Dataset Information

Bayesian disease classification using copy number data.

ABSTRACT: DNA copy number variations (CNVs) have been shown to be associated with cancer development and progression. The detection of these CNVs has the potential to impact the basic knowledge and treatment of many types of cancers, and can play a role in the discovery and development of molecular-based personalized cancer therapies. One of the most common types of high-resolution chromosomal microarrays is array-based comparative genomic hybridization (aCGH) methods that assay DNA CNVs across the whole genomic landscape in a single experiment. In this article we propose methods to use aCGH profiles to predict disease states. We employ a Bayesian classification model and treat disease states as outcome, and aCGH profiles as covariates in order to identify significant regions of the genome associated with disease subclasses. We propose a principled two-stage method where we first make inferences on the underlying copy number states associated with the aCGH emissions based on hidden Markov model (HMM) formulations to account for serial dependencies in neighboring probes. Subsequently, we infer associations with disease outcomes, conditional on the copy number states, using Bayesian linear variable selection procedures. The selected probes and their effects are parameters that are useful for predicting the disease categories of any additional individuals on the basis of their aCGH profiles. Using simulated datasets, we investigate the method's accuracy in detecting disease category. Our methodology is motivated by and applied to a breast cancer dataset consisting of aCGH profiles assayed on patients from multiple disease subtypes.

SUBMITTER: Guha S

PROVIDER: S-EPMC4196891 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Bayesian disease classification using copy number data.

Guha Subharup S Ji Yuan Y Baladandayuthapani Veerabhadran V

Cancer informatics 20141001 Suppl 2

DNA copy number variations (CNVs) have been shown to be associated with cancer development and progression. The detection of these CNVs has the potential to impact the basic knowledge and treatment of many types of cancers, and can play a role in the discovery and development of molecular-based personalized cancer therapies. One of the most common types of high-resolution chromosomal microarrays is array-based comparative genomic hybridization (aCGH) methods that assay DNA CNVs across the whole ...[more]

PMID: 25336897

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Project description:BACKGROUND: Cancer and other disorders are due to genomic lesions. SNP-microarrays are able to measure simultaneously both genotype and copy number (CN) at several Single Nucleotide Polymorphisms (SNPs) along the genome. CN is defined as the number of DNA copies, and the normal is two, since we have two copies of each chromosome. The genotype of a SNP is the status given by the nucleotides (alleles) which are present on the two copies of DNA. It is defined homozygous or heterozygous if the two alleles are the same or if they differ, respectively. Loss of heterozygosity (LOH) is the loss of the heterozygous status due to genomic events. Combining CN and LOH data, it is possible to better identify different types of genomic aberrations. For example, a long sequence of homozygous SNPs might be caused by either the physical loss of one copy or a uniparental disomy event (UPD), i.e. each SNP has two identical nucleotides both derived from only one parent. In this situation, the knowledge of the CN can help in distinguishing between these two events. RESULTS: To better identify genomic aberrations, we propose a method (called gBPCR) which infers the type of aberration occurred, taking into account all the possible influence in the microarray detection of the homozygosity status of the SNPs, resulting from an altered CN level. Namely, we model the distributions of the detected genotype, given a specific genomic alteration and we estimate the parameters involved on public reference datasets. The estimation is performed similarly to the modified Bayesian Piecewise Constant Regression, but with improved estimators for the detection of the breakpoints.Using artificial and real data, we evaluate the quality of the estimation of gBPCR and we also show that it outperforms other well-known methods for LOH estimation. CONCLUSIONS: We propose a method (gBPCR) for the estimation of both LOH and CN aberrations, improving their estimation by integrating both types of data and accounting for their relationships. Moreover, gBPCR performed very well in comparison with other methods for LOH estimation and the estimated CN lesions on real data have been validated with another technique.

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.

Dataset Information

Bayesian disease classification using copy number data.

Publications

Bayesian disease classification using copy number data.

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