Cancer gene prioritization for targeted resequencing using FitSNP scores.
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 b...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2012-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC3291573?pdf=render |
id |
doaj-c7f3bebe6bc840aa954bc37988d4b45b |
---|---|
record_format |
Article |
spelling |
doaj-c7f3bebe6bc840aa954bc37988d4b45b2020-11-25T02:16:00ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0173e3133310.1371/journal.pone.0031333Cancer gene prioritization for targeted resequencing using FitSNP scores.Annelies FieuwBram De WildeFrank SpelemanJo VandesompeleKatleen De PreterBACKGROUND: 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.http://europepmc.org/articles/PMC3291573?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Annelies Fieuw Bram De Wilde Frank Speleman Jo Vandesompele Katleen De Preter |
spellingShingle |
Annelies Fieuw Bram De Wilde Frank Speleman Jo Vandesompele Katleen De Preter Cancer gene prioritization for targeted resequencing using FitSNP scores. PLoS ONE |
author_facet |
Annelies Fieuw Bram De Wilde Frank Speleman Jo Vandesompele Katleen De Preter |
author_sort |
Annelies Fieuw |
title |
Cancer gene prioritization for targeted resequencing using FitSNP scores. |
title_short |
Cancer gene prioritization for targeted resequencing using FitSNP scores. |
title_full |
Cancer gene prioritization for targeted resequencing using FitSNP scores. |
title_fullStr |
Cancer gene prioritization for targeted resequencing using FitSNP scores. |
title_full_unstemmed |
Cancer gene prioritization for targeted resequencing using FitSNP scores. |
title_sort |
cancer gene prioritization for targeted resequencing using fitsnp scores. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2012-01-01 |
description |
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. |
url |
http://europepmc.org/articles/PMC3291573?pdf=render |
work_keys_str_mv |
AT anneliesfieuw cancergeneprioritizationfortargetedresequencingusingfitsnpscores AT bramdewilde cancergeneprioritizationfortargetedresequencingusingfitsnpscores AT frankspeleman cancergeneprioritizationfortargetedresequencingusingfitsnpscores AT jovandesompele cancergeneprioritizationfortargetedresequencingusingfitsnpscores AT katleendepreter cancergeneprioritizationfortargetedresequencingusingfitsnpscores |
_version_ |
1724893424482516992 |