Quantification of subclonal selection in cancer from bulk sequencing data.
View/ Open
Date
2018-05-28Author
Williams, MJ
Werner, B
Heide, T
Curtis, C
Barnes, CP
Sottoriva, A
Graham, TA
Type
Journal Article
Metadata
Show full item recordAbstract
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.
Collections
Subject
Humans
Neoplasms
Cell Proliferation
High-Throughput Screening Assays
High-Throughput Nucleotide Sequencing
Research team
Evolutionary Genomics & Modelling
Language
eng
Date accepted
2018-03-23
License start date
2018-06
Citation
Nature genetics, 2018, 50 (6), pp. 895 - 903
Publisher
NATURE PUBLISHING GROUP