Computational methods for detecting copy number variations in cancer genome using next generation sequencing: principles and challenges

Liu, Biao; Morrison, Carl; Johnson, Candace S.; Trump, Donald L.; Qin, Maochun; Conroy, Jeffrey; Wang, Jianmin; Liu, Song

doi:10.18632/oncotarget.1537

Cited by 83 publications

(73 citation statements)

References 95 publications

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“…The different forms of CNV, and the mechanisms by which they arise, have been reviewed in relation to their risk of various diseases and the genetic risk of radiation (101). High throughput screening of CNVs can be performed by next generation sequencing (NGS) and used in identifying targets for cancer treatment (102). Recently, CNVs in XRCC1 was found to be significantly associated with rectal bleeding in prostate RT patients, and marginally significant for erectile dysfunction, and the integration into a risk model improved prediction of late normal-tissue toxicity (103).…”

Section: ‘Omics’ Approachesmentioning

confidence: 99%

Radiogenomics: A systems biology approach to understanding genetic risk factors for radiotherapy toxicity?

et al. 2016

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Adverse reactions in normal tissue after radiotherapy (RT) limit the dose that can be given to tumour cells. Since 80% of individual variation in clinical response is estimated to be caused by patient-related factors, identifying these factors might allow prediction of patients with increased risk of developing severe reactions. While inactivation of cell renewal is considered a major cause of toxicity in early-reacting normal tissues, complex interactions involving multiple cell types, cytokines, and hypoxia seem important for late reactions. Here, we review ‘omics’ approaches such as screening of genetic polymorphisms or gene expression analysis, and assess the potential of epigenetic factors, posttranslational modification, signal transduction, and metabolism. Furthermore, functional assays have suggested possible associations with clinical risk of adverse reaction. Pathway analysis incorporating different ‘omics’ approaches may be more efficient in identifying critical pathways than pathway analysis based on single ‘omics’ data sets. Integrating these pathways with functional assays may be powerful in identifying multiple subgroups of RT patients characterized by different mechanisms. Thus ‘omics’ and functional approaches may synergize if they are integrated into radiogenomics ‘systems biology’ to facilitate the goal of individualised radiotherapy.

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Section: ‘Omics’ Approachesmentioning

confidence: 99%

Radiogenomics: A systems biology approach to understanding genetic risk factors for radiotherapy toxicity?

et al. 2016

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“…NGS-based CNV algorithms frequently manage WGS and WES data. A number of somatic CNV-detection programs for NGS data have been developed, each of them based on a different approach 64. However, with regard to targeted sequencing, the approach used in diagnostic settings, the bioinformatic challenge remains open.…”

Section: Variant Calling and Copy-number Variationsmentioning

confidence: 99%

Next-generation sequencing: advances and applications in cancer diagnosis

Serratì¹,

Summa²,

Pilato³

et al. 2016

OTT

167

113

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Technological advances have led to the introduction of next-generation sequencing (NGS) platforms in cancer investigation. NGS allows massive parallel sequencing that affords maximal tumor genomic assessment. NGS approaches are different, and concern DNA and RNA analysis. DNA sequencing includes whole-genome, whole-exome, and targeted sequencing, which focuses on a selection of genes of interest for a specific disease. RNA sequencing facilitates the detection of alternative gene-spliced transcripts, posttranscriptional modifications, gene fusion, mutations/single-nucleotide polymorphisms, small and long noncoding RNAs, and changes in gene expression. Most applications are in the cancer research field, but lately NGS technology has been revolutionizing cancer molecular diagnostics, due to the many advantages it offers compared to traditional methods. There is greater knowledge on solid cancer diagnostics, and recent interest has been shown also in the field of hematologic cancer. In this review, we report the latest data on NGS diagnostic/predictive clinical applications in solid and hematologic cancers. Moreover, since the amount of NGS data produced is very large and their interpretation is very complex, we briefly discuss two bioinformatic aspects, variant-calling accuracy and copy-number variation detection, which are gaining a lot of importance in cancer-diagnostic assessment.

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“…For instance, their lengths and diversities in genome are very distinguishable, besides the specific concern in dealing with the existence of an inevitable normal cell contamination in tumor cell. Because of that and other challenges that cause some misperceptions in the signal variation, there are excellent germline CNV detection tools that are not suitable for CNAs detection [71]. Tools exclusively focused on CNAs detections have been developed, such as CNASeg, CNAnorm, and ReadDepth.…”

Section: Cnv Detection Tools For Cancer Studiesmentioning

confidence: 99%

Copy Number Variations Detection: Unravelling the Problem in Tangible Aspects

Nascimento

Guimarães

2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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In the midst of the important genomic variants associated to the susceptibility and resistance to complex diseases, Copy Number Variations (CNV) has emerged as a prevalent class of structural variation. Following the flood of next-generation sequencing data, numerous tools publicly available have been developed to provide computational strategies to identify CNV at improved accuracy. This review goes beyond scrutinizing the main approaches widely used for structural variants detection in general, including Split-Read, Paired-End Mapping, Read-Depth, and Assembly-based. In this paper, (1) we characterize the relevant technical details around the detection of CNV, which can affect the estimation of breakpoints and number of copies, (2) we pinpoint the most important insights related to GC-content and mappability biases, and (3) we discuss the paramount caveats in the tools evaluation process. The points brought out in this study emphasize common assumptions, a variety of possible limitations, valuable insights, and directions for desirable contributions to the state-of-the-art in CNV detection tools.

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Computational methods for detecting copy number variations in cancer genome using next generation sequencing: principles and challenges

Cited by 83 publications

References 95 publications

Radiogenomics: A systems biology approach to understanding genetic risk factors for radiotherapy toxicity?

Radiogenomics: A systems biology approach to understanding genetic risk factors for radiotherapy toxicity?

Next-generation sequencing: advances and applications in cancer diagnosis

Copy Number Variations Detection: Unravelling the Problem in Tangible Aspects

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