Dataset Information

Identification of SNP-containing regulatory motifs in the myelodysplastic syndromes model using SNP arrays and gene expression arrays.

ABSTRACT: Myelodysplastic syndromes have increased in frequency and incidence in the American 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 polymor-phism(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 TFBS. We confirmed 21 of the 28 motifs with ChIP-chip fragments in the TRANSFAC database. Another six motifs were validated by TRANSFAC via searching binding fragments on co-regulated genes. The identified motifs and their location genes can be considered potential biomarkers for myelodysplastic syndromes. 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.

SUBMITTER: Fan J

PROVIDER: S-EPMC3845573 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Identification of SNP-containing regulatory motifs in the myelodysplastic syndromes model using SNP arrays and gene expression arrays.

Fan Jing J Dy Jennifer G JG Chang Chung-Che CC Zhou Xiaobo X

Chinese journal of cancer 20130118 4

Myelodysplastic syndromes have increased in frequency and incidence in the American 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 polymor-phism(SNP) array and gene expression array--with gene motifs considered transc ...[more]

PMID: 23327800

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Project description:Copy Number Aberration (CNA) in myelodysplastic syndromes (MDS) study using single nucleotide polymorphism (SNP) arrays have been received increasingly attentions in the recent years. In the current study, a new Constraint Moving Average (CMA) algorithm is adopted to determine the regions of CNA regions first. In addition to large regions of CNA, using the proposed CMA algorithm, small regions of CNA can also be detected. Real-time Polymerase Chain Reaction (qPCR) results prove that the CMA algorithm presents an insightful discovery of both large and subtle regions. Based on the results of CMA, two independent applications are studied. The first one is power analysis for sample estimation. An accurate estimation of sample size needed for the desired purpose of an experiment will be important for effort-efficiency and cost-effectiveness. The power analysis is performed to determine the minimum sample size required for ensuring at least (0<lambda <or=) detected regions statistically different from normal references. As expected, power increase with increasing sample size for a fixed significance level. The second application is the distinguishment of high-grade MDS patients from low-grade ones. We propose to calculate the General Variant Level (GVL) score to integrate the general information of each patient at genotype level, and use it as the unified measurement for the classification. Traditional MDS classifications usually refer to cell morphology and The International Prognostic Scoring System (IPSS), which belongs to the classification at the phenotype level. The proposed GVL score integrates the information of CNA region, the number of abnormal chromosomes and the total number of the altered SNPs at the genotype level. Statistical tests indicate that the high and low grade MDS patients can be well separated by GVL score, which appears to correlate better with clinical outcome than the traditional classification approaches using morphology and IPSS sore at the phenotype level.

Project description:Traditional response criteria in MDS and AML are based on bone marrow morphology and may not accurately reflect tumor burden in patients treated with hypomethylating agents. We used massively parallel sequencing of serial bone marrow samples to monitor MDS and AML tumor burden during treatment with epigenetic therapy (decitabine and panobinostat). Serial bone marrow samples (and skin as a source of normal DNA) from 25 MDS and AML patients were sequenced . We observed that responders, including those in complete remission (CR), can have persistent measurable tumor burden for at least one year without disease progression. Using an ultra-sensitive barcode sequencing approach, we detected extremely rare mutations months to years prior to disease relapse. While patients can live with persistent clonal hematopoiesis in a CR or stable disease, ultimately we find evidence that expansion of a rare subclone can drive relapse or progression. Digital sequencing provides an alternative measure of tumor burden and disease response which may augment traditional clinical response criteria in patients treated with hypomethylating agents and its use should be explored in future clinical trials. BCR-ABL1 lymphoblastic leukaemia is characterized by the deletion of Ikaros. The Philadelphia chromosome, encoding BCR-ABL1, is the defining lesion of chronic myelogenous leukemia (CML) and a subset of acute lymphoblastic leukemia (ALL) cases. To define oncogenic lesions that cooperate with BCR-ABL1 to induce ALL, we performed genome-wide analysis of leukemic samples from 23 CML cases and 304 ALL cases, including 43 BCR-ABL1 B-ALL cases. IKZF1 (encoding the transcription factor Ikaros) was deleted in 83.7% of BCR-ABL1 B-ALL cases, but not in chronic phase CML. Deletion of IKZF1 was also identified as an acquired lesion in lymphoid blast crisis of CML. The IKZF1 deletions resulted in haploinsufficiency, expression of a dominant negative Ikaros isoform or the complete loss of Ikaros expression. Sequencing of IKZF1 deletion breakpoints suggested that aberrant V(D)J recombination is responsible for the deletions. These findings suggest that genetic lesions resulting in the loss of Ikaros function are a key event in the development of BCR-ABL1 ALL.

Dataset Information

Identification of SNP-containing regulatory motifs in the myelodysplastic syndromes model using SNP arrays and gene expression arrays.

Publications

Identification of SNP-containing regulatory motifs in the myelodysplastic syndromes model using SNP arrays and gene expression arrays.

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