In:
Science, American Association for the Advancement of Science (AAAS), Vol. 376, No. 6591 ( 2022-04-22)
Abstract:
Mutational signatures—imprints of DNA damage and repair processes that have been operative during tumorigenesis—provide insights into environmental and endogenous causes of each patient’s cancer. Cancer genome sequencing studies permit exploration of mutational signatures. We investigated a very large number of whole-genome–sequenced cancers of many tumor types, substantially more than in previous efforts, to comprehensively reinforce our understanding of mutational signatures. RATIONALE We present mutational signature analyses of 12,222 whole-genome–sequenced cancers collected prospectively via the UK National Health Service (NHS) for the 100,000 Genomes Project. We identified single-base substitution (SBS) and double-base substitution (DBS) signatures independently in each organ. Exploiting this unusually large cohort, we developed a method to enhance discrimination of common mutational processes from rare, lower-frequency mutagenic processes. We validated our findings by independently performing analyses with data from two publicly available cohorts: 3001 primary cancers from the International Cancer Genome Consortium (ICGC) and 3417 metastatic cancers from the Hartwig Medical Foundation. We produced a set of reference signatures by comparing and contrasting the independently derived tissue-specific signatures and performing clustering analysis to unite mutational signatures from different tissues that could be due to similar processes. We included additional quality control measures such as dimensionality reduction of mixed signatures and gathered evidence that could help elucidate mechanisms and etiologies such as transcriptional and replication strand bias, associations with somatic drivers, and germline predisposition mutations. We also investigated additional mutation context and examined past clinical and treatment histories when possible, to explore potential etiologies. RESULTS Each organ contained a limited number of common SBS signatures (typically between 5 and 10). The number of common signatures was independent of cohort size. By contrast, the number of rare signatures was dependent on sample size, as the likelihood of detecting a rare signature is a function of its population prevalence. The same biological process produced slightly different signatures in diverse tissues, reinforcing that mutational signatures are tissue specific. Across organs, we clustered all tissue-specific signatures to ascertain mutational processes that were equivalent but occurring in different tissues (i.e., reference signatures). We obtained 82 high-confidence SBS reference signatures and 27 high-confidence DBS reference signatures. We compared these with previously reported mutational signatures, revealing 40 and 18 previously unidentified SBS and DBS signatures, respectively. Because we are cognizant of increasing complexity in mutational signatures and want to enable general users, we developed an algorithm called Signature Fit Multi-Step (FitMS) that seeks signatures in new samples while taking advantage of our recent findings. In a first step, FitMS detects common, organ-specific signatures; in a second step, it determines whether an additional rare signature is also present. CONCLUSION Mutational signature analysis of 18,640 cancers, the largest cohort of whole-genome–sequenced samples to date, has required methodological advances, permitting knowledge expansion. We have identified many previously unreported signatures and established the concept of common and rare signatures. The FitMS algorithm has been designed to exploit these advances to aid users in accurately identifying mutational processes in new samples. Discovery and application of common and rare mutational signatures. Analysis of three large whole-genome–sequenced cancer cohorts revealed that per-organ common signatures are limited in number, whereas numbers of rare signatures increase with increasing cohort size. Reference signatures permit comparisons across organs and cohorts. Henceforth, a new algorithm, FitMS, which accounts for common and rare signatures, can be used to analyze new samples. GEL, Genomics England cohort.
Type of Medium:
Online Resource
ISSN:
0036-8075
,
1095-9203
DOI:
10.1126/science.abl9283
Language:
English
Publisher:
American Association for the Advancement of Science (AAAS)
Publication Date:
2022
detail.hit.zdb_id:
128410-1
detail.hit.zdb_id:
2066996-3
detail.hit.zdb_id:
2060783-0
SSG:
11
