The mechanisms underlying genetic susceptibility at loci discovered by genome-wide association study (GWAS) approaches in human cancer remain largely undefined. In this study we characterized the high-risk neuroblastoma association at the BRCA1-related locus, BARD1, showing that disease-associated variations correlate with increased expression of the oncogenically activated isoform, BARD1β. In neuroblastoma cells, silencing of BARD1β showed genotype-specific cytotoxic effects, including decreased substrate-adherent, anchorage-independent, and foci growth. In established murine fibroblasts, overexpression of BARD1β was sufficient for neoplastic transformation. BARD1β stabilized the Aurora family of kinases in neuroblastoma cells, suggesting both a mechanism for the observed effect and a potential therapeutic strategy. Together, our findings identify BARD1β as an oncogenic driver of high-risk neuroblastoma tumorigenesis, and more generally, they illustrate how robust GWAS signals offer genomic landmarks to identify molecular mechanisms involved in both tumor initiation and malignant progression. The interaction of BARD1β with the Aurora family of kinases lends strong support to the ongoing work to develop Aurora kinase inhibitors for clinically aggressive neuroblastoma.

Neuroblastoma is a childhood cancer of the sympathetic nervous system that accounts for approximately 10% of all paediatric oncology deaths1,2. To identify genetic risk factors for neuroblastoma, we performed a genome-wide association study (GWAS) on 2,251 patients and 6,097 control subjects of European ancestry from four case series. Here we report a significant association within LIM domain only 1 (LMO1) at 11p15.4 (rs110419, combined P = 5.2 × 10−16, odds ratio of risk allele = 1.34 (95% confidence interval 1.25–1.44)). The signal was enriched in the subset of patients with the most aggressive form of the disease. LMO1 encodes a cysteine-rich transcriptional regulator, and its paralogues (LMO2, LMO3 and LMO4) have each been previously implicated in cancer. In parallel, we analysed genome-wide DNA copy number alterations in 701 primary tumours. We found that the LMO1 locus was aberrant in 12.4% through a duplication event, and that this event was associated with more advanced disease (P < 0.0001) and survival (P = 0.041). The germline single nucleotide polymorphism (SNP) risk alleles and somatic copy number gains were associated with increased LMO1 expression in neuroblastoma cell lines and primary tumours, consistent with a gain-of-function role in tumorigenesis. Short hairpin RNA (shRNA)-mediated depletion of LMO1 inhibited growth of neuroblastoma cells with high LMO1 expression, whereas forced expression of LMO1 in neuroblastoma cells with low LMO1 expression enhanced proliferation. These data show that common polymorphisms at the LMO1 locus are strongly associated with susceptibility to developing neuroblastoma, but also may influence the likelihood of further somatic alterations at this locus, leading to malignant progression.

Neuroblastoma is a malignant neoplasm of the developing sympathetic nervous system that is notable for its phenotypic diversity. High-risk patients typically have widely disseminated disease at diagnosis and a poor survival probability, but low-risk patients frequently have localized tumors that are almost always cured with little or no chemotherapy. Our genome-wide association study (GWAS) has identified common variants within FLJ22536, BARD1, and LMO1 as significantly associated with neuroblastoma and more robustly associated with high-risk disease. Here we show that a GWAS focused on low-risk cases identified SNPs within DUSP12 at 1q23.3 (P = 2.07×10−6), DDX4 and IL31RA both at 5q11.2 (P = 2.94×10−6 and 6.54×10−7 respectively), and HSD17B12 at 11p11.2 (P = 4.20×10−7) as being associated with the less aggressive form of the disease. These data demonstrate the importance of robust phenotypic data in GWAS analyses and identify additional susceptibility variants for neuroblastoma.

Author Summary

Neuroblastoma is the most common solid tumor outside the central nervous system and is accountable for 10% of the mortality rate of all children's cancers. It has distinctive clinical behaviors and is categorized into different risk groups: high-risk, intermediate-risk, and low-risk. Genome-wide association studies have reported a number of genetic variations predisposing to high-risk neuroblastoma. This study focuses on the low-risk neuroblastoma group and identifies four novel genes (DUSP12, DDX4, IL31RA, and HSD17B12) at three distinct genomic positions that harbor disease-causing variants. This study also reports several gene sets that are enriched in overall neuroblastoma as well as in both high-risk and low-risk groups. Also of importance is that this study adopts a new computational method that identifies genes, instead of only one single nucleotide polymorphism, as disease-causing variants. Shown to have superior power of detection genome-wide association signals for neuroblastoma, the methodology presented in this study has great potential applications in case-control association studies in other diseases.

We conducted a SNP-based genome-wide association study (GWAS) focused on the high-risk subset of neuroblastoma1. As our previous unbiased GWAS showed strong association of common 6p22 SNP alleles with aggressive neuroblastoma2, we now restricted our analysis to 397 high-risk cases compared to 2,043 controls. We detected new significant association of six SNPs at 2q35 within the BARD1 gene locus (Pallelic = 2.35×10−9 − 2.25×10−8). Each SNP association was confirmed in a second series of 189 high-risk cases and 1,178 controls (Pallelic = 7.90×10−7 − 2.77×10−4). The two most significant SNPs (rs6435862, rs3768716) were also tested in two additional independent high-risk neuroblastoma case series, yielding combined allelic odds-ratios of 1.68 each (P = 8.65×10−18 and 2.74×10−16, respectively). Significant association was also found with known BARD1 nsSNPs. These data show that common variation in BARD1 contributes to the etiology of the aggressive and most clinically relevant subset of human neuroblastoma.

Common copy number variations (CNVs) represent a significant source of genetic diversity, yet their influence on phenotypic variability, including disease susceptibility, remains poorly understood. To address this problem in cancer, we performed a genome-wide association study (GWAS) of CNVs in the childhood cancer neuroblastoma, a disease where SNP variations are known to influence susceptibility1,2. We first genotyped 846 Caucasian neuroblastoma patients and 803 healthy Caucasian controls at 550,000 single nucleotide polymorphisms, and performed a CNV-based test for association. We then replicated significant observations in two independent sample sets comprised of a total of 595 cases and 3,357 controls. We identified a common CNV at 1q21.1 associated with neuroblastoma in the discovery set, which was confirmed in both replication sets (Pcombined = 2.97 × 10−17; OR = 2.49, 95% CI: 2.02 to 3.05). This CNV was validated by quantitative PCR, fluorescent in situ hybridization, and analysis of matched tumor specimens, and was shown to be heritable in an independent set of 713 cancer-free trios. We identified a novel transcript within the CNV which showed high sequence similarity to several “Neuroblastoma breakpoint family” (NBPF) genes3,4 and represents a new member of this gene family (NBPFX). This transcript was preferentially expressed in fetal brain and fetal sympathetic nervous tissues, and expression level was strictly correlated with CNV state in neuroblastoma cells. These data demonstrate that inherited copy number variation at 1q21.1 is associated with neuroblastoma and implicate a novel NBPF gene in early tumorigenesis of this childhood cancer.

Background

Neuroblastoma is a malignancy of the developing sympathetic nervous system that most commonly affects young children and is often lethal. The etiology of this embryonal cancer is not known.

Methods

We performed a genome-wide association study by first genotyping 1,032 neuroblastoma patients and 2,043 controls of European descent using the Illumina HumanHap550 BeadChip. Three independent groups of neuroblastoma cases (N=720) and controls (N=2128) were then genotyped to replicate significant associations.

Results

We observed highly significant association between neuroblastoma and the common minor alleles of three single nucleotide polymorphisms (SNPs) within a 94.2 kilobase (Kb) linkage disequilibrium block at chromosome band 6p22 containing the predicted genes FLJ22536 and FLJ44180 (P-value range = 1.71×10-9-7.01×10-10; allelic odds ratio range 1.39-1.40). Homozygosity for the at-risk G allele of the most significantly associated SNP, rs6939340, resulted in an increased likelihood of developing neuroblastoma of 1.97 (95% CI 1.58-2.44). Subsequent genotyping of these 6p22 SNPs in the three independent case series confirmed our observation of association (P=9.33×10-15 at rs6939340 for joint analysis). Furthermore, neuroblastoma patients homozygous for the risk alleles at 6p22 were more likely to develop metastatic (Stage 4) disease (P=0.02), show amplification of the MYCN oncogene in the tumor cells (P=0.006), and to have disease relapse (P=0.01).

Conclusion

Common genetic variation at chromosome band 6p22 is associated with susceptibility to neuroblastoma.

Whole-genome microarrays with large-insert clones designed to determine DNA copy number often show variation in hybridization intensity that is related to the genomic position of the clones. We found these ‘genomic waves’ to be present in Illumina and Affymetrix SNP genotyping arrays, confirming that they are not platform-specific. The causes of genomic waves are not well-understood, and they may prevent accurate inference of copy number variations (CNVs). By measuring DNA concentration for 1444 samples and by genotyping the same sample multiple times with varying DNA quantity, we demonstrated that DNA quantity correlates with the magnitude of waves. We further showed that wavy signal patterns correlate best with GC content, among multiple genomic features considered. To measure the magnitude of waves, we proposed a GC-wave factor (GCWF) measure, which is a reliable predictor of DNA quantity (correlation coefficient = 0.994 based on samples with serial dilution). Finally, we developed a computational approach by fitting regression models with GC content included as a predictor variable, and we show that this approach improves the accuracy of CNV detection. With the wide application of whole-genome SNP genotyping techniques, our wave adjustment method will be important for taking full advantage of genotyped samples for CNV analysis.

Genomic aberrations recurrent in a particular cancer type can be important prognostic markers for tumor progression. Typically in early tumorigenesis, cells incur a breakdown of the DNA replication machinery that results in an accumulation of genomic aberrations in the form of duplications, deletions, translocations, and other genomic alterations. Microarray methods allow for finer mapping of these aberrations than has previously been possible; however, data processing and analysis methods have not taken full advantage of this higher resolution. Attention has primarily been given to analysis on the single sample level, where multiple adjacent probes are necessarily used as replicates for the local region containing their target sequences. However, regions of concordant aberration can be short enough to be detected by only one, or very few, array elements. We describe a method called Multiple Sample Analysis for assessing the significance of concordant genomic aberrations across multiple experiments that does not require a-priori definition of aberration calls for each sample. If there are multiple samples, representing a class, then by exploiting the replication across samples our method can detect concordant aberrations at much higher resolution than can be derived from current single sample approaches. Additionally, this method provides a meaningful approach to addressing population-based questions such as determining important regions for a cancer subtype of interest or determining regions of copy number variation in a population. Multiple Sample Analysis also provides single sample aberration calls in the locations of significant concordance, producing high resolution calls per sample, in concordant regions. The approach is demonstrated on a dataset representing a challenging but important resource: breast tumors that have been formalin-fixed, paraffin-embedded, archived, and subsequently UV-laser capture microdissected and hybridized to two-channel BAC arrays using an amplification protocol. We demonstrate the accurate detection on simulated data, and on real datasets involving known regions of aberration within subtypes of breast cancer at a resolution consistent with that of the array. Similarly, we apply our method to previously published datasets, including a 250K SNP array, and verify known results as well as detect novel regions of concordant aberration. The algorithm has been fully implemented and tested and is freely available as a Java application at http://www.cbil.upenn.edu/MSA.

Author Summary

Cancer is a genetic disease caused by genomic mutations that confer an increased ability to proliferate and survive in a specific environment. It is now known that many regions of genomic DNA are deleted or amplified in specific cancer types. These aberrations are believed to occur randomly in the genome. If these aberrations overlap more than would be expected by chance across individual occurrences of the cancer this suggests a selective pressure on this aberration. These conserved aberrations likely represent regions that are important for the development, progression, and survival of a specific cancer type in its environment. We present a method for identifying these conserved aberrations within a class of samples. The applications for this method include accurate high resolution mapping of aberrations characteristic of cancer subtypes as well as other genetic diseases and determination of conserved copy number variations in the population. With the use of high resolution microarray methods we have profiled different tumor types. We have been able to create high resolution profiles of conserved aberrations in specific cancer types. These conserved aberrations are prime targets for cancer therapies and many of these regions have already been used to develop effective cancer therapeutics.

PlasmoDB (http://PlasmoDB.org) is the official database of the Plasmodium falciparum genome sequencing consortium. This resource incorporates finished and draft genome sequence data and annotation emerging from Plasmodium sequencing projects. PlasmoDB currently houses information from five parasite species and provides tools for cross-species comparisons. Sequence information is also integrated with other genomic-scale data emerging from the Plasmodium research community, including gene expression analysis from EST, SAGE and microarray projects. The relational schemas used to build PlasmoDB [Genomics Unified Schema (GUS) and RNA Abundance Database (RAD)] employ a highly structured format to accommodate the diverse data types generated by sequence and expression projects. A variety of tools allow researchers to formulate complex, biologically based queries of the database. A version of the database is also available on CD-ROM (Plasmodium GenePlot), facilitating access to the data in situations where Internet access is difficult (e.g. by malaria researchers working in the field). The goal of PlasmoDB is to enhance utilization of the vast quantities of data emerging from genome-scale projects by the global malaria research community.