SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering

BMC Bioinformatics

Jimmy Van den Eynden¹  Ana Carolina Fierro²  Lieven PC Verbeke²  Kathleen Marchal¹ 

[1] Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium

[2] Department of Information Technology, Ghent University - iMinds, Ghent, Belgium

关键词: Tumour suppressor gene;    Oncogene;    Driver gene;    Mutation;    Cancer;   

Others  :  1177463
DOI  :  10.1186/s12859-015-0555-7

 received in 2014-12-19, accepted in 2015-03-30,  发布年份 2015

【 摘 要 】

Background

With the advances in high throughput technologies, increasing amounts of cancer somatic mutation data are being generated and made available. Only a small number of (driver) mutations occur in driver genes and are responsible for carcinogenesis, while the majority of (passenger) mutations do not influence tumour biology. In this study, SomInaClust is introduced, a method that accurately identifies driver genes based on their mutation pattern across tumour samples and then classifies them into oncogenes or tumour suppressor genes respectively.

Results

SomInaClust starts from the observation that oncogenes mainly contain mutations that, due to positive selection, cluster at similar positions in a gene across patient samples, whereas tumour suppressor genes contain a high number of protein-truncating mutations throughout the entire gene length. The method was shown to prioritize driver genes in 9 different solid cancers. Furthermore it was found to be complementary to existing similar-purpose methods with the additional advantages that it has a higher sensitivity, also for rare mutations (occurring in less than 1% of all samples), and it accurately classifies candidate driver genes in putative oncogenes and tumour suppressor genes. Pathway enrichment analysis showed that the identified genes belong to known cancer signalling pathways, and that the distinction between oncogenes and tumour suppressor genes is biologically relevant.

Conclusions

SomInaClust was shown to detect candidate driver genes based on somatic mutation patterns of inactivation and clustering and to distinguish oncogenes from tumour suppressor genes. The method could be used for the identification of new cancer genes or to filter mutation data for further data-integration purposes.

【 授权许可】

   
2015 Van den Eynden et al.; licensee BioMed Central.

【 图 表 】

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