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A bayesian analysis for identifying DNA copy number variations using a compound poisson process.

ABSTRACT: To study chromosomal aberrations that may lead to cancer formation or genetic diseases, the array-based Comparative Genomic Hybridization (aCGH) technique is often used for detecting DNA copy number variants (CNVs). Various methods have been developed for gaining CNVs information based on aCGH data. However, most of these methods make use of the log-intensity ratios in aCGH data without taking advantage of other information such as the DNA probe (e.g., biomarker) positions/distances contained in the data. Motivated by the specific features of aCGH data, we developed a novel method that takes into account the estimation of a change point or locus of the CNV in aCGH data with its associated biomarker position on the chromosome using a compound Poisson process. We used a Bayesian approach to derive the posterior probability for the estimation of the CNV locus. To detect loci of multiple CNVs in the data, a sliding window process combined with our derived Bayesian posterior probability was proposed. To evaluate the performance of the method in the estimation of the CNV locus, we first performed simulation studies. Finally, we applied our approach to real data from aCGH experiments, demonstrating its applicability.

SUBMITTER: Chen J

PROVIDER: S-EPMC3171362 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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A bayesian analysis for identifying DNA copy number variations using a compound poisson process.

Chen Jie J Yiğiter Ayten A Wang Yu-Ping YP Deng Hong-Wen HW

EURASIP journal on bioinformatics & systems biology 20100927

To study chromosomal aberrations that may lead to cancer formation or genetic diseases, the array-based Comparative Genomic Hybridization (aCGH) technique is often used for detecting DNA copy number variants (CNVs). Various methods have been developed for gaining CNVs information based on aCGH data. However, most of these methods make use of the log-intensity ratios in aCGH data without taking advantage of other information such as the DNA probe (e.g., biomarker) positions/distances contained in ...[more]

PMID: 20976296

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Project description:BACKGROUND: Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of DNA copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents. Bayesian Piecewise Constant Regression (BPCR) is a Bayesian regression method for data that are noisy observations of a piecewise constant function. The method estimates the unknown segment number, the endpoints of the segments and the value of the segment levels of the underlying piecewise constant function. The Bayesian Regression Curve (BRC) estimates the same data with a smoothing curve. However, in the original formulation, some estimators failed to properly determine the corresponding parameters. For example, the boundary estimator did not take into account the dependency among the boundaries and succeeded in estimating more than one breakpoint at the same position, losing segments. RESULTS: We derived an improved version of the BPCR (called mBPCR) and BRC, changing the segment number estimator and the boundary estimator to enhance the fitting procedure. We also proposed an alternative estimator of the variance of the segment levels, which is useful in case of data with high noise. Using artificial data, we compared the original and the modified version of BPCR and BRC with other regression methods, showing that our improved version of BPCR generally outperformed all the others. Similar results were also observed on real data. CONCLUSION: We propose an improved method for DNA copy number estimation, mBPCR, which performed very well compared to previously published algorithms. In particular, mBPCR was more powerful in the detection of the true position of the breakpoints and of small aberrations in very noisy data. Hence, from a biological point of view, our method can be very useful, for example, to find targets of genomic aberrations in clinical cancer samples.

Project description:ContextFine-needle aspiration (FNA) is the best diagnostic tool for preoperative evaluation of thyroid nodules but is often inconclusive as a guide for surgical management.ObjectiveOur hypothesis was that thyroid tumor subtypes may show characteristic DNA copy number variation (CNV) patterns, which may further improve the preoperative classification.DesignOur study cohorts included benign follicular adenomas (FAs), classic papillary thyroid carcinomas (PTCs), and follicular variant PTCs (FVPTCs), the three subtypes most commonly associated with inconclusive preoperative cytopathology.SettingTissue and FNA samples were obtained at an academic tertiary referral center.PatientsCases were identified that underwent partial or complete thyroidectomy for malignant or indeterminate thyroid lesions between 2000 and 2008 and had adequate snap-frozen tissue.InterventionsPairs of tumor tissue and matching normal thyroid tissue-derived DNA were compared using 550K single-nucleotide polymorphism arrays.Main outcome measureStatistically significant differences in CNV patterns between tumor subtypes were identified.ResultsSegmental amplifications in chromosomes (Ch) 7 and 12 were more common in FAs than in PTCs or FVPTCs. Additionally, a subset of FAs and FVPTCs showed deletions in Ch22. We identified the 5 CNV-associated genes best at discriminating between FAs and PTCs/FVPTCs, which correctly classified 90% of cases. These 5 Ch12 genes were validated by quantitative genomic PCR and gene expression array analyses on the same patient cohort. The 5-gene signature was then successfully validated against an independent test cohort of benign and malignant tumor samples. Finally, we performed a feasibility study on matched FA-derived intraoperative FNA samples and were able to correctly identify FAs harboring the Ch12 amplification signature, whereas FAs without amplification showed a normal Ch12 signature.ConclusionsThyroid tumor subtypes possess characteristic genomic profiles that may further our understanding of structural genetic changes in thyroid tumor subtypes and may lead to the development of new diagnostic biomarkers in FNA samples.

Dataset Information

A bayesian analysis for identifying DNA copy number variations using a compound poisson process.

Publications

A bayesian analysis for identifying DNA copy number variations using a compound poisson process.

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