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).