Dataset Information

DEFOR: depth- and frequency-based somatic copy number alteration detector.

ABSTRACT: MOTIVATION:Detection of somatic copy number alterations (SCNAs) using high-throughput sequencing has become popular because of rapid developments in sequencing technology. Existing methods do not perform well in calling SCNAs for the unstable tumor genomes. RESULTS:We developed a new method, DEFOR, to detect SCNAs in tumor samples from exome-sequencing data. The evaluation showed that DEFOR has a higher accuracy for SCNA detection from exome sequencing compared with the five existing tools. This advantage is especially apparent in unstable tumor genomes with a large proportion of SCNAs. AVAILABILITY AND IMPLEMENTATION:DEFOR is available at https://github.com/drzh/defor. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Zhang H

PROVIDER: S-EPMC6761943 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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DEFOR: depth- and frequency-based somatic copy number alteration detector.

Zhang He H Zhan Xiaowei X Brugarolas James J Xie Yang Y

Bioinformatics (Oxford, England) 20191001 19

<h4>Motivation</h4>Detection of somatic copy number alterations (SCNAs) using high-throughput sequencing has become popular because of rapid developments in sequencing technology. Existing methods do not perform well in calling SCNAs for the unstable tumor genomes.<h4>Results</h4>We developed a new method, DEFOR, to detect SCNAs in tumor samples from exome-sequencing data. The evaluation showed that DEFOR has a higher accuracy for SCNA detection from exome sequencing compared with the five exist ...[more]

PMID: 30860569

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Project description:Breast cancer is a heterogeneous disease described in well-recognized biological subtypes. Particularly, gene expression profiling has revealed 5 intrinsic subtypes of breast cancer characterized by different biological and clinical features. The diversity of the intrinsic subtypes across human population has been limited described, and there is few information about the genomic architecture of breast tumors in Mexican or Hispanic populations. In this study, we performed PAM50 assay, based in Affymetrix microarray profiling of 128 fresh frozen tumors from Mexican Latino Hispanic population, to describe the overall distribution of subtypes, and characterize the relation to clinicopathologic characteristics. As well, we correlated the mRNA expression patterns with specific copy number alterations (CPA), in order to analyze their role in breast tumors. A total of 100 blood-tumor samples were assayed using Affymetrix 6.0 SNP arrays; segmentation analysis and GISTIC were performed to identify focal amplifications or deletions. The distribution of PAM50 intrinsic subtypes in our cohort was computed to be 44% luminal A, 20% luminal B, 12.0% HER2-enriched, 12% basal-like, and 12% normal-like. Study comparison with the literature mainly TCGA and METABRIC (most of the patients came from Caucasian population), as well as LACE (which describe a population study) show a similar distribution of the intrinsic subtypes within Hispanic and Caucasian populations. Interestingly, basal-like subtype is less represented than in African-American race. The sum of sensitivity and specificity between the clinicopathologic and intrinsic subtype categories across 4 groups (excluding normal-like) was 50% and 87.5%, respectively. Differentially expression profiles within the subtypes reveal a set of genes altered in each group with biological relevance to stablish the phenotypical characteristics of each subtype. Our analyses confirmed the already reported copy number data. Importantly, many of the copy number profiles correlated with mRNA subtype. With this analysis we can conclude that breast cancer intrinsic subtypes have been reproduced in Mexican population contributing to the description of the PAM50 subtypes among multiple ethnic groups based on a gene expression assay. Our observation based in the integrative genomic analysis of mRNA expression and CPA allowed us to define gene circuits and phenotypic characteristics that can explain the heterogeneity of breast cancer subtypes.

Dataset Information

DEFOR: depth- and frequency-based somatic copy number alteration detector.

Publications

DEFOR: depth- and frequency-based somatic copy number alteration detector.

Similar Datasets

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