A sequential Monte Carlo algorithm for inference of subclonal structure in cancer.

ABSTRACT: Tumors are heterogeneous in the sense that they consist of multiple subpopulations of cells, referred to as subclones, each of which is characterized by a distinct profile of genomic variations such as somatic mutations. Inferring the underlying clonal landscape has become an important topic in that it can help in understanding cancer development and progression, and thereby help in improving treatment. We describe a novel state-space model, based on the feature allocation framework and an efficient sequential Monte Carlo (SMC) algorithm, using the somatic mutation data obtained from tumor samples to estimate the number of subclones, as well as their characterization. Our approach, by design, is capable of handling any number of mutations. Via extensive simulations, our method exhibits hig

SUBMITTER: Ogundijo OE 

PROVIDER: S-EPMC6347199 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature