TY - JOUR
T1 - Challenges in identifying cancer genes by analysis of exome sequencing data
AU - Hofree, Matan
AU - Carter, Hannah
AU - Kreisberg, Jason F.
AU - Bandyopadhyay, Sourav
AU - Mischel, Paul S.
AU - Friend, Stephen
AU - Ideker, Trey
N1 - Funding Information:
This work was supported by the National Institutes of Health (R01 ES014811 and U24 CA184427) and a generous donation from the Fred Luddy Family Foundation.
PY - 2016/7/15
Y1 - 2016/7/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84978732015&partnerID=8YFLogxK
U2 - 10.1038/ncomms12096
DO - 10.1038/ncomms12096
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C2 - 27417679
AN - SCOPUS:84978732015
SN - 2041-1723
VL - 7
JO - Nature Communications
JF - Nature Communications
M1 - 12096
ER -