CODEX: A Normalization and Copy Number Variation Detection Method for Whole Exome Sequencing

High-throughput sequencing of DNA coding regions has become a common way of assaying genomic variation in the study of human diseases. Copy number variation (CNV) is an important type of genomic variation, but detecting and characterizing CNV from exome sequencing is challenging due to the high level of biases and artifacts. We propose CODEX, a normalization and CNV calling procedure for whole exome sequencing data. The Poisson latent factor model in CODEX includes terms that specifically remove biases due to GC content, exon capture and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. CODEX is compared to existing methods on a population analysis of HapMap samples from the 1000 Genomes Project, and shown to be more accurate on three microarray-based validation data sets. We further evaluate performance on 222 neuroblastoma samples with matched normals and focus on a well-studied rare somatic CNV within the ATRX gene. We show that the cross-sample normalization procedure of CODEX removes more noise than normalizing the tumor against the matched normal and that the segmentation procedure performs well in detecting CNVs with nested structures

Assessing the Significance of Conserved Genomic Aberrations Using High Resolution Genomic Microarrays

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

A family-based study of gene variants and maternal folate and choline in neuroblastoma: a report from the Children’s Oncology Group

Neuroblastoma is a childhood cancer of the sympathetic nervous system with embryonic origins. Previous epidemiologic studies suggest maternal vitamin supplementation during pregnancy reduces the risk of neuroblastoma. We hypothesized offspring and maternal genetic variants in folate-related and choline-related genes are associated with neuroblastoma and modify the effects of maternal intake of folate, choline and folic acid

MIBG avidity correlates with clinical features, tumor biology, and outcomes in neuroblastoma: A report from the Children’s Oncology Group

BackgroundPrior studies suggest that neuroblastomas that do not accumulate metaiodobenzylguanidine (MIBG) on diagnostic imaging (MIBG non‐avid) may have more favorable features compared with MIBG avid tumors. We compared clinical features, biologic features, and clinical outcomes between patients with MIBG nonavid and MIBG avid neuroblastoma.ProcedurePatients had metastatic high‐ or intermediate‐risk neuroblastoma and were treated on Children’s Oncology Group protocols A3973 or A3961. Comparisons of clinical and biologic features according to MIBG avidity were made with chi‐squared or Fisher exact tests. Event‐free (EFS) and overall (OS) survival compared using log–rank tests and modeled using Cox models.ResultsThirty of 343 patients (8.7%) had MIBG nonavid disease. Patients with nonavid tumors were less likely to have adrenal primary tumors (34.5 vs. 57.2%; P = 0.019), bone metastases (36.7 vs. 61.7%; P = 0.008), or positive urine catecholamines (66.7 vs. 91.0%; P < 0.001) compared with patients with MIBG avid tumors. Nonavid tumors were more likely to be MYCN amplified (53.8 vs. 32.6%; P = 0.030) and had lower norepinephrine transporter expression. Patients with MIBG nonavid disease had a 5‐year EFS of 50.0% compared with 38.7% for patients with MIBG avid disease (P = 0.028). On multivariate testing in high‐risk patients, MIBG avidity was the sole adverse prognostic factor for EFS identified (hazard ratio 1.77; 95% confidence interval 1.04–2.99; P = 0.034).ConclusionsPatients with MIBG nonavid neuroblastoma have lower rates of adrenal primary tumors, bone metastasis, and catecholamine secretion. Despite being more likely to have MYCN‐amplified tumors, these patients have superior outcomes compared with patients with MIBG avid disease.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138438/1/pbc26545_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138438/2/pbc26545.pd

Phenotype Restricted Genome-Wide Association Study Using a Gene-Centric Approach Identifies Three Low-Risk Neuroblastoma Susceptibility Loci

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

Software comparison for evaluating genomic copy number variation for Affymetrix 6.0 SNP array platform

<p>Abstract</p> <p>Background</p> <p>Copy number data are routinely being extracted from genome-wide association study chips using a variety of software. We empirically evaluated and compared four freely-available software packages designed for Affymetrix SNP chips to estimate copy number: Affymetrix Power Tools (APT), Aroma.Affymetrix, PennCNV and CRLMM. Our evaluation used 1,418 GENOA samples that were genotyped on the Affymetrix Genome-Wide Human SNP Array 6.0. We compared bias and variance in the locus-level copy number data, the concordance amongst regions of copy number gains/deletions and the false-positive rate amongst deleted segments.</p> <p>Results</p> <p>APT had median locus-level copy numbers closest to a value of two, whereas PennCNV and Aroma.Affymetrix had the smallest variability associated with the median copy number. Of those evaluated, only PennCNV provides copy number specific quality-control metrics and identified 136 poor CNV samples. Regions of copy number variation (CNV) were detected using the hidden Markov models provided within PennCNV and CRLMM/VanillaIce. PennCNV detected more CNVs than CRLMM/VanillaIce; the median number of CNVs detected per sample was 39 and 30, respectively. PennCNV detected most of the regions that CRLMM/VanillaIce did as well as additional CNV regions. The median concordance between PennCNV and CRLMM/VanillaIce was 47.9% for duplications and 51.5% for deletions. The estimated false-positive rate associated with deletions was similar for PennCNV and CRLMM/VanillaIce.</p> <p>Conclusions</p> <p>If the objective is to perform statistical tests on the locus-level copy number data, our empirical results suggest that PennCNV or Aroma.Affymetrix is optimal. If the objective is to perform statistical tests on the summarized segmented data then PennCNV would be preferred over CRLMM/VanillaIce. Specifically, PennCNV allows the analyst to estimate locus-level copy number, perform segmentation and evaluate CNV-specific quality-control metrics within a single software package. PennCNV has relatively small bias, small variability and detects more regions while maintaining a similar estimated false-positive rate as CRLMM/VanillaIce. More generally, we advocate that software developers need to provide guidance with respect to evaluating and choosing optimal settings in order to obtain optimal results for an individual dataset. Until such guidance exists, we recommend trying multiple algorithms, evaluating concordance/discordance and subsequently consider the union of regions for downstream association tests.</p

Genomic Profiling of Childhood Tumor Patient-Derived Xenograft Models to Enable Rational Clinical Trial Design.

Accelerating cures for children with cancer remains an immediate challenge as a result of extensive oncogenic heterogeneity between and within histologies, distinct molecular mechanisms evolving between diagnosis and relapsed disease, and limited therapeutic options. To systematically prioritize and rationally test novel agents in preclinical murine models, researchers within the Pediatric Preclinical Testing Consortium are continuously developing patient-derived xenografts (PDXs)-many of which are refractory to current standard-of-care treatments-from high-risk childhood cancers. Here, we genomically characterize 261 PDX models from 37 unique pediatric cancers; demonstrate faithful recapitulation of histologies and subtypes; and refine our understanding of relapsed disease. In addition, we use expression signatures to classify tumors for TP53 and NF1 pathway inactivation. We anticipate that these data will serve as a resource for pediatric oncology drug development and will guide rational clinical trial design for children with cancer

Epigenomic profiling of neuroblastoma cell lines.

Understanding the aberrant transcriptional landscape of neuroblastoma is necessary to provide insight to the underlying influences of the initiation, progression and persistence of this developmental cancer. Here, we present chromatin immunoprecipitation sequencing (ChIP-Seq) data for the oncogenic transcription factors, MYCN and MYC, as well as regulatory histone marks H3K4me1, H3K4me3, H3K27Ac, and H3K27me3 in ten commonly used human neuroblastoma-derived cell line models. In addition, for all of the profiled cell lines we provide ATAC-Seq as a measure of open chromatin. We validate specificity of global MYCN occupancy in MYCN amplified cell lines and functional redundancy of MYC occupancy in MYCN non-amplified cell lines. Finally, we show with H3K27Ac ChIP-Seq that these cell lines retain expression of key neuroblastoma super-enhancers (SE). We anticipate this dataset, coupled with available transcriptomic profiling on the same cell lines, will enable the discovery of novel gene regulatory mechanisms in neuroblastoma

A G316A polymorphism in the ornithine decarboxylase gene promoter modulates MYCN-driven childhood neuroblastoma

Simple Summary Neuroblastoma is a devasting childhood cancer in which multiple copies (amplification) of the cancer-causing gene MYCN strongly predict poor outcome. Neuroblastomas are reliant on high levels of cellular components called polyamines for their growth and malignant behavior, and the gene regulating polyamine synthesis is called ODC1. ODC1 is often coamplified with MYCN, and in fact is regulated by MYCN, and like MYCN is prognostic of poor outcome. Here we studied a naturally occurring genetic variant or polymorphism that occurs in the ODC1 gene, and used gene editing to demonstrate the functional importance of this variant in terms of ODC1 levels and growth of neuroblastoma cells. We showed that this variant impacts the ability of MYCN to regulate ODC1, and that it also influences outcome in neuroblastoma, with the rarer variant associated with a better survival. This study addresses the important topic of genetic polymorphisms in cancer. Ornithine decarboxylase (ODC1), a critical regulatory enzyme in polyamine biosynthesis, is a direct transcriptional target of MYCN, amplification of which is a powerful marker of aggressive neuroblastoma. A single nucleotide polymorphism (SNP), G316A, within the first intron of ODC1, results in genotypes wildtype GG, and variants AG/AA. CRISPR-cas9 technology was used to investigate the effects of AG clones from wildtype MYCN-amplified SK-N-BE(2)-C cells and the effect of the SNP on MYCN binding, and promoter activity was investigated using EMSA and luciferase assays. AG clones exhibited decreased ODC1 expression, growth rates, and histone acetylation and increased sensitivity to ODC1 inhibition. MYCN was a stronger transcriptional regulator of the ODC1 promoter containing the G allele, and preferentially bound the G allele over the A. Two neuroblastoma cohorts were used to investigate the clinical impact of the SNP. In the study cohort, the minor AA genotype was associated with improved survival, while poor prognosis was associated with the GG genotype and AG/GG genotypes in MYCN-amplified and non-amplified patients, respectively. These effects were lost in the GWAS cohort. We have demonstrated that the ODC1 G316A polymorphism has functional significance in neuroblastoma and is subject to allele-specific regulation by the MYCN oncoprotein

The genetic landscape of high-risk neuroblastoma

Neuroblastoma is a malignancy of the developing sympathetic nervous system that often presents with widespread metastatic disease, resulting in survival rates of less than 50%1. To determine the spectrum of somatic mutation in high-risk neuroblastoma, we studied 240 cases using a combination of whole exome, genome and transcriptome sequencing as part of the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative. Here we report a low median exonic mutation frequency of 0.60 per megabase (0.48 non-silent), and remarkably few recurrently mutated genes in these tumors. Genes with significant somatic mutation frequencies included ALK (9.2% of cases), PTPN11 (2.9%), ATRX (2.5%, an additional 7.1% had focal deletions), MYCN (1.7%, a recurrent p.Pro44Leu alteration), and NRAS (0.83%). Rare, potentially pathogenic germline variants were significantly enriched in ALK, CHEK2, PINK1, and BARD1. The relative paucity of recurrent somatic mutations in neuroblastoma challenges current therapeutic strategies reliant upon frequently altered oncogenic drivers