SCONCE: A method for profiling Copy Number Alterations in Cancer Evolution using Single Cell Whole Genome Sequencing.

ABSTRACT:

Motivation

Copy number alterations are a significant driver in cancer growth and development, but remain poorly characterized on the single cell level. Although genome evolution in cancer cells is Markovian through evolutionary time, copy number alterations are not Markovian along the genome. However, existing methods call copy number profiles with Hidden Markov Models or change point detection algorithms based on changes in observed read depth, corrected by genome content, and do not account for the stochastic evolutionary process.

Results

We present a theoretical framework to use tumor evolutionary history to accurately call copy number alterations in a principled manner. In order to model the tumor evolutionary process and account for technical noise from low coverage single cell whole genome sequencing data, we developed SCONCE, a method based on a Hidden Markov Model to analyze read depth data from tumor cells using matched normal cells as negative controls. Using a combination of public data sets and simulations, we show SCONCE accurately decodes copy number profiles, and provides a useful tool for understanding tumor evolution.

Availability

SCONCE is implemented in C++ 11 and is freely available from https://github.com/NielsenBerkeleyLab/sconce.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Hui S 

PROVIDER: S-EPMC8963318 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature