Abstract
Neuroblastoma is a childhood cancer that originates in the developing sympathetic nervous system. We previously reported a crucial role of mitochondrial DNA haplogroups in the pathology of neuroblastoma. To pinpoint mitochondrial DNA variants associated with neuroblastoma risk, we applied a mitochondrial genome imputation pipeline to the single nucleotide polymorphisms array data of 2 pediatric cohorts containing a total of 2404 neuroblastoma patients and 9310 cancer-free controls. All statistical tests were 2-sided. The single nucleotide variant, rs2853493, was statistically significantly associated with neuroblastoma risk in the discovery cohort (odds ratio = 0.62, 95% confidence interval = 0.53 to 0.72, P < .001) and further confirmed in the replication cohort (odds ratio = 0.75, 95% confidence interval = 0.62 to 0.90, P = .002). Further, expression quantitative trait loci analysis indicated genotypes of rs2853493 were associated with expression levels of MT-CYB gene expression in neuroblastoma cells, suggesting rs2853493 may confer risk to neuroblastoma via regulating the expression level of its nearby genes.
Neuroblastoma originates from the developing sympathetic nervous system and accounts for approximately 12% of all pediatric oncology deaths (1). It is one the most fatal childhood cancers, with approximately one-half of affected individuals having disease dissemination at diagnosis and poor outcome from therapy (1,2). Survival rate for these high-risk group remains less than 50% despite aggressive multimodal therapies (1,2).
Genetic studies have uncovered a polygenic model of inheritance with the discovery of multiple genes associated with neuroblastoma through genetic studies (3-12). Besides identification of genetic risk factors from nuclear DNA, cumulative evidence suggests that mitochondrial dysfunction may link to neuroblastoma tumorigenesis (13-15). Our recent study revealed statistically significant associations between neuroblastoma risk and human mitochondrial DNA (mtDNA) haplogroup K (16). A haplogroup represents a group of similar haplotypes inherited from a common ancestor (17). The formation of mtDNA haplogroups is a result of the sequential accumulation of mutations through maternal lineages during ancient migrations of human populations (17). However, it is still unknown which haplogroup K-associated variants or mitochondrial-encoded genes may contribute to neuroblastoma risk. To extend our previous observations, we have designed studies to investigate the mitochondrial genome in more depth, using an mtDNA imputation pipeline (Supplementary Methods, available online), further pinpointing statistically significantly associated mtDNA variants by analyzing a total of 2404 neuroblastoma cases and 9310 cancer-free controls (Supplementary Methods; Supplementary Figures 1 and 2, available online). This study was approved by the Institutional Research Ethics Board of CHOP (The Children’s Hospital of Philadelphia). Written informed consent was obtained from all patients by nursing and medical assistant study staff under the direction of CHOP clinicians.
A 2-sided logistic regression test indicated rs2853493, a synonymous variant in MT-ND4, was statistically significantly (the cutoff for statistical significance is P < .05) associated with neuroblastoma risk in the discovery cohort of 1474 case patients and 5699 cancer-free controls (odds ratio [OR] = 0.62, 95% confidence interval [CI] = 0.53 to 0.72, P < .001; Table 1; Supplementary Table 1, available online). This association was further replicated in the replication cohort comprised of 930 cases and 3611 controls (OR= 0.75, 95% CI = 0.62 to 0.90, P = .002; Table 1; Supplementary Table 1, available online) and confirmed by a sensitivity analysis (Supplementary Methods, available online). Meta-analysis of 2 studies yielded a P value that extends well beyond the statistical significance threshold (OR = 0.67, 95% CI = 0.59 to 0.76, P < .001; Table 1) and additionally detected a synonymous variant, rs2853499, in MT-ND5 (OR = 0.71, 95% CI = 0.63 to 0.8, P < .001; Table 1). Consistent with our previous observation, minor alleles of both rs2853493 (11467G) and rs2853499 (12372A) are listed as top-level haplogroup markers of haplogroup K, which should be present at 80% or more in the top-level haplogroups (18). In our data, the fractions of haplogroup K carriers possessing minor alleles of rs2853493, rs2853499, or both were 91.2%, 97.2%, and 90.8% respectively. We next conditioned our analysis on rs2853493 (Figure 1; Supplementary Table 2, available online) and found the association between rs2853499 and neuroblastoma disappeared (OR = 0.73, 95% CI = 0.18 to 2.46, P = .68). Likewise, the association between rs2853493 and neuroblastoma was not statistically significant (OR = 0.89, 95% CI = 0.24 to 3.34, P = .86) after conditioning on rs2853499 indicating a single signal residing in this region. Our results also indicated the previously described association between risk of neuroblastoma and haplogroup K was explained by these 2 variants, because this association was not statistically significant (OR = 1.01, 95% CI = 0.91 to 1.11, P = .93) when genotypes of rs2853493 and rs2853499 were included as covariates. Moreover, we replicated this signal in another independent cohort (rs2853493, OR = 0.65, 95% CI = 0.49 to 0.89, P = .008) of 344 European cases from the United Kingdom and 2752 ancestry-matched controls from CHOP. Although the reported variants were not statistically significant in the African American cohort of 332 cases and 2748 controls, the estimated odds ratios for both variants (rs2853493, OR = 0.71, 95% CI = 0.39 to1.27, P = .26; rs2853499, OR = 0.69, 95% CI = 0.38 to 1.22, P = .21) were very similar to the estimates given above for Europeans. This suggests that this signal may overlap in both racial groups.
Table 1.
Association results of rs2853493 and rs2853499
| Risk group | RSID | POS | A1/A2 | Discovery |
Replication |
Meta-analysis |
|||
|---|---|---|---|---|---|---|---|---|---|
| OR (95% CI)a | P a | OR (95% CI)a | P a | OR (95% CI)b |
P b | ||||
| All | rs2853493 | 11 467 | G/A | 0.62 (0.53 to 0.72) | <.001 | 0.75 (0.62 to 0.90) | .002 | 0.67 (0.59 to 0.76) | <.001 |
| rs2853499 | 12 372 | A/G | 0.69 (0.59 to 0.81) | <.001 | 0.74 (0.61 to 0.89) | .001 | 0.71 (0.63 to 0.80) | <.001 | |
| High | rs2853493 | 11 467 | G/A | 0.59 (0.47 to 0.75) | <.001 | 0.73 (0.55 to 0.95) | .02 | 0.65 (0.54 to 0.77) | <.001 |
| rs2853499 | 12 372 | A/G | 0.64 (0.51 to 0.82) | <.001 | 0.72 (0.55 to 0.94) | .02 | 0.68 (0.57 to 0.81) | <.001 | |
| Intermediate | rs2853493 | 11 467 | G/A | 0.55 (0.39 to 0.78) | <.001 | 0.69 (0.48 to 0.99) | .05 | 0.61 (0.48 to 0.79) | <.001 |
| rs2853499 | 12 372 | A/G | 0.62 (0.44 to 0.87) | .006 | 0.69 (0.48 to 0.98) | .04 | 0.65 (0.51 to 0.83) | <.001 | |
| Low | rs2853493 | 11 467 | G/A | 0.64 (0.5 to 0.82) | <.001 | 0.90 (0.67 to 1.21) | .48 | 0.74 (0.61 to 0.89) | .001 |
| rs2853499 | 12 372 | A/G | 0.73 (0.57 to 0.92) | .007 | 0.87 (0.64 to 1.17) | .34 | 0.78 (0.65 to 0.93) | .007 | |
aTwo-sided logistic regression test. A1 = minor allele (coded allele); A2 = major allele; CI = confidence interval; OR = odds ratio; POS = genomic coordinates; RSID = rsID number of reported mitochondrial variants.
bTwo-sided meta-analysis.
Figure 1.
Mitochondrial DNA variants associated with neuroblastoma. A) Circos plot of the association results. Circles from the outside to the inside indicate the following: position of a variant on the mitochondrial genome; statistical significance of tested mitochondrial DNA variants (the radial axis corresponds to the −log10-transformed P values, 2 genome-wide statistically significant variants are colored in red, haplogroup K markers are colored in yellow); and regions corresponding to the different mtDNA genes (green, coding region; yellow, ribosomal RNAs; orange, transfer RNAs). B) Circos plot of the association results conditioned on rs2853493.
We next tested rs2853493 and rs2853499 for association with genotypes at 7 known neuroblastoma loci (3,4,7,8,19). No statistically significant interaction was detected (Supplementary Table 3, available online), suggesting the neuroblastoma risk attributed to this mitochondrial locus was independent from other loci. Also, no additional variants were identified as associated with a specific risk group of neuroblastoma (Table 1) (20). Cochrane’s Q tests for heterogeneity of the odds ratios over the 3 risk groups were not statistically significant (rs2853493, P = .90; rs2853499, P = .93). Further survival analysis based on the log rank test detected no statistically significant differences between patients with minor (G) and major (A) alleles of rs2853493 (Supplementary Figure 3, available online). In addition, no statistically significant association was detected between the reported variants and demographic variables.
We next analyzed the RNAseq data of neuroblastoma primary tumor tissues (Supplementary Methods, available online) and found genotypes of rs2853493 were statistically significantly associated with expression levels of MT-CYB (Supplementary Figure 4, available online). We also conducted an expression quantitative trait loci analysis of rs2853493 based on the Genotype-Tissue Expression Program v8 data to explore the potential regulatory effect of rs2853493 in normal human tissues (Supplementary Methods, available online). Statistically significant associations between rs2853493 and nearby genes were detected (Supplementary Table 4, available online), including MT-CYB, MT-ND4, MT-ND4L, and MT-ND5. However, none of the reported mtDNA variants resides in the regulatory region according to the epigenetic data (Supplementary Methods, available online).
The genomic region containing MT-ND4 and MT-ND5 has been reported as a genetic hotspot region for multiple cancers and neurological disorders (21-25). Triska et al. (23) reported that mtDNA mutations of pediatric tumors clustered at a statistically significant hotspot region in MT-ND4 and MT-ND5. Jaberi et al. (21) observed that the MT-ND5 region is prone to mutations in hematological cancers and in dopaminergic neurons involved in Parkinson disease. High incidence of MT-CYB mutations has also been reported in esophageal cancer (25) and glioblastoma (26). Consistently, we found 2 regions (Supplementary Table 5, available online) within MT-ND5 and MT-CYB exhibited a statistically significant enrichment of mutations indicating the presence of potential mutation hotspots in these regions (Supplementary Methods, available online).
A limitation of this study is that the imputation analysis is limited by poor coverage of mtDNA variants in the genotyping arrays, because only 485 of 3617 mtDNA variants were successfully imputed. Future studies using whole-genome sequencing are likely to detect additional mtDNA variants near this region associated with neuroblastoma.
This study presents an extension of our previous findings by uncovering mtDNA variants and candidate genes for future functional studies on mitochondrial mechanisms in the development of neuroblastoma.
Funding
The study was supported by Institutional Development Funds from The Children’s Hospital of Philadelphia (HH), Endowed Chair in Genomic Research (HH), and by National Institutes of Health (NIH) grants U01HG006830 (HH), R01CA124709 (JMM), R01CA180692 (JMM), and R35CA220500 (JMM).
Notes
Role of the funder: The funders had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the article; and the decision to submit the article for publication.
Disclosures: The authors have no conflicts of interest to disclose.
Author contributions: All authors have read and approved the final version. Additional contributions are as follows: experimental design (XC, HH), data curation (XC, YL, HC, QH, JG, KN), data analysis and interpretation of the data (XC, YL, PS, SJD, JMM, JG, QH), Supervision (HH, JMM).
Disclaimers: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Data Availability
Individual-level genotyping data are available in dbGAP, and can be accessed with accession phs000124.v3.p1.
Supplementary Material
Contributor Information
Xiao Chang, The Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA.
Yichuan Liu, The Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA.
Joseph Glessner, The Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA.
Cuiping Hou, The Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA.
Huiqi Qu, The Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA.
Kenny Nguyen, The Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA.
Patrick Sleiman, The Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, PA, USA; Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA.
Lobin Lee, Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
Sharon J Diskin, Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, PA, USA; Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA; Division of Oncology and Center for Childhood Cancer Research, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA.
John M Maris, Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, PA, USA; Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA , USA; Division of Oncology and Center for Childhood Cancer Research, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA.
Hakon Hakonarson, The Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, University of Pennsylvania School of Medicine , Philadelphia, PA, USA; Division of Human Genetics, The Children’s Hospital of Philadelphia, Ph iladelphia, PA, USA; Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
References
- 1. Maris JM. Recent advances in neuroblastoma. N Engl J Med. 2010;362(23):2202–2211. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Maris JM, Hogarty MD, Bagatell R, et al. Neuroblastoma. Lancet. 2007;369(9579):2106–2120. [DOI] [PubMed] [Google Scholar]
- 3. Maris JM, Mosse YP, Bradfield JP, et al. Chromosome 6p22 locus associated with clinically aggressive neuroblastoma. N Engl J Med. 2008;358(24):2585–2593. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Capasso M, Devoto M, Hou C, et al. Common variations in BARD1 influence susceptibility to high-risk neuroblastoma. Nat Genet. 2009;41(6):718–723. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Wang K, Diskin SJ, Zhang H, et al. Integrative genomics identifies LMO1 as a neuroblastoma oncogene. Nature. 2011;469(7329):216–220. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Nguyen Le B, Diskin SJ, Capasso M, et al. Phenotype restricted genome-wide association study using a gene-centric approach identifies three low-risk neuroblastoma susceptibility Loci. PLoS Genet. 2011;7(3):e1002026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Diskin SJ, Capasso M, Schnepp RW, et al. Common variation at 6q16 within HACE1 and LIN28B influences susceptibility to neuroblastoma. Nat Genet. 2012;44(10):1126–1130. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. McDaniel LD, Conkrite KL, Chang X, et al. Common variants upstream of MLF1 at 3q25 and within CPZ at 4p16 associated with neuroblastoma. PLoS Genet. 2017;13(5):e1006787. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Chang X, Zhao Y, Hou C, et al. Common variants in MMP20 at 11q22.2 predispose to 11q deletion and neuroblastoma risk. Nat Commun. 2017;8(1):569. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Eleveld TF, Oldridge DA, Bernard V, et al. Relapsed neuroblastomas show frequent RAS-MAPK pathway mutations. Nat Genet. 2015;47(8):864–871. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Pugh TJ, Morozova O, Attiyeh EF, et al. The genetic landscape of high-risk neuroblastoma. Nat Genet. 2013;45(3):279–284. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Tolbert VP, Coggins GE, Maris JM. Genetic susceptibility to neuroblastoma. Curr Opin Genet Dev. 2017;42:81–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Galluzzi L, Kepp O, Vander Heiden MG, et al. Metabolic targets for cancer therapy. Nat Rev Drug Discov. 2013;12(11):829–846. [DOI] [PubMed] [Google Scholar]
- 14. Morscher RJ, Aminzadeh-Gohari S, Feichtinger RG, et al. Inhibition of neuroblastoma tumor growth by ketogenic diet and/or calorie restriction in a CD1-Nu mouse model. PLoS One. 2015;10(6):e0129802. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Ashton TM, McKenna WG, Kunz-Schughart LA, et al. Oxidative phosphorylation as an emerging target in cancer therapy. Clin Cancer Res. 2018;24(11):2482–2490. [DOI] [PubMed] [Google Scholar]
- 16. Chang X, Bakay M, Liu Y, et al. Mitochondrial DNA haplogroups and susceptibility to neuroblastoma. J Natl Cancer Inst. 2020;112(12):1259–1266. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Wallace DC. Mitochondrial DNA variation in human radiation and disease. Cell. 2015;163(1):33–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Ruiz-Pesini E, Lott MT, Procaccio V, et al. An enhanced MITOMAP with a global mtDNA mutational phylogeny. Nucleic Acids Res. 2007;35(Database issue):D823–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Wang K, Zhang H, Ma D, et al. Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature. 2009;459(7246):528–533. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Pinto NR, Applebaum MA, Volchenboum SL, et al. Advances in risk classification and treatment strategies for neuroblastoma. J Clin Oncol. 2015;33(27):3008–3017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Jaberi E, Tresse E, Grønbæk K, et al. Identification of unique and shared mitochondrial DNA mutations in neurodegeneration and cancer by single-cell mitochondrial DNA structural variation sequencing (MitoSV-seq). EBioMedicine. 2020;57:102868. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Cavalcante GC, Marinho ANR, Anaissi AK, et al. Whole mitochondrial genome sequencing highlights mitochondrial impact in gastric cancer. Sci Rep. 2019;9(1):15716. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Triska P, Kaneva K, Merkurjev D, et al. Landscape of germline and somatic mitochondrial DNA mutations in pediatric malignancies. Cancer Res. 2019;79(7):1318–1330. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Arezi P, Rezvani Z. The variation of mitochondrial NADH dehydrogenase subunit 4 (mtND4) and molecular dynamics simulation of SNPs among Iranian women with breast cancer. J Mol Graph Model. 2018;85:242–249. [DOI] [PubMed] [Google Scholar]
- 25. Liu ZW, Guo ZJ, Chu AL, et al. High incidence of coding gene mutations in mitochondrial DNA in esophageal cancer. Mol Med Rep. 2017;16(6):8537–8541. [DOI] [PubMed] [Google Scholar]
- 26. Lloyd RE, Keatley K, Littlewood DT, et al. Identification and functional prediction of mitochondrial complex III and IV mutations associated with glioblastoma. Neuro Oncol. 2015;17(7):942–952. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Individual-level genotyping data are available in dbGAP, and can be accessed with accession phs000124.v3.p1.