Abstract
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 () 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.
Highlights
Myelodysplastic syndromes (MDS) are a heterogeneous group of clonal hematopoietic disorders characterized by peripheral cytopenia, morphologic dysplasia and susceptibility to leukemic transformation [1,2]
There is evidence suggesting that MDS may start with multiple minor clones [6], which may be missed with conventional cytogenetic studies at the initial presentation
Our novel contributions are: (i) we develop a new patternselection based method to detect the regions of Copy Number Aberration (CNA) for a heterogeneity disease such as MDS by using sorted bone marrow single nucleotide polymorphism (SNP) arrays
Summary
Myelodysplastic syndromes (MDS) are a heterogeneous group of clonal hematopoietic disorders characterized by peripheral cytopenia, morphologic dysplasia and susceptibility to leukemic transformation [1,2]. Cytogenetic abnormality is one of the most determinants in the prognosis. While a large database of cytogenetic data based on metaphase karyotyping is generated in MDS, and only about 50% clonal abnormalities of primary MDS are detected by conventional cytogenetic studies [3,4,5]. There is evidence suggesting that MDS may start with multiple minor clones [6], which may be missed with conventional cytogenetic studies at the initial presentation. The detection of copy number variants and related studies of MDS using single nucleotide polymorphism (SNP) array data has received increasing attention in recent years and is used as a powerful tool for molecular karyotyping
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