Dataset Information

Copy number variants landscape of multiple cancers and clinical applications based on NGS gene panel.

ABSTRACT:

Background

The rapid adoption of next-generation sequencing in clinical oncology has enabled detection of molecular biomarkers which are shared between multiple tumour types. Intra-tumour heterogeneity is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the tumour-related copy number variants (CNVs), as key regulators of cancer origination, development, and progression, across various types of cancers are poorly understood.

Methods

We performed pan-cancer CNV analysis of cancer-related genes in 15 types of cancers including 1438 cancerous patients by next-generation sequencing using a commercially available pan-cancer panel (Onco PanScan™). Downstream bioinformatics analysis was performed in order to detect CNVs, cluster analysis of the found CNVs, and comparison of the frequency of gained CNVs between different types of cancers. LASSO analysis was used for identification of the most important CNVs.

Results

We also identified 523 CNVs among which 16 CNVs were common while 22 CNVs were caner-specific CNVs. Meanwhile, FAM58A was most commonly found in all studied cancers in this study and significant differences were found in FAM58A between female and male patients (p = .001). Common CNVs, such as FOXA1, NFKBIA, HEY1, MECOM, CHD7, AGO2, were mutated in 6.79%, 8.45%, 7.51%, 6.43%, 7.59%, 8.16% of tumours, while most of these mutations have proven roles in positive regulation of transcription from RNA polymerase II promoter. 11 features including sex, DIS3, EPHB1, ERBB2, FLT1, HCK, KEAP1, MYD88, PARP3, TBX3, and TOP2A were found as the key features for classification of cancers using CNVs.

Conclusion

The 16 common CNVs between cancers can be used to identify the target of pan-cancer drug design and targeted therapies. Additionally, 22 caner-specific CNVs can be used as unique diagnostic markers for each cancer type.

SUBMITTER: Yan K

PROVIDER: S-EPMC10653745 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Copy number variants landscape of multiple cancers and clinical applications based on NGS gene panel.

Yan Kangpeng K Niu Li L Wu Boyu B He Chongwu C Deng Lei L Chen Chuan C Lan Zhangzhang Z Lin Chao C Kuang Weihua W Lin Huihong H Zou Jun J Zhang Wenyong W Luo Zhiqiang Z

Annals of medicine 20231115 2

<h4>Background</h4>The rapid adoption of next-generation sequencing in clinical oncology has enabled detection of molecular biomarkers which are shared between multiple tumour types. Intra-tumour heterogeneity is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the tumour-related copy number variants (CNVs), as key regulators of cancer origination, development, and progression, across various types of cancers are poorly understood.<h4>Methods</h4>We p ...[more]

PMID: 37967237

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Project description:Background: Targeted next generation sequencing (NGS) panels are increasingly being used in clinical genomics to increase capacity, throughput and affordability of gene testing. Identifying whole exon deletions or duplications (termed exon copy number variants, 'exon CNVs') in exon-targeted NGS panels has proved challenging, particularly for single exon CNVs. Methods: We developed a tool for the Detection of Exon Copy Number variants (DECoN), which is optimised for analysis of exon-targeted NGS panels in the clinical setting. We evaluated DECoN performance using 96 samples with independently validated exon CNV data. We performed simulations to evaluate DECoN detection performance of single exon CNVs and to evaluate performance using different coverage levels and sample numbers. Finally, we implemented DECoN in a clinical laboratory that tests BRCA1 and BRCA2 with the TruSight Cancer Panel (TSCP). We used DECoN to analyse 1,919 samples, validating exon CNV detections by multiplex ligation-dependent probe amplification (MLPA). Results: In the evaluation set, DECoN achieved 100% sensitivity and 99% specificity for BRCA exon CNVs, including identification of 8 single exon CNVs. DECoN also identified 14/15 exon CNVs in 8 other genes. Simulations of all possible BRCA single exon CNVs gave a mean sensitivity of 98% for deletions and 95% for duplications. DECoN performance remained excellent with different levels of coverage and sample numbers; sensitivity and specificity was >98% with the typical NGS run parameters. In the clinical pipeline, DECoN automatically analyses pools of 48 samples at a time, taking 24 minutes per pool, on average. DECoN detected 24 BRCA exon CNVs, of which 23 were confirmed by MLPA, giving a false discovery rate of 4%. Specificity was 99.7%. Conclusions: DECoN is a fast, accurate, exon CNV detection tool readily implementable in research and clinical NGS pipelines. It has high sensitivity and specificity and acceptable false discovery rate. DECoN is freely available at www.icr.ac.uk/decon.

Project description:BACKGROUND: Algorithms and software for CNV detection have been developed, but they detect the CNV regions sample-by-sample with individual-specific breakpoints, while common CNV regions are likely to occur at the same genomic locations across different individuals in a homogenous population. Current algorithms to detect common CNV regions do not account for the varying reliability of the individual CNVs, typically reported as confidence scores by SNP-based CNV detection algorithms. General methodologies for identifying these recurrent regions, especially those directed at SNP arrays, are still needed. RESULTS: In this paper, we describe two new approaches for identifying common CNV regions based on (i) the frequency of occurrence of reliable CNVs, where reliability is determined by high confidence scores, and (ii) a weighted frequency of occurrence of CNVs, where the weights are determined by the confidence scores. In addition, motivated by the fact that we often observe partially overlapping CNV regions as a mixture of two or more distinct subregions, regions identified using the two approaches can be fine-tuned to smaller sub-regions using a clustering algorithm. We compared the performance of the methods with sequencing-based results in terms of discordance rates, rates of departure from Hardy-Weinberg equilibrium (HWE) and average frequency and size of the identified regions. The discordance rates as well as the rates of departure from HWE decrease when we select CNVs with higher confidence scores. We also performed comparisons with two previously published methods, STAC and GISTIC, and showed that the methods we consider are better at identifying low-frequency but high-confidence CNV regions. CONCLUSIONS: The proposed methods for identifying common CNV regions in multiple individuals perform well compared to existing methods. The identified common regions can be used for downstream analyses such as group comparisons in association studies.

Project description:BackgroundCopy number variants (CNVs) are a type of polymorphism found to underlie phenotypic variation, both in humans and livestock. Most surveys of CNV in livestock have been conducted in the cattle genome, and often utilise only a single approach for the detection of copy number differences. Here we performed a study of CNV in sheep, using multiple methods to identify and characterise copy number changes. Comprehensive information from small pedigrees (trios) was collected using multiple platforms (array CGH, SNP chip and whole genome sequence data), with these data then analysed via multiple approaches to identify and verify CNVs.ResultsIn total, 3,488 autosomal CNV regions (CNVRs) were identified in this study, which substantially builds on an initial survey of the sheep genome that identified 135 CNVRs. The average length of the identified CNVRs was 19 kb (range of 1 kb to 3.6 Mb), with shorter CNVRs being more frequent than longer CNVRs. The total length of all CNVRs was 67.6Mbps, which equates to 2.7 % of the sheep autosomes. For individuals this value ranged from 0.24 to 0.55 %, and the majority of CNVRs were identified in single animals. Rather than being uniformly distributed throughout the genome, CNVRs tended to be clustered. Application of three independent approaches for CNVR detection facilitated a comparison of validation rates. CNVs identified on the Roche-NimbleGen 2.1M CGH array generally had low validation rates with lower density arrays, while whole genome sequence data had the highest validation rate (>60 %).ConclusionsThis study represents the first comprehensive survey of the distribution, prevalence and characteristics of CNVR in sheep. Multiple approaches were used to detect CNV regions and it appears that the best method for verifying CNVR on a large scale involves using a combination of detection methodologies. The characteristics of the 3,488 autosomal CNV regions identified in this study are comparable to other CNV regions reported in the literature and provide a valuable and sizeable addition to the small subset of published sheep CNVs.

Dataset Information

Copy number variants landscape of multiple cancers and clinical applications based on NGS gene panel.

Background

Methods

Results

Conclusion

Publications

Copy number variants landscape of multiple cancers and clinical applications based on NGS gene panel.

Similar Datasets

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