Engineered in-vitro cell line mixtures and robust evaluation of computational methods for clonal decomposition and longitudinal dynamics in cancer.

ABSTRACT: Characterization and quantification of tumour clonal populations over time via longitudinal sampling are essential components in understanding and predicting the response to therapeutic interventions. Computational methods for inferring tumour clonal composition from deep-targeted sequencing data are ubiquitous, however due to the lack of a ground truth biological data, evaluating their performance is difficult. In this work, we generate a benchmark data set that simulates tumour longitudinal growth and heterogeneity by in vitro mixing of cancer cell lines with known proportions. We apply four different algorithms to our ground truth data set and assess their performance in inferring clonal composition using different metrics. We also analyse the performance of these algorithms on breast t

SUBMITTER: Farahani H 

PROVIDER: S-EPMC5647443 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature