A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data.

ABSTRACT:

Motivation

Tumours are composed of distinct cancer cell populations (clones), which continuously adapt to their local micro-environment. Standard methods for clonal deconvolution seek to identify groups of mutations and estimate the prevalence of each group in the tumour, while considering its purity and copy number profile. These methods have been applied on cross-sectional data and on longitudinal data after discarding information on the timing of sample collection. Two key questions are how can we incorporate such information in our analyses and is there any benefit in doing so?

Results

We developed a clonal deconvolution method, which incorporates explicitly the temporal spacing of longitudinally sampled tumours. By merging a Dirichlet Process Mixture Model with Gaussian

SUBMITTER: Vavoulis DV 

PROVIDER: S-EPMC8055230 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature