Abstract
Genetic variation may be an important risk factor for multiple myeloma. A hallmark of tumor formation and growth is cell cycle dysregulation and apoptosis avoidance. We previously reported the association of genetic variation in caspase genes, the apoptotic-regulating family, and multiple myeloma risk. To further examine if genetic variation in key cell cycle and apoptosis genes alters multiple myeloma risk, we genotyped 276 tag SNPs in 27 gene regions in a population-based case-control study of non-Hispanic Caucasian women (108 cases; 482 controls) in Connecticut. Logistic regression assessed the effect of each SNP on multiple myeloma risk and the minP test assessed the association at the gene region level. Three gene regions were significantly associated with risk of multiple myeloma (BAX minP = 0.018, CASP9 minP = 0.025, and RIPK1 minP = 0.037). Further explorations identified the most significant variant of BAX, RIPK1, and CASP9 to be rs1042265, rs9391981, and rs751643, respectively. The A variant at rs1042265 (ORGA+AA = 0.40, 95%CI = 0.21 – 0.78) and the C variant at rs9391981 (ORGC+CC = 0.32, 95%CI = 0.12 – 0.81) were associated with a decreased risk of multiple myeloma. The G variant at rs7516435 was associated with an increased risk of multiple myeloma (ORAG = 1.48, 95%CI = 0.94 – 2.32; ORGG = 2.59, 95%CI = 1.30 – 5.15; ptrend = 0.005). Haplotype analyses supported the SNP findings. These findings suggest that genetic variation in cell cycle and apoptosis genes may play a key role in multiple myeloma and warrant further investigation through replication studies.
Keywords: multiple myeloma, caspase, BAX, RIPK1, cell cycle
Introduction
Multiple myeloma is a B-cell hematological malignancy that accounts for an estimated 19,900 incident cancer cases per year in the United States (1). Multiple myeloma cells affect the bone marrow microenvironment by adhering to extracellular matrix proteins, as well as bone marrow stromal cells. These adhesions made by the tumor cells stimulate cytokine-mediated tumor growth by activating numerous cell growth and survival pathways. Pathways that are activated include the extracellular signal-regulated kinase (ERK), the Janus kinase 2 / signal transducer and activator of transcription 3 (JAK2/STAT3), the phosphatidylinositol 3-kinase (PI3K/Akt), the Ras-Raf-MAPK kinase (MEK), and the nuclear factor κβ (NFκβ) pathways (2). As multiple myeloma cells localize in the bone microenvironment, the transforming growth factor β (TNFβ) and vascular epithelial growth factor (VEGF) are also upregulated, leading to further activation of cell growth and survival pathways. The downstream biologic effect of these pathways is the upregulation of cell cycle and apoptosis related genes, such as the pro-apoptotic gene, BAX (3) and the two main apoptotic pathways in humans, which both utilize the caspase enzyme cascade (4;5). Therefore, it is hypothesized that genetic variation in cell cycle and apoptosis genes could alter multiple myeloma susceptibility.
Although multiple myeloma initiation and progression are well characterized, there have been few known risk factors identified, besides race, for this incurable disease (6). Family history of multiple myeloma in a first-degree relative is also thought to contribute to the risk of myeloma (7), with several studies showing a 2-fold or more increased risk of multiple myeloma associated with family history of multiple myeloma (8;9). These studies also suggest that genetic variation may play a role in the etiology of myeloma.
The genetic susceptibility to multiple myeloma has not been extensively studied. While studies have evaluated polymorphisms in immune response (10-12), xenobiotic metabolism (13-15), and DNA repair genes (16;17), few studies have evaluated genetic variation in the cell cycle pathway. Our previous study evaluating the four key apoptosis related caspase genes and myeloma risk found significant associations with CASP3 rs1049216 and CASP9 rs1052576 (18). To extend these initial findings and to test our hypothesis that cell cycle polymorphisms may also play a role in multiple myeloma risk, we genotyped 276 tag single nucleotide polymorphisms (SNPs) in 27 cell cycle and apoptosis gene regions in a population-based case-control study.
Design and Methods
This population-based case-control study has been described previously (18). Briefly, female cases residing in Connecticut were histologically-confirmed incident multiple myeloma cases diagnosed between 1996 and 2000, aged 21 to 84 years, with no previous diagnosis of cancer, except non-melanoma skin cancer. Cases were alive at the time of interview and 183 interviews were obtained from the 323 eligible cases. Population-based female controls (n = 691) aged 21 to 84 years, were recruited for a parallel study of non-Hodgkin lymphoma, by random digit dialing methods for those below age 65 years and Health Care Financing Administration files for those 65 years and older. Participation rates for cases, controls below age 65, and controls 65 years and older were 57%, 69% and 47%, respectively.
Genomic DNA for genotyping was extracted from peripheral blood or buccal cells for 128 cases and 517 controls, using a phenol-chloroform extraction method (19). Three cases were excluded because of insufficient DNA. Tag SNPs were chosen from the designable set of common SNPs (minor allele frequency >5%) genotyped in the Caucasian population sample of the HapMap Project (Data Release 20/Phase II, NCBI Build 35 assembly, dpSNPb125) using Tagzilla (http://tagzilla.nci.nih.gov/), which implements a tagging algorithm based on the pairwise binning method (20). For each gene region, SNPs located within 20kb 5′ of the start of transcription (exon 1) and 10kb 3′ of the end of the last exon were grouped and selected using a binning threshold of r2>0.8. When there were multiple transcripts available for the gene, the primary transcript was assessed. Tag SNPs were genotyped using a GoldenGate assay (www.illumina.com), at the National Cancer Institute's Core Genotyping Facility (Gaithersburg, MD). Genes were included in this custom assay if they were previously reported to be associated with or hypothesized to be associated with hematopoietic diseases. Blinded duplicate and replicate samples were interspersed throughout the genotyping plates to assess quality control. All tag SNPs had concordance rates ≥ 95% and completion rates ≥ 90%. Subjects with a completion rate <90% were excluded (n = 2 controls). Thus, genotyping for the 290 cell cycle and apoptosis tag SNPs was successful for 125 cases and 515 controls. After restriction to only non-Hispanic Caucasian subjects to maximize study population homogeneity, 108 non-Hispanic Caucasian female cases and 482 non-Hispanic Caucasian female controls remained for analysis.
Of the 290 genotyped tag SNPs, 12 were excluded from analysis due to low minor allele frequency (<0.05). Hardy-Weinberg equilibrium (HWE) for each tag SNP was tested in controls with a Pearson χ2 test or a Fischer's exact test if any of the cell counts were less than five. Fourteen tag SNPs (4.8%) deviated from HWE (p ≤0.05), which was expected by chance. Two SNPs deviated substantially from HWE (p ≤0.001) and were consequently removed from further analyses. Therefore, a total of 276 tag SNPs in 27 gene regions were analyzed. Unconditional logistic regression estimated the odds ratio (OR) and 95% confidence interval (CI) for the association between multiple myeloma risk and each tag SNP. The homozygote of the common allele was used as the reference group and the ORs and 95% CIs were adjusted for age (<50, 50–59, 60-69, and ≥70 years). Gene-dose effects were estimated by a linear trend test based on the number of variant alleles present (0, 1, 2). To assess the significance of the association between each gene region and multiple myeloma, MatLab was used to perform a minP test that assesses the significance of the minimal p-value in each gene region using a permutation-based resampling procedure (1,000 permutations) that takes into account the number of SNPs genotyped in each gene region and their underlying linkage disequilibrium structure (21).
Finally, haplotype blocks and structure were determined by the solid spine LD algorithm in Haploview (www.broad.mit.edu/mpg/haploview) using data from controls for the significant gene regions. Haplotype frequencies were estimated using the expectation-maximization algorithm (22). Haplotypes with frequencies less than 1% were excluded. The overall difference in haplotype frequencies between cases and controls was assessed using a global score test (23). Haplotype ORs and 95% CIs were estimated, adjusted for age (<50, 50–59, 60-69, and ≥70 years). All statistical analyses were performed with SAS unless stated otherwise.
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.
Results
Cases were slightly older than controls in our study population of non-Hispanic Caucasian females (p = 0.04) (Table I). Cases and controls had similar education, marital, and smoking statuses (p > 0.05).
Table I. Characteristics of multiple myeloma cases and controls.
| Cases | % | Controls | % | p-value | |
|---|---|---|---|---|---|
| Age (years) | 0.04 | ||||
| < 50 | 8 | 7.4 | 91 | 18.9 | |
| 50 – 59 | 22 | 20.4 | 89 | 18.5 | |
| 60 – 69 | 30 | 27.8 | 109 | 22.6 | |
| 70 + | 48 | 44.4 | 193 | 40.0 | |
| Education | 0.09 | ||||
| <12 years | 15 | 13.9 | 52 | 10.8 | |
| High School Graduate | 37 | 34.3 | 119 | 24.7 | |
| Vocational / some college | 32 | 29.6 | 144 | 29.9 | |
| College degree | 15 | 13.9 | 97 | 20.1 | |
| Graduate / professional degree | 9 | 8.3 | 70 | 14.5 | |
| Marital Status | 0.20 | ||||
| Married / living as married | 58 | 53.7 | 285 | 59.1 | |
| Widowed | 28 | 25.9 | 119 | 24.7 | |
| Seperated / Divorced | 13 | 12.0 | 60 | 12.4 | |
| Never married | 9 | 8.3 | 18 | 3.7 | |
| Smoking (pack years) | 0.59 | ||||
| Never smoker | 57 | 52.8 | 221 | 45.9 | |
| <6.3 | 11 | 10.2 | 65 | 13.5 | |
| >6.3 - 16.5 | 10 | 9.3 | 61 | 12.7 | |
| >16.5 - 33.0 | 13 | 12.0 | 60 | 12.4 | |
| >33.0 | 14 | 13.0 | 74 | 15.4 |
Three of the 27 gene regions involved in the cell cycle and apoptosis pathways were significantly associated with risk of multiple myeloma (BAX minP = 0.018, CASP9 minP = 0.025, and RIPK1 minP = 0.037) (Table II). Of the 23 tag SNPs that were genotyped in these significant regions, four were significantly associated (ptrend ≤ 0.05) with multiple myeloma risk (Table III). Supplemental table I provides the allele distribution among cases and controls, as well as, multiple myeloma risk associated with all genotyped tag SNPs. Although several SNPs were associated with multiple myeloma risk in the BLC2L10, CASP1/4/5, and BCL2A1 gene regions, those regions had a non-significant minP and thus those SNPs were thought to be more likely chance findings.
Table II. Cell cycle and apoptosis genomic regions associated with multiple myeloma.
| Genomic Region* | Gene Name | Genes (and # of SNPs) included | Total SNPs | minP** |
|---|---|---|---|---|
| BAX | Bcl2-associated X protein | bax (4), dhdh (3), gys1 (2) | 9 | 0.018 |
| CASP9 | caspase 9 | casp9 (4), dnajc16 (1), ela2b (2) | 7 | 0.025 |
| RIPK1 | receptor-interacting serine-threonine kinase 1 | ripk1 (6), serpinbp1 (1) | 7 | 0.037 |
| BCL2L10 | BCL2-like 10 | bcl2l10 (2), gnb5 (3) | 5 | 0.104 |
| CASP1/4/5 | caspase 1, 4, and 5 | casp1 (4), casp4 (6), casp5 (9), loc440067 (2) | 21 | 0.148 |
| BCL2A1 | BCL2-related protein A1 | bcl2a1 (8) | 8 | 0.155 |
| TP53I3 | tumor protein p53 inducible protein 3 | tp53i3 (2), pfn4 (1), sf3b14 (1) | 4 | 0.155 |
| CCND1 | cyclin D1 | ccnd1 (4) | 4 | 0.236 |
| FAS | TNF receptor superfamily, member 6 | fas (20) | 20 | 0.26 |
| BCL2L1 | BCL2-like 1 | bcl2l1 (3), tpx2 (1) | 4 | 0.417 |
| BCL2L11 | BCL2-like 11 | acoxl (2), bcl2l11 (10) | 12 | 0.467 |
| MDM2 | p53 binding protein homolog | mdm2 (3) | 3 | 0.487 |
| CASP2 | caspase 2 | casp2 (3), clcn1 (1), flj90586 (1) | 5 | 0.544 |
| BCL10 | B-cell CLL/lymphoma 10 | bcl10 (10), c1orf52 (2) | 12 | 0.552 |
| CASP3 | caspase 3 | casp3 (6), flj33167 (3) | 9 | 0.598 |
| CASP14 | caspase 14 | casp14 (6), flj40365 (2) | 8 | 0.603 |
| CASP6 | caspase 6 | casp6 (4), flj20647 (3), pla2g612a (1) | 8 | 0.647 |
| CASP8AP2 | caspase 8 associated protien 2 | casp8ap2 (9), cx62 (4), mdn1 (3) | 16 | 0.691 |
| RIPK2 | receptor-interacting serine-threonine kinase 2 | ripk2 (6) | 6 | 0.736 |
| BCL2L2 | BCL2-like 2 | pabpn1 (1), ppp1r3e (1) | 2 | 0.769 |
| FASLG | Fas ligand (TNF superfamily, member 6) | faslg (9) | 9 | 0.772 |
| CASP7 | caspase 7 | casp7 (12) | 12 | 0.809 |
| BCL2 | B-cell CLL/lymphoma 2 | bcl2 (56), fvt1 (5) | 61 | 0.89 |
| MYC | v-myc myelocytomatosis viral oncogene homolog | myc (9) | 9 | 0.924 |
| TP53 | tumor protein p53 | tp53 (1) | 1 | 0.949 |
| PIM1 | pim-1 oncogene | pim1 (1) | 1 | 0.958 |
| CASP8/10 | caspase 8 and 10 | als2cr12 (1), casp8 (9), casp10 (3) | 13 | 0.976 |
defined as SNPs between 20kb 5′ of the start of transcription (exon 1) and 10kb 3′ of the end of the last exon were grouped using a binning threshold of r2>0.8;
adjusted for age (<50, 50-59, 60-69, ≥ 70 years)
Table III. Risk of multiple myeloma associated with all genotyped tag SNPs of signficant cell cycle and apoptosis genes.
| Gene Region | SNP | Alleles | Heterozygotes | Homozygotes | Dominant Model | p - trend | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Major | Minor | OR* | 95% CI* | p | OR* | 95% CI* | p | OR* | 95% CI* | p | |||
| Bax | rs1805419 | G | A | 1.00 | 0.64-1.54 | 0.982 | 1.12 | 0.50-2.48 | 0.789 | 1.01 | 0.67-1.54 | 0.952 | 0.868 |
| rs11667200 | T | A | 0.56 | 0.32-1.00 | 0.049 | 1.18 | 0.12-11.60 | 0.886 | 0.58 | 0.33-1.02 | 0.057 | 0.077 | |
| rs11667229 | T | C | 0.98 | 0.60-1.61 | 0.951 | 0.87 | 0.46-1.64 | 0.667 | 0.95 | 0.60-1.52 | 0.843 | 0.689 | |
| rs11667351 | T | G | 0.56 | 0.33-0.97 | 0.040 | 0.77 | 0.09-6.69 | 0.809 | 0.57 | 0.33-0.98 | 0.041 | 0.051 | |
| rs2270939 | T | C | 1.07 | 0.67-1.71 | 0.789 | 1.86 | 0.74-4.67 | 0.189 | 1.16 | 0.74-1.80 | 0.519 | 0.316 | |
| rs3765148 | G | A | 1.24 | 0.70-2.19 | 0.460 | not applicable | 1.31 | 0.75-2.29 | 0.351 | 0.253 | |||
| rs4802527 | C | G | 0.77 | 0.48-1.24 | 0.285 | 1.26 | 0.34-4.73 | 0.728 | 0.80 | 0.50-1.27 | 0.347 | 0.475 | |
| rs1042265 | G | A | 0.42 | 0.21-0.81 | 0.010 | not applicable | 0.40 | 0.21-0.78 | 0.007 | 0.007 | |||
| rs2270938 | A | T | 0.94 | 0.59-1.50 | 0.798 | 1.05 | 0.55-2.01 | 0.886 | 0.96 | 0.62-1.50 | 0.871 | 0.977 | |
| CASP9 | |||||||||||||
| rs2020902 | A | G | 0.81 | 0.49-1.32 | 0.396 | 0.39 | 0.05-3.08 | 0.372 | 0.77 | 0.48-1.26 | 0.303 | 0.243 | |
| rs4646047 | C | T | 0.76 | 0.47-1.23 | 0.267 | 0.51 | 0.27-0.95 | 0.034 | 0.68 | 0.43-1.07 | 0.095 | 0.033 | |
| rs4646092 | C | T | 0.89 | 0.57-1.38 | 0.589 | 0.73 | 0.24-2.20 | 0.578 | 0.87 | 0.57-1.33 | 0.517 | 0.472 | |
| rs4661636 | C | T | 0.90 | 0.57-1.41 | 0.639 | 0.91 | 0.46-1.80 | 0.796 | 0.90 | 0.59-1.37 | 0.631 | 0.686 | |
| rs7516435 | A | G | 1.48 | 0.94-2.32 | 0.090 | 2.59 | 1.30-5.15 | 0.007 | 1.65 | 1.08-2.53 | 0.020 | 0.005 | |
| rs12130370 | T | C | 1.28 | 0.78-2.12 | 0.327 | 1.51 | 0.82-2.78 | 0.183 | 1.34 | 0.84-2.16 | 0.221 | 0.174 | |
| rs3766160 | G | A | 1.22 | 0.77-1.91 | 0.396 | 2.34 | 1.05-5.23 | 0.038 | 1.35 | 0.88-2.06 | 0.165 | 0.059 | |
| RIPK1 | |||||||||||||
| rs10498658 | C | T | 0.99 | 0.64-1.55 | 0.981 | 1.40 | 0.63-3.12 | 0.409 | 1.05 | 0.69-1.60 | 0.818 | 0.598 | |
| rs2326173 | G | A | 0.93 | 0.60-1.45 | 0.764 | 0.59 | 0.27-1.32 | 0.200 | 0.86 | 0.57-1.31 | 0.485 | 0.269 | |
| rs6596945 | C | A | 1.21 | 0.77-1.88 | 0.410 | 1.33 | 0.47-3.73 | 0.587 | 1.22 | 0.79-1.87 | 0.366 | 0.360 | |
| rs6920337 | G | A | 0.96 | 0.62-1.50 | 0.868 | 0.78 | 0.29-2.11 | 0.630 | 0.94 | 0.61-1.44 | 0.770 | 0.677 | |
| rs7765221 | G | A | 1.49 | 0.87-2.58 | 0.149 | not applicable | 1.40 | 0.82-2.41 | 0.221 | 0.353 | |||
| rs9391981 | G | C | 0.34 | 0.13-0.86 | 0.023 | not applicable | 0.32 | 0.12-0.81 | 0.017 | 0.016 | |||
| rs7775816 | G | A | 1.34 | 0.85-2.10 | 0.208 | 1.43 | 0.39-5.31 | 0.592 | 1.34 | 0.87-2.09 | 0.188 | 0.196 | |
adjusted for age (<50, 50-59, 60-69, ≥ 70 years)
The most significant variant in the BAX region was the A variant at rs1042265, which was associated with a 60% decreased risk of multiple myeloma (ORGA+AA = 0.40, 95%CI = 0.21 – 0.78) (Table III). The C variant at RIPK1 rs9391981 was also associated with a decreased risk of multiple myeloma (ORGC+CC = 0.32, 95%CI = 0.12 – 0.81). The CASP9 genomic region had two significant tag SNPs, rs4646047 and rs7516435, which were moderately correlated (D' = 0.99, r2 = 0.35). When including rs4646047 and rs7416435 in the same model, only rs7516435 remained significantly associated with multiple myeloma risk. The G variant at rs7516435 was associated with an increased risk of multiple myeloma (ORAG+GG = 1.65, 95%CI = 1.08 – 2.53).
The linkage disequilibrium structures for the three genetic regions associated with risk of multiple myeloma (BAX, CASP9, and RIPK1) are shown in supplemental figure 1. The linkage disequilibrium plot for CASP9 identified only one haplotype block and it was significantly associated with multiple myeloma risk (Pglobal test = 0.011). Haplotype analyses for the CASP9 block suggested that the increased risk associated with CASP9 and multiple myeloma was driven by rs7516435 (Table IV). The addition of our previously reported CASP9 rs1052576 to our haplotype analysis did not add further information. Haplotype analyses for BAX and RIPK1 did not provide any additional information beyond the individual tag SNP analyses.
Table IV. Haplotype analyses for significant cell cycle and apoptosis genomic regions associated with multiple myeloma.
| Gene Region | Block | Haplotype | Frequency | OR* | 95% CI* | Global test* | Order of SNPs |
|---|---|---|---|---|---|---|---|
| BAX | 1 | TG | 0.74 | 1.00 | reference | 0.192 | rs2270939 (T>C), rs3765148 (G>A) |
| CG | 0.18 | 1.29 | 0.89 - 1.86 | ||||
| TA | 0.07 | 1.48 | 0.85 - 2.58 | ||||
| 2 | CT | 0.82 | 1.00 | reference | 0.279 | rs4802527 (C>G), rs11667200 (T>A) | |
| GA | 0.11 | 0.64 | 0.37 - 1.12 | ||||
| GT | 0.06 | 1.18 | 0.65 - 2.12 | ||||
| CA | 0.01 | 0.37 | 0.05 - 2.95 | ||||
| 3 | TTGGT | 0.37 | 1.00 | reference | 0.277 | rs11667229 (T>C), rs11667351 (T>G), rs1805419 (G>A), rs1042265 (G>A), rs2270938 (A>T) | |
| CTGGA | 0.29 | 1.22 | 0.83 - 1.79 | ||||
| TTAGA | 0.14 | 1.17 | 0.41 - 3.28 | ||||
| CGAAA | 0.09 | 0.47 | 0.23 - 1.00 | ||||
| CGAGA | 0.03 | 1.34 | 0.53 - 3.37 | ||||
| TTGGA | 0.02 | 1.17 | 0.41 - 3.28 | ||||
| CGGGA | 0.01 | 0.87 | 0.18 - 4.11 | ||||
| CTAGT | 0.01 | 2.57 | 0.80 - 8.26 | ||||
| CTGAA | 0.01 | 0.47 | 0.06 - 3.79 | ||||
| CASP9 | 1 | GTCTTAA | 0.34 | 1.00 | reference | 0.011 | rs3766160 (G>A), rs12130370 (T>C), rs4646092 (C>T), rs4661636 (C>T), rs4646047 (C>T), rs2020902 (A>G), rs7516435 (A>G) |
| ACCCCAG | 0.21 | 1.37 | 0.92 - 2.03 | ||||
| GTCCTAA | 0.15 | 0.60 | 0.34 - 1.05 | ||||
| GCTCCGA | 0.13 | 0.89 | 0.53 - 1.49 | ||||
| GCTCCAA | 0.07 | 1.10 | 0.57 - 2.12 | ||||
| GTCCCAG | 0.06 | 1.95 | 1.01 - 3.74 | ||||
| GTTCCGA | 0.01 | 0.44 | 0.06 - 3.38 | ||||
| RIPK1 | 1 | GC | 0.63 | 1.00 | reference | 0.211 | rs7775816 (G>A), rs6596945 (C>A) |
| GA | 0.20 | 1.27 | 0.87 - 1.86 | ||||
| AC | 0.16 | 1.42 | 0.95 - 2.13 | ||||
| 2 | GG | 0.67 | 1.00 | reference | 0.547 | rs6920337 (G>A), rs2326173 (G>A) | |
| AA | 0.23 | 0.84 | 0.58 - 1.22 | ||||
| GA | 0.09 | 0.78 | 0.45 - 1.35 | ||||
| AG | 0.01 | 1.54 | 0.47 - 5.10 | ||||
| 3 | CG | 0.66 | 1.00 | reference | 0.517 | rs10498658 (C>T), rs7765221 (G>A) | |
| TG | 0.26 | 1.15 | 0.82 - 1.63 | ||||
| TG | 0.08 | 1.29 | 0.76 - 2.17 | ||||
adjusted for age (<50, 50-59, 60-69, ≥ 70 years)
Discussion
Our exploratory analysis of genetic variation in the cell cycle and apoptosis pathways identified three gene regions that were significantly associated with multiple myeloma risk (BAX, CASP9, and RIPK1). Further explorations identified the most significant variant as rs1042265, rs7516435, and rs9391981 for BAX, CASP9, and RIPK1, respectively.
The two main apoptotic pathways in humans, the extrinsic or receptor-mediated pathway and the intrinsic or mitochondrial pathway, both utilize the caspase enzyme cascade (4;5). CASP9 is a member of the intrinsic pathway, which is activated as a result of mitochondrial damage and cytochrome c release. After cytochrome c is released into the cytoplasm, the apoptosome is formed and the caspase-9 cascade is activated (24). Our previous study in this population found a significant inverse association between the A variant at CASP9 rs1052576 and multiple myeloma risk (18). The A variant at CASP9 rs1052576 was also associated with a decreased risk of non-Hodgkin's lymphoma in our parallel study (25). CASP9 rs7516435 and rs1052576 were moderately correlated in our population (D' = 0.98; r2 = 0.35). Our study found the G variant at CASP9 rs7516435 to also be associated with risk of multiple myeloma and inclusion of both SNPs in the same model revealed that the association was driven by the rs7516435 variant. It should be noted that rs7516435 is an intron in DNAJC16, upstream of CASP9; therefore, this variant may affect DNAJC16 as well as CASP9. DnaJ proteins play critical roles in many biological processes, including cell cycle regulation (26) and have been recently associated with breast and brain cancer etiology (27;28). If our findings in the CASP9 gene region are replicated in another population, further research should determine if one of these SNPs or another SNP in linkage disequilibrium with these SNPs is functionally important.
Although not fully understood, the activation of CASP9 by mitochondrial release of cytochrome c is believed to be regulated by the BCL-2-related protein family, through both anti-apoptotic (BCL-2) and the pro-apoptotic protein BAX, which are both regulated by p53 (3). Interestingly, our study also found genetic variation in the BAX gene region, particularly in an exon located in the 3′UTR (rs1042265), to be associated with decreased risk of multiple myeloma. Previous studies evaluating genetic susceptibility to BAX polymorphisms have focused on another B-cell neoplasm, chronic lymphocytic leukemia (CLL). The A variant at BAX rs4645878 has been shown to be associated with decreased BAX expression and overall survival in CLL patients (29); however, a larger study did not replicate these findings (30). When evaluating acute lymphoblastic leukemia (ALL) and acute myeloblastic leukemia (AML) patients, overexpression of BAX mRNA suggested that alteration of BAX transcription may be involved with leukemogenesis (31).
RIPK1 is a member of a kinase family that are integral mediators of cellular response to stress (32). RIPK1 aids the cell in oxidative stress management by interacting with the cell surface receptor death domains, such as TNF, to activate NFκβ (33). This initiation of NFκβ leads to the activation of the caspase cascade and consequently cell death. Reports of genetic susceptibility to multiple myeloma involving the TNF pathway have focused on candidate polymorphisms in TNF-α (34-36), with one study finding a significant inverse association with the A variant at TNF-α -308 (34) and the other two studies founding no association with the A variant (35;36). Our study is the first, to the best of our knowledge, to suggest that genetic variation in a RIPK1 intron (rs9391981) may be important to the risk of multiple myeloma.
Finally, the current analysis did not find a significant association between multiple myeloma risk and CASP3. Our previously reported CASP3 rs1049216 (18) was highly correlated with CASP3 rs2705897 (D' = 0.97; r2 = 0.94) from this tag SNP analysis; however, this analysis has a smaller sample and thus lower power to detect an association. Another possible explanation is that the previously reported association was a chance finding. Replication studies are needed to further evaluate this association.
Our study had several limitations. Our study had a modest sample size, which may have led to false positive and negative findings (37). We attempted to account for spurious findings by using the minP permutation method. The use of this gene-based permutation analysis identifies genes' true significance by comparing the observed association with the distribution of gene-disease associations seen in 1000 randomly generated populations. This robust method can be useful in identifying important genomic regions associated with multiple myeloma. However, it is possible that some associations significant at the SNP level but deemed non-significant at the gene level are indeed true positive findings. Similarly, it is possible some of our findings even if significant at the gene level are false positives. However, our results are biologically plausible, given in the importance of the cell cycle and apoptosis pathways in multiple myeloma pathogenesis. Another concern is that our study had a relatively low participation rate; however, bias in our results is unlikely, since genotyping frequencies have been found to be very similar based on participation status among controls in other studies (38). As our analysis included only non-Hispanic whites, the generalizability of our findings to other ethnic groups is limited. While the allelic frequency variation by ethnicity in HapMap is minimal for rs1042265 and rs9391981, the G allele at rs7516435 is only 22% among individuals of European descent, compared to 57% and 62% for Asian and African descent, respectively. Further, it is unknown if genetic variation at rs1042265, rs9391981, and rs7516435 alters the protein coding sequence, functionality, or expression levels of their respective genes. Finally, our findings may not generalize to all multiple myeloma cases since disease stage and aggressiveness were not available.
In conclusion, this population-based case-control study is the first to comprehensively evaluate the risk of multiple myeloma associated with genetic variation in the cell cycle and apoptosis pathways, to the best of our knowledge. Our study suggests that polymorphisms in the BAX, CASP9, and RIPK1 gene regions may be important for the etiology of multiple myeloma. However, given that candidate gene analyses can hardly distinguish functional SNPs from genetic markers, our results should be viewed as exploratory until they are replicated in larger studies.
Supplementary Material
Acknowledgments
All authors had full access to all of the study data and have contributed to, seen, and approved the final version of the manuscript. TZ, QL, SZ, and SC designed this study, managed data collection, and participated in data processing. HDH participated in data processing, conducted most of the analyses, and was primarily responsible for writing the paper. IM was also involved with data processing. The analysis incorporated suggestions by DB, YZ, SB, LMM, KL, MY, and QL.
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 U.S. Government.
Footnotes
No authors have any or potential conflicts of interest.
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