Abstract
BackgroundCopy number alteration is a main genetic structural variation that plays an important role in tumor initialization and progression. Accurate detection of copy number alterations is necessary for discovering cancer-causing genes. Whole-exome sequencing has become a widely used technology in the last decade for detecting various types of genomic aberrations in cancer genomes. However, there are several major issues encountered in these detection problems, including normal cell contamination, tumor aneuploidy, and intra-tumor heterogeneity. Especially, deciphering the intra-tumor heterogeneity is imperative for identifying clonal and subclonal copy number alterations.ResultsWe introduce CloneCNA, a novel bioinformatics tool for efficiently addressing these issues and automatically detecting clonal and subclonal somatic copy number alterations from heterogeneous tumor samples. CloneCNA fully explores the log ratio of read counts between paired tumor-normal samples and tumor B allele frequency of germline heterozygous SNP positions, further employs efficient statistical models to quantitatively represent copy number status of tumor sample containing multiple clones. We examine CloneCNA on simulated heterogeneous and real tumor samples, and the results demonstrate that CloneCNA has higher power to detect copy number alterations than existing methods.ConclusionsCloneCNA, a novel algorithm is developed to efficiently and accurately identify somatic copy number alterations from heterogeneous tumor samples. We demonstrate the statistical framework of CloneCNA represents a remarkable advance for tumor whole-exome sequencing data. We expect that CloneCNA will promote cancer-focused studies for investigating the role of clonal evolution and elucidating critical events benefiting tumor tumourigenesis and progression.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1174-7) contains supplementary material, which is available to authorized users.
Highlights
Copy number alteration is a main genetic structural variation that plays an important role in tumor initialization and progression
Our analysis indicate that the log read counts ratio (LCR) signals demonstrate a significant correlation with GC-content, and require a normalization procedure before being used to identify aberrant exome regions
Results on simulated data We first investigate the LCR and B allele frequency (BAF) distributions associated with different types of aberrations to analyze the influence of normal cell contamination for whole-exome sequencing (WES) data and the results are shown in Additional file 2: Figure S3
Summary
Copy number alteration is a main genetic structural variation that plays an important role in tumor initialization and progression. The theory of clonal evolution [1] states that once a single precursor cell is initiated, the proceeding of neoplastic proliferation can to some extent be considered as a natural selection process – sequential selection by an evolutionary process Over time, both similar and divergent genetic alterations beneficial for tumor persistence and growth are acquired by different tumor cells through clonal expansions. Both similar and divergent genetic alterations beneficial for tumor persistence and growth are acquired by different tumor cells through clonal expansions This results in the emergence of variant cell populations in tumors with each cell population containing a distinct complement of genetic alterations, which is known as the intra-tumor heterogeneity [1,2,3]. Copy number alteration (CNA) has emerged as one of the main categories of genetic structural variations that plays an important role in tumor progression [4]
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