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Cancer gene prioritization for targeted resequencing using fitSNP scores

Annelies Fieuw UGent, Bram De Wilde UGent, Franki Speleman UGent, Jo Vandesompele UGent and Katleen De Preter UGent (2012) PLOS ONE. 7(3).
abstract
Background: Although the throughput of next generation sequencing is increasing and at the same time the cost is substantially reduced, for the majority of laboratories whole genome sequencing of large cohorts of cancer samples is still not feasible. In addition, the low number of genomes that are being sequenced is often problematic for the downstream interpretation of the significance of the variants. Targeted resequencing can partially circumvent this problem; by focusing on a limited number of candidate cancer genes to sequence, more samples can be included in the screening, hence resulting in substantial improvement of the statistical power. In this study, a successful strategy for prioritizing candidate genes for targeted resequencing of cancer genomes is presented. Results: Four prioritization strategies were evaluated on six different cancer types: genes were ranked using these strategies, and the positive predictive value (PPV) or mutation rate within the top-ranked genes was compared to the baseline mutation rate in each tumor type. Successful strategies generate gene lists in which the top is enriched for known mutated genes, as evidenced by an increase in PPV. A clear example of such an improvement is seen in colon cancer, where the PPV is increased by 2.3 fold compared to the baseline level when 100 top fitSNP genes are sequenced. Conclusions: A gene prioritization strategy based on the fitSNP scores appears to be most successful in identifying mutated cancer genes across different tumor entities, with variance of gene expression levels as a good second best.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
GLIOBLASTOMA, CARCINOMA, HUMAN BREAST, COLORECTAL CANCERS, GENOMIC LANDSCAPES
journal title
PLOS ONE
PLoS One
volume
7
issue
3
article_number
e31333
pages
7 pages
Web of Science type
Article
Web of Science id
000303005000005
JCR category
MULTIDISCIPLINARY SCIENCES
JCR impact factor
3.73 (2012)
JCR rank
7/56 (2012)
JCR quartile
1 (2012)
ISSN
1932-6203
DOI
10.1371/journal.pone.0031333
project
HPC-UGent: the central High Performance Computing infrastructure of Ghent University
language
English
UGent publication?
yes
classification
A1
copyright statement
I have retained and own the full copyright for this publication
id
1987022
handle
http://hdl.handle.net/1854/LU-1987022
date created
2012-01-16 12:08:06
date last changed
2013-09-17 10:49:47
@article{1987022,
  abstract     = {Background: Although the throughput of next generation sequencing is increasing and at the same time the cost is substantially reduced, for the majority of laboratories whole genome sequencing of large cohorts of cancer samples is still not feasible. In addition, the low number of genomes that are being sequenced is often problematic for the downstream interpretation of the significance of the variants. Targeted resequencing can partially circumvent this problem; by focusing on a limited number of candidate cancer genes to sequence, more samples can be included in the screening, hence resulting in substantial improvement of the statistical power. In this study, a successful strategy for prioritizing candidate genes for targeted resequencing of cancer genomes is presented. 
Results: Four prioritization strategies were evaluated on six different cancer types: genes were ranked using these strategies, and the positive predictive value (PPV) or mutation rate within the top-ranked genes was compared to the baseline mutation rate in each tumor type. Successful strategies generate gene lists in which the top is enriched for known mutated genes, as evidenced by an increase in PPV. A clear example of such an improvement is seen in colon cancer, where the PPV is increased by 2.3 fold compared to the baseline level when 100 top fitSNP genes are sequenced. 
Conclusions: A gene prioritization strategy based on the fitSNP scores appears to be most successful in identifying mutated cancer genes across different tumor entities, with variance of gene expression levels as a good second best.},
  articleno    = {e31333},
  author       = {Fieuw, Annelies and De Wilde, Bram and Speleman, Franki and Vandesompele, Jo and De Preter, Katleen},
  issn         = {1932-6203},
  journal      = {PLOS ONE},
  keyword      = {GLIOBLASTOMA,CARCINOMA,HUMAN BREAST,COLORECTAL CANCERS,GENOMIC LANDSCAPES},
  language     = {eng},
  number       = {3},
  pages        = {7},
  title        = {Cancer gene prioritization for targeted resequencing using fitSNP scores},
  url          = {http://dx.doi.org/10.1371/journal.pone.0031333},
  volume       = {7},
  year         = {2012},
}

Chicago
Fieuw, Annelies, BRAM DE WILDE, Franki Speleman, Jo Vandesompele, and Katleen De Preter. 2012. “Cancer Gene Prioritization for Targeted Resequencing Using fitSNP Scores.” Plos One 7 (3).
APA
Fieuw, A., DE WILDE, B., Speleman, F., Vandesompele, J., & De Preter, K. (2012). Cancer gene prioritization for targeted resequencing using fitSNP scores. PLOS ONE, 7(3).
Vancouver
1.
Fieuw A, DE WILDE B, Speleman F, Vandesompele J, De Preter K. Cancer gene prioritization for targeted resequencing using fitSNP scores. PLOS ONE. 2012;7(3).
MLA
Fieuw, Annelies, BRAM DE WILDE, Franki Speleman, et al. “Cancer Gene Prioritization for Targeted Resequencing Using fitSNP Scores.” PLOS ONE 7.3 (2012): n. pag. Print.