scholarly journals Integrative analysis of DNA copy number, DNA methylation and gene expression in multiple myeloma reveals alterations related to relapse

Oncotarget ◽  
2016 ◽  
Vol 7 (49) ◽  
pp. 80664-80679 ◽  
Author(s):  
Patryk Krzeminski ◽  
Luis A. Corchete ◽  
Juan L. García ◽  
Lucía López-Corral ◽  
Encarna Fermiñán ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Multiple Myeloma ◽  
Copy Number ◽  
Integrative Analysis ◽  
Dna Copy Number

scholarly journals Integrated DNA Copy Number and Gene Expression Regulatory Network Analysis of Non-small Cell Lung Cancer Metastasis

2014 ◽  
Vol 13s5 ◽  
pp. CIN.S14055 ◽  
Author(s):  
Seyed M. Iranmanesh ◽  
Nancy L. Guo
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Copy Number ◽  
Regulatory Networks ◽  
Cancer Metastasis ◽  
Integrative Analysis ◽  
Transcriptional Level ◽  
Dna Copy Number ◽  
Driver Genes ◽  
Lung Cancer Metastasis

Integrative analysis of multi-level molecular profiles can distinguish interactions that cannot be revealed based on one kind of data in the analysis of cancer susceptibility and metastasis. DNA copy number variations (CNVs) are common in cancer cells, and their role in cell behaviors and relationship to gene expression (GE) is poorly understood. An integrative analysis of CNV and genome-wide mRNA expression can discover copy number alterations and their possible regulatory effects on GE. This study presents a novel framework to identify important genes and construct potential regulatory networks based on these genes. Using this approach, DNA copy number aberrations and their effects on GE in lung cancer progression were revealed. Specifically, this approach contains the following steps: (1) select a pool of candidate driver genes, which have significant CNV in lung cancer patient tumors or have a significant association with the clinical outcome at the transcriptional level; (2) rank important driver genes in lung cancer patients with good prognosis and poor prognosis, respectively, and use top-ranked driver genes to construct regulatory networks with the COpy Number and Expression In Cancer (CONEXIC) method; (3) identify experimentally confirmed molecular interactions in the constructed regulatory networks using Ingenuity Pathway Analysis (IPA); and (4) visualize the refined regulatory networks with the software package Genatomy. The constructed CNV/mRNA regulatory networks provide important insights into potential CNV-regulated transcriptional mechanisms in lung cancer metastasis.

Genome-Wide Analysis of DNA Copy Number in Multiple Myeloma Using High-Density SNP Arrays Reveals Clustering Patterns with Distinct Transcriptional Profiles.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 2482-2482
Author(s):  
Laura Mosca ◽  
Luca Agnelli ◽  
Ivo Kwee ◽  
Sonia Fabris ◽  
Domenica Ronchetti ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Plasma Cell ◽  
Copy Number ◽  
Dna Copy Number ◽  
Snp Arrays ◽  
Chromosome 13 ◽  
Genome Wide ◽  
Group A ◽  
Group B

Abstract Multiple myeloma (MM) is characterized by a high genomic instability that involves both ploidy and structural rearrangements. Nearly half of MM tumors are non-hyperdiploid and are frequently associated with 13q deletion and chromosomal translocations involving the immunoglobulin heavy chain (IGH) locus on chromosome 14q32. The remaining tumors are hyperdiploid, showing low prevalence of both IGH translocations and chromosome 13 deletions. Our study was aimed at providing insights into the genomic heterogeneity associated with plasma cell neoplasms by defining the genome-wide pattern of genetic lesions in a representative and stratified panel of MM patients. To this end, genome-wide profiling data of 45 plasma cell dyscrasia patients (41 MM and 4 plasma cell leukemia) were generated on GeneChip® Human Mapping 50K Xba SNP arrays, and the local DNA copy number variations were calculated using the DNAcopy Bioconductor package. The patients were clustered using the non-negative matrix factorization (NMF) algorithm to identify, within the natural grouping of profiles, the strongest clusters on the basis of their genomic characteristics. We identified three consistent clusters, characterized byrecurrent gains of odd-chromosomes, suggestive of the hyperdiploid status (Group A),high frequency of chromosome 13 deletion and 1q gains (Group B), orhigh frequency of chromosomes 13, 14, 16 and 22 deletions and losses of 1p and 4p regions, together with some cases showing 1q gains (Group C). To determine whether peculiar transcription fingerprints characterized these groups, gene expression profiles of 40 out of 45 corresponding samples generated on GeneChip® HG-U133A arrays were analyzed using the Prediction Analysis of Microarray (PAM) software. The multi-class analysis identified 229 transcripts (corresponding to 195 genes), which specifically marked the three groups. In particular, Group A was characterized by the overexpression of genes involved in the translational machinery or thought to be involved in MM pathogenesis such as the HGF, the tumor necrosis factor ligand TRAIL, DKK1, and c-KIT. Upregulation of the CKS1B gene was present in Group B and C, most likely reflecting the high frequencies of 1q gains in tumors within group B and C and its consequent deregulation. Group C was marked by the specific downregulation of genes mainly mapped to 1p arm: AMPD1, CSDE1, AKR1A1 and the PRKACB kinase, suggesting a relationship with the recurrent 1p loss within the group. Our data further supported the notion that structural abnormalities in multiple myeloma are associated with gene expression imbalances, and provide novel analytical approaches for the identification of genetic lesions and molecular patterns of the disease.

DNA Copy Number Changes Have Gene Dosage Effects with Consequent Impact On Disease Biology and Prognosis in Multiple Myeloma

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 3984-3984
Author(s):  
Mehmet Kemal Samur ◽  
Parantu K Shah ◽  
Xujun Wang ◽  
Norman Huang ◽  
Stephane Minvielle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Copy Number ◽  
Dosage Effect ◽  
Chromosome 9 ◽  
Copy Number Alterations ◽  
Dna Copy Number ◽  
A Genome ◽  
The Impact ◽  
Affect Gene Expression

Abstract Abstract 3984 Copy number alterations, deletions and amplifications, are very frequent in multiple myeloma (MM), however, it is less clear how these alterations affect gene expression. We performed a genome-wide analysis of 170 newly-diagnosed uniformly treated MM patients using high-density SNP arrays and Exon ST 1.0 gene expression arrays, and evaluated how copy number alterations affect gene expression in MM. Using SNP array data just over 40% patients had hyperdiploid MM (HMM) while the rest had non-hyperdiploid MM (N-HMM). We used two-step procedure to identify dosage effect scores of genes. At first, for each gene, percentage of copy number altered samples was calculated. Then for each gene percentage of samples that had dosage effect was calculated. Finally dosage effect score for each gene was calculated as a ratio of dosage effect samples percentage to copy number alteration sample percentage. We show that dosage effect in MM is wide-spread and some chromosomal locations are affected by dosage effect more compared to other locations. The dosage effect tracks can be observed at trisomy chromosomes and chromosome 1q, but most explicitly at chromosome 9, 11, 15 and 19. Also for deleted genes, dosage effect can be mostly observed at chromosome 13 and 16q. Separate analysis of HMM and N-HMM patients also showed that HMM patients have higher dosage effect especially in chromosome 15 compared to the others. In addition, relation between dosage effect and gene expression analysis show that the highly expressed genes have significantly higher dosage effect compared to the lowly expressed genes. Also function enrichment analysis showed that genes involved with crucial biological processes including translation, RNA processing and transcription factor genes are enriched in genes with higher dosage effect. Interstingly, dosage resistant genes are enriched in cell death and GTPase processes. These results help us understand the impact of aneuploidy in MM on global gene expression changes. In conclusion, our analysis identifies concordant and discordant gene expression changes associated with DNA copy number alterations, identifying genes and pathways that may play an important role in myeloma disease behavior as well as prognosis. Disclosures: No relevant conflicts of interest to declare.

Genetic and Epigenetic Profile Of Early Relapsed Childhood ALL

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1380-1380
Author(s):  
Yucel Erbilgin ◽  
Ozden Hatirnaz Ng ◽  
Sinem Firtina ◽  
Tiraje Celkan ◽  
Sema Anak ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Dna Methylation ◽  
Bone Marrow ◽  
Drug Resistance ◽  
Expression Profile ◽  
High Throughput ◽  
Copy Number ◽  
Dna Copy Number ◽  
Early Relapse

Abstract Relapsed acute lymphoblastic leukemia (ALL) is one of the leading causes of death among children with cancer. While children who relapse late (≥ 3 years from initial diagnosis) fare better than those who relapse early in treatment, the prognosis for these children remains poor, even with aggressive treatment. Thus new approaches to prevent and treat relapsed ALL are needed. To discover the underlying biological pathways that may play a role in drug resistance and relapse, we integrated three high-throughput assays. Using matched diagnosis/relapse bone marrow samples from children with early relapsed Pre B-ALL (n=10) and T-ALL (n=6) we evaluated gene expression, DNA methylation and DNA copy number alterations (CNAs) analysis. All diagnosis and relapsed samples had more than 80% bone marrow blast account and were initially treated on BFM protocol. As controls, we used flow-sorted normal B cell progenitor subpopulations and CD4+CD8+ T cell samples purified from thymus. Gene expression profile of RNA samples were detected by Illumina HumanHT-12v4 ExpressionChip and bisulfite converted DNA materials were hybridized to the Infinium HumanMethylation450K BeadChip. DNA copy number and LOH data were obtained using Illumina HumanCytoSNP-12. Analysis variant CpG sites in an unsupervised manner, we identified three clearly distinct DNA methylome profiles in samples. T-ALL cases clustered separately from B-ALL samples and B-ALL samples clustered two branch according to their structural variations. We performed supervised comparisons of methylation data between B-ALL and normal B cells, as well as comparing B-ALL with T-ALL cases and T-ALL cases with normal T cells. Differentially methylated regions were identified using limma and lumi packages. ALL cases did not show significant differences between paired diagnose/ relapse samples though they had hyper DNA methylation than control samples. Aberrant DNA methylation had been detected in negative regulator of cell cycle genes and DNA damage repair genes in ALL. Unsupervised expression analysis of the pairs, T-ALL cases clustered separately from B-ALL samples. We defined a signature of 5,762 probe sets that differentially expressed between samples and control. Matched-pair analyses revealed significant differences in the expression of genes involved in cell-cycle regulation, and apoptosis between diagnostic and early relapse samples. Copy number analysis of patients revealed varying numbers of genetic lesions ranging from 0 to 45 CNAs per sample. The vast majority of CNAs observed were shared between diagnosis and relapse in the same patients. For the B-ALL majority of the copy number changes were gross, beside that T-ALL samples mostly had cryptic lesions. One of the most frequent CNAs involved deletions of CDKN2A/B at 9p21.3, occurring in 8 patients, 2 of 8 patients lost the CDKN2A/B at relapse time. CDKN2A, a benchmark gene and had been found hypermethylated at relapse parallel to recent studies. Early relapse samples were more likely to be similar to their respective diagnostic sample that suggests early-relapse results from the emergence of a related clone. Differential expression profile had been observed between relapse and diagnose samples that was independent from the methylation profile. By combining three high-throughput platforms, we demonstrated that both methylation and genomic alterations contribute to evolution of relapse and drug resistance. Disclosures: Karakas: Novartis: Honoraria, Research Funding. Sayitoglu:Roche Diagnostics: Research Support Other.

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