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Cancer gene prioritization for targeted resequencing using FitSNP scores

Journal Contribution - Journal Article

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.

Journal: PLOS ONE
ISSN: 1932-6203
Issue: 3
Volume: 7
Publication year:2012
Keywords:Algorithms, Cell Line, Tumor, Cohort Studies, Computational Biology/methods, DNA Mutational Analysis, Early Detection of Cancer, Gene Dosage, Gene Expression Profiling, Genetic Predisposition to Disease, Genome, Humans, Mutation, Neoplasms/genetics, Polymorphism, Single Nucleotide, Predictive Value of Tests