Germline fitness-based scoring of cancer mutations

• Published in: Genetics (Austin) Vol. 188; no. 2; pp. 383 - 393
• Main Authors: Fischer, Andrej, Greenman, Chris, Mustonen, Ville
• Format: Journal Article
• Language: English
• Published: United States Genetics Society of America 01.06.2011
• Subjects: Algorithms; Amino Acid Sequence; Base Sequence; Cancer; Genes; Genetic Fitness; Genetic Predisposition to Disease - genetics; Genetics; Genome, Human - genetics; Germ-Line Mutation; Humans; Investigations; Models, Genetic; Mutation; Mutation, Missense; Neoplasms - genetics; Oncogenes - genetics; Polymorphism, Genetic; Selection, Genetic; Sequence Homology, Amino Acid; Tumor Suppressor Proteins - genetics
• ISSN: 1943-2631; 0016-6731; 1943-2631
• DOI: 10.1534/genetics.111.127480

A key goal in cancer research is to find the genomic alterations that underlie malignant cells. Genomics has proved successful in identifying somatic variants at a large scale. However, it has become evident that a typical cancer exhibits a heterogenous mutation pattern across samples. Cases where the same alteration is observed repeatedly seem to be the exception rather than the norm. Thus, pinpointing the key alterations (driver mutations) from a background of variations with no direct causal link to cancer (passenger mutations) is difficult. Here we analyze somatic missense mutations from cancer samples and their healthy tissue counterparts (germline mutations) from the viewpoint of germline fitness. We calibrate a scoring system from protein domain alignments to score mutations and their target loci. We show first that this score predicts to a good degree the rate of polymorphism of the observed germline variation. The scoring is then applied to somatic mutations. We show that candidate cancer genes prone to copy number loss harbor mutations with germline fitness effects that are significantly more deleterious than expected by chance. This suggests that missense mutations play a driving role in tumor suppressor genes. Furthermore, these mutations fall preferably onto loci in sequence neighborhoods that are high scoring in terms of germline fitness. In contrast, for somatic mutations in candidate onco genes we do not observe a statistically significant effect. These results help to inform how to exploit germline fitness predictions in discovering new genes and mutations responsible for cancer.