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Uncovering the biology of multiple myeloma among African Americans: a comprehensive genomics approach.

ABSTRACT: Epidemiological data have suggested that African American (AA) persons are twice as likely to be diagnosed with multiple myeloma (MM) compared with European American (EA) persons. Here, we have analyzed a set of cytogenetic and genomic data derived from AA and EA MM patients. We have compared the frequency of IgH translocations in a series of data from 115 AA patients from 3 studies and 353 EA patients from the Eastern Cooperative Oncology Group (ECOG) studies E4A03 and E9487. We have also interrogated tumors from 45 AA and 196 EA MM patients for somatic copy number abnormalities associated with poor outcome. In addition, 35 AA and 178 EA patients were investigated for a transcriptional profile associated with high-risk disease. Overall, based on this cohort, genetic profiles were similar except for a significantly lower frequency of IgH translocations (40% vs 52%; P = .032) in AA patients. Frequency differences of somatic copy number aberrations were not significant after correction for multiple testing. There was also no significant difference in the frequency of high-risk disease based on gene expression profiling. Our study represents the first comprehensive comparisons of the frequency and distribution of molecular alterations in MM tumors between AA and EA patients. ECOG E4A03 is registered with ClinicalTrials.gov, number NCT00098475. ECOG E9487 is a companion validation set to the ECOG study E9486 and is registered with the National Institutes of Health, National Cancer Institute, Clinical Trials (PDQ), number EST-9486.

SUBMITTER: Baker A

PROVIDER: S-EPMC3630830 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Uncovering the biology of multiple myeloma among African Americans: a comprehensive genomics approach.

Baker Angela A Braggio Esteban E Jacobus Susanna S Jung Sungwon S Larson Dirk D Therneau Terry T Dispenzieri Angela A Van Wier Scott A SA Ahmann Gregory G Levy Joan J Perkins Louise L Kim Seungchan S Henderson Kimberly K Vesole David D Rajkumar S Vincent SV Jelinek Diane F DF Carpten John J Fonseca Rafael R

Blood 20130219 16

Epidemiological data have suggested that African American (AA) persons are twice as likely to be diagnosed with multiple myeloma (MM) compared with European American (EA) persons. Here, we have analyzed a set of cytogenetic and genomic data derived from AA and EA MM patients. We have compared the frequency of IgH translocations in a series of data from 115 AA patients from 3 studies and 353 EA patients from the Eastern Cooperative Oncology Group (ECOG) studies E4A03 and E9487. We have also inter ...[more]

PMID: 23422747

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Project description:A SNP microarray and FISH-based procedure to detect allelic imbalances in multiple myeloma: an integrated genomics approach reveals a wide dosage effect on gene and microRNA expression Multiple myeloma (MM) is characterized by marked genomic instability. Beyond structural rearrangements, a relevant role in its biology is represented by allelic imbalances leading to significant variations in ploidy status. To better elucidate the genomic complexity of MM, we analyzed a panel of 45 patients using combined FISH and microarray approaches. Using a self-developed procedure to infer exact local copy numbers for each sample, we identified a significant fraction of patients showing marked aneuploidy. A conventional clustering analysis showed that aneuploidy, chromosome 1 alterations, hyperdiploidy and recursive deletions at 1p and chromosomes 13, 14 and 22 were the main aberrations driving samples grouping. Then, we integrated mapping information with gene and microRNAs expression profiles: a multiclass analysis of the identified clusters showed a marked gene-dosage effect, particularly concerning 1q transcripts, also confirmed by correlating gene expression levels and local copy number alterations. A wide dosage effect affected also microRNAs, indicating that structural abnormalities in MM closely reflect in their expression imbalances. Finally, we identified several loci in which genes and microRNAs expression correlated with loss-of-heterozygosity occurrence. Our results provide insights into the composite network linking genome structure and gene/microRNA transcriptional features in MM. Keywords: Integrated genomics approach based on SNP microarray and FISH procedures to detect allelic imbalances in multiple myeloma. Pathological bone marrow specimens from 41 MM and four plasma cell leukemia (PCL) patients at diagnosis. 250 nanograms of genomic DNA was processed and, in accordance with the manufacturer's protocols, 40 micrograms of fragmented biotin-labelled DNA were hybridized on GeneChip Human Mapping 50K XbaI Arrays (Affymetrix Inc.). The arrays were scanned using the GeneChip Scanner 3000 7G. The images were acquired using Affymetrix GeneChip® Operating Software (GCOS version 1.4). Copy number values for individual SNPs were extracted and converted from CEL files into signal intensities using GTYPE 4.1 and Affymetrix Copy Number Analysis Tool (CNAT 4.0.1) softwares. Genomic Smoothing analysis was performed by using the smoothing window of 0 Mb, and inferred copy number states were derived from a Hidden Markov Model (HMM) based algorithm implemented in CNAT 4.0.1. Circular Binary Segmentation (Ohlsen et al., 2004) was applied using DNAcopy package for R Bioconductor on raw data. FBN procedure was finally applied to infer exact local copy number as described in the mentioned Reference.

Project description:Imputation of genome-wide single-nucleotide polymorphism (SNP) arrays to a larger known reference panel of SNPs has become a standard and an essential part of genome-wide association studies. However, little is known about the behavior of imputation in African Americans with respect to the different imputation algorithms, the reference population(s) and the reference SNP panels used. Genome-wide SNP data (Affymetrix 6.0) from 3207 African American samples in the Atherosclerosis Risk in Communities Study (ARIC) was used to systematically evaluate imputation quality and yield. Imputation was performed with the imputation algorithms MACH, IMPUTE and BEAGLE using several combinations of three reference panels of HapMap III (ASW, YRI and CEU) and 1000 Genomes Project (pilot 1 YRI June 2010 release, EUR and AFR August 2010 and June 2011 releases) panels with SNP data on chromosomes 18, 20 and 22. About 10% of the directly genotyped SNPs from each chromosome were masked, and SNPs common between the reference panels were used for evaluating the imputation quality using two statistical metrics-concordance accuracy and Cohen's kappa (?) coefficient. The dependencies of these metrics on the minor allele frequencies (MAF) and specific genotype categories (minor allele homozygotes, heterozygotes and major allele homozygotes) were thoroughly investigated to determine the best panel and method for imputation in African Americans. In addition, the power to detect imputed SNPs associated with simulated phenotypes was studied using the mean genotype of each masked SNP in the imputed data. Our results indicate that the genotype concordances after stratification into each genotype category and Cohen's ? coefficient are considerably better equipped to differentiate imputation performance compared with the traditionally used total concordance statistic, and both statistics improved with increasing MAF irrespective of the imputation method. We also find that both MACH and IMPUTE performed equally well and consistently better than BEAGLE irrespective of the reference panel used. Of the various combinations of reference panels, for both HapMap III and 1000 Genomes Project reference panels, the multi-ethnic panels had better imputation accuracy than those containing only single ethnic samples. The most recent 1000 Genomes Project release June 2011 had substantially higher number of imputed SNPs than HapMap III and performed as well or better than the best combined HapMap III reference panels and previous releases of the 1000 Genomes Project.

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Uncovering the biology of multiple myeloma among African Americans: a comprehensive genomics approach.

Publications

Uncovering the biology of multiple myeloma among African Americans: a comprehensive genomics approach.

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