The MOBSTER R package for tumour subclonal deconvolution from bulk DNA whole-genome sequencing data
- PDF / 1,877,387 Bytes
- 11 Pages / 595.276 x 790.866 pts Page_size
- 25 Downloads / 234 Views
Open Access
SOFTWARE
The MOBSTER R package for tumour subclonal deconvolution from bulk DNA whole‑genome sequencing data Giulio Caravagna1,4* , Guido Sanguinetti2, Trevor A. Graham3 and Andrea Sottoriva4* *Correspondence: [email protected]; andrea. [email protected] 1 University of Trieste, Trieste, Italy 4 The Institute of Cancer Research, London, UK Full list of author information is available at the end of the article
Abstract Background: The large-scale availability of whole-genome sequencing profiles from bulk DNA sequencing of cancer tissues is fueling the application of evolutionary theory to cancer. From a bulk biopsy, subclonal deconvolution methods are used to determine the composition of cancer subpopulations in the biopsy sample, a fundamental step to determine clonal expansions and their evolutionary trajectories. Results: In a recent work we have developed a new model-based approach to carry out subclonal deconvolution from the site frequency spectrum of somatic mutations. This new method integrates, for the first time, an explicit model for neutral evolutionary forces that participate in clonal expansions; in that work we have also shown that our method improves largely over competing data-driven methods. In this Software paper we present mobster, an open source R package built around our new deconvolution approach, which provides several functions to plot data and fit models, assess their confidence and compute further evolutionary analyses that relate to subclonal deconvolution. Conclusions: We present the mobster package for tumour subclonal deconvolution from bulk sequencing, the first approach to integrate Machine Learning and Population Genetics which can explicitly model co-existing neutral and positive selection in cancer. We showcase the analysis of two datasets, one simulated and one from a breast cancer patient, and overview all package functionalities. Keywords: Tumour subclonal deconvolution, Cancer evolution, Population genetics, Dirichlet mixture model, Whole-genome DNA sequencing
Background One of the most exciting recent developments in cancer informatics is the ability to reconstruct the evolutionary history and clonal composition of tumours from wholegenome DNA sequencing (WGS) data [1, 2]. This analysis leverages statistical models and bioinformatics tools that can recapitulate patient-level intra-tumour heterogeneity, and that we can use to study, from an evolutionary point of view, tumour evolutionary patterns across multiple patients [3–6]. An investigation of the evolutionary forces underpinning a tumour usually begins by performing a subclonal deconvolution of the © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third
Data Loading...
