title: The somatic autosomal mutation matrix in cancer genomes.
abstract: DNA damage in somatic cells originates from both environmental and endogenous sources, giving rise to mutations through multiple mechanisms. When these mutations affect the function of critical genes, cancer may ensue. Although identifying genomic subsets of mutated genes may inform therapeutic options, a systematic survey of tumor mutational spectra is required to improve our understanding of the underlying mechanisms of mutagenesis involved in cancer etiology. Recent studies have presented genome-wide sets of somatic mutations as a 96-element vector, a procedure that only captures the immediate neighbors of the mutated nucleotide. Herein, we present a 32 × 12 mutation matrix that captures the nucleotide pattern two nucleotides upstream and downstream of the mutation. A somatic autosomal mutation matrix (SAMM) was constructed from tumor-specific mutations derived from each of 909 individual cancer genomes harboring a total of 10,681,843 single-base substitutions. In addition, mechanistic template mutation matrices (MTMMs) representing oxidative DNA damage, ultraviolet-induced DNA damage, (5m)CpG deamination, and APOBEC-mediated cytosine mutation, are presented. MTMMs were mapped to the individual tumor SAMMs to determine the maximum contribution of each mutational mechanism to the overall mutation pattern. A Manhattan distance across all SAMM elements between any two tumor genomes was used to determine their relative distance. Employing this metric, 89.5% of all tumor genomes were found to have a nearest neighbor from the same tissue of origin. When a distance-dependent 6-nearest neighbor classifier was used, 10.4% of the SAMMs had an Undetermined tissue of origin, and 92.2% of the remaining SAMMs were assigned to the correct tissue of origin. [corrected]. Thus, although tumors from different tissues may have similar mutation patterns, their SAMMs often display signatures that are characteristic of specific tissues.
authors.affiliation: In Silico Research Centers of Excellence, Advanced Biomedical Computing Center, Information Systems Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., P.O. Box B, Frederick, MD, 21702, USA.
authors.forename: Nuri A
authors.initials: NA
authors.lastname: Temiz
authors.forename: Duncan E
authors.initials: DE
authors.lastname: Donohue
authors.forename: Albino
authors.initials: A
authors.lastname: Bacolla
authors.forename: Karen M
authors.initials: KM
authors.lastname: Vasquez
authors.forename: David N
authors.initials: DN
authors.lastname: Cooper
authors.forename: Uma
authors.initials: U
authors.lastname: Mudunuri
authors.forename: Joseph
authors.initials: J
authors.lastname: Ivanic
authors.forename: Regina Z
authors.initials: RZ
authors.lastname: Cer
authors.forename: Ming
authors.initials: M
authors.lastname: Yi
authors.forename: Robert M
authors.initials: RM
authors.lastname: Stephens
authors.forename: Jack R
authors.initials: JR
authors.lastname: Collins
authors.forename: Brian T
authors.initials: BT
authors.lastname: Luke
mesh_terms: D004249:DNA Damage; D004273:DNA, Neoplasm / Q000235:genetics*; D030541:Databases, Genetic; D005260:Female; D015894:Genome, Human; D006801:Humans; D008297:Male; D020125:Mutation, Missense; D009369:Neoplasms / Q000235:genetics*
chemical_list: D004273:DNA, Neoplasm