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Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig.

ABSTRACT: The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3-5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes.

SUBMITTER: Rubanova Y

PROVIDER: S-EPMC7002414 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig.

Rubanova Yulia Y Shi Ruian R Harrigan Caitlin F CF Li Roujia R Wintersinger Jeff J Sahin Nil N Deshwar Amit G AG Morris Quaid D QD

Nature communications 20200205 1

The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-geno ...[more]

PMID: 32024834

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Project description:Resistance to the frontline antibiotic rifampicin constitutes a challenge to the treatment and control of tuberculosis. Here, we analyzed the mutational landscape of Mycobacterium smegmatis during long-term evolution with increasing concentrations of rifampicin, using a mutation accumulation assay combined with whole-genome sequencing. Antibiotic treatment enhanced the acquisition of mutations, doubling the genome-wide mutation rate of the wild-type cells. While antibiotic exposure led to extinction of almost all wild-type lines, the hypermutable phenotype of the ΔnucS mutant strain (noncanonical mismatch repair deficient) provided an efficient response to the antibiotic, leading to high rates of survival. This adaptative advantage resulted in the emergence of higher levels of rifampicin resistance, an accelerated acquisition of drug resistance mutations in rpoB (β RNA polymerase), and a wider diversity of evolutionary pathways that led to drug resistance. Finally, this approach revealed a subset of adaptive genes under positive selection with rifampicin that could be associated with the development of antibiotic resistance. IMPORTANCE Rifampicin is the most important first-line antibiotic against mycobacterial infections, including tuberculosis, one of the top causes of death worldwide. Acquisition of rifampicin resistance constitutes a major global public health problem that makes the control of the disease challenging. Here, we performed an experimental evolution assay under antibiotic selection to analyze the response and adaptation of mycobacteria, leading to the acquisition of rifampicin resistance. This approach explored the total number of mutations that arose in the mycobacterial genomes under long-term rifampicin exposure, using whole-genome sequencing. Our results revealed the effect of rifampicin at a genomic level, identifying different mechanisms and multiple pathways leading to rifampicin resistance in mycobacteria. Moreover, this study detected that an increase in the rate of mutations led to enhanced levels of drug resistance and survival. In summary, all of these results could be useful to understand and prevent the emergence of drug-resistant isolates in mycobacterial infections.

Project description:The clonal relationship between ovarian high-grade serous carcinoma (HGSC) and its presumed precursor lesion, serous tubal intraepithelial carcinoma (STIC), has been reported. However, when analyzing patients with concurrent ovarian carcinoma and tubal lesion, the extensive carcinoma tissues present at diagnosis may have effaced the natural habitat of precursor clone(s), obscuring tumor clonal evolutionary history, or may have disseminated to anatomically adjacent fimbriae ends, masquerading as precursor lesions. To circumvent these limitations, we analyzed the genomic landscape of incidental tubal precursor lesions including p53 signature, dormant STIC or serous tubal intraepithelial lesion (STIL) and proliferative STIC in women without ovarian carcinoma or any cancer diagnosis using whole-exome sequencing and amplicon sequencing. In three of the four cancer-free women with multiple discrete tubal lesions we observed non-identical TP53 mutations between precursor lesions from the same individual. In one of the four women with co-existing ovarian HGSC and tubal precursor lesion we found non-identical TP53 mutations and a lack of common mutations shared between her precursor lesion and carcinoma. Analyzing the evolutionary history of multiple tubal lesions in the same four patients with concurrent ovarian carcinoma indicated distinct evolution trajectories. Collectively, the results support diverse clonal origins of tubal precursor lesions at the very early stages of tumorigenesis. Mathematical modeling based on lesion-specific proliferation rates indicated that p53 signature and dormant STIC may take a prolonged time (two decades or more) to develop into STIC, whereas STIC may progress to carcinoma in a much shorter time (6 years). The above findings may have implications for future research aimed at prevention and early detection of ovarian cancer. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Dataset Information

Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig.

Publications

Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig.

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