemia and 14 non-WM) cases. Human Agilent 244A platform was used by array-based comparative genomic hybridization (aCGH) analysis. Overall, 93% of patients have an altered genome. LPL had the highest amount of copy number abnormalities (median of 7.5 per patient; average of 11.7; range: 0–44), followed by npMALT (6.5; 7.6; 0–33), NMZL (5; 8.3; 0–31), SMZL (4; 7.1; 0–56) and pMALT (3.5; 4.3; 1–11). When the overall size of the abnormalities was considered, LPL was also the most affected disease with an average size of 245.15Mb included in copy number changes per patient, followed by NMZL (188.6Mb), npMALT (187.2Mb), SMZL (126.4Mb) and pMALT (109Mb). Deletions were more commonly observed than amplifications but the affected area was significantly smaller (average of 55.2Mb and 118.6Mb included in deletions and amplifications per genome, respectively). Partial or total gains of chromosome 3 and 18 were recurrently identified in all the entities, being the most common abnormalities in npMALT (60% and 50%, respectively) and pMALT (27% each). Trisomy 12 was also ubiquitously observed, ranging from 24% of NMZL to 9% of pMALT. Deletions in 6q were commonly identified in LPL (55%), npMALT (35%) and, in a less extent, in SMZL (12%) and NMZL (5%), but not in pMALT. Biallelic and focal monoallelic deletions at 6q23 clearly delineate a minimal deleted region (MDR) that includes TNFAIP3 as being the target gene of the abnormality. Deletions on 7q were found in SMZL and npMALT, thus delineating two MDR. Interstitial 11q deletions were found in LPL, NMZL and SMZL, but not in MALT. Chromosome 13 abnormalities were monosomies in NMZL and interstitial 13q14 deletions in LPL and SMZL, comprising MIRN15A and MIRN16-1 in the MDR. Partial X chromosome gains were common to all the entities but involving different areas: Xq27.3–q28 gains were recurrent events in LPL and NMZL, but not in SMZL and MALT, which were characterized by p arm gains or trisomies. Finally, some recurrent abnormalities were exclusive of specific entities: 1p deletions and 1q gains were only common in NMZL; a focal 2p gain including BCL11A and REL was found in npMALT and partial 8q gains were only identified in LPL. Trisomy 4 was only identified in the subgroup of LPL with WM, but not in the remaining LPL cases or in the remaining diseases. In this study we made a compendium of chromosomal abnormalities present in a group of related diseases, in which the molecular basis remains poorly defined. Using this high-resolution approach we were able to establish recurrent and disease-specific patterns of abnormalities and minimal deleted/amplified regions were redefined. Furthermore, TNFAIP3, a negative regulator of the NF-kB pathway, has been identified as being a commonly affected gene in MALT, LPL and NMZL, highlighting its potential role in pathogenesis.

Abstract Background: The free light chain (FLC) assay allows a nephelometric measurement of this free kappa and lambda light chains that circulate as monomers or dimers, not bound to the immunoglobulin heavy chain. Estimation of the free light chain levels and their ratio has utility in screening, prognostication and treatment response assessment in multiple myeloma, and is particularly useful in light chain myeloma. Previous studies have suggested that absence of a functional rearrangement on at least one of the IgH allele is the basis of light chain myeloma. Translocations involving the immunoglobulin heavy chain (IgH) locus on chromosome 14 can be detected using FISH in over half of the patients with myeloma. We hypothesized that presence of an IgH translocation may be associated with compromised production of heavy chains and leads to unbalanced, excess production of clonal light chain. Methods: From February 1988 to May 1992, 653 patients with previously untreated multiple myeloma were enrolled on the phase III clinical trial E9486. Serum free light chains were run on stored sera obtained at baseline in 495 patients as part of a previous study. Among these patients, 314 patients were previously studied using FISH for presence or absence of IgH translocations, p53 abnormalities and deletion 13 and were included in the current analysis. Results: The median difference between the involved and uninvolved FLC (FLCdiff) was higher among the group of patients with any IgH translocation abnormality compared to those with none (Figure 1). This was particularly evident among the patients with a t(14;16) who had the highest FLCdiff. In a multivariate analysis, we examined if the prognostic value seen with FLCdiff was independent of the presence of IgH translocations t(4:14) and t(14;16), which are associated with a worse outcome. The presence of t(4;14) or t(14;16) was associated with FLCdiff groups using a cutoff point of 185, with 13% of those with FLCd ≤185 and 27% of those with FLCd > 185 having the translocations (P= 0.008). At the cutoff value of 185, the prognostic value of the FLC-diff on progression free and overall survival appears to be independent of these high risk IgH translocations (P-value=0.03 and 0.003, respectively for PFS and OS) We then examined the prognostic value of the FLC-diff in the group of patients without these two high risk abnormalities and were able to confirm that they remained prognostic for PFS (P=0.009) and OS (p=0.005). However, among the group of patients with either of the two high risk IgH abnormalities, FLCdiff estimate added little to assessment of their outcome (P=0.97 and 0.20, respectively for PFS and OS). Conclusion: Elevated free light chain levels are associated with presence of IgH translocations and these patients are more likely to harbor the high risk genetic abnormalities. Routine FISH testing for patients with newly diagnosed myeloma will allow us to confirm these and allow better risk stratification of the patients. Additional work needs to be done to help us better understand the molecular basis for unbalanced light chain excretion by myeloma cells and the current finding of the association with IgH translocation abnormalities should form the basis for this work. Figure 1. Distribution of FLC ratio (inv vs. uninv) and FLC difference (inv − uninv) among patients. Solid squares represent patients with t(4;14) or t(14;16) or t(11;14) or other 14q32 translocation; open circles represent patients without t(4;14), t(14;16), t(11;14) and any other 14q32 translocations. The horizontal dashed lines refer to FLC ratio original values of 100 and 1000, and vertical dashed lines refer to FLC difference original values of 10 and 100. Solid line indicates significant association (P<=0.05) between the dichotomized FLC groups and translocation status. Figure 1. Distribution of FLC ratio (inv vs. uninv) and FLC difference (inv − uninv) among patients. Solid squares represent patients with t(4;14) or t(14;16) or t(11;14) or other 14q32 translocation; open circles represent patients without t(4;14), t(14;16), t(11;14) and any other 14q32 translocations. The horizontal dashed lines refer to FLC ratio original values of 100 and 1000, and vertical dashed lines refer to FLC difference original values of 10 and 100. Solid line indicates significant association (P<=0.05) between the dichotomized FLC groups and translocation status.

Abstract Deletion 13 multiple myeloma (MM) is detected in nearly 50 % of patients diagnosed with MM and confers a shorter survival. A region of minimal deletion was identified for chromosome 13 using Agilent 500K aCGH arrays that included microRNAs 15a and 16- 1. MicroRNAs (miRs) are small RNAs that negatively regulate gene expression through degradation of mRNA transcripts or translational inhibition. In order to determine the contribution of deletion of miRs 15a and 16-1 to MM progression, miR precursors were transfected into KMS-11 and JJN3 adherent myeloma cell lines and total RNA hybridized to Affymetrix U133 Plus 2.0 gene expression arrays for the purpose of identification of mRNA target transcripts. Thirty nanomolar miR 15a and 16-1 precursors and a nonsilencing siRNA control were transfected into adherent KMS-11 and JJN3 myeloma cell lines. Cultures were harvested 16 hours after transfection to minimize the downregulation of transcripts that are not direct targets of miRs 15a and 16-1. Total RNA was extracted using the miRNeasy kit to allow retention of the miR fraction for RT-PCR confirmation of miR over-expression following transfection. Following transfection of miR precursors, expression of miRs 15a and 16-1 were increased 64 and 128-fold, respectively, compared to non-silencing control. Total RNA was hybridized to Affymetrix gene expression arrays using protocols supplied by the manufacturer. Transcripts down-regulated following miR transfection were compared to mathematical models for prediction of miR targets. Additionally, the 3′ UTRs of down-regulated transcripts were inspected for complementarity to miR 15a and 16-1 seed sequences. RT-PCR validation of identified targets was performed. Cross reference of down-regulated transcripts with the TargetSCAN and PictarVERT miR prediction algorithms resulted in a list of 9 genes that represented potential miR-15a/16-1 targets in MM. This list included: FGF2, BCL2, CCNE1, V-MYB, WEE1, E2F7, CDK6, CDC25A and CDC27. Following target identification, reporter constructs were used to confirm direct regulation of transcripts by miRs 15a and 16-1. Functional investigation of miR targets was performed using siRNA reduction of identified targets followed by MTT assay and cell cycle analysis.

Abstract In an effort to identify novel genetic abnormalities in multiple myeloma (MM) we analyzed an initial set, as part of the Multiple Myeloma Research Consortium (MMRC) Genomics Initiative, of 94 MM patient samples on high-resolution array-based comparative genomic hybridization (aCGH) and gene expression profiling (GEP) technologies. The MMRC Genomics Initiative is a three-year research program spanning a full-spectrum of genomic technologies with the goal of analyzing 250 MM patient samples by 2009. Raw data from the Initiative are released in near real time through the MMRC Multiple Myeloma Genomics Portal. This initial aCGH dataset from the Agilent 244K platform represents the highest resolution gene copy number analysis with corresponding GEP released to date in MM. It has confirmed the cytogenetic definitions of MM with the data set being split between patients with hyperdiploid and non-hyperdiploid MM. Furthermore, the 244K platform has helped to refine minimal regions of change present on 1p, 1q, 6q, 8p, 13q, 14q, and 16q. A comprehensive screen of the dataset for genes residing in regions of homozygous deletion identified 105 genes. The most commonly affected gene was CDKN2C(p18) (8.3%) while 12 other regions were recurrently targeted. Several of these regions were recently implicated in the pathogenesis of MM but the large majority represents novel observations. The aCGH and GEP data were integrated using two different programs developed by the core institutions (The Broad Institute of Harvard & MIT and Translational Genomics Research Institute). First, Genomic Identification of Significant Targets in Cancer (GISTIC), which identifies highly significant regions of change and a limited set of candidate genes within these regions, was used to identify 285 genes associated with DNA content alterations. Second, Breakpoint Expression Correlation (BEC), which identifies dysregulated genes on either side of a copy number alteration, was used to identify 194 genes with expression changes associated with a breakpoint. This latter gene list includes all known IgH translocation target genes identified to date in myeloma. Overlapping the gene lists generated by GISTIC, BEC, and the homozygous deletion screen identified a number of genes previously implicated in MM such as CDKN2C(p18), CYLD, BIRC2/BIRC3, and two novel genes, FAM46C and RNF6. Ongoing data integration with RNAi and high content sequencing projects of the current Initiative holds great promise in furthering our understanding of MM. The MMRC Genomics Initiative and resulting efforts from the myeloma research community will increase our understanding of MM at the molecular level, help to identify new targets, and ultimately lead to the development of better, more effective therapies for myeloma.

Abstract PURPOSE: Recent advances in understanding of the molecular alterations that occur at the genetic and epigenetic levels in Multiple Myeloma (MM) have led to major leaps in identifying molecular pathways that regulate progression and resistance to therapeutic agents. However, despite great scientific advances at the genomic level, studies to identify signaling pathways deregulated at the functional proteomic level in MM are limited. In this study, we used a rapid and reproducible antibody-based protein microarray technique to screen the functional differences between malignant plasma cells in samples obtained from patients with MM compared to normal plasma cells (NPC) from the bone marrow of healthy volunteers. METHODS: We determined the protein expression level of 512 polypeptides in 12 samples of newly diagnosed patients with MM using high-throughput proteomic analysis with antibody-based protein microarray. Primary CD138+ sorted MM cells were obtained from the bone marrow of patients after informed consent. MM1.S was used in this study. Using immunohistochemistry and immunoblotting were confirmed. Lentivirus was used to knockdown CRIK. Gene expression datasets from the Mayo Clinic (accession number GSE 6477) and the UAMS (accession number GSE 2658) were obtained from the Gene Expression Omnibus for analysis. The Mayo dataset was generated using Affymetrix U133A platform whereas the UAMS dataset was generated using Affymetrix U133plus 2.0 platform. RESULTS: We identified four subgroups of MM using unsupervised clustering analysis. We confirmed overexpression of some of these proteins including CRIK and CDK4 using immunohistochemistry and immunoblotting. Many of these proteins are known to be deregulated in MM, indicating that this technique can accurately identify proteins that are over or under-expressed in MM in a high-throughput fashion. In addition, we identified novel proteins that are not previously known to be differentially expressed in MM, including the small GTPase member of the Rho family, CRIK protein. We then showed using knockdown of CRIK that this novel protein specifically regulates migration and adhesion in MM. There was no effect on survival of MM cells using the CRIK knockdown. Analyzing the GEP data of the 15 NPC, 46 monoclonal gammopathy of undetermined significance (MGUS) or smoldering myeloma (SMM) and 101 MM samples from the Mayo Clinic, there was a significant increase in expression of CIT from NPC to MM. Among the 351 patients entered into TTII trials from UAMS, CIT expression was similar across the different TC class. Using a cut-off normalized expression level of 1.25 (a level above expression in NPC), MM with a high CIT expression (n=81) had a significantly shorter survival than the other patients. CONCLUSION: In this study, we show that MM cells express a high level of CRIK, and that inhibition of this protein leads to significant inhibition of adhesion and migration of MM cells. In addition, CRIK protein expression correlated with CIT gene expression, with high expression in MM samples compared to NPC. In addition, high CIT expression correlated with poor survival in patients with MM.