Title |
Clinical applications of next generation sequencing in cancer: from panels, to exomes, to genomes
|
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Published in |
Frontiers in Genetics, June 2015
|
DOI | 10.3389/fgene.2015.00215 |
Pubmed ID | |
Authors |
Tony Shen, Stefan Hans Pajaro-Van de Stadt, Nai Chien Yeat, Jimmy C.-H. Lin |
Abstract |
This article will review recent impact of massively parallel next-generation sequencing (NGS) in our understanding and treatment of cancer. While whole exome sequencing (WES) remains popular and effective as a method of genetically profiling different cancers, advances in sequencing technology has enabled an increasing number of whole-genome based studies. Clinically, NGS has been used or is being developed for genetic screening, diagnostics, and clinical assessment. Though challenges remain, clinicians are in the early stages of using genetic data to make treatment decisions for cancer patients. As the integration of NGS in the study and treatment of cancer continues to mature, we believe that the field of cancer genomics will need to move toward more complete 100% genome sequencing. Current technologies and methods are largely limited to coding regions of the genome. A number of recent studies have demonstrated that mutations in non-coding regions may have direct tumorigenic effects or lead to genetic instability. Non-coding regions represent an important frontier in cancer genomics. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
India | 2 | 15% |
United Kingdom | 2 | 15% |
Spain | 1 | 8% |
Israel | 1 | 8% |
Ireland | 1 | 8% |
United States | 1 | 8% |
Switzerland | 1 | 8% |
Unknown | 4 | 31% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 8 | 62% |
Scientists | 5 | 38% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | <1% |
United Kingdom | 1 | <1% |
Unknown | 216 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 40 | 18% |
Student > Master | 30 | 14% |
Other | 28 | 13% |
Student > Ph. D. Student | 25 | 11% |
Student > Bachelor | 19 | 9% |
Other | 37 | 17% |
Unknown | 40 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 50 | 23% |
Agricultural and Biological Sciences | 50 | 23% |
Medicine and Dentistry | 47 | 21% |
Computer Science | 5 | 2% |
Pharmacology, Toxicology and Pharmaceutical Science | 4 | 2% |
Other | 16 | 7% |
Unknown | 47 | 21% |