Detection Of Somatic Copy Number Alterations Research Articles

Harnessing Cell-Free DNA Biological Features for Early Treatment Response Prediction in High Grade B-Cell Lymphoma

Introduction The application of circulating cell-free DNA (cfDNA) has attracted recent interest for cancer detection and monitoring. The presence of somatic copy number alterations (SCNAs) can be used to differentiate between cancer cases and healthy controls (PMID: 29109393). This information can be retrieved from low-coverage whole genome sequencing (lcWGS) and used to determine the proportion of tumor derived cfDNA (ctDNA) in a tube of blood at a reasonable cost. Previous studies observed that cancer patients tend to have a higher fraction of shorter cfDNA fragments compared to healthy controls, irrespective of the presence of genomic events (PMID: 30404863). Here, we evaluate if a combination of cfDNA genomic and epigenomic features that can be retrieved from a single run of lcWGS, has potential for the early prediction of end of induction (EoI) response, in patients with high-grade lymphoma with MYC and BCL2 and/or BLC6 rearrangements (HGBL-DH/TH). Methods In the HOVON-152 phase II trial (NCT03620578) HGBL-DH/TH patients were treated with induction immunochemotherapy with one cycle of R-CHOP followed by five cycles of DA-EPOCH-R. Patients in complete metabolic response (CMR, defined as Deauville score 1-3) at the end of induction receive 1-year nivolumab consolidation. Blood was collected from 40 patients using PAXgene ccfDNA tubes after one cycle (timepoint 1, T1), three (T2) and six (T3) cycles of treatment. At T3, response to induction treatment was determined using 18F-FDG-PET scan. Patients with CMR at EoI were considered responders. In this study, we included 40 out of 97 patients from the HOVON-152 phase II trial, and enriched for patients with no CMR. Plasma was separated using dual centrifugation protocol, and cfDNA was isolated using the QIASymphony kit (QIAGEN). Sequencing libraries were prepared using the ThruPLEX Plasma-seq kit (Takara). lcWGS was performed on a Novaseq 6000 (Illumina). Somatic copy number aberrations (SCNAs) were retrieved with ichorCNA, insert size profiles were generated using Picard tools. SCNAs were classified as detected when the tumor fraction quantified with ichorCNA exceeded 3%. Results We assessed the detection of SCNAs and the SCNA-derived tumor fraction using the ichorCNA software. The ctDNA tumor fraction was found to be significantly increased in non-responders (NR, n=24) compared to responders (R, n=16), irrespective of sampling timepoint (Wilcoxon, p<0.001). At T1, the tumor fraction was significantly elevated in non-responders (Wilcoxon, p=0.014), and SCNAs were detected in 6/16 (38%) of the responders versus 19/24 (79%) of the non-responders, with negative predictive value (NPV): 71%, positive predictive value (PPV): 74%. Therefore, non-responders tend to have a higher fraction of tumor-derived signal which is already apparent at earlier timepoints. To evaluate the potential of using cfDNA fragmentation for the early detection of response, we retrieved the fragment size profiles. The proportion of short fragments (P20-150 bp), was found to be increased in non-responders compared to responders at T1 (Wilcoxon, p<0.001) and T2 (Wilcoxon, p=0.033), which suggests that the plasma of non-responders contains a higher fraction of tumor-signal (Figure 1A). Previous reports leveraged the size properties of ctDNA in order to increase the sensitivity of SCNA based methods (PMID: 30404863). In order to utilize both epigenomic and genomic properties to increase the detection rate of tumor-signal, we performed in silico size selection to computationally filter out fragments in the 20-150bp size range. Upon repeating the SCNA analysis, we observed that 20/24 (83%) non-responders and 1/15 (19%) responders had detectable ctDNA at T1 (Figure 1B). This yields a NPV and PPV of 80% and 86% respectively for EoI response detection at T1. By leveraging the fragmentomic properties of cfDNA, the sensitivity of SCNA-based methods can be improved, which leads to an increased detection of response at earlier timepoints. Discussion In patients with HGBL-DH/TH, after one cycle of R-CHOP, the presence of SCNAs enhanced by fragment size analysis in plasma, is significantly associated with an unfavourable EoI response, with a NPV and PPV of 80% and 86% respectively. This novel approach can be performed without prior knowledge of genetic alterations, and may have high potential to guide early risk-adapted treatment strategies in HGBL-DH/TH. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

A Validation Framework for Somatic Copy Number Detection in Targeted Sequencing Panels

Somatic copy number alterations (SCNAs) in tumors are clinically significant diagnostic, prognostic, and predictive biomarkers. SCNA detection from targeted next-generation sequencing panels is increasingly common in clinical practice; however, detailed descriptions of optimization and validation of SCNA pipelines for small targeted panels are limited. This study describes the validation and implementation of a tumor-only SCNA pipeline using CNVkit, augmented with custom modules and optimized for clinical implementation by testing reference materials and clinical tumor samples with different classes of copy number variation (CNV; amplification, single copy loss, and biallelic loss). Using wet-bench and in silico methods, various parameters impacting CNV calling, including assay-intrinsic variables (establishment of normal reference and sequencing coverage), sample-intrinsic variables (tumor purity and sample quality), and CNV algorithm-intrinsic variables (bin size), were optimized. The pipeline was trained and tested on an optimization cohort and validated using an independent cohort with a sensitivity and specificity of 100% and 93%, respectively. Using custom modules, intragenic CNVs with breakpoints within tumor suppressor genes were uncovered. Using the validated pipeline, re-analysis of 28 pediatric solid tumors that had been previously profiled for mutations identified SCNAs in 86% (24/28) samples, with 46% (13/28) samples harboring findings of potential clinical relevance. Our report highlights the importance of rigorous establishment of performance characteristics of SCNA pipelines and presents a detailed validation framework for optimal SCNA detection in targeted sequencing panels.

Chromosomal imbalances detected via RNA-sequencing in 28 cancers.

MotivationRNA-sequencing (RNA-seq) of tumor tissue is typically only used to measure gene expression. Here, we present a statistical approach that leverages existing RNA-seq data to also detect somatic copy number alterations (SCNAs), a pervasive phenomenon in human cancers, without a need to sequence the corresponding DNA.ResultsWe present an analysis of 4942 participant samples from 28 cancers in The Cancer Genome Atlas (TCGA), demonstrating robust detection of SCNAs from RNA-seq. Using genotype imputation and haplotype information, our RNA-based method had a median sensitivity of 85% to detect SCNAs defined by DNA analysis, at high specificity (∼95%). As an example of translational potential, we successfully replicated SCNA features associated with breast cancer subtypes. Our results credential haplotype-based inference based on RNA-seq to detect SCNAs in clinical and population-based settings.Availability and implementationThe analyses presented use the data publicly available from TCGA Research Network (http://cancergenome.nih.gov/). See Methods for details regarding data downloads. hapLOHseq software is freely available under The MIT license and can be downloaded from http://scheet.org/software.html.Supplementary information Supplementary data are available at Bioinformatics online.

Limits and Applications of Genomic Analysis of Circulating Tumor DNA as a Liquid Biopsy in Asymptomatic Forms of Multiple Myeloma.

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Abstract 5119: Customizable all-in-one cancer panels to detect copy number variation, DNA rearrangement and mutations by targeted NGS

Abstract Background: The current standard of genomic profiling of cancer tissues relies upon multiple separate technologies to aid in tumor characterization. Here we describe a customizable NGS design and analysis workflow for cancer samples that can detect single nucleotide polymorphisms (SNPs), insertions and deletions (INDEL), somatic copy number alterations (SCNAs), and translocations (TLs) in a single assay. Methods: We have created a library of strategically placed target-enrichment probes to provide for specific detection of SNP/INDEL, SCNAs, and translocation in genes identified as important in several cancer subtypes. The probes are optimized with the SureSelect XTHS protocol, which enables extremely sensitive detection of rare exonic mutation events within a heterogeneous sample. SCNA detection is further enhanced with additional probes targeting high minor allele frequency SNPs within the target gene boundary. Translocation detection is enabled by targeting intronic regions of fusion gene candidates. The data analysis is performed by the software SureCall that calls SNPs across all regions covered by probes. SCNA detection utilizes either a matched normal or standard control DNA and aberrant regions are identified by jointly segmenting the read depth ratios and SNP allele frequencies. The clonal structure is discerned using two Bayesian cancer models in a hierarchical manner. Translocation are identified by analyzing split reads across putative fusion breakpoints that are supported by both high- and low-confidence reads that pass quality filters. Results: We demonstrate rare allele detection in cancer cells down to 1%. We also show detection of 3 copies of MET at 20% tumor fraction and 16 copies of ERBB2 at 2% tumor fraction. For translocations, DNA level gene fusions were detected for ALK and ROS fusion genes at &amp;lt;=3% tumor fraction. In 39 ERBB2 FISH-positive FFPE samples, ERBB2 CNV detected by AIO panel was 97.4% concordant with FISH (scored according to standard ASCO/CAP convention). Using FISH as the gold standard, the false negative rate was 2.6% and false positive rate was 2.4%. In 43 ALK FISH-positive FFPE samples, ALK fusion was detected using a small AIO lung panel which includes probes spanning introns 18/19 of the ALK gene and is 90.7% concordant with FISH (Agilent SureFISH ALK BA). Using FISH as the gold standard, the false negative rate was 9.3% and false positive rate was 2.3%. Conclusions: This work represents an important advancement in the development of a single assay to detect copy number variation, DNA rearrangement and mutations. Citation Format: Arjun Vadapalli, Ashutosh Ashutosh, Heather Tao, Linus Forsmark, Christian Le Cocq, Akansha Khare, Bahram Arezi, Gilbert Amparo, Michael Ruvolo, Carlos Pabon, Jayati Ghosh, Jimmy Jin, Tracy Liu, Douglas Roberts. Customizable all-in-one cancer panels to detect copy number variation, DNA rearrangement and mutations by targeted NGS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5119.

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

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. 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. DEFOR is available at https://github.com/drzh/defor. Supplementary data are available at Bioinformatics online.

Somatic DNA Copy-Number Alterations Detection for Esophageal Adenocarcinoma Using Digital Polymerase Chain Reaction.

Somatic copy-number alterations are commonly found in cancer and play key roles in activating oncogenes and deactivating tumor suppressor genes. Digital polymerase chain reaction is an effective way to detect the changes in copy number. In esophageal adenocarcinoma, detection of somatic copy-number alterations could predict the prognosis of patients as well as the response to therapy. This chapter will review the methods involved in digital polymerase chain reaction for the research or potential clinical applications in esophageal adenocarcinoma.

ENVE: a novel computational framework characterizes copy-number mutational landscapes in colorectal cancers from African American patients.

Reliable detection of somatic copy-number alterations (sCNAs) in tumors using whole-exome sequencing (WES) remains challenging owing to technical (inherent noise) and sample-associated variability in WES data. We present a novel computational framework, ENVE, which models inherent noise in any WES dataset, enabling robust detection of sCNAs across WES platforms. ENVE achieved high concordance with orthogonal sCNA assessments across two colorectal cancer (CRC) WES datasets, and consistently outperformed a best-in-class algorithm, Control-FREEC. We subsequently used ENVE to characterize global sCNA landscapes in African American CRCs, identifying genomic aberrations potentially associated with CRC pathogenesis in this population. ENVE is downloadable at https://github.com/ENVE-Tools/ENVE.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0192-9) contains supplementary material, which is available to authorized users.

Comparative analysis of methods for identifying somatic copy number alterations from deep sequencing data

Somatic copy-number alterations (SCNAs) are an important type of structural variation affecting tumor pathogenesis. Accurate detection of genomic regions with SCNAs is crucial for cancer genomics as these regions contain likely drivers of cancer development. Deep sequencing technology provides single-nucleotide resolution genomic data and is considered one of the best measurement technologies to detect SCNAs. Although several algorithms have been developed to detect SCNAs from whole-genome and whole-exome sequencing data, their relative performance has not been studied. Here, we have compared ten SCNA detection algorithms in both simulated and primary tumor deep sequencing data. In addition, we have evaluated the applicability of exome sequencing data for SCNA detection. Our results show that (i) clear differences exist in sensitivity and specificity between the algorithms, (ii) SCNA detection algorithms are able to identify most of the complex chromosomal alterations and (iii) exome sequencing data are suitable for SCNA detection.

COPS: A Sensitive and Accurate Tool for Detecting Somatic Copy Number Alterations Using Short-Read Sequence Data from Paired Samples

Copy Number Alterations (CNAs) such as deletions and duplications; compose a larger percentage of genetic variations than single nucleotide polymorphisms or other structural variations in cancer genomes that undergo major chromosomal re-arrangements. It is, therefore, imperative to identify cancer-specific somatic copy number alterations (SCNAs), with respect to matched normal tissue, in order to understand their association with the disease. We have devised an accurate, sensitive, and easy-to-use tool, COPS, COpy number using Paired Samples, for detecting SCNAs. We rigorously tested the performance of COPS using short sequence simulated reads at various sizes and coverage of SCNAs, read depths, read lengths and also with real tumor:normal paired samples. We found COPS to perform better in comparison to other known SCNA detection tools for all evaluated parameters, namely, sensitivity (detection of true positives), specificity (detection of false positives) and size accuracy. COPS performed well for sequencing reads of all lengths when used with most upstream read alignment tools. Additionally, by incorporating a downstream boundary segmentation detection tool, the accuracy of SCNA boundaries was further improved. Here, we report an accurate, sensitive and easy to use tool in detecting cancer-specific SCNAs using short-read sequence data. In addition to cancer, COPS can be used for any disease as long as sequence reads from both disease and normal samples from the same individual are available. An added boundary segmentation detection module makes COPS detected SCNA boundaries more specific for the samples studied. COPS is available at ftp://115.119.160.213 with username “cops” and password “cops”.