Large genomic aberrations detected by SNP array are independent prognosticators of a shorter time to first treatment in chronic lymphocytic leukemia patients with normal FISH.
Ann Oncol. 2013 Jan 31;
Authors: Mian M, Rinaldi A, Mensah AA, Rossi D, Ladetto M, Forconi F, Marasca R, Uhr M, Stussi G, Kwee I, Cavalli F, Gaidano G, Zucca E, Bertoni F
BackgroundGenomic complexity can predict the clinical course of patients affected by chronic lymphocytic leukemia (CLL) with a normal FISH. However, large studies are still lacking. Here, we analyzed a large series of CLL patients and also carried out the so far largest comparison of FISH versus single-nucleotide polymorphism (SNP) array in this disease.Patients and methodsSNP-array data were derived from a previously reported dataset.ResultsSeventy-seven of 329 CLL patients (23%) presented with a normal FISH. At least one large (>5 Mb) genomic aberration was detected by SNP array in 17 of 77 patients (22%); this finding significantly affected TTT. There was no correlation with the presence of TP53 mutations. In multivariate analysis, including age, Binet stage, IGHV genes mutational status and large genomic lesion, the latter three factors emerged as independent prognosticators. The concordance between FISH and SNP array varied between 84 and 97%, depending on the specific genomic locus investigated.ConclusionsSNP array detected additional large genomic aberrations not covered by the standard FISH panel predicting the outcome of CLL patients.
PMID: 23372049 [PubMed - as supplied by publisher]
Genome Fusion Detection: A novel method to detect fusion genes from SNP-array data.
Bioinformatics. 2013 Jan 22;
Authors: Thieme S, Groth P
MOTIVATION: Fusion genes result from genomic rearrangements, such as deletions, amplifications, and translocations. Such rearrangements can also frequently be observed in cancer and have been postulated as driving event in cancer development. In order to detect them, one needs to analyze the transition region of two segments with different copy number, the location where fusions are known to occur. Finding fusion genes is essential to understanding cancer development and may lead to new therapeutic approaches. RESULTS: Here we present a novel method, the Genomic Fusion Detection algorithm, to predict fusion genes on a genomic level based on SNP-array data. This algorithm detects genes at the transition region of segments with copy number variation. With the application of defined constraints, certain properties of the detected genes are evaluated in order to predict whether they may be fused. We evaluated our prediction by calculating the observed frequency of known fusions in both primary cancers and cell lines. We tested a set of cell lines positive for the BCR-ABL1 fusion and prostate cancers positive for the TMPRSS2-ERG fusion. We could detect the fusions in all positive cell lines, but not in the negative controls. AVAILABILITY: The algorithm is available from the supplement CONTACT: email@example.com.
PMID: 23341502 [PubMed - as supplied by publisher]
Identification of SNP-containing regulatory motifs in the myelodysplastic syndromes model using SNP arrays and gene expression arrays.
Chin J Cancer. 2013 Jan 18;
Authors: Fan J, Dy JG, Chang CC, Zhou XB
Myelodysplastic syndromes have increased in frequency and incidence in the U.S. population, but patient prognosis has not significantly improved over the last decade. Such improvements could be realized if biomarkers for accurate diagnosis and prognostic stratification were successfully identified. In this study, we propose a method that associates two state-of-the-art array technologies--single nucleotide polymorphism (SNP) array and gene expression array--with gene motifs considered transcription factor binding sites (TFBS). We are particularly interested in SNP-containing motifs introduced by genetic variation and mutation as TFBS. The potential regulation of SNP-containing motifs affects only when certain mutations occur. These motifs can be identified from a group of co-expressed genes with copy number variation. Then, we used a sliding window to identify motif candidates near SNPs on gene sequences. The candidates were filtered by coarse thresholding and fine statistical testing. Using the regression-based LARS-EN algorithm and a level-wise sequence combination procedure, we identified 28 SNP-containing motifs as candidate transcription factor binding sites. We confirmed 21 of the 28 motifs with ChIP-Chip fragments in the TRANSFAC database. Another six motifs were validated by TRANSFAC by searching binding fragments on co-regulated genes. The identified motifs and their location genes can be considered potential biomarkers for MDS. Thus, our proposed method, a novel strategy for associating two data categories, is capable of integrating information from different sources to identify reliable candidate regulatory SNP-containing motifs introduced by genetic variation and mutation.
PMID: 23327800 [PubMed - as supplied by publisher]
Development and Evaluation of a Genome-Wide 6K SNP Array for Diploid Sweet Cherry and Tetraploid Sour Cherry.
PLoS One. 2012;7(12):e48305
Authors: Peace C, Bassil N, Main D, Ficklin S, Rosyara UR, Stegmeir T, Sebolt A, Gilmore B, Lawley C, Mockler TC, Bryant DW, Wilhelm L, Iezzoni A
High-throughput genome scans are important tools for genetic studies and breeding applications. Here, a 6K SNP array for use with the Illumina Infinium® system was developed for diploid sweet cherry (Prunus avium) and allotetraploid sour cherry (P. cerasus). This effort was led by RosBREED, a community initiative to enable marker-assisted breeding for rosaceous crops. Next-generation sequencing in diverse breeding germplasm provided 25 billion basepairs (Gb) of cherry DNA sequence from which were identified genome-wide SNPs for sweet cherry and for the two sour cherry subgenomes derived from sweet cherry (avium subgenome) and P. fruticosa (fruticosa subgenome). Anchoring to the peach genome sequence, recently released by the International Peach Genome Initiative, predicted relative physical locations of the 1.9 million putative SNPs detected, preliminarily filtered to 368,943 SNPs. Further filtering was guided by results of a 144-SNP subset examined with the Illumina GoldenGate® assay on 160 accessions. A 6K Infinium® II array was designed with SNPs evenly spaced genetically across the sweet and sour cherry genomes. SNPs were developed for each sour cherry subgenome by using minor allele frequency in the sour cherry detection panel to enrich for subgenome-specific SNPs followed by targeting to either subgenome according to alleles observed in sweet cherry. The array was evaluated using panels of sweet (n = 269) and sour (n = 330) cherry breeding germplasm. Approximately one third of array SNPs were informative for each crop. A total of 1825 polymorphic SNPs were verified in sweet cherry, 13% of these originally developed for sour cherry. Allele dosage was resolved for 2058 polymorphic SNPs in sour cherry, one third of these being originally developed for sweet cherry. This publicly available genomics resource represents a significant advance in cherry genome-scanning capability that will accelerate marker-locus-trait association discovery, genome structure investigation, and genetic diversity assessment in this diploid-tetraploid crop group.
PMID: 23284615 [PubMed - in process]