Analysis Of DNA Copy Number Data Research Articles

Functional genomics identifies AMPD2 as a new prognostic marker for undifferentiated pleomorphic sarcoma.

Soft-tissue sarcomas are rare, heterogeneous, and often aggressive mesenchymal cancers. Many of them are associated with poor outcome, partially because biomarkers that can identify high-risk patients are lacking. Studies on sarcomas are often limited by small sample-sizes rendering the identification of biomarkers difficult when focusing on individual cohorts. However, the increasing number of publicly available 'omics' data opens inroads to overcome this obstacle. Here, we combine transcriptome analyses, immunohistochemistry, and functional assays to show that high adenosine monophosphate deaminase 2 (AMPD2) is a robust prognostic biomarker for worse outcome in undifferentiated pleomorphic sarcoma (UPS). Gene expression and survival data for UPS from two independent studies were subjected to survival association-testing. Genes, whose high expression was significantly correlated with worse outcome in both cohorts, were considered as biomarker candidates. The best candidate, AMPD2, was validated in a tissue microarray. Analysis of DNA copy-number data and matched transcriptomes indicated that high AMPD2 expression is significantly correlated with gains at the AMPD2 locus. Gene set enrichment analyses of AMPD2 co-expressed genes in both transcriptome datasets suggested that AMPD2-high UPS are enriched in tumorigenic signatures. Consistently, knockdown of AMPD2 by RNA interference in an UPS cell line inhibited proliferation in vitro and tumorigenicity in vivo. Collectively, we provide evidence that AMPD2 may serve as a biomarker for outcome prediction in UPS. Our study exemplifies how the integration of 'omics' data, immunohistochemistry, and functional experiments can identify novel biomarkers even in a rare sarcoma, which may serve as a blueprint for biomarker identification for other rare cancers.

Abstract 45: Detection of focal somatic copy number variants in whole genome, whole exome, and targeted next-generation sequencing data of tumor/normal pairs

Abstract We describe the development and validation of an analysis pipeline for detection of focal somatic copy number aberrations from next-generation sequencing (NGS) data. In the clinical setting, tumor samples may be derived from historical tissue or from recently collected fresh frozen biopsies. DNA extracted from samples preserved as formalin-fixed, paraffin-embedded (FFPE) tissue are often highly degraded. We describe challenges inherent to sequencing different tissue sources and algorithmic approaches for optimizing detection of focal alterations in these samples with a high degree of accuracy. The first area of optimization takes into account clonal or fragment coverage based on user-defined intervals by accounting for coverage between paired-end reads. Normalized log2 fold-changes between tumor and normal samples are then calculated and a smoothing window is applied. In addition, we utilize tumor allele frequencies of known heterozygous germline SNPs identified within the normal to both evaluate potential false positives and correct biases. Lastly, a segmentation algorithm (circular binary segmentation as implemented in DNAcopy package1) is applied to summarize the individual log2 fold-changes into intervals with a constant copy number state. The results from the segmentation algorithm are then annotated and reformatted into the VCF format. We have analytically optimized and validated our algorithm across a series of samples with known alterations for implementation on both clinical and research samples. 1. Seshan VE and Olshen A. DNAcopy: DNA copy number data analysis. R package version 1.40.0. Citation Format: Jessica Aldrich, Jonathan J. Keats, Winnie S. Liang, John D. Carpten, David W. Craig. Detection of focal somatic copy number variants in whole genome, whole exome, and targeted next-generation sequencing data of tumor/normal pairs. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Integrating Clinical Genomics and Cancer Therapy; Jun 13-16, 2015; Salt Lake City, UT. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(1_Suppl):Abstract nr 45.

Assessing Population Level Genetic Instability via Moving Average

Tumoral tissues tend to generally exhibit aberrations in DNA copy number that are associated with the development and progression of cancer. Genotyping methods such as array-based comparative genomic hybridization (aCGH) provide means to identify copy number variation across the entire genome. To address some of the shortfalls of existing methods of DNA copy number data analysis, including strong model assumptions, lack of accounting for sampling variability of estimators, and the assumption that clones are independent, we propose a simple graphical approach to assess population-level genetic alterations over the entire genome based on moving average. Furthermore, existing methods primarily focus on segmentation and do not examine the association of covariates with genetic instability. In our methods, covariates are incorporated through a possibly mis-specified working model and sampling variabilities of estimators are approximated using a resampling method that is based on perturbing observed processes. Our proposal, which is applicable to partial, entire or multiple chromosomes, is illustrated through application to aCGH studies of two brain tumor types, meningioma and glioma.

Efficient Calculation of Interval Scores for DNA Copy Number Data Analysis

DNA amplifications and deletions characterize cancer genome and are often related to disease evolution. Microarray-based techniques for measuring these DNA copy-number changes use fluorescence ratios at arrayed DNA elements (BACs, cDNA, or oligonucleotides) to provide signals at high resolution, in terms of genomic locations. These data are then further analyzed to map aberrations and boundaries and identify biologically significant structures. We develop a statistical framework that enables the casting of several DNA copy number data analysis questions as optimization problems over real-valued vectors of signals. The simplest form of the optimization problem seeks to maximize phi(I) = Sigmanu(i)/radical|I| over all subintervals I in the input vector. We present and prove a linear time approximation scheme for this problem, namely, a process with time complexity O (nepsilon(-2)) that outputs an interval for which phi(I) is at least Opt/alpha(epsilon), where Opt is the actual optimum and alpha(epsilon) --> 1 as epsilon --> 0. We further develop practical implementations that improve the performance of the naive quadratic approach by orders of magnitude. We discuss properties of optimal intervals and how they apply to the algorithm performance. We benchmark our algorithms on synthetic as well as publicly available DNA copy number data. We demonstrate the use of these methods for identifying aberrations in single samples as well as common alterations in fixed sets and subsets of breast cancer samples.