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. Author manuscript; available in PMC: 2013 Jun 17.
Published in final edited form as: Leukemia. 2009 Aug 6;23(10):1913–1919. doi: 10.1038/leu.2009.129

Genetic polymorphisms of EPHX1, Gsk3β, TNFSF8 and myeloma cell DKK-1 expression linked to bone disease in myeloma

BGM Durie 1, B Van Ness 2, C Ramos 2, O Stephens 3, M Haznadar 2, A Hoering 4, J Haessler 4, MS Katz 5, GR Mundy 6, RA Kyle 7, GJ Morgan 8, J Crowley 4, B Barlogie 3, J Shaughnessy Jr 3
PMCID: PMC3684359  NIHMSID: NIHMS454484  PMID: 19657367

Abstract

Bone disease in myeloma occurs as a result of complex interactions between myeloma cells and the bone marrow microenvironment. A custom-built DNA single nucleotide polymorphism (SNP) chip containing 3404 SNPs was used to test genomic DNA from myeloma patients classified by the extent of bone disease. Correlations identified with a Total Therapy 2 (TT2) (Arkansas) data set were validated with Eastern Cooperative Oncology Group (ECOG) and Southwest Oncology Group (SWOG) data sets. Univariate correlates with bone disease included: EPHX1, IGF1R, IL-4 and Gsk3β SNP signatures were linked to the number of bone lesions, log2 DKK-1 myeloma cell expression levels and patient survival. Using stepwise multivariate regression analysis, the following SNPs: EPHX1 (P = 0.0026); log2 DKK-1 expression (P = 0.0046); serum lactic dehydrogenase (LDH) (P = 0.0074); Gsk3β (P = 0.02) and TNFSF8 (P = 0.04) were linked to bone disease. This assessment of genetic polymorphisms identifies SNPs with both potential biological relevance and utility in prognostic models of myeloma bone disease.

Keywords: myeloma, bone disease, SNP, molecular, prognosis

Introduction

Multiple myeloma is a tumor of plasma cells that depends on the bone marrow microenvironment for growth and survival.1,2 Bone disease in myeloma occurs as a result of the complex interactions between myeloma cells and the bone marrow osteoclasts, osteoblasts plus other accessory cells and microenvironmental components.2

Myeloma bone disease is characterized by a unique combination of enhanced osteoclast numbers and function plus reduced osteoblast differentiation and function.1–12 The important elements in osteoclast activation are myeloma cellderived MIP-1α, which activates osteoclast CCRX-5 plus microenvironmental-derived RANK-ligand (RANK-L), which activates osteoclast RANK and competes with stromal-derived osteoprotegrin (OPG).10–12 Recent studies have emphasized the central role of the Wnt (Wingless-type MMTV integration site family (mammalian homologue))-signaling inhibitor DKKK-1 in the pathogenesis of the osteolytic bone lesions in myeloma.6 DKK-1 inhibits both osteoblast differentiation and function and increases osteoclast activity. Attention is focused both on the mechanisms responsible for the upregulation of DKK-1 synthesis in plasma cells and the interactions with the microenvironment. 7–10 Expression of DKK-1 is regulated by a combination of intrinsic genomic factors and interactions with the bone marrow microenvironment.8

To assess the predilection to bone disease, it was elected to study the effect of single nucleotide DNA polymorphisms (SNP) in a well-characterized population of myeloma patients for whom DKK-1 expression and gene expression profile (GEP) gene signature data were also available.13 We focused on several pathways involved in the pathogenesis of myeloma bone disease, including the Wnt pathway, in particular GSK3, as well as insulin growth factor, interleukin 4, bradykinin receptors and β3 adrenergic receptors.

Peripheral blood DNA from 282 patients enrolled in the UARK 2003–33 ‘Total Therapy 2’ (TT2) protocol was studied using the previously reported Affymetrix 3k BOAC custom DNA chip to assess the presence or absence of relevant genetic polymorphisms.14–16 Here, we present evidence that several SNPs significantly correlate with both the clinical extent of the bone disease, as well as DKK-1 expression.

Patients, materials and methods

Patients

These analyses included 282 patients with previously untreated multiple myeloma enrolled in the TT2 trial between October 1998 and February 2004. Details of patient characteristics plus treatment and clinical outcomes have been reported.14 All participants had provided written informed consent in keeping with institutional and National Cancer Institute (NIH, Bethesda, MD, USA) guidelines. All details of the protocol had been approved by institutional guidelines and the United States Food and Drug Administration, and were monitored by a data safety and monitoring board as required for Phase III trials. The multiple myeloma baseline evaluation included serum and urine protein electrophoresis, quantitative immunoglobulin measurements, total 24-h urine protein excretion, serum β2-microglobulin (Sβ2M), C-reactive protein, and lactic dehydrogenase (LDH) plus bone marrow aspirate and biopsy evaluations.

Bone studies

Imaging included baseline magnetic resonance imaging (MRI) and complete skeletal survey radiological examination (myeloma bone survey (MBS)) in a prospective manner.14 The MRI included the axial skeleton and pelvis plus any additional areas requiring diagnostic evaluation for pain or other medical issues. MRI studies were carried out with a series of sequences to permit identification of focal or diffuse bone marrow involvement, including spin echo (T2-wt), short T, inversion recovery (STIR) and gadolinium-enhanced spin echo sequences with and without fat suppression. Myeloma bone survey encompassed the long bones and were carried out with digital radiographs incorporating two views of the chest; views of ribs, lateral skull, vertebral column; anteroposterior views of the pelvis, shoulders; and the extremities including hands and feet.

Focal lesions on both MRI and myeloma bone survey were identified as areas with an axial diameter of at least 0.5 cm. The MRIs were reviewed independently by four individuals who recorded the size, number and location of all focal lesions compatible with myeloma. Full details have been previously published.14

Classification of bone disease

X-ray was the primary classification system for bone disease. The exception was 12 patients with extensive focal MRI disease, but no focal changes on X-ray. On the basis of detailed previous analyses,14 this 4% subset was added to the ‘extensive bone disease’ category to give 183/282 (65%) within this extensive bone disease group. The remaining 99 patients (35%) all had negative X-rays and no extensive focal disease on MRI.

Using X-ray results only, validation of the TT2 findings was conducted comparing results in separate Eastern Cooperative Oncology Group (ECOG) and Southwest Oncology Group (SWOG) data sets.15,16 For these analyses, patients with completely negative X-rays were compared with those having > 3 focal lesions on X-ray.

Genotyping

Peripheral blood was collected in heparinized green top tubes and centrifuged to recover mononuclear cell pellets. DNA was extracted from the mononuclear cell pellets and genotyped using the Affymetrix (Santa Clara, CA, USA) Genchip scanner 3000 Targeted Genotyping System (GCS 3000 TG System) using molecular inversion probes to simultaneously identify the 3404 pre-selected SNPs in 983 genes.15,16 All genotyping experiments were carried out in strict adherence to the manufacturer’s protocol.

Custom SNP Chip design and content

A directed, custom SNP chip design was developed with specific criteria from public and commercial databases. Full details are described elsewhere.15,16 In essence, a custom SNP chip was developed, focusing on functionally relevant polymorphisms known to have a role in normal and abnormal cellular functions related to inflammation, immunity and drug responses.

Statistical analysis

Overview

Several methods were used to assess possible correlations between SNPs and the presence or absence of bone disease.

  1. Univariate correlations of individual SNPs were assessed. This was first carried out for the TT2 data set and then for validation with the Eastern Cooperative Oncology Group and Southwest Oncology Group data sets.

  2. Recursive partitioning was used to identify the best combinations of SNPs correlated with bone disease.

  3. The validity of correlations with individual SNPs and combinations of SNPs was assessed using multivariate logistic regression analyses that incorporated known standard prognostic factors, gene expression profile results (risk groups: TT2 only) and Dkk-1expression results (TT2 only).

  4. Correlations between individual SNPs as combinations of SNPs and patient outcomes were assessed including progression- free (PFS) and overall survivals (OS).

  5. The Eastern Cooperative Oncology Group and Southwest Oncology Group data sets were evaluated with respect to SNP signatures identified in the TT2 data.

Statistical analysis details

We used Fisher’s exact test as a univariate screening tool to determine the association of SNPs with bone disease. The top 50 rank-ordered SNPs were selected and a recursive-partitioning algorithm was carried out to determine the combination of SNPs that best distinguished the bone disease subgroups. In recursive partitioning, each genotype was evaluated on its ability to make a correct prediction, creating a decision node.17 Recursive partitioning allowed for interactions of SNPs and also included SNPs further down the rank-ordered list. Univariate association between clinical parameters was assessed using continuous and categorical variables.18 The non-parametric Kruskal–Wallace test was used for continuous variables and the χ2-test was used for categorical variables.19 Multivariate logistic regression was used to test for associations of SNPs and clinical parameters with bone disease.20 Survival curves were constructed according to Kaplan and Meier.21

Results

Classification of bone disease

The 282 patients were divided into 99 patients (35%) with no bone disease (X-rays negative) and 183 patients (65%) with definite/extensive bone disease (X-rays positive and/or extensive focal lesions on MRI (12 patients)). This separation best identified the two sub-populations in detailed analyses of the imaging results for the TT2 data set.14

1. Univariate correlations between bone disease and SNPs in TT2 data set: Fisher’s exact test was used as a univariatescreening tool to determine the association of SNPs with bone disease. Results are shown in Table 1, which displays the top SNPs most highly correlated with bone disease. The top-ranked SNP, EPHX1(P = 0.0003), is rs3766934 (GG), which is an expoxide hydrolase SNP. Several SNPs linked to bone biology were among the top-ranked SNPs, including IGFIR (P=0.003: #6), IL-4 (P=0.009: #16) and Gsk3β (P=0.015: #23).

Table 1.

‘Top 30’ SNPs: Univariate correlation using TT2 model

RS. Number Univariate P-value SNP function Gene symbol Rank
rs3766934 0.000309446 mRNA-UTR EPHX1 1a
rs514658 0.00168139 3'UTR TATDN2 2
rs2307340 0.002064057 Coding non-synonymous MCM5 3
rs4646227 0.002516714 Coding non-synonymous SLC15A1 4
rs520354 0.002945155 Intron APOB 5
rs2684773 0.003235843 Intron IGF1R 6
rs2303428 0.004614435 Intron (boundary) MSH2 7
rs934197 0.00468514 Promoter APOB 8
rs3176162 0.005103038 Coding non-synonymous POLG 9
rs4905475 0.005428397 Promoter BDKRB1 10
rs730566 0.005494049 3'UTR TREX1 11
rs7102464 0.00622144 Coding non-synonymous SBF2 12
rs698708 0.007090614 Promoter FVT1 13
rs7009367 0.007336344 UTR ADRB3 14
rs693 0.009103226 Coding synonymous APOB 15
rs2243289 0.009591359 Intron (boundary) IL4 16
rs2274405 0.011215443 Coding synonymous ABCC4 17
rs1805403 0.012224939 Intron (boundary) PARP1 18
rs2280712 0.012409921 Intron (boundary) PARP1 19
rs2664538 0.013514229 Coding non-synonymous MMP9 20
rs2974938 0.014298606 Coding non-synonymous PPP1R3A 21
rs2274750 0.01464092 Coding non-synonymous TNC 22
rs3783408 0.015425683 Promoter Gsk3β 23a
rs7080536 0.015452676 Coding non-synonymous HABP2 24
rs1329568 0.015522769 Promoter PAX5 25
rs1052637 0.01560948 Coding non-synonymous DDX18 26
rs8187710 0.015968731 Coding –non-synonymous ABCC2 27
rs1399291 0.015974764 Intron, TagSNP:DPYD DPYD 28
rs3181366 0.016310866 Intron TNFSF8 29a
rs12659 0.016329684 Coding synonymous SLC19A1 30
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Abbreviations: mRNA, messenger RNA; SNP, single nucleotide polymorphism; TT2, total therapy 2 and UTR, untranslated region.

a

Identified with recursive partitioning and other correlations.

2. Recursive partitioning: The top 50 SNPs with the lowest P-values were selected for recursive partitioning analysis. The results of recursive partitioning analysis are shown in Figure 1. The 4 SNPs providing the best correlation were: rs3766934, EPHX1, RANK #1; rs3783408, Gsk3β, RANK #23; rs1052637, DDX18, RANK #26; and rs3181366, TNFSF8, RANK #29 in the univariate correlations (Table 1). The 4 SNP combination was then used as a search engine to identify further correlations. The results are shown in Figures 2a and b, respectively. There were excellent correlations with both numbers of individual focal bone lesions (P values=0.001) and the directly measured DKK-1 expression levels for individual patients (P=0.05).

Figure 1.

Figure 1

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Recursive partitioning using ‘Top SNPs’ with Total Therapy 2 (TT2) model. Recursive partitioning branching tree displaying the four single nucleotide polymorphisms (SNPs) used in the model: rs3766934 (EPHX1); rs3783408 (Gsk3β); rs1052637 (DDX18); and rs3181366 (TNSF8). The SNP genotypes are identified: EPHX1(GT/TT versus GG); Gsk3 β (GG versus AG/AA); DDX18 (CC versus CG/CC); and TNFSF8 (CC versus CT/TT). The appended table shows the univariate P-values for each SNP and SNP function.

Figure 2.

Figure 2

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Baseline focal bone lesions and baseline log2 DKK-1 by predicted disease using the recursive-partitioning model. (a) The number of focal bone lesions (per patient) is plotted for patients with limited bone disease and extensive bone disease predicted by the four single nucleotide polymorphism (SNP) model illustrated in Figure 1. The mean values are identified. The P-value for the difference is P = 0.001. (b) The directly measured log2 DKK-1 expression values are plotted for patients with limited bone disease and extensive bone disease predicted by the four SNP model illustrated in Figure 1. The P-value for the difference is P = 0.05.

3. Stepwise multivariate regression analyses: Several logistic regression models were used to further assess the correlations with the identified SNPs. Results are displayed in Table 2. Again, the previously identified SNPs prove to be statistically significantly associated with the bone disease status. The individual SNPs (EPHX1, Gsk3β and TNSF8), DKK-1 and lactic dehydrogenase (serum level) are predictive in the displayed multivariate analysis.

Table 2.

Stepwise multivariate regression analyses for the TT2 dataset†

Variable Bone
N With factor Without factor OR (95% CI) P-value SNP/GEP
Univariate
   rs3766934 = 0 282 166/241 (69%) 17/41 (41%) 3.12 (1.59,6.16) 0.0010 EPHX1
   dkk1 282 N/A N/A 1.24 (1.07,1.44) 0.0053 Dkk1
   ldh 282 N/A N/A 1.01 (1.00,1.01) 0.0131 LDH
   g17high risk 282 32/40 (80%) 151/242 (62%) 2.41 (1.06, 5.46) 0.0348 G17high
   rs3181366>0 280 123/177 (69%) 59/103 (57%) 1.70 (1.03,2.81) 0.0396 TNSF8
   rs1052637>0 282 159/237 (67%) 24/45 (53%) 1.78 (0.94,3.40) 0.0788 DDX18
   rs3783408<2 279 82/116 (71%) 99/163 (61%) 1.56 (0.94,2.59) 0.0870 Gsk3β
   crp 279 N/A N/A 1.01 (1.00,1.03) 0.1404 CRP
Multivariate
   rs3766934 = 0 275 162/234 (69%) 17/41 (41%) 3.05 (1.48,6.29) 0.0026 EPHX1
   ddk1 275 N/A N/A 1.27 (1.08,1.50) 0.0046 Dkk1
   ldh 275 N/A N/A 1.01 (1.00,1.01) 0.0074 LDH
   rs3783408<2 275 81/114 (71%) 98/161 (61%) 1.93 (1.11,3.37) 0.0202 Gsk3β
   rs3181366>0 275 120/173 (69%) 59/102 (58%) 1.73 (1.01,2.98) 0.0470 TNSF8
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Abbreviations: CI, confidence interval; GEP, gene expression profile; OR, odds ratio and TT2, total therapy 2.

P-value from Wald’s χ2-Test in Logistic Regression.

NS2-Multivariate results not statistically significant at 0.05 level. Univariate P-values reported regardless of significance.

Multivariate model uses stepwise selection with entry level 0.1 and variable remains if meets the 0.05 level.

A multivariate P-value greater than 0.05 indicates variable forced into model with significant variables chosen using stepwise selection.

†

Using the variables already identified as significant in preliminary analyses, we used stepwise logistic regression to find the best prognostic model in all 282 of the TT2 patients. The multivariate results are in order of selection into the model.

4. Correlations with progression-free and overall survival: Figure 3 shows the correlations between SNP pattern and outcomes. The cross correlations between known and predicted survivals are highly significant.

Figure 3.

Figure 3

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Overall survival (OS) and Event-free survival (EFS) for both actual and predicted bone disease (Total Therapy 2 (TT2) model). (a) OS is shown for patients with known limited and extensive bone disease and compared with the survival for patients predicted by the four single nucleotide polymorphism (SNP) model to have limited and extensive disease. The listed P-values indicate that OS is statistically inferior for patients with both actual and predicted extensive versus limited bone disease (P = 0.0183). The actual versus predicted outcomes are not different (P-values 0.693 and 0.881, respectively). (b) EFS is shown for patients with known limited and extensive bone disease and compared with EFS for patients predicted to have limited and extensive bone disease based on the four SNP model (Figure 1). The P-values indicate that EFS is not different for limited versus extensive disease, but this is true for both the actual and predicted patient populations (P-values: overall 0.185; and 0.327 and 0.924 for comparisons).

Cross-validation in additional clinical data sets with bone disease defined by X-ray only. These statistical analyses used 207 patients with zero or more than three X-ray focal lesions from the original TT2 data set plus 62 patients from Southwest Oncology Group (S9321) and 69 patients from Eastern Cooperative Oncology Group (E1A00 and E9486). Collectively, there were 163 patients with no X-ray evidence of bone disease and 175 patients with more then three focal lesions evident on X-ray. A majority of the SNPs from the TT2 only analyses were again highly ranked in combined analyses. For example, EPHX1 (previously ranked #1, now #9); IGFIR (previously ranked #6, now #13) and IL-4 (previously ranked #16, now #19) again showed significant correlations. Conversely, the SNPs for BDKRB1, ADRB3 and DDX18 were not highly ranked.

Stepwise multivariate logistic regression analysis was then repeated incorporating top SNPs identified by cross-validation. The results are displayed in Table 3. This further cross-validation assessment shows that the EPHX1 SNP is still the top SNP in both the univariate and multivariate regressions. The TREX1 SNP, previously ranked number 11, acquires greater significance in these univariate and multivariate regressions. Other significant correlations were with DDK-1, lactic dehydrogenase, the 17-gene gene expression profile high risk, plus again the SNPs for Gsk3β and TNFSF8.

Table 3.

Stepwise multivariate regression analyses incorporating SNPs identified with bone disease classified by X-rays only (0 versus >3 lesions)a

Variable Bone
N With factor Without factor OR (95% CI) P-value SNP/GEP
Univariate
   rs3766934 = 0 282 166/241 (69%) 17/41 (41%) 312 (1.59,6.16) 0.0010 EPHX1
   rs730566<2 282 172/254 (68%) 11/28 (39%) 3.24 (1.45,7.23) 0.0041 TREX1
   dkk1 282 N/A N/A 1.24 (1.07,1.44) 0.0053 Dkk1
   ldh 282 N/A N/A 1.01 (1.00,1.01) 0.0131 LDH
   g17high 282 32/40 (80%) 151/242 (62%) 2.41 (1.06,5.46) 0.0348 G17high
   rs3181366>0 280 123/177 (69%) 59/103 (57%) 1.70 (1.03,2.81) 0.0396 TNSF8
   rs7120118 = 0 281 21/25 (84%) 161/256 (63%) 3.10 (1.03,9.30) 0.0437 NRIH3
   rs1052637>0 282 159/237 (67%) 24/45 (53%) 1.78 (0.94,3.40) 0.0788 DDX18
   rs3783408<2 279 82/116 (71%) 99/163 (61%) 1.56 (0.94,2.59) 0.0870 Gsk3β
   crp 279 N/A N/A 1.01 (1.00,1.03) 0.1404 CRP
Multivariate
   rs3766934 = 0 274 161/233 (69%) 17/41 (41%) 2.90 (1.36,6.17) 0.0057 EPHX1
   rs730566<2 274 168/247 (68%) 10/27 (37%) 3.40 (1.38,8.37) 0.0077 TREX1
   ldh 274 N/A N/A 1.01 (1.00,1.01) 0.0105 LDH
   rs3783408<2 274 80/113 (71%) 98/161 (61%) 2.09 (1.17,3.70) 0.0121 GSK3β
   ddk1 274 N/A N/A 1.23 (1.04,11.08) 0.0178 Dkk1
   rs3181366>0 274 119/172 (69%) 59/102 (58%) 1.78 (1.02,3.10) 0.0423 TNSF8
   rs7120118 = 0 274 21/25 (84%) 157/249 (63%) 3.39 (1.04,11.08) 0.0430 NRIH3
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Abbreviations: CI, confidence interval; GEP, gene expression profile; OR, odds ratio and SNP, single nucleotide polymorphism.

P-value from Wald’s χ2-Test in Logistic Regression.

NS2-Multivariate results not statistically significant at 0.05 level. Univariate P-values reported regardless of significance.

Multivariate model uses stepwise selection with entry level 0.1 and variable remains if meets the 0.05 level.

A multivariate P-value greater than 0.05 indicates variable forced into model with significant variables chosen using stepwise selection.

a

Logistic regression on all 282 Total Therapy 2 (TT2) patients. The variables considered in Table 2 together with top two SNPs from the X-ray only analysis are considered.

Discussion

In this study, several SNPs are correlated with the likelihood of bone disease. The top SNP is EPHX1 (rs3766934: GG genotype versus GT/TT), an epoxide hydrolase. Although EPHX1 has been evaluated in multiple studies of genetic polymorphisms of biotransformation enzymes related to cancer, the functional significance of this specific GG genotype is currently unclear.22 Nonetheless, it is known that epoxide hydrolase is involved in both the inflammatory response linked to the bioactivation of leukotoxins23 and the activation of the dioxin response element by benzo[a] pyrene compounds.24 Further studies are necessary to investigate the potential significance of this EPHX1 SNP genotype in laboratory, clinical and epidemiological studies.

The Gsk3β SNP (Table 1 and Figure 1) was the second SNP selected as part of the recursive partitioning decision tree. This SNP is especially interesting as binding of GSK3βi with axin and APC forms a critical complex involved in Wnt-activated release or stabilization of β-catenin.25–29 This pathway is central to osteoblast function.30 Increased Wnt signaling through Wnt 3A results in an increase in the bone mineral density and a decrease in the osteoclast/osteoblast ratio.31–34 Gsk3 β is the target of upregulation by thalidomide and is central to reactive oxygen species-mediated thalidomide-induced apoptosis.28

Other identified SNPs linked to bone related pathways (see Table 4) included the following: insulin-like growth factor 1 receptor (ranked number 6: Table 1);35–39 bradykinin receptor B1 (ranked number 10: Table 1);40 adrenergic receptor B3 (ranked number 14: Table 1);41,42 and interleukin-4 (ranked number 16: Table 1).43 Several SNPs linked to drug and/or toxin metabolism and/or DNA metabolism and repair were noted and are summarized in Table 4. As dioxins have been linked to the etiology of myeloma,44 it is noteworthy that EPHX145–47 is important in dioxin and polycyclic aromatic hydrocarbon metabolism. In addition, the DPYD SNP (rs1399291) ranked number 28 (Table 1) is involved with pyrimidine metabolism, and has, in addition, been identified in a separate recent largescale screening.48

Table 4.

Biological significance of correlated SNPs

SNP Identification Comments/Discussion
rs 3783408 Gsk3β Binding to GSK3βi in the Wnt pathway stabilizes β-catenin
rs 2684773 IGF1R Insulin-like growth factor triggers osteoblast functions
rs 2243289 IL-4 Interleukin-4 modulates the activity of osteoblasts
rs 3766934 EPHX1 Epoxide hydrolase is a multifunctional protein involved in the metabolism of carcinogenic xenobiotics
rs 730566 TREX-1 Trex-1 is an exonuclease involved in processing and clearing anomalous DNA structures. Absence is linked to familial lupus with DNA auto antibodies. In this study the SNP is linked to the absence of bone lesions.
rs 7120118 NRIH3 Key regulator in cholesterol homeostasis: absence results in the rapid accumulation of cholesterol esters and failure to induce CYP7A
rs 1052637 DDX18 Dead box viral RNA helicase that allows unwinding of double stranded RNA. Linked to C-myc function and oncogenic cell activation
rs 4905475 BDKRB1 Bradykinin receptor B1 involved in pro inflammatory cytokine and prostaglandin (PGE2) signaling and bone disease
rs 7009367 ADR B3 β3-adrenergic receptor linked to bone mass index, bone mineral density and fracture risk
rs 3760413 EME1 Essential meiotic endonuclease 1, which has a key role in DNA repair and maintenance of genome integrity.
rs 1399291 DPYD DPYD encodes the rate-limiting enzyme in the catabolism of uracil and thymidine.
rs 10916 CYP 1B1 Cytochrome-P450 enzyme B1: multifunctional enzyme involved in estrogen metabolism and aryl hydrocarbon receptor expression
rs 520354 APOB Apolipoprotein B the structural protein required for lipoprotein assembly and secretion. Crucial for triglyceride transfer
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Abbreviation: SNP, single nucleotide polymorphism.

Testing with the 3400 SNP custom chip has, thus, revealed several SNPs that are significantly correlated with the likelihood of bone disease in patients with myeloma. Larger studies are currently underway, for example, in collaboration with the National Cancer Institute (NCI) epidemiology branch, to further explore the relationships with identified SNPs.48

Acknowledgements

This investigation was supported in part by an unrestricted grant from the International Myeloma Foundation (Bank on a Cure project), as well as by the following PHS Cooperative Agreement grant numbers awarded by the National Cancer Institute, DHHS: CA32102 and CA38926 (SWOG); and CA21115 (ECOG); plus CA 97513 (JDS and BB).

Footnotes

Conflict of interest

The authors declare no conflict of interest.

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