Format

Send to

Choose Destination
Nat Genet. 2018 Jun;50(6):895-903. doi: 10.1038/s41588-018-0128-6. Epub 2018 May 28.

Quantification of subclonal selection in cancer from bulk sequencing data.

Author information

1
Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London, UK.
2
Department of Cell and Developmental Biology, University College London, London, UK.
3
Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), University College London, London, UK.
4
Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, Institute of Cancer Research, London, UK.
5
Departments of Medicine and Genetics, Stanford University School of Medicine, Stanford, CA, USA.
6
Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
7
Department of Cell and Developmental Biology, University College London, London, UK. christopher.barnes@ucl.ac.uk.
8
UCL Genetics Institute, University College London, London, UK. christopher.barnes@ucl.ac.uk.
9
Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, Institute of Cancer Research, London, UK. andrea.sottoriva@icr.ac.uk.
10
Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London, UK. t.graham@qmul.ac.uk.

Abstract

Subclonal architectures are prevalent across cancer types. However, the temporal evolutionary dynamics that produce tumor subclones remain unknown. Here we measure clone dynamics in human cancers by using computational modeling of subclonal selection and theoretical population genetics applied to high-throughput sequencing data. Our method determined the detectable subclonal architecture of tumor samples and simultaneously measured the selective advantage and time of appearance of each subclone. We demonstrate the accuracy of our approach and the extent to which evolutionary dynamics are recorded in the genome. Application of our method to high-depth sequencing data from breast, gastric, blood, colon and lung cancer samples, as well as metastatic deposits, showed that detectable subclones under selection, when present, consistently emerged early during tumor growth and had a large fitness advantage (>20%). Our quantitative framework provides new insight into the evolutionary trajectories of human cancers and facilitates predictive measurements in individual tumors from widely available sequencing data.

PMID:
29808029
PMCID:
PMC6475346
DOI:
10.1038/s41588-018-0128-6
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center