Citations

Abstract Introduction Gene Expression Profiling studies have resulted in signatures capable of providing robust prognosis for Multiple Myeloma (MM) patients, such as the EMC92 [SKY92, Kuiper et al. Leukemia 2012]. Recently, data from 4720 MM patients from the HOVON-65/GMMG-HD4, UAMS-TT2, UAMS-TT3, MRC-IX, APEX and IFM trials were employed to assessed the majority of currently identified prognostic markers and their combinations (GEP, FISH, and biochemistry data, [Kuiper et al ASH 2014]). The "SKY92 + ISS" was identified and validated as the statistically most optimal (i.e. most significant and robust) prognostic marker combination for MM patients. The combination of the EMC92 (=SKY92) and ISS prognostic features proves to be a very powerful, unprecedented and straightforward prognostic system. It identifies four prognostic risk classes: low risk (ISS I-SKY92 standard risk (SR)), intermediate-low (ISS II-SKY92 SR), intermediate-high (ISS III-SKY92 SR) and high risk (ISS I-III, SKY92 high risk). Previously, the SKY92 has been validated on the 91 MM cases in the MMGI cohort [Van Beers et al. ASH 2013]. Here we present an extension of that validation with the SKY92 + ISS combination on the 78 MM cases for whom both GEP and ISS is available, by both assessing as the "4 risk group" model defined above, but also a "3 risk group" model (the two intermediate groups combined) as this may be more relevant and useful for clinical application. Materials and Methods A public untreated MM dataset (Multiple Myeloma Genomics Initiative, MMGI) had n=78 cases for which OS, GEP, and ISS were available for analysis. The prognostic markers SKY92 and ISS were applied as proposed [Kuiper et al ASH 2014] to classify cases into the risk categories. Results The risks for the 4 group classification model are shown in Table 1 and Figure 1, and the 3 risk group model is shown in Table 2 and Figure 1. In the 4 group model, the intermediate-low group (SKY92 standard + ISS II) was a small and not significantly different (p=0.79) from the low risk. The intermediate-high group (SKY92 standard + ISS III) was also a small group, with significantly worse outcome compared with the low risk group (p=0.012). Although stratification into four groups was statistically superior in the training and validation data [Kuiper et al ASH 2014] the interpretability benefits from aggregation of the middle two groups [Fig 1 right]. Table 1. Classification results for the four risk groups (Fig 1 left) Risk group n % Median OS HR vs low risk SKY92 High Risk regardless of ISS 19 24 28.4 m 10.8 SKY92 standard risk + ISS-III 10 13 63.0 m 4.1 SKY92 standard risk + ISS-II 16 21 NR (0.8) SKY92 standard risk + ISS-I (low risk) 33 42 NR NA NR= Not Reached at 96 months, HR= hazard ratio Table 2. Classification results for the three risk groups (Fig 1 right) Risk group n % Median OS HR vs low risk SKY92 High Risk regardless of ISS 19 24 28.4 m 10.1 SKY92 standard risk + ISS-II/III 26 34 78.5 m (1.8) SKY92 standard risk + ISS-I 33 42 NR NA NR= Not Reached at 96 months, HR= hazard ratio, () not significant Conclusions By applying the SKY92 + ISS risk stratification model in an independent validation cohort of newly diagnosed Multiple Myeloma patients, besides a high risk group of 19 patients (24%), a group of 33 patients (42%) with superior prognosis could be predicted (SKY92 standard risk and ISS I) that translated into 62% OS at 96 months. The 19 high risk (SKY92 high risk) cases had very poor prognosis (median survival of 28 months). The combination of SKY92 Standard Risk and ISS II and III seems useful for definition of "intermediate risk" although sample size currently is insufficient for significance compared to low risk. The intention is to also perform this validation on the relapsed samples from the MMGI cohort, once OS data has been collected. This validated risk model could play a role in the design of future treatment strategies for high and low risk MM patients. Figure 1. Kaplan Meier curves on the 78 MMGI cases, split into four (left) or three (right) risk groups based on the combination of SKY92 + ISS. Hazard Ratios (HR) are from a Cox proportional Hazards model comparing a particular group to the low risk group. Figure 1. Kaplan Meier curves on the 78 MMGI cases, split into four (left) or three (right) risk groups based on the combination of SKY92 + ISS. Hazard Ratios (HR) are from a Cox proportional Hazards model comparing a particular group to the low risk group. Disclosures van Beers: SkylineDx: Employment. van Vliet:SkylineDx: Employment. de Best:SkylineDx: Employment. Chari:Novartis: Consultancy, Research Funding; Millennium/Takeda: Consultancy, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Biotest: Other: Institutional Research Funding; Array Biopharma: Consultancy, Other: Institutional Research Funding, Research Funding; Onyx: Consultancy, Research Funding. Jagganath:Millennium: Honoraria; Celgene: Honoraria. Jakubowiak:Sanofi-Aventis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: institutional funding for support of clinical trial conduct, Speakers Bureau; SkylineDx: 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, Speakers Bureau; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; SkylineDx: Membership on an entity's Board of Directors or advisory committees; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sanofi-Aventis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Millennium: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Millennium: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Kumar:Celgene, Millenium, Sanofi, Skyline, BMS, Onyx, Noxxon,: Other: Consultant, no compensation,; Janssen: Research Funding; AbbVie: Research Funding; Sanofi: Research Funding; Millenium/Takeda: Research Funding; Celgene: Research Funding; Onyx: Research Funding; Skyline, Noxxon: Honoraria. Lebovic:Onyx: Speakers Bureau; Celgene: Speakers Bureau. Lonial:Millennium: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Onyx: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Reece:Onyx: Honoraria; Novartis: Honoraria; Millennium: Research Funding; Merck: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Research Funding. Richardson:Millennium Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gentium S.p.A.: 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. Siegel:Celgene Corporation: Consultancy, Speakers Bureau; Amgen: Speakers Bureau; Takeda: Speakers Bureau; Novartis: Speakers Bureau; Merck: Speakers Bureau. Stewart:SkylineDx: Membership on an entity's Board of Directors or advisory committees. Vij:Onyx: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Millennium: Honoraria, Speakers Bureau; BMS: Consultancy; Takeda: Consultancy, Research Funding; Novartis: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Merck: Consultancy. Zimmerman:Celgene: Honoraria, Speakers Bureau; Millennium: Honoraria, Speakers Bureau; Onyx: Honoraria; Amgen: Honoraria, Speakers Bureau. Fonseca:Onyx/Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Bayer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Binding Site: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Millennium: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Applied Biosciences: Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding.

Abstract Introduction Tumor specific somatic mutations can potentially influence patient response to targeted immunotherapies. Since somatic mutations generate neoantigens, novel tumor-specific peptides can be processed, loaded into MHC class I molecules, and presented to T-cells as potentially immunogenic targets. Indeed, both somatic mutation burden and neoantigen burden have been correlated with responsiveness to checkpoint inhibitor blockade in highly mutagenic advanced melanoma and lung cancer. However, correlation of response with mutation and neoantigen burden has not been reported for cancers of lower characteristic mutational burden such as multiple myeloma (MM). Indeed early results of checkpoint inhibitors in MM have been underwhelming. We therefore wished to better understand the mutation and neoantigen landscape of MM patients as it relates to responsiveness to current and future therapies. Materials/Methods Somatic mutation burden, neoantigen load, and response to therapy was obtained using interim data from theCoMMpassstudy (NCT01454297), a study by the Multiple Myeloma Research Foundation that collects longitudinal clinical and molecular data on 1,000 newly diagnosed MM patients from baseline through treatment and disease progression. Next-generation exome sequencing data was used to call somatic mutations andbioinformaticallydetermine patient specific HLA type. Neoantigen binding affinity was predicted for all unique patient specific somatic missense mutation and HLA allele combinations using a well validated neural network and machine learning based algorithm. Binding affinity of the corresponding non-mutated peptide was also predicted to identify mutated neoantigens witha high binding affinity while the wild type counterpart has low predicted binding affinity. Results For 627 patients with available sequencing and clinical data, mutation and neoantigen profiles were created. Within this population, the mean overall canonical somatic mutation load was 405.84 (s=608.55) mutations per patient. These mutations included 38,222 unique missense mutations in 12,994 genes. The mean somatic missense mutation load was 64.51(s=95.29) mutations per patient including immunoglobulin genes (52.70, s=94.85 excluding immunoglobulins). There was a positive linear correlation between mutation burden and neoantigen burden. The mean neoantigen load using thresholds of mutant peptide binding affinity (half maximal inhibitory concentration, IC50) less than 500nM was 69.57 (s=124.76) neoantigens (58.53, s=123.58 excluding immunoglobulins). Limiting binding affinity of wild type peptide sequence to greater than 500nM decreased the mean neoantigen load to 23.84 (s=53.55) neoantigens (20.74, s=53.28 excluding immunoglobulins). Progression free survival (PFS) following induction therapy was used to determine correlates of mutation burden excluding immunoglobulin mutations with response. Kaplan-Meier survival analysis revealed significantly shorter PFS time in patients with greater than average tumor somatic missense mutation load (N=126) relative to those with less than average mutation load (49.1% vs. 73.0% PFS at 2yrs, p=0.0055, Figure 1) Similarly, patients with greater than average neoantigen load (N=164) had significantly shorter PFS relative to those with less than average neoantigen load (48.0% vs. 74.2% PFS at 2yrs, p=0.00259, Figure 2). Moreover, patients with both high mutation and neoantigen load (N=85) had significantly shorter PFS relative to those with less than average mutation and neoantigen load (45.3% vs. 72% PFS at 2yrs, p= 0.0168). Conclusion While mutation and neoantigen load has been correlated with response to targeted immunotherapies in highly mutated solid tumors, we report here a negative correlation between mutation, neoantigen load and MM patient survival. This indicates that a high mutation andneoantigen load negatively impacts survival of myeloma patients under current standard of care. It is hypothesized that this is because current treatments fail to recruit T cell immunity necessary to exploit tumor neoantigens fundamental to immune-mediated killing of cancer cells. This presents an encouraging basis for pursuing targeted and personalized immunotherapies for multiple myeloma, especially for patients with a higher mutational burden. PFS and mutation load. PFS and mutation load. PFS and neoantigen load. PFS and neoantigen load. Disclosures Lonial: BMS: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Onyx: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Janssen: Consultancy; Onyx: Consultancy; Novartis: Consultancy; BMS: Consultancy; Millenium: Consultancy; Merck: Consultancy.

Abstract Abstract 832 We hypothesized that new therapeutic targets for multiple myeloma (MM) could be discovered through the integrative computational analysis of genomic data. Accordingly, we generated gene expression profiling and copy number data on 250 clinically-annotated MM patient samples. Utilizing an outlier statistical approach, we identified HOXA9 as the top candidate gene for further investigation. HOXA9 expression was particularly high in patients lacking canonical MM chromosomal translocations, and allele-specific expression analysis suggested that this overexpression was mono-allelic. Indeed, focal copy number amplifications at the HOXA locus were observed in some patients. Outlier HOXA9 expression was further validated in both a collection of 52 MM cell lines and 414 primary patient samples previously described. To further verify the aberrant expression of HOXA9 in MM, we performed quantitative RT-PCR, which confirmed expression in all MM patients and cell lines tested, with high-level expression in a subset. To further investigate the mechanism of aberrant HOXA9 expression, we interrogated the pattern of histone modification at the HOXA locus because HOXA gene expression is particularly regulated by such chromatin marks. Accordingly, immunoprecipitation studies showed an aberrantly low level of histone 3 lysine 27 trimethylation marks (H3K27me3) at the HOXA9 locus. H3K27me3 modification is normally associated with silencing of HOXA9 in normal B-cell development. As such, it appears likely that the aberrant expression of HOXA9 in MM is due at least in part to defects in histone modification at this locus. To determine the functional consequences of HOXA9 expression in MM, we performed RNAi-mediated knock-down experiments in MM cell lines. Seven independent HOXA9 shRNAs that diminished HOXA9 expression resulted in growth inhibition of 12/14 MM cell lines tested. Taken together, these experiments indicate that HOXA9 is essential for survival of MM cells, and that the mechanism of HOXA9 expression relates to aberrant histone modification at the HOXA9 locus. The data thus suggest that HOXA9 is an attractive new therapeutic target for MM. Disclosures: No relevant conflicts of interest to declare.

Key Points Using the largest set of patients with newly diagnosed myeloma, we identified 63 mutated driver genes. We identified oncogenic dependencies, particularly relating to primary translocations, indicating a nonrandom accumulation of genetic hits.

Abstract Treatment of myeloma has benefited from the introduction of more effective and better tolerated agents, improvements in supportive care, better understanding of disease biology, revision of diagnostic criteria, and new sensitive and specific tools for disease prognostication and management. Assessment of minimal residual disease (MRD) in response to therapy is one of these tools, as longer progression-free survival (PFS) is seen consistently among patients who have achieved MRD negativity. Current therapies lead to unprecedented frequency and depth of response, and next-generation flow and sequencing methods to measure MRD in bone marrow are in use and being developed with sensitivities in the range of 10−5 to 10−6 cells. These technologies may be combined with functional imaging to detect MRD outside of bone marrow. Moreover, immune profiling methods are being developed to better understand the immune environment in myeloma and response to immunomodulatory agents while methods for molecular profiling of myeloma cells and circulating DNA in blood are also emerging. With the continued development and standardization of these methodologies, MRD has high potential for use in gaining new drug approvals in myeloma. The FDA has outlined two pathways by which MRD could be qualified as a surrogate endpoint for clinical studies directed at obtaining accelerated approval for new myeloma drugs. Most importantly, better understanding of MRD should also contribute to better treatment monitoring. Potentially, MRD status could be used as a prognostic factor for making treatment decisions and for informing timing of therapeutic interventions. Clin Cancer Res; 23(15); 3980–93. ©2017 AACR.

To gain insight into the genomic basis of diffuse large B-cell lymphoma (DLBCL), we performed massively parallel whole-exome sequencing of 55 primary tumor samples from patients with DLBCL and matched normal tissue. We identified recurrent mutations in genes that are well known to be functionally relevant in DLBCL, including MYD88 , CARD11 , EZH2 , and CREBBP . We also identified somatic mutations in genes for which a functional role in DLBCL has not been previously suspected. These genes include MEF2B , MLL2 , BTG1 , GNA13 , ACTB , P2RY8 , PCLO , and TNFRSF14 . Further, we show that BCL2 mutations commonly occur in patients with BCL2 / IgH rearrangements as a result of somatic hypermutation normally occurring at the IgH locus. The BCL2 point mutations are primarily synonymous, and likely caused by activation-induced cytidine deaminase–mediated somatic hypermutation, as shown by comprehensive analysis of enrichment of mutations in WRCY target motifs. Those nonsynonymous mutations that are observed tend to be found outside of the functionally important BH domains of the protein, suggesting that strong negative selection against BCL2 loss-of-function mutations is at play. Last, by using an algorithm designed to identify likely functionally relevant but infrequent mutations, we identify KRAS , BRAF , and NOTCH1 as likely drivers of DLBCL pathogenesis in some patients. Our data provide an unbiased view of the landscape of mutations in DLBCL, and this in turn may point toward new therapeutic strategies for the disease.

Abstract The plasma cell malignancy Multiple Myeloma (MM) is a rare but deadly form of cancer with 5 year survival rates of ∼30%. The disease exacts an especially heavy burden on African American (AA) patients. In fact, African Americans are twice a likely to be diagnosed with and die from MM as compared to Caucasian Americans. Although socio-economic factors likely influence this disparity, the role of biology has not been ruled out. We have profiled ∼250 MM tumors for somatic copy number alterations using array-based Comparative Genomic Hybridization (aCGH) and Gene Expression Profiling as part of the Multiple Myeloma Genomics Initiative. To determine if somatic alterations may play a role in the disparity in incidence and/or severity of MM in AA, we compared the frequency of specific chromosomal aberrations in genes and genomic regions previously found to be involved in progression and poor outcome in MM among a cohort of one hundred ninety-six (196) patients including 16 African Americans and 180 Caucasians. The frequency of occurrence (expressed as a percentage) was determined for each of the following aberrations: 1p deletion, homozygous deletion of p18, chromosome 13 deletion, and chromosome 13 monosomy. No significant difference was detected in the frequency of chromosome 13 deletion and chromosome 13 monosomy between the two groups. However, significant differences were observed for chromosome 1p alterations. Homozygous deletion at the p18 locus at 1p occurred in 12.5% (2/16) of tumors from AA patients and 2.8% (5/180) of CA patient tumors (p = 0.045). We further stratified tumors by hyperdiploid or non-hyperdiploid status using a-CGH profiles to determine ploidy status. Most significantly, p18 homozygous deletion was found in 25% (2/8) of AA patients with non-hyperdiploid tumors and in only 1.2% (1/83) of CA patients with non-hyperdiploid tumors (p=3.2 × 10-4). A nominally significant difference among non-hyperdiploid patients was also present for overall 1p deletion, which was observed in 2/8 (25%) of AA non-hyperdiploid tumors and 33/83 (40%) of CA with non-hyperdiploid tumors (p=0.05). No differences were observed for 1p deletion or p18 homozygous deletion among hyperdiploid tumors between AA and CA patients. These results suggest that deletions at 1p are more common among African American MM patients. Furthermore, homozygous deletion encompassing the p18 locus is more significantly common among AA MM patients, especially AA patients with non-hyperdiploid tumors. As 1p deletion and p18 homozygous deletion have been linked to poor outcome, our results suggest that the increased frequency of these alterations in tumors from AA patients may in part explain the disparity in unfavorable outcomes seen among AA MM patients Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4822.

Abstract Multiple myeloma (MM) is a cancer of the plasma cells in the bone marrow. Although rare, MM is a deadly form of cancer with a 5 year survival rate of only 37%. African Americans (AA) are twice as likely to be diagnosed with and die from MM as compared to European Americans (EA). Socioeconomic factors may contribute to this disparity, however, biological factors may also be an influence. We have identified somatic copy number alterations in MM as a part of the Multiple Myeloma Genomics Initiative by profiling 250 MM tumors using array-based comparative genomic hybridization (aCGH). Using this cohort, we identified 16 African American and 180 European American patients using a MMRC database that indicated the racial demographics of each patient. To determine if somatic alterations may play a role in the disparity in incidence and/or severity of MM in AA, we compared the frequency of specific chromosomal aberrations in genes and genomic regions found to be involved in progression and poor outcome. The number of events of occurring was calculated and a univariate analysis was conducted using the Fisher's exact test. P-values were used to determine statistical significance. Results revealed significant differences for chromosome 1 p alterations. Specifically, homozygous deletion at the p18 locus at 1 p occurred in 12.5% (2/16) of tumors from AA patients and 2.8% (5/180) of EA patient tumors (p = 0.045). Further stratification of tumors by hyperdiploid or non-hyperdiploid status revealed significant differences in p18 homozygous deletion. Homozygous deletion of p18 was found in 25% (2/8) of AA patients and only 1.2% (1/83) of EA patients with non-hyperdiploid tumors (p=3.2 × 10-4). A nominally significant difference among non-hyperdiploid patients was also present for overall 1 p deletion, which was observed in 2/8 (25%) of AA non-hyperdiploid tumors and 33/83 (40%) of EA with non-hyperdiploid tumors (p=0.05). No differences were observed for 1 p deletion or p18 homozygous deletion among hyperdiploid tumors between AA and EA patients. We then combined our AA dataset with an independent dataset of 8 AA MM patients generated at the Mayo Clinic, Scottsdale AZ and repeated the analysis. Interestingly, very little or no aberrations were found in the dataset from the Mayo Clinic, however, differences of p18 homozygous deletion and 16q deletion among AA and EA remained statistically significant. To determine the correlation between hemizygous and biallelic loss of p18 and poor prognosis we used the 70 gene signature (Shaughnessy, et al., 2007) as a surrogate to identify patients with poor prognosis within the Multiple Myeloma Genomics Initiative dataset. Genomic identification of significant targets in cancer (GISTIC) was used (Beroukhim et al., 2007) to identify regions of aberrant copy number that were over-represented among samples. A relationship between poor prognosis and biallelic loss was found to be statistically significant (p = 0002) while samples with hemizygous loss showed no relationship (p = 0.57). Although the size of the current AA cohort is small, these results suggest that homozygous deletions at the p18 locus may be more common among African American MM patients with non-hyperdiploid tumors which may help to explain the disparity in AA patients with MM. Array CGH is currently being conducted on an additional 40 tumors from AA MM patients for a more robust analysis. Citation Information: Cancer Epidemiol Biomarkers Prev 2010;19(10 Suppl):B64.

Abstract Multiple myeloma (MM) is a plasma cell malignancy of the bone marrow, which evolves from a premalignant stage called monoclonal gammopathy of undetermined significance (MGUS). In some patients, an intermediate stage referred to as smoldering multiple myeloma (SMM) is clinically recognized, with the full-bore malignancy termed MM. We conducted a study to assess differential CpG methylation at 1,500 genic loci during MM progression and profiled CD138+ plasma cells from MGUS, SMM, and MM specimens; human myeloma cell lines; and normal plasma cell (NPC) samples. We showed that the number of differentially methylated loci (DML) increased with tumor grade, and the vast majority were due to hypomethylation. Hierarchical clustering analysis revealed samples that coclustered tightly with NPC. These cases, referred to as “normal-like,” contained significantly fewer DML when compared with their non–normal-like counterparts and displayed overall methylation levels resembling NPC. This study represents one of the first methylome interrogation studies in MM and points toward global hypomethylation at genic CpG loci as an important and early mechanism driving myelomagenesis. Determining the set of critical genes and pathways based on the myeloma methylome is expected to lead to an improved understanding of biological mechanisms involved in myelomagenesis. Cancer Res; 70(17); 6934–44. ©2010 AACR.