TY  - JOUR
AU  - Rubanova, Yulia
AU  - Shi, Ruian
AU  - Harrigan, Caitlin F
AU  - Li, Roujia
AU  - Wintersinger, Jeff
AU  - Sahin, Nil
AU  - Deshwar, Amit
AU  - PCAWGEvolution
AU  - Group, Heterogeneity Working
AU  - Morris, Quaid
AU  - PCAWGConsortium
TI  - Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig.
JO  - Nature Communications
VL  - 11
IS  - 1
SN  - 2041-1723
CY  - [London]
PB  - Nature Publishing Group UK
M1  - DKFZ-2021-02570
SP  - 731
PY  - 2020
N1  - siehe Correction: DKFZ Autoren affiliiert im PCAWG Consortium: https://inrepo02.dkfz.de/record/212434   /    https://doi.org/10.1038/s41467-022-32336-7
AB  - 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
KW  - Computational Biology: methods
KW  - Computer Simulation
KW  - Evolution, Molecular
KW  - Gene Frequency
KW  - Genome, Human
KW  - Humans
KW  - Mutation
KW  - Neoplasms: genetics
KW  - Neoplasms: pathology
KW  - Polymorphism, Single Nucleotide
KW  - Whole Genome Sequencing
LB  - PUB:(DE-HGF)16
C6  - pmid:32024834
C2  - pmc:PMC7002414
DO  - DOI:10.1038/s41467-020-14352-7
UR  - https://inrepo02.dkfz.de/record/177483
ER  -