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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Hematol Oncol. Author manuscript; available in PMC 2012 March 1.
Published in final edited form as:
PMCID: PMC2980579
NIHMSID: NIHMS224702

Variation in innate immunity genes and risk of multiple myeloma

Abstract

Multiple myeloma (MM) is a B-cell lymphoid malignancy suspected to be associated with immunologic factors. Given recent findings associating single-nucleotide polymorphisms (SNPs) in innate immunity genes with non-Hodgkin lymphoma, we conducted an investigation of innate immune gene variants using specimens from a population-based case-control study of MM conducted in Connecticut women. Tag SNPs (N=1,461) summarizing common variation in 149 gene regions were genotyped in non-Hispanic Caucasian subjects (103 cases, 475 controls). Odds ratios (OR) and 95% confidence intervals (CI) relating SNP associations with MM were computed using unconditional logistic regression, while the MinP test was used to investigate associations with MM at the gene level. We calculated permutation-adjusted P-values and false discovery rates (FDR) to account for the number of comparisons performed in SNP-level and gene-level tests, respectively. Three genes were associated with MM when controlling for a FDR of ≤10%: SERPINE1 (PMinP<0.0001; FDR=0.02), HGF (PMinP=0.0006; FDR=0.06) and CCR7 (PMinP=0.001; FDR=0.08). Two SNPs demonstrated robust associations: SERPINE1 rs2227667 (P=2.1×10−5, Ppermutation=0.03) and HGF rs17501108 (P=5.0×10−5, Ppermutation=0.07). Our findings suggest that genetic variants in SERPINE1 and HGF, and possibly CCR7, are associated with MM risk, and warrant further investigation in other studies.

Keywords: epidemiology, myeloma, genetics

INTRODUCTION

Multiple myeloma is an incurable B-cell malignancy characterized by plasma cell overproduction of monoclonal immunoglobulin (Ig) G, IgA, and/or light chains [1]. It accounts for approximately 10% of hematological malignancies and 1% of cancer deaths in Western countries [2]. The etiology of this malignancy is poorly understood, with only age and race (elevated risk in African Americans) having been established as risk factors to date [1]. Familial clusters of multiple myeloma and an increased risk of myeloma among individuals with a family history of myeloma or hematological malignancy have been reported, suggesting that genetic or shared environmental factors contribute to myeloma susceptibility [36]. Immunologic factors may also contribute to the development of myeloma, with reports of increased risk associated with chronic antigen stimulation, infections and autoimmune condition [7].

A small number of studies have investigated whether genetic polymorphisms in immunoregulatory genes influence multiple myeloma susceptibility; associations with variants in TNF, IL4R, FCGR2 and IL6 [810] have been reported, and warrant further investigation. Recent studies of non-Hodgkin lymphoma, another malignancy of predominantly B-cell origin, suggest that lymphomagenesis may be influenced by common variants in genes regulating innate immunity, a system of immune cells and mechanisms which recognizes and responds to infections by microbial pathogens in a non-adaptive manner [1113]. To our knowledge, no studies to date have investigated whether innate immunity gene variants are associated with multiple myeloma. We genotyped 1,461 tag SNPs summarizing common genetic variation in 149 gene regions involved with the innate immune system in a case-control study of women conducted in Connecticut.

METHODS

The study methods have been previously described [14]. Histologically confirmed incident cases of multiple myeloma diagnosed between 1996 and 2000 among female Connecticut residents aged 21 to 84 years were identified through the Connecticut Tumor Registry. Of the 409 cases identified during this time period, 86 (21%) died prior to enrollment and 140 (34%) declined to participate, resulting in a total of 183 interviewed cases (57% of living cases; 45% overall participation rate). Controls aged 21–84 years were recruited as part of a parallel population-based case-control study of non-Hodgkin lymphoma. Controls below age 65 years were enrolled through random digit dialing methods, while Medicare files were used to select eligible control aged 65 years or older. A total of 717 controls participated in the study; the participation rate was 69% for those contacted by random-digit dialing and 47% for those identified through Medicare files. The study was approved by the Yale University School of Medicine’s Human Investigations Committee, the Connecticut Department of Public Health, and the National Cancer Institute’s Special Studies Institutional Review Board. Participation was voluntary, and written informed consent was obtained from all participants.

Following an in-person interview, subjects provided 10mL of peripheral blood or buccal cell cotton swab samples. Genomic DNA was extracted from peripheral blood and buccal cells by phenol-chloroform extraction (Gentra Systems, Minneapolis, MN). Samples from non-Hispanic Caucasian participants with blood-based DNA were genotyped at the NCI Core Genotyping Facility using the 1,536-SNP Illumina GoldenGate platform, using an oligonucleotide pool designed to tag 149 gene regions (210 genes) selected from known innate immune pathways (oxidative response, pattern recognition molecules and antimicrobials, integrins and adhesion molecules, complement, chemokines with their receptors and signaling molecules, and response genes and tissue factors). Tag SNPs were selected from the designable set of common SNPs (minor allele frequency ≥0.05) genotyped in the Caucasian (CEU) population sample of the International HapMap Project using the software application TagZilla (v1.1; http://tagzilla.nci.nih.gov/), which employs the pairwise binning algorithm of Carlson et al. [15]. For each gene, SNPs within the region 20kb 5’ of the ATG-translation initiation codon and 10kb 3’ of the end of the last exon were binned using a binning threshold of r2 > 0.80, with greater weighting for SNPs with a design score of 1.1. Less than 5% of SNPs were forced into the panel, creating a negligible effect on the tagging algorithm. Samples from 103 cases and 475 controls were successfully genotyped. After excluding assays with completion rates <90% or with genotype concordance rates <95%, 1,461 SNPs were available for analysis. A list of the gene, chromosomal position, and base change for each of the 1,461 SNPs in the final data set is provided in Supplementary Table 1.

SAS version 9.1 (SAS Institute Inc., Cary, NC, USA) was used to compute odds ratios (OR) and 95% confidence intervals (CI) for each SNP through unconditional logistic regression modeling with adjustment for age. Tests for trend were performed by modeling the number of rare alleles (0, 1 or 2) as a continuous variable. Gene-level statistical tests of association were conducted using the “MinP” test, which assess the true statistical significance of the smallest p-trend within each gene by permutation-based resampling methods (10,000 permutations) that automatically adjust for the number of tag SNPs tested within that gene and the underlying linkage disequilibrium pattern [16;17]. These analyses were performed using the MATLAB Statistics ToolboxTM 6.2 (The Mathworks, Inc., Natick, MA, USA).

We used different methods to assess the potential for our gene-level and SNP-level findings to have arisen due to multiple comparisons. For gene-level MinP tests we calculated the false discovery rate (FDR), the expected ratio of erroneous rejections of the null hypothesis to the total number of rejected hypotheses among all the variants assessed [18]. Statistically significant findings with a FDR of 10% or less were considered to be noteworthy. For SNP-level trend tests, we computed permutation-adjusted P-values (10,000 permutations) which account for the total number of evaluated SNPs [19].

We further investigated whether SNPs associated with multiple myeloma after permutation adjustment were also associated with expression levels of the gene of interest. To do this, we analyzed publicly available expression data (GENEVAR project, www.sanger.ac.uk/humgen/genevar/) from 90 Epstein-Barr virus-transformed lymphoblastoid cell lines derived from the HapMap CEU population sample [20]. Tests of association between SNP genotype and gene expression levels were performed by one-way ANOVA and by two-sample t-tests of dichotomized genotypes (homozygous rare allele / heterozygous vs. homozygous common allele).

RESULTS

One hundred and three cases and 475 controls were successfully genotyped for 1,461 SNPs in 149 gene regions (210 genes). The observed distribution of SNP trend test P-values did not differ from the expected distribution, suggesting no evidence of systematic bias between cases and controls (Supplementary Figure 1). Table 1 lists all of the investigated genes with MinP P-values <0.05 and summarizes the results of the SNP with the smallest trend test P-value within each gene. Three of the 210 investigated genes had MinP P-values that were noteworthy at a FDR of less than 10%: SERPINE1 (PMinP<0.0001, FDR=0.02), CCR7 (PMinP=0.0006, FDR=0.06) and HGF (PMinP=0.001, FDR=0.08). Within SERPINE1, the SNP rs2227667 was associated with multiple myeloma risk at a P-value of 2.1×10−5; other SNPs with P<0.05 included rs2227672 (P=0.0004), rs1050813 (P=0.03) and rs2227692 (P=0.049). One SNP within CCR7 was significantly associated with risk (rs3136685, P=0.0001), as were two HGF SNPs (rs17501108, P=5.0×10−5; rs1558001, P=0.009). After computing permutation-based P-values to account for all of the evaluated SNPs, noteworthy findings remained for SERPINE1 rs2227667 (Ppermutation=0.03) and HGF rs17501108 (Ppermutation=0.07). Associations between multiple myeloma risk and genotypes of rs2227667, rs17501108 and rs3136685 are shown in Table 2. None of the three SNPs were found to be associated with expression levels of SERPINE1, HGF or CCR7 in lymphoblastoid cell lines derived from the HapMap CEU samples (results not shown).

Table 1
Summary of gene-level and SNP-level association test results for genes with PMinP <0.05
Table 2
Associations with multiple myeloma for rs2227667, rs3136685 and rs17501108 genotypes

DISCUSSION

In this case-control study investigating 1,461 tag SNPs in 149 gene regions regulating innate immunity, genetic variants in the genes SERPINE1, CCR7 and HGF were associated with myeloma risk. In particular, robust associations were observed for two SNPs, SERPINE1 rs2227667 and HGF rs17501108.

SERPINE1, located on chromosome region 7q21, encodes plasminogen activator inhibitor type 1 (PAI-1), an important inhibitor of fibrinolysis. Multiple myeloma patients have previously been shown to have significantly elevated levels of PAI-1 compared to healthy patients [21], and are known to be particularly susceptible to deep vein thrombosis [22]. Moreover, SERPINE1 overexpression has been found in cultured bone marrow mesenchymal stem cells (BMMSC) of multiple myeloma patients compared to cultured BMMSC from healthy donors [23]. Apart from its role in hemostasis, PAI-1 is suspected to influence tumor progression and metastasis for several types of cancer; reported effects of PAI-1 include inhibition of apoptosis [24;25]and increased angiogenesis [26;27], cell adhesion [28]and cell migration [29]. The SERPINE1 polymorphism rs2227667 which was significantly associated with multiple myeloma risk in this study has been previously associated with plasma levels of PAI-1 [30] and the hemostatic marker D-dimer [31], although we did not observe an association between this SNP and SERPINE1 expression within lymphoblastoid cell lines derived from the HapMap CEU samples. In total, these findings suggest that rs2227667 may have functional relevance, or is in linkage disequilibrium with a functional variant.

Our observed association with variation in HGF, located 19Mb centromeric of SERPINE1 on 7q21, is intriguing given that its gene product, hepatocyte growth factor (HGF), is already known to play a role in multiple myeloma biology. In clinical studies, serum levels of HGF have been found to be elevated in MM patients compared to healthy individuals and to be associated with reduced survival [3235]. Experimental investigations have demonstrated that HGF exerts strong proliferative and antiapoptotic effects in MM cells [36], promotes cell invasiveness [37], stimulates angiogenesis [38] and inhibits bone formation [39]. The SNP rs17501108, which was significantly associated with MM in our analysis, has also been associated with myopia in a previous study [40], although we did not observe an association between this SNP and HGF expression from the lymphoblastoid cell line data.

CCR7 encodes C-C chemokine receptor type 7 (CCR7), a receptor expressed on B- and T-lymphocytes that is involved in the trafficking of lymphocytes and dendritic cells to lymph nodes [41;42]. Although CCR7 expression in tumor cells from several cancer types has been associated with an increased risk of lymph node metastasis [43;44], CCR7 is typically not expressed in multiple myeloma [45]. The rs3136685 genotype was not associated with gene expression in the lymphoblastoid cell line data and, unlike with SERPINE1 rs2227667 and HGF rs17501108, its association with multiple myeloma did not survive a permutation-based test accounting for the multiple comparisons. In light of this, and the absence of previous evidence linking it to MM, the findings for CCR7 should be interpreted with caution.

An important strength of this study is its detailed assessment of innate immunity pathways, with 149 gene regions assessed. Given the large number of genes and SNPs evaluated in this analysis, we were careful to consider the potential for false-positive findings when interpreting our results. With that in mind, our findings for SERPINE1 rs2227667 and HGF rs17501108 are particularly important given that they remained noteworthy following permutation-based tests accounting for the total number of evaluated SNPs, suggesting that they are not due to chance. This study also has limitations. The sample size of this study is small, resulting in low power to detect genetic effects of modest magnitude as well as an increased probability of generating false-positive findings [46]. Another limitation is the low participation rate among cases and controls. While willingness to participate and provide blood is unlikely to be related to genotype differences [47], we cannot rule out the possibility that the results may reflect selection bias due to possible effects of the identified SNPs on survival. It is thus essential for these results to be replicated in other studies before meaningful inferences regarding causation are made.

CONCLUSION

In conclusion, our findings suggest that genetic variants in SERPINE1 and HGF, and possibly CCR7, are associated with multiple myeloma risk, and warrant further investigation in other studies.

Supplementary Material

Supp Fig S1

Supp Table S1

Acknowledgments

This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract N01-CO-12400. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government.

Footnotes

Conflicts of Interest: The authors have no conflicts of interest to disclose.

References

1. De Roos AJ, Baris D, Weiss NS, Herrinton LJ. Multiple Myeloma. In: Schottenfeld D, Fraumeni JF, editors. Cancer Epidemiology and Prevention. 3. New York: Oxford University Press; 2006. pp. 919–45.
2. Kastrinakis NG, Gorgoulis VG, Foukas PG, Dimopoulos MA, Kittas C. Molecular aspects of multiple myeloma. Ann Oncol. 2000;11 :1217–28. [PubMed]
3. Maldonado JE, Kyle RA. Familial myeloma. Report of eight families and a study of serum proteins in their relatives. Am J Med. 1974;57 :875–84. [PubMed]
4. Shoenfeld Y, Berliner S, Shaklai M, Gallant LA, Pinkhas J. Familial multiple myeloma. A review of thirty-seven families. Postgrad Med J. 1982;58 :12–6. [PMC free article] [PubMed]
5. Brown LM, Linet MS, Greenberg RS, Silverman DT, Hayes RB, Swanson GM, et al. Multiple myeloma and family history of cancer among blacks and whites in the U.S. Cancer. 1999;85 :2385–90. [PubMed]
6. Landgren O, Linet MS, McMaster ML, Gridley G, Hemminki K, Goldin LR. Familial characteristics of autoimmune and hematologic disorders in 8,406 multiple myeloma patients: a population-based case-control study. Int J Cancer. 2006;118 :3095–8. [PubMed]
7. Kristinsson SY, Goldin LR, Bjorkholm M, Koshiol J, Turesson I, Landgren O. Genetic and immune-related factors in the pathogenesis of lymphoproliferative and plasma cell malignancies. Haematologica. 2009;94 :1581–9. [PubMed]
8. Cozen W, Gebregziabher M, Conti DV, Van Den Berg DJ, Coetzee GA, Wang SS, et al. Interleukin-6-related genotypes, body mass index, and risk of multiple myeloma and plasmacytoma. Cancer Epidemiol Biomarkers Prev. 2006;15 :2285–91. [PubMed]
9. Brown EE, Lan Q, Zheng T, Zhang Y, Wang SS, Hoar-Zahm S, et al. Common variants in genes that mediate immunity and risk of multiple myeloma. Int J Cancer. 2007;120 :2715–22. [PubMed]
10. Birmann BM, Tamimi RM, Giovannucci E, Rosner B, Hunter DJ, Kraft P, et al. Insulin-like growth factor-1- and interleukin-6-related gene variation and risk of multiple myeloma. Cancer Epidemiol Biomarkers Prev. 2009;18 :282–8. [PMC free article] [PubMed]
11. Forrest MS, Skibola CF, Lightfoot TJ, Bracci PM, Willett EV, Smith MT, et al. Polymorphisms in innate immunity genes and risk of non-Hodgkin lymphoma. Br J Haematol. 2006;134 :180–3. [PubMed]
12. Nieters A, Beckmann L, Deeg E, Becker N. Gene polymorphisms in Toll-like receptors, interleukin-10, and interleukin-10 receptor alpha and lymphoma risk. Genes Immun. 2006;7 :615–24. [PubMed]
13. Purdue MP, Lan Q, Wang SS, Kricker A, Menashe I, Zheng TZ, et al. A pooled investigation of Toll-like receptor gene variants and risk of non-Hodgkin lymphoma. Carcinogenesis. 2009;30 :275–81. [PMC free article] [PubMed]
14. Landgren O, Zhang Y, Zahm SH, Inskip P, Zheng T, Baris D. Risk of multiple myeloma following medication use and medical conditions: a case-control study in Connecticut women. Cancer Epidemiol Biomarkers Prev. 2006;15 :2342–7. [PubMed]
15. Carlson CS, Eberle MA, Rieder MJ, Yi Q, Kruglyak L, Nickerson DA. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am J Hum Genet. 2004;74 :106–20. [PubMed]
16. Westfall PH, Zaykin DV, Young SS. Multiple tests for genetic effects in association studies. Methods Mol Biol. 2002;184 :143–68. [PubMed]
17. Chen BE, Sakoda LC, Hsing AW, Rosenberg PS. Resampling-based multiple hypothesis testing procedures for genetic case-control association studies. Genet Epidemiol. 2006;30 :495–507. [PubMed]
18. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society B. 1995;57 :289–300.
19. Churchill GA, Doerge RW. Empirical threshold values for quantitative trait mapping. Genetics. 1994;138 :963–71. [PubMed]
20. Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C, et al. Population genomics of human gene expression. Nat Genet. 2007;39 :1217–24. [PMC free article] [PubMed]
21. Yagci M, Sucak GT, Haznedar R. Fibrinolytic activity in multiple myeloma. Am J Hematol. 2003;74 :231–7. [PubMed]
22. Kristinsson SY, Fears TR, Gridley G, Turesson I, Mellqvist UH, Bjorkholm M, et al. Deep vein thrombosis after monoclonal gammopathy of undetermined significance and multiple myeloma. Blood. 2008;112 :3582–6. [PubMed]
23. Corre J, Mahtouk K, Attal M, Gadelorge M, Huynh A, Fleury-Cappellesso S, et al. Bone marrow mesenchymal stem cells are abnormal in multiple myeloma. Leukemia. 2007;21 :1079–88. [PMC free article] [PubMed]
24. Kwaan HC, Wang J, Svoboda K, Declerck PJ. Plasminogen activator inhibitor 1 may promote tumour growth through inhibition of apoptosis. Br J Cancer. 2000;82:1702–8. [PMC free article] [PubMed]
25. Chen Y, Kelm RJ, Jr, Budd RC, Sobel BE, Schneider DJ. Inhibition of apoptosis and caspase-3 in vascular smooth muscle cells by plasminogen activator inhibitor type-1. J Cell Biochem. 2004;92 :178–88. [PubMed]
26. Bajou K, Noel A, Gerard RD, Masson V, Brunner N, Holst-Hansen C, et al. Absence of host plasminogen activator inhibitor 1 prevents cancer invasion and vascularization. Nat Med. 1998;4 :923–8. [PubMed]
27. McMahon GA, Petitclerc E, Stefansson S, Smith E, Wong MK, Westrick RJ, et al. Plasminogen activator inhibitor-1 regulates tumor growth and angiogenesis. J Biol Chem. 2001;276 :33964–8. [PubMed]
28. Palmieri D, Lee JW, Juliano RL, Church FC. Plasminogen activator inhibitor-1 and -3 increase cell adhesion and motility of MDA-MB-435 breast cancer cells. J Biol Chem. 2002;277 :40950–7. [PubMed]
29. Chazaud B, Ricoux R, Christov C, Plonquet A, Gherardi RK, Barlovatz-Meimon G. Promigratory effect of plasminogen activator inhibitor-1 on invasive breast cancer cell populations. Am J Pathol. 2002;160 :237–46. [PubMed]
30. Morange PE, Saut N, Alessi MC, Yudkin JS, Margaglione M, Di MG, et al. Association of plasminogen activator inhibitor (PAI)-1 (SERPINE1) SNPs with myocardial infarction, plasma PAI-1, and metabolic parameters: the HIFMECH study. Arterioscler Thromb Vasc Biol. 2007;27 :2250–7. [PubMed]
31. Lange LA, Reiner AP, Carty CL, Jenny NS, Cushman M, Lange EM. Common genetic variants associated with plasma fibrin D-dimer concentration in older European- and African-American adults. J Thromb Haemost. 2008;6 :654–9. [PubMed]
32. Seidel C, Borset M, Turesson I, Abildgaard N, Sundan A, Waage A. Elevated serum concentrations of hepatocyte growth factor in patients with multiple myeloma. The Nordic Myeloma Study Group. Blood. 1998;91 :806–12. [PubMed]
33. Iwasaki T, Hamano T, Ogata A, Hashimoto N, Kitano M, Kakishita E. Clinical significance of vascular endothelial growth factor and hepatocyte growth factor in multiple myeloma. Br J Haematol. 2002;116 :796–802. [PubMed]
34. Ludek P, Hana S, Zdenek A, Martina A, Dana K, Tomas B, et al. Treatment response to bortezomib in multiple myeloma correlates with plasma hepatocyte growth factor concentration and bone marrow thrombospondin concentration. Eur J Haematol. 2009;84 :332–6. [PubMed]
35. Pour L, Svachova H, Adam Z, Mikulkova Z, Buresova L, Kovarova L, et al. Pretreatment hepatocyte growth factor and thrombospondin-1 levels predict response to high-dose chemotherapy for multiple myeloma. Neoplasma. 2010;57 :29–34. [PubMed]
36. Derksen PW, de Gorter DJ, Meijer HP, Bende RJ, van DM, Lokhorst HM, et al. The hepatocyte growth factor/Met pathway controls proliferation and apoptosis in multiple myeloma. Leukemia. 2003;17 :764–74. [PubMed]
37. Vande BI, Asosingh K, Allegaert V, Leleu X, Facon T, Vanderkerken K, et al. Bone marrow endothelial cells increase the invasiveness of human multiple myeloma cells through upregulation of MMP-9: evidence for a role of hepatocyte growth factor. Leukemia. 2004;18 :976–82. [PubMed]
38. Bussolino F, Di Renzo MF, Ziche M, Bocchietto E, Olivero M, Naldini L, et al. Hepatocyte growth factor is a potent angiogenic factor which stimulates endothelial cell motility and growth. J Cell Biol. 1992;119 :629–41. [PMC free article] [PubMed]
39. Standal T, Abildgaard N, Fagerli UM, Stordal B, Hjertner O, Borset M, et al. HGF inhibits BMP-induced osteoblastogenesis: possible implications for the bone disease of multiple myeloma. Blood. 2007;109 :3024–30. [PubMed]
40. Yanovitch T, Li YJ, Metlapally R, Abbott D, Viet KN, Young TL. Hepatocyte growth factor and myopia: genetic association analyses in a Caucasian population. Mol Vis. 2009;15 :1028–35. [PMC free article] [PubMed]
41. Dieu MC, Vanbervliet B, Vicari A, Bridon JM, Oldham E, Ait-Yahia S, et al. Selective recruitment of immature and mature dendritic cells by distinct chemokines expressed in different anatomic sites. J Exp Med. 1998;188 :373–86. [PMC free article] [PubMed]
42. Hirao M, Onai N, Hiroishi K, Watkins SC, Matsushima K, Robbins PD, et al. CC chemokine receptor-7 on dendritic cells is induced after interaction with apoptotic tumor cells: critical role in migration from the tumor site to draining lymph nodes. Cancer Res. 2000;60 :2209–17. [PubMed]
43. Mashino K, Sadanaga N, Yamaguchi H, Tanaka F, Ohta M, Shibuta K, et al. Expression of chemokine receptor CCR7 is associated with lymph node metastasis of gastric carcinoma. Cancer Res. 2002;62 :2937–41. [PubMed]
44. Kodama J, Hasengaowa, Kusumoto T, Seki N, Matsuo T, Ojima Y, et al. Association of CXCR4 and CCR7 chemokine receptor expression and lymph node metastasis in human cervical cancer. Ann Oncol. 2007;18 :70–6. [PubMed]
45. Trentin L, Miorin M, Facco M, Baesso I, Carraro S, Cabrelle A, et al. Multiple myeloma plasma cells show different chemokine receptor profiles at sites of disease activity. Br J Haematol. 2007;138 :594–602. [PubMed]
46. Wacholder S, Chanock S, Garcia-Closas M, El GL, Rothman N. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst. 2004;96 :434–42. [PubMed]
47. Bhatti P, Sigurdson AJ, Wang SS, Chen J, Rothman N, Hartge P, et al. Genetic variation and willingness to participate in epidemiologic research: data from three studies. Cancer Epidemiol Biomarkers Prev. 2005;14 :2449–53. [PubMed]