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. Author manuscript; available in PMC: 2017 Aug 30.
Published in final edited form as: Clin Cancer Res. 2016 Feb 4;22(16):4039–4044. doi: 10.1158/1078-0432.CCR-15-2366

Impact of genes highly correlated with MMSET myeloma on survival of Non-MMSET myeloma patients

S Peter Wu 1, Ruth M Pfeiffer 2, Inhye E Ahn 1, Sham Mailankody 3, Pieter Sonneveld 4, Mark van Duin 4, Nikhil Munshi 5, Brian Walker 6, Gareth Morgan 7, Ola Landgren 3
PMCID: PMC5576175  NIHMSID: NIHMS757967  PMID: 26847058

Abstract

Purpose

The poor prognosis of multiple myeloma with t(4;14) is driven by the fusion of genes encoding MMSET and immunoglobulin heavy chain. Specific genes affected by MMSET and their clinical implications in Non-MMSET myeloma remain undetermined.

Experimental design

We obtained gene-expression profiles of 1,032 newly diagnosed myeloma patients enrolled in Total Therapy 2, Total Therapy 3, Myeloma IX, and HOVON65-GMMGHD4 trials, and 156 patients from Multiple Myeloma Resource Collection. Probes most correlated with MMSET myeloma were selected based on a multivariable linear regression and Bonferroni correction, and refined based on the strength of association with survival in Non-MMSET patients.

Results

Ten MMSET-like probes were associated with poor survival in Non-MMSET myeloma. Non-MMSET myeloma patients in the highest quartile of the 10-gene signature (MMSET-like myeloma) had 5-year overall survival similar to that of MMSET myeloma (highest quartile vs. lowest quartile hazard ratio [HR]=2.0, 95% confidence interval [CI] 1.5-2.8 in MMSET-like myeloma; HR=2.3, 95% CI: 1.6-3.3 in MMSET myeloma). Analyses of MMSET-like gene signature suggested the involvement of p53 and MYC pathways.

Conclusion

MMSET-like gene signature captures a subset of high-risk myeloma patients under-represented by conventional risk stratification platforms, and defines a distinct biological subtype.

Keywords: multiple myeloma, high risk myeloma, MMSET, t(4;14), cDNA microarray

Introduction

Multiple myeloma has extremely heterogeneous outcomes. Among many prognostic factors utilized in myeloma, translocation t(4;14)(p16.3;q32.3) is an oncogenic event associated with poor prognosis. (1) The key molecular target of t(4;14) is multiple myeloma SET domain (MMSET) at chromosomal band 4p16.3. (2-5) The detection of MMSET overexpression with gene-expression profiling (GEP) consistently identifies a high-risk subgroup in multiple myeloma. (6) While the prognostic significance of MMSET is well established, the underlying mechanism of its excess risk is poorly understood. Given MMSET encodes histone methyltransferase, its overexpression has been attributed to alter epigenetic regulation of genes involved in cell cycle progression and DNA damage repair. (7) However, downstream gene targets and molecular pathways regulated by MMSET remain unclear.

What is also unknown in myeloma is the presence of biological homology shared between high-risk and non-high-risk subgroups. This question comes within the context of the recent advancement of genetic sequencing, which identified diverse spectrum of disease biology that, at times, redefined conventional risk stratification and management. For instance, “BRCA-ness” was identified in up to 14% of non-small cell lung cancer and 15% of head and neck cancer patients due to epigenetic inactivation of genes responsible for DNA damage repair, such as BRCA1 and FNACF. (8) In breast and ovarian cancers, next-generation sequencing demonstrated the presence of certain genes beyond BRCA1/2, such as PALB2, ATM or CHEK2, was strongly associated with an increased risk of cancer diagnosis and early death. (9-11) Recent discoveries in solid tumor suggest a substantial proportion of cancer patients harbors molecular signatures similar to those of high-risk subtypes.

We hypothesize there is an overlap of disease biology between the established high-risk myeloma and its non-high-risk counterpart. Specifically, the same genes involved in the pathogenesis and adverse outcomes of MMSET myeloma (6) could also be relevant to a subset of Non-MMSET patients with poor clinical outcomes (hereby refer to “MMSET-like myeloma”). To characterize genes and molecular pathways influencing survival across different myeloma subtypes, we assessed expression levels of 54,675 genes in 1,188 newly diagnosed multiple myeloma patients. Among 71 genes significantly altered in MMSET myeloma, 10 genes most strongly associated with survival were selected and combined into a GEP risk score. Patients who did not have detectable MMSET but were at the top quartile of the 10-gene risk score were categorized as MMSET-like myeloma. Five-year survivals were similar between patients with MMSET myeloma and MMSET-like myeloma. Pathway analysis identified MYC and TP53 transcriptional regulators as lead candidates targeted by the observed genes within the risk score. Our findings suggest there is a homology of aggressive disease biology and clinical outcomes shared between MMSET myeloma and a subset of non-MMSET myeloma.

Methods

Study design

From the NCBI Gene Expression Omnibus (GEO), we downloaded unprocessed CEL files from the following datasets: Total Therapy (TT) 2 (N=345, accession number GSE2658, NCT00083551); TT 3 (N=214, accession number GSE2658, NCT00081939); HOVON65/GMMG-HD4, (N=320, accession number GSE19784, ISRCTN64455289); Myeloma IX (N=247, accession number GSE15695, ISRCTN68454111); and Multiple Myeloma Reference Collection (MMRC) (N=288, accession number GSE26760). The sample size of each data set was determined after excluding 8 profiles (accession number GSE19784) that were normal plasma cells and 16 patients (accession number GSE26760) who were smoldering myeloma (n=11), MGUS (n=2), or plasma cell leukemia (n=3). Anonymized patient characteristics of TT trials were obtained from GEO and were identified with the same accession numbers. Anonymized patient characteristics of Myeloma IX and HOVON65/GMMG-HD4 trials were obtained through personal correspondence with Mark van Duin and Ping Wu, respectively. Anonymized patient characteristics of MMRC were obtained from Multiple Myeloma Genome Portal. (12) Selected characteristics of patients from the five studies are shown in Table 1.

Table 1. Patient and study characteristics.

Induction Therapy Maintenance Median Age, Years (range) Women, N (%) ISS 1, N (%) ISS 2, N (%) ISS 3, N (%) Maximum Follow-up (years)
TT2 (N=345)
TT3 (N=208) 		D(T)-PACE (N=345) Thalidomide 57 (24-77) 148 (43) 184 (53) 90 (26) 71 (21) 8.2
VTD-PACE (N=208) Bort-Thal-Dex 60 (32-75) 72 (34) 100 (48) 64 (31) 44 (21) 4.4
HOVON65/GMMGHD5 (N=296) VAD (N=143) Thalidomide 58 (27-65) 61 (43) 55 (38) 44 (31) 44 (31) 6.1
PAD (N=153) Bortezomib 56 (31-65) 58 (38) 51 (33) 62 (41) 40 (26) 5.8
Myeloma IX (N=183) CTD (N=58) (+/-) Thalidomide (N=30) 59.5 (45-69) 11 (37) 8 (27) 12 (40) 10 (33) 8.1
NULL (28) 61 (35-68) 13 (46) 10 (36) 10 (36) 8 (28) 7.6
CVAD (N=48) (+/-) Thalidomide (N=23) 57 (39-69) 6 (26) 8 (35) 7 (30) 8 (35) 7.6
NULL (N=25) 60 (48-68) 10 (40) 6 (24) 9 (36) 10 (40) 7.4
CTDa (N=41) (+/-) Thalidomide (N=19) 73 (67-83) 8 (42) 2 (11) 5 (27) 12 (63) 7.5
NULL (N=22) 73.5 (61-84) 12 (55) 1 (5) 11 (50) 10 (46) 7.7
Melphalan (N=36) (+/-) Thalidomide (N=15) 70 (63-80) 8 (53) 5 (33) 5 (33) 5 (33) 7.2
NULL (N=21) 74 (62-89) 8 (38) 2 (10) 7 (34) 12 (57) 6.5
MMRC* (N=156) -* -* 60 (24-89) 51 (32) 74 (48) 46 (30) 36 (23) -*
All Studies (N=1188) -* -* 59 (24-89) 466 (39%) 506 (43%) 372 (31%) 310 (26%) 8.2
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D(T)-PACE: Dexamethasone with or without thalidomide, cisplatin P, doxorubicin A, cyclophosphamide C, etoposide E. VTD-PACE: V Bortezomib. PAD: Bortezomib P, Doxorubicin A, Dexamethasone D, VAD: Vincristine V, Doxorubicin A, Dexamethasone D. CTD/CVAD: Cyclophosphamide C, Thalidomide T, Doxorubicin A, Dexamethasone D.

*

=information not available

All gene-expression data were derived from CD138+ purified plasma cells of newly diagnosed myeloma patients, which were hybridized to Affymetrix Human Genome U133 Plus 2.0 cDNA microarray (Santa Clara, CA). All raw CEL files were processed using the justMAS function in the R statistical programming language, and gene-expression levels were log2 transformed. The final dataset included GEPs of 1,188 myeloma patients with complete data for age, sex, beta-2 microglobulin, and albumin. For the HOVON65/GMMG-HD4 trial, FISH data regarding MMSET status was available for 241 patients; MMSET status by FISH versus gene expression revealed a correlation of 0.81 (Spearman's rho). For the analysis of survival outcomes, we excluded 156 patients from MMRC as it was not a clinical trial, and only used the remaining data from 1,032 patients.

Institutional Review Boards of respective institutions approved all studies. All subjects provided written informed consents approving the use of their samples for research purposes.

Statistical analysis

MMSET myeloma patients, non-MMSET myeloma patients, and genes associated with MMSET myeloma

To classify patients into MMSET or Non-MMSET myeloma, we used the previously reported microarray model using 700 gene probes to assign subjects into one of seven molecular subtypes. (13) We assessed the association of MMSET myeloma with individual expression levels of 54,675 available probes. By using linear regression models for each probe and for each study, gene-expression levels were dependent variables of MMSET status, age (divided by 50 years or less, 51-60 years, 61-70 years, 71 years or older), sex, and International Staging System (ISS) stage. (14) For each probe, study-specific linear regression coefficients for MMSET myeloma were then combined across studies using a random effects meta-analysis. (15) Prior to finalizing the probes that were significantly associated with MMSET myeloma, all 700 gene probes used in the Arkansas model (13) were removed. We performed a random effects meta-analysis after Bonferroni correction for multiple testing (p<0.05/54,675=9.14×10-7). The absolute value of the random effects slope parameter for MMSET myeloma was 2 or greater, indicating MMSET myeloma had 2-fold or greater changes in log expression of a given gene.

Identification of probes associated with survival in Non-MMSET patients

To identify probes relevant to survival of non-MMSET patients, results from the aforementioned meta-analysis were analyzed by a stepwise variable selection (proc phreg, SAS 9.3) in a Cox proportional hazards model. Duration of follow-up was defined by the start of treatment until death or censoring. Censoring occurred when a subject reached 5 years or was lost to follow up. For the initial selection of probes, we included probes that passed Bonferroni correction for multiple testing, and those showed log 2 or greater changes of expression. For each probe, a minimal p<0.1 in a marginal Cox proportional hazards model was set for initial inclusion, and a criteria of p<0.05 was set to retain a probe in the model. All models were adjusted for age, sex, ISS stage (14) and treatment. (16-18) The risk score was calculated based on the adjusted Cox regression model (Appendix 1).

Validation

To assess the unbiased association of the risk score and survival, we conducted a 5-fold cross-validation. (18) Briefly, the original dataset was divided into five equal parts, with equal numbers of patients from individual studies in each part. Four of the five parts were used to develop a gene signature following the aforementioned procedures (training set). The remaining fifth part was used to compute the association of the risk score and survival using Cox regression models (test set). Validation was performed five times with each part serving as a test set once. Risk scores from five test sets were median-centered and combined to form an independently scored measure of risk.

Sensitivity analysis

To assess stability of our results, we conducted three separate sensitivity analyses (Supplemental Tables 1 and 2). First, we used survival outcomes throughout the full follow-up time of up to 98 months instead of censoring at 60 months. Second, we excluded patients who were treated with proteasome inhibitor-based regimens, such as VTD-PACE and PAD, from the analysis. Third, patients on Myeloma IX trial were coded separately if they encountered death or censoring before the second randomization for thalidomide maintenance. In all three sensitivity analyses, the main results remained unchanged.

Pathway analysis

To determine biological functions of the identified gene probes, pathway analysis was performed using Ingenuity Pathway Analysis software package and the molecular signaling database from the Broad Institute (MsigDB). (19) Gene networks were constructed using the upstream regulator analysis to identify transcription factors with the most interactions with selected genes (Figure 1).

Figure 1.

Figure 1

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Full lines represent direct interactions while dashed lines indicate indirect interactions. An arrow pointing from one protein to another indicates that the first protein acts on or activates the second protein (at which the arrow is pointing).

Results

Among 1,032 myeloma patients included in this study, 139 (13.4%) had MMSET myeloma defined by GEP (Table 1). (13) In MMSET myeloma, the median age was 59 years (range 24-89), and 68% were males. Distributions of ISS stage I, II, and III were 48%, 30% and 22%, respectively. Similar to prior reports (6), MMSET myeloma was associated with a higher mortality after adjusting for age, sex, ISS stage and treatment (hazard ratio (HR) =1.7, p<0.001).

To determine if the same genes involved in MMSET myeloma were also relevant to survival of Non-MMSET myeloma patients, we took the following analytical approach: First, as described in the Methods, we obtained GEPs of 1,188 newly diagnosed myeloma patients and defined 71 gene probes correlated with MMSET myeloma (Supplemental Table 3). From these probes, we further identified those associated with 5-year survival in Non-MMSET patients and created a 10-gene risk score predictive of survival. Lastly, we conducted a functional pathway analysis.

Gene probes correlated with MMSET myeloma

After the random effects meta-analysis, we identified 71 gene probes (0.13%) correlated with MMSET myeloma. The selected genes showed 2-fold or greater changes in log-expressions (range 2.0 to 3.7 or -2.0 to -3.7) in MMSET patients compared to Non-MMSET patients, and meta-analytic p-values ranged from 1.9×10-11 to 5.2×10-36 (Supplemental Table 3). Genes highly correlated with MMSET myeloma included cyclin D1 (CCND1), cyclin D2 (CCND2), a transcription factor Kruppel-like factor 4 (KLF4), ubiquitin carboxyl-terminal esterase L1 (UCHL1), and alpha-2-glycoprotein (AZGP1) (Supplemental Table 3).

Probes enriched in MMSET myeloma, Non-MMSET myeloma, and survival

From the identified 71 gene probes, 10 genes were strongly associated with 5-year survival of Non-MMSET patients (Table 2). AZGP1 and CCND1 were most significantly associated with survival (probe-specific HRs: 0.89-0.91 for CCND1 and 1.07-1.14 for AZGP1, p<0.001). To define risk scores relevant to survival of non-MMSET myeloma patients, a multivariable Cox proportional hazards model was applied to the 10 genes. Risk score groups of the first quartile (low-risk) and the fourth quartile (high-risk) were compared within Non-MMSET patients in cross-validation. High-risk Non-MMSET patients (here by referred as “MMSET-like myeloma”) had a similarly increased risk of mortality (HR=2.0, 95% confidence interval (CI) 1.5-2.8, p<0.001) comparable to MMSET patients (HR=2.3, 95% CI 1.6-3.3, p<0.001).

Table 2. Genes identified as highly correlated to MMSET myeloma and associated with survival in Non-MMSET patients.

gene probe Lin Reg coef1 p-value HR2 (95% CI) p-value TP53 Interacting MYC Interacting annotation
CCND1 208712_at -3.6 1.70E-33 0.90 (0.86-0.94) <.0001 Yes Yes cyclin D1
AZGP1 209309_at 3.08 6.40E-25 1.10 (1.05-1.16) <.0001 No Yes alpha-2-glycoprotein 1 zinc
SGCB 205120_s_at 2.51 3.90E-17 1.09 (1.03-1.15) 0.0014 No No sarcoglycan beta (dystrophin-associated glycoprotein)
MAGED4 223313_s_at 2.95 5.00E-23 0.92 (0.87-0.97) 0.0033 No No melanoma antigen family D 4
PXDN 212012_at 2.21 1.30E-13 1.09 (1.03-1.15) 0.0035 No No peroxidasin homolog (Drosophila)
RNF130 217865_at 2.47 1.40E-16 0.93 (0.89-0.98) 0.0053 No Yes ring finger protein 130
MYBL1 213906_at 2.5 5.30E-17 1.08 (1.02-1.14) 0.0086 Yes Yes v-myb myeloblastosis viral oncogene homolog (avian)-like 1
PTP4A3 206574_s_at 2.51 4.70E-17 0.94 (0.90-0.99) 0.0104 Yes Yes protein tyrosine phosphatase type IVA member 3
MPPE1 213924_at 2.28 2.10E-14 0.92 (0.86-0.98) 0.0109 No No Metallophosphoesterase 1
ROBO1 213194_at 2.23 8.60E-14 1.06 (1.01-1.11) 0.0288 Yes No roundabout axon guidance receptor homolog 1 (Drosophila)
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1

linear regression coefficient associated with MMSET from meta analysis that uses log2 transformed probe levels as outcome

2

HRs from adjusted Cox regression model that fit each probe separately to 5-year survival

Pathway analysis

To characterize genes and molecular pathways influencing survival across different myeloma subtypes, we conducted analysis of the 10 genes associated with 5-year survival in MMSET-like myeloma by using IPA (Ingenuity® Systems). Pathway analysis identified MYC and TP53 transcriptional regulators as lead candidates for the observed gene expression changes within the gene signature risk score (Figure 1). TP53 was identified as a transcriptional regulator of four genes (CCND1, PTP4A3, MYBL1, and ROBO1) (p=1.9×10-3), and MYC was a transcriptional regulator of five genes (CCND1, AZGP1, PTP4A3, MYBL1, and RNF130) (p=3.1×10-4).

Discussion

To characterize genes and molecular pathways influencing survival across myeloma subtypes, we assessed expression levels of over 55,000 gene probes from tumor cells obtained from 1,188 newly diagnosed myeloma patients. 71 genes were significantly altered in patients with the MMSET molecular subtype. Selecting from these genes, 10-gene risk score demonstrated similar 5-year survivals between MMSET myeloma and Non-MMSET patients categorized as the top quartile risk score (MMSET-like myeloma). A 5-fold cross-validation was conducted to determine the unbiased association of the risk score and survival. Pathway analysis identified MYC and TP53 transcriptional regulators were associated with the observed gene-expresssion changes of 10 genes.

Of clinical relevance, our findings suggest an overlap of disease biology between conventionally divided groups of high-risk and non-high-risk myelomas. The study findings should be interpreted within the context of recent advancement of genetic sequencing, which refined tumor subtypes based on recurrent genetic alterations. In ovarian cancer, approximately half of the patients were found to have homologous recombination deficiency (HRD) mimicking the genetic phenotype of BRCA mutation (i.e. “BRCA-ness”). (20) Intriguingly, the presence of HRD in BRCA wild-type patients predicted striking sensitivity to PARP inhibition in a prospective trial (overall response rate: 32% with HRD vs. 11% without HRD), albeit less than the true BRCA mutated group (66%). Evolving knowledge in biological homology across different tumor subtypes proposes a new therapeutic strategy is required to improve the outcome of patients with MMSET-like gene signature. As seen in differential responsiveness to PARP inhibition in cancers with BRCA-ness, MMSET-like subgroup may also benefit from established or investigational regimens developed for high-risk myeloma, rather than those developed for standard-risk population. Such regimens tested in high-risk myeloma include proteasome inhibitors (21-23) and other investigational agents aimed at novel targets such as FGFR3, (5) CD38, (24) and MEK pathway. (25) Further research needs to validate the role of genomic risk stratification tools to capture high-risk population, and to prospectively assess clinical outcome to potential treatment options within the identified subgroup.

Another important observation of this study is the demonstration of TP53 and MYC as downstream targets of MMSET gene signature. t(4;14) accounts for 15% of myeloma population and is linked to universal overexpression of MMSET gene. (3, 4) Histone methyltransferase encoded at catalytic SET domain methylates lysine residue of histone, leading to epigenetic regulation of genes involved in cell cycle progression, p53 pathway, and integrin signaling. (7) The role of MMSET as a myeloma oncogene is supported by an experimental knock-down of MMSET in myeloma cell-lines, which led to decreased proliferation and increased apoptosis. (2, 26, 27), Among many targets altered by MMSET overexpression, c-myc is an important downstream pathway enhanced by MMSET through down-regulation of miR-126. (28, 29) The overlap of p53 and MYC pathways has also been described in model systems of other malignancies. In vitro, p53 represses c-myc transcription by deacetylation of histone located at c-myc promoter (30) and by miR-145-mediated gene silencing, (31) and arrests cell cycle. These findings support the primary role of MMSET as a regulator of epigenetic machineries, rather than genetic instability, and is corroborated by findings from whole exome sequencing which demonstrated only a few mutational changes in the t(4;14) subgroup. (32) Taken together, an aggressive clinical phenotype of MMSET overexpression is attributable to the fine-tuning of selected genes. Functional studies are required to assess direct binding or indirect modulation of 10 genes by MSMET and to validate downstream activity of MMSET-like signature converging into selected signaling pathways, such as MYC and p53.

Gene-expression profiling is a mature and robust technology with many validated platforms in multiple myeloma reported to date. (33) Compared to previously established platforms, MMSET-like signature has several unique aspects. First, MMSET-like gene signature was developed from a biologically homogeneous population with a single genetically defined abnormality, and was applied to the overall population with an aim to select patients influenced by similar pathobiology. This sequence of development is reversed from what had been done in conventional studies, which performed hierarchical gene clustering among biologically heterogeneous population. (34) By using the latter method, a given gene-expression group can contain several different genetic abnormalities within the subtype, (6) which may have led to inconsistent results in predicting therapeutic responses. (35) The 10-gene signature proposed by the current study was developed from a homogenous subgroup, hence may be more representative of a single biological entity and can serve a useful risk stratification tool for treatment trials. Second, with the exception of one gene, 10-gene signature did not overlap with previously reported platforms such as EMC 92-gene, (34) UAMS 70-gene (6) and IFM 15-gene signatures. (36) This finding further supports that MMSET-like gene signature represents a distinct biological subtype utilizing a selected set of genes. Interestingly, ROBO1 was the only gene within our 10-gene platform that was previously reported in another gene expression profile (37) and in a sequencing study as a candidate gene in myeloma. (32) Downstream of ROBO1 is associated with E-cadherin mediated regulation of WNT signaling in pancreatic cancer, and its functional role in myeloma remains to be studied.

We demonstrated 10-gene signature that were significantly altered in MMSET myeloma and associated with inferior survival in Non-MMSET myeloma patients. Pathway analysis of the MMSET-like gene signature recapitulated clustering of important signaling pathways in myeloma, specifically TP53 and MYC pathways. MMSET-like gene expression profile was able to capture a distinct biological subtype under-represented by conventional platforms, and was strongly linked poor clinical outcome. The proposed gene signature can serve as a reliable screening platform representative of high-risk disease biology, as we move towards personalized therapy for myeloma.

Supplementary Material

1

Statement of Translational Relevance.

Multiple myeloma is a biologically and clinically heterogeneous disease. The presence of biological homology shared between conventional high-risk and non-high-risk myeloma subgroups has not been reported to date. We hypothesized that molecular risk stratification can capture biological homology between patients with or without MMSET overexpression, and be used as a prognostic tool. We identified 10-gene signature associated with MMSET myeloma. We obtained gene-expression profiles of 1,032 newly diagnosed myeloma patients enrolled in Total Therapy 2, Total Therapy 3, Myeloma IX, and HOVON65-GMMGHD4 trials, and 156 patients from Multiple Myeloma Resource Collection. Expression of MMSET-like gene signature in Non-MMSET subgroup was associated with similarly poor survival. Pathway analysis of MMSET-like gene signature revealed the involvement of p53 and MYC signaling pathways. MMSET-like gene signature captures a subset of high-risk myeloma patients under-represented by conventional risk stratification platforms, and defines a distinct biological subtype.

Acknowledgments

None

Grant Support: This work was supported by the intramural research program of the National Institutes of Health. Research support for S.P.W. was made possible through the National Institutes of Health (NIH) Medical Research Scholars Program, a public-private partnership supported jointly by the NIH and generous contributions to the Foundation for the NIH from Pfizer Inc, The Leona M. and Harry B. Helmsley Charitable Trust, and the Howard Hughes Medical Institute, as well as other private donors. For a complete list, please visit the Foundation website at http://www.fnih.org/work/programs-development/medical-research-scholars-program).

Footnotes

Disclosure of Potential Conflict of Interest: Dr. Landgren has given scientific presentations at meeting funded by Onyx Pharmaceuticals/AMGEN, Celgene, BMS and Jansen; has served on the Independent Data Monitoring Committee for clinical trials by Millennium Pharmaceuticals/Takeda; and has served on the advisory committee for Medscape Educations, Myeloma program.

Authors' Contributions: Contribution: S.P.W, R.M.P., and O.L. designed the study and performed bioinformatics analysis; S.P.W, R.M.P., I.E.A., S.M., P.S., M.D., N.M., B.W., and O.L. reviewed data analysis and wrote manuscript.

Supplementary information contains 3 tables and 1 figure, which are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).

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