% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@ARTICLE{Rubanova:177483,
author = {Y. Rubanova and R. Shi and C. F. Harrigan and R. Li and J.
Wintersinger and N. Sahin and A. Deshwar and PCAWGEvolution
and H. W. Group and Q. Morris and PCAWGConsortium},
title = {{R}econstructing evolutionary trajectories of mutation
signature activities in cancer using {T}rack{S}ig.},
journal = {Nature Communications},
volume = {11},
number = {1},
issn = {2041-1723},
address = {[London]},
publisher = {Nature Publishing Group UK},
reportid = {DKFZ-2021-02570},
pages = {731},
year = {2020},
note = {siehe Correction: DKFZ Autoren affiliiert im PCAWG
Consortium: https://inrepo02.dkfz.de/record/212434 /
https://doi.org/10.1038/s41467-022-32336-7},
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.},
keywords = {Computational Biology: methods / Computer Simulation /
Evolution, Molecular / Gene Frequency / Genome, Human /
Humans / Mutation / Neoplasms: genetics / Neoplasms:
pathology / Polymorphism, Single Nucleotide / Whole Genome
Sequencing},
cin = {B370 / B330 / B240 / HD01 / B080 / B060 / B062 / B360 /
B260 / BE01 / B063 / B087 / W610 / W190 / B066},
ddc = {500},
cid = {I:(DE-He78)B370-20160331 / I:(DE-He78)B330-20160331 /
I:(DE-He78)B240-20160331 / I:(DE-He78)HD01-20160331 /
I:(DE-He78)B080-20160331 / I:(DE-He78)B060-20160331 /
I:(DE-He78)B062-20160331 / I:(DE-He78)B360-20160331 /
I:(DE-He78)B260-20160331 / I:(DE-He78)BE01-20160331 /
I:(DE-He78)B063-20160331 / I:(DE-He78)B087-20160331 /
I:(DE-He78)W610-20160331 / I:(DE-He78)W190-20160331 /
I:(DE-He78)B066-20160331},
pnm = {312 - Functional and structural genomics (POF3-312)},
pid = {G:(DE-HGF)POF3-312},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:32024834},
pmc = {pmc:PMC7002414},
doi = {10.1038/s41467-020-14352-7},
url = {https://inrepo02.dkfz.de/record/177483},
}