UV signature mutations

Abstract

Sequencing complete tumor genomes and exomes has sparked the cancer field's interest in mutation signatures for identifying the tumor's carcinogen. This review and meta-analysis discusses signatures and their proper use. We first distinguish between a mutagen's canonical mutations—deviations from a random distribution of base changes to create a pattern typical of that mutagen—and the subset of signature mutations, which are unique to that mutagen and permit inference backward from mutations to mutagen. To verify UV signature mutations, we assembled literature datasets on cells exposed to UVC, UVB, UVA, or solar simulator light (SSL) and tested canonical UV mutation features as criteria for clustering datasets. A confirmed UV signature was: ≥60% of mutations are C→T at a dipyrimidine site, with ≥5% CC→TT. Other canonical features such as a bias for mutations on the nontranscribed strand or at the 3' pyrimidine had limited application. The most robust classifier combined these features with criteria for the rarity of non-UV canonical mutations. In addition, several signatures proposed for specific UV wavelengths were limited to specific genes or species; UV's nonsignature mutations may cause melanoma BRAF mutations; and the mutagen for sunlight-related skin neoplasms may vary between continents.

Figure 1

Inverse relationship of canonical mutation patterns and mutation signatures for inferring the mutagen from mutations. Two mutagens are illustrated. A mutagen's canonical mutations deviate from random base changes, establishing a pattern typical for that mutagen. Different mutagens can produce the same canonical mutations (non-informative mutations). Signature mutations are the subset of canonical mutations that, in addition, are unique to that mutagen and permit inference backward from mutations to mutagen. A mutagen therefore produces signature mutations plus non-informative mutations. The latter are real and were produced by the mutagen, but are not useful for identifying that mutagen or carcinogen.

Figure 2

Heat map of canonical mutation patterns after UVC, UVB, UVA, SSL, and chemical carcinogens. When many gene targets are analyzed, the canonical mutation patterns of all four UV wavelength regions are similar. Yet they differ from the chemical mutagens. Finer patterns are discussed in the text. Colors: dark blue, row minimum; white, row average; dark red, row maximum. Lines, distinctive subsets. Abbreviations: diPY, the mutated base was a member of a dipyrimidine site; C->T, C → T mutation (G:C → A:T); Ts, transition mutation (Py:Pu → Py:Pu); CG muts, the mutated base was a member of a CG dinucleotide; NTS, nontranscribed strand.

Figure 3

Clustering of canonical mutation patterns by non-negative matrix factorization (NMF). Colors indicate the degree of correlation between datasets listed on both the vertical and horizontal axes. Two to three stable groups were identifiable (dark red): a) repair-defective cells (any UV wavelength) + wild type UVC + UVB + SSL + Sun (US/Sweden) + UVA group I; b) Sun (Australia) + UVA group II + UVA-riboflavin + UVB&SSL hamster + BPDE + AFB₁; and c) Sun (Australia) + UVA group II + UVA-porphyrin + UVC&UVA hamster + H₂O₂. Clusters were separated from each other by vertical and horizontal bands representing datasets that clustered into one or the other of its neighbors on different runs (green, orange, and light blue bands). Abbreviations indicate a particular report's mutagen, gene target, author, and year; these are written in full in Table S1.

Figure 4

Discrimination of UV-induced mutations from chemically-induced mutations is enhanced by including negative-control mutation features. a) Using only the 2 canonical UV features, “dipyrimidine site” and “C→T”, yielded clusters that showed much greater dispersion than in Fig. 3 and it intermixed datasets that had been separated in the NMF analysis when using 9 features. Dispersion is apparent visually and as a lower cophenetic score. Dispersion was even greater when three clusters were stipulated at the outset. b) Adding 2 additional UVlike features, “transition mutations” and “CG mutations that are at PyCG>T”, improved dispersion slightly. c) Much greater resolution, revealing three clusters, was obtained by instead supplementing the 2 original canonical UV features with 2 negative controls: features rare with UV (A:T→C:G and G:C→T:A) (lower row). These are non-signature canonical UV mutations and non-UV canonical mutations.