Identification Of Copy Number Aberrations Research Articles

15. Interpreting TP53 variants identified by NGS in the setting of complex karyotypes: examples of potential cryptic copy number alterations and copy-neutral loss of heterozygosity

Diagnosis and follow-up of hematologic malignancies often involve both cytogenetic and molecular testing, and while they target different types of genetic aberrations, the two have findings that are complementary. Chromosome analysis provides a low-resolution genome-wide view, whereas, next-generation sequencing (NGS) offers a high resolution view but may overlook the larger picture. One way in which these technologies complement each other is in the identification of copy number aberrations (CNAs). CNAs can be determined from NGS data by quantitatively assessing the number of amplicon reads (coverage) from one case compared to all cases in the batch (log normalized copy number state or LN-CNS), which can then be correlated with concurrent karyotype studies. We reviewed hematologic malignancy cases received in our laboratories over a period of one year to identify samples with possible loss of heterozygosity (LOH) for the TP53 locus based on NGS allele frequency. Of 21 cases with possible LOH, loss of 17p was confirmed in 14 cases by cytogenetic studies, either karyotype or fluorescence in situ hybridization (FISH). Of the seven cases without evidence of a loss by cytogenetics, six revealed a normal LN-CNS by NGS, suggestive of copy-neutral LOH. The remaining case showed abnormal LN-CNS by NGS. Importantly, this case had an apparently balanced translocation involving 17p (confirmed by FISH) that was discrepant with the NGS results. Overall, we conclude that using the read depth from NGS panels may aid in determining the genetic mechanism of disease. To illustrate the usefulness of CNA detection via NGS, we present three interesting hematologic malignancy cases with LOH for the TP53 locus in patients with complex karyotypes but no TP53 loss identified by cytogenetic testing, highlighting how integrating molecular and cytogenetic methods can provide a more complete genetic diagnosis.

Characterization of Somatically-Acquired Copy Number Alterations in Chronic Lymphocytic Leukaemia Using Shallow Whole Genome Sequencing.

Shallow whole genome sequencing (sWGS) is a simple, robust, and cost-effective technique recently optimized for the identification of copy number aberrations (CNAs) in tumor samples. This multiplexed methodology sequences 50bp from one end of the DNA molecule, generating ˜0.1× coverage, and utilizes the observed sequence depth across the genome to infer copy number. It is amenable to low quantities of input DNA, sequencing costs are modest, processing is compatible with low-output instruments, and downstream analysis is simplified by the use of freely available bioinformatics tools and a data analysis package written especially for the analysis of sWGS data. It is the aim of this chapter to introduce the fundamental concepts of sWGS and to provide an overview of the steps involved in a successful sWGS experiment.

Identification of Copy Number Aberrations in Breast Cancer Subtypes Using Persistence Topology.

DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Identification of tumor-driving CNAs (driver CNAs) however remains a challenging task, because they are frequently hidden by CNAs that are the product of random events that take place during tumor evolution. Experimental detection of CNAs is commonly accomplished through array comparative genomic hybridization (aCGH) assays followed by supervised and/or unsupervised statistical methods that combine the segmented profiles of all patients to identify driver CNAs. Here, we extend a previously-presented supervised algorithm for the identification of CNAs that is based on a topological representation of the data. Our method associates a two-dimensional (2D) point cloud with each aCGH profile and generates a sequence of simplicial complexes, mathematical objects that generalize the concept of a graph. This representation of the data permits segmenting the data at different resolutions and identifying CNAs by interrogating the topological properties of these simplicial complexes. We tested our approach on a published dataset with the goal of identifying specific breast cancer CNAs associated with specific molecular subtypes. Identification of CNAs associated with each subtype was performed by analyzing each subtype separately from the others and by taking the rest of the subtypes as the control. Our results found a new amplification in 11q at the location of the progesterone receptor in the Luminal A subtype. Aberrations in the Luminal B subtype were found only upon removal of the basal-like subtype from the control set. Under those conditions, all regions found in the original publication, except for 17q, were confirmed; all aberrations, except those in chromosome arms 8q and 12q were confirmed in the basal-like subtype. These two chromosome arms, however, were detected only upon removal of three patients with exceedingly large copy number values. More importantly, we detected 10 and 21 additional regions in the Luminal B and basal-like subtypes, respectively. Most of the additional regions were either validated on an independent dataset and/or using GISTIC. Furthermore, we found three new CNAs in the basal-like subtype: a combination of gains and losses in 1p, a gain in 2p and a loss in 14q. Based on these results, we suggest that topological approaches that incorporate multiresolution analyses and that interrogate topological properties of the data can help in the identification of copy number changes in cancer.

Abstract 2375: A method to identify copy number aberrations (CNAs) from whole exome sequence (WES) data and its application to multiple myeloma cell lines and patient samples

Abstract Duplications and deletions of large-scale genomic regions, including whole chromosomes, are a hallmark of malignant tumors and represent an important predictor of outcome for many tumor types. In multiple myeloma, a core set of structural variants are regularly used for disease prognosis (e.g., del17p, t(4;14), t(14,16)). The presence of one or more of these lesions is predictive of a more aggressive disease and poorer outcomes for patients. The application of next-generation sequencing (NGS) to cancer samples has enhanced our ability to detect prognostic and predictive copy number aberrations (CNAs). However, most NGS-based methods for the identification of CNAs in tumors require whole genome sequencing (WGS). Relatively fewer methods exist for the detection of CNAs from exome sequencing and, to our knowledge, these methods all rely on having matched normal data. However, WES is still far more common than WGS and matched normal samples are frequently not available. Therefore, we have developed a novel method for the identification of CNAs from single-sample WES data. Our method combines LOWESS smoothing and discrete-wavelet-transformation to normalize the exome coverage data with an HMM for coverage segmentation. We have applied our new method to WES data from 40 myeloma cell-lines for which aCGH copy number calls were also available for training and validation. Our method shows a high correlation with the aCGH calls (mean = 95%). In addition we have currently analyzed 42 (of 102) tumor samples that were collected during screening of relapsed/refractory multiple myeloma patients on three Onyx-sponsored Phase 2 clinical trials of the proteasome inhibitor carfilzomib (PX-171-003,PX-171-004, PX-171-005). Based on analysis of the completed subset we find that the frequency of known and novel lesions within these samples is similar to what has previously been observed in other publically available myeloma CNA datasets. The most frequent event that we find is loss of chr13 followed by gains on chr1q and chr15 all at near 40% frequency. We also find gains of chr3, chr5, chr9 and chr11 in ∼30% of patients. These results indicate that we can apply this new method to our larger set of clinical samples in order to discover new prognostic markers for patient outcomes and response to therapy in MM. Citation Format: Jeremiah D. Degenhardt, Kenneth B. Hoehn, Kevin A. Kwei, Kristi Stephenson, Jonathan J. Keats, Chris J. Kirk, Brian B. Tuch. A method to identify copy number aberrations (CNAs) from whole exome sequence (WES) data and its application to multiple myeloma cell lines and patient samples. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2375. doi:10.1158/1538-7445.AM2014-2375

A high-throughput computational framework for identifying significant copy number aberrations from array comparative genomic hybridisation data.

Reliable identification of copy number aberrations (CNA) from comparative genomic hybridization data would be improved by the availability of a generalised method for processing large datasets. To this end, we developed swatCGH, a data analysis framework and region detection heuristic for computational grids. swatCGH analyses sequentially displaced (sliding) windows of neighbouring probes and applies adaptive thresholds of varying stringency to identify the 10% of each chromosome that contains the most frequently occurring CNAs. We used the method to analyse a published dataset, comparing data preprocessed using four different DNA segmentation algorithms, and two methods for prioritising the detected CNAs. The consolidated list of the most commonly detected aberrations confirmed the value of swatCGH as a simplified high-throughput method for identifying biologically significant CNA regions of interest.