Original Paper

Oncogene (2005) 24, 6133–6142. doi:10.1038/sj.onc.1208745; published online 16 May 2005

In silico whole-genome scanning of cancer-associated nonsynonymous SNPs and molecular characterization of a dynein light chain tumour variant

Abdel Aouacheria1,3, Vincent Navratil1,3, Wenyu Wen2, Ming Jiang2, Dominique Mouchiroud1, Christian Gautier1, Manolo Gouy1 and Mingjie Zhang2

  1. 1Laboratoire de Biométric et Biologie Evolutive, CNRS UMR 5558, Université Claude Bernard Lyon 1, F-69622 Villeurbanne Cedex, France
  2. 2Department of Biochemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, PR China

Correspondence: A Aouacheria, E-mail: aouacher@biomserv.univ-lyon1.fr

3These authors contributed equally to this work

Received 14 September 2004; Revised 24 March 2005; Accepted 11 April 2005; Published online 16 May 2005.



Last decade has led to the accumulation of large amounts of data on cancer genetics, opening an unprecedented access to the mapping of cancer genes in the human genome. Single-nucleotide polymorphisms (SNPs), the most common form of DNA variation in humans, emerge as an invaluable tool for cancer association studies. These genotypic markers can be used to assay how alleles of candidate genes correlate with the malignant phenotype, and may provide new clues into the genetic modifications that characterize cancer onset. In this cancer-oriented study, we detail an SNP mining strategy based on the analysis of expressed sequence tags among publicly available databases. Our whole-genome approach provides a comprehensive and unbiased description of nonsynonymous SNPs (nsSNPs) in tumoral versus normal tissues. To gain further insights into the possible relationships between genetic variation and altered phenotype, locations of a subset of nsSNPs were mapped onto protein domains known to be critical for protein function. Computational methods were also used to predict the potential impact of these cancer-associated nsSNPs on protein structure and function. We illustrate our approach through the detailed biochemical and structural characterization of a previously unknown cancer-associated mutation (G79C) affecting the 8 kDa dynein light chain (DNCL1).


single-nucleotide polymorphism, DNCL1, cancer genomics, cancer association study, dynein light chain, expressed sequence tags