Challenges in identifying cancer genes by analysis of exome sequencing data
Massively parallel sequencing has permitted an unprecedented examination of the cancer exome, leading to predictions that all genes important to cancer will soon be identified by genetic analysis of tumours. To examine this potential, here we evaluate the ability of state-of-the-art sequence analysi...
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Nature Publishing Group
2016
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4947162/ |
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pubmed-49471622016-07-27 Challenges in identifying cancer genes by analysis of exome sequencing data Hofree, Matan Carter, Hannah Kreisberg, Jason F. Bandyopadhyay, Sourav Mischel, Paul S. Friend, Stephen Ideker, Trey Article Massively parallel sequencing has permitted an unprecedented examination of the cancer exome, leading to predictions that all genes important to cancer will soon be identified by genetic analysis of tumours. To examine this potential, here we evaluate the ability of state-of-the-art sequence analysis methods to specifically recover known cancer genes. While some cancer genes are identified by analysis of recurrence, spatial clustering or predicted impact of somatic mutations, many remain undetected due to lack of power to discriminate driver mutations from the background mutational load (13–60% recall of cancer genes impacted by somatic single-nucleotide variants, depending on the method). Cancer genes not detected by mutation recurrence also tend to be missed by all types of exome analysis. Nonetheless, these genes are implicated by other experiments such as functional genetic screens and expression profiling. These challenges are only partially addressed by increasing sample size and will likely hold even as greater numbers of tumours are analysed. Nature Publishing Group 2016-07-15 /pmc/articles/PMC4947162/ /pubmed/27417679 http://dx.doi.org/10.1038/ncomms12096 Text en Copyright © 2016, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved. http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
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Open Access Journal |
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Foreign Institution |
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US National Center for Biotechnology Information |
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NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Hofree, Matan Carter, Hannah Kreisberg, Jason F. Bandyopadhyay, Sourav Mischel, Paul S. Friend, Stephen Ideker, Trey |
| spellingShingle |
Hofree, Matan Carter, Hannah Kreisberg, Jason F. Bandyopadhyay, Sourav Mischel, Paul S. Friend, Stephen Ideker, Trey Challenges in identifying cancer genes by analysis of exome sequencing data |
| author_facet |
Hofree, Matan Carter, Hannah Kreisberg, Jason F. Bandyopadhyay, Sourav Mischel, Paul S. Friend, Stephen Ideker, Trey |
| author_sort |
Hofree, Matan |
| title |
Challenges in identifying cancer genes by analysis of exome sequencing data |
| title_short |
Challenges in identifying cancer genes by analysis of exome sequencing data |
| title_full |
Challenges in identifying cancer genes by analysis of exome sequencing data |
| title_fullStr |
Challenges in identifying cancer genes by analysis of exome sequencing data |
| title_full_unstemmed |
Challenges in identifying cancer genes by analysis of exome sequencing data |
| title_sort |
challenges in identifying cancer genes by analysis of exome sequencing data |
| description |
Massively parallel sequencing has permitted an unprecedented examination of the cancer exome, leading to predictions that all genes important to cancer will soon be identified by genetic analysis of tumours. To examine this potential, here we evaluate the ability of state-of-the-art sequence analysis methods to specifically recover known cancer genes. While some cancer genes are identified by analysis of recurrence, spatial clustering or predicted impact of somatic mutations, many remain undetected due to lack of power to discriminate driver mutations from the background mutational load (13–60% recall of cancer genes impacted by somatic single-nucleotide variants, depending on the method). Cancer genes not detected by mutation recurrence also tend to be missed by all types of exome analysis. Nonetheless, these genes are implicated by other experiments such as functional genetic screens and expression profiling. These challenges are only partially addressed by increasing sample size and will likely hold even as greater numbers of tumours are analysed. |
| publisher |
Nature Publishing Group |
| publishDate |
2016 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4947162/ |
| _version_ |
1613610249405595648 |