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

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...

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Bibliographic Details
Published in:PloS one 2019-01, Vol.14 (1), p.e0211213-e0211213
Main Authors: Ogundijo, Oyetunji E, Zhu, Kaiyi, Wang, Xiaodong, Anastassiou, Dimitris
Format: Article
Language:English
Subjects:
Algorithms
Binomial distribution
Biology
Biology and Life Sciences
Breast cancer
Cancer
Cell Count
Clone Cells - chemistry
Clone Cells - cytology
Computer and Information Sciences
Computer simulation
Consortia
Control theory
Disease Progression
Downloading
Electrical engineering
Gene expression
Genetic aspects
Genetic variation
Genotype
Humans
Lung cancer
Medical research
Medicine and Health Sciences
Models, Theoretical
Monte Carlo Method
Monte Carlo methods
Mutation
Neoplasms - genetics
Parameter estimation
Phylogeny
Physical Sciences
Population
Prostate cancer
Research and Analysis Methods
State space models
Subpopulations
Tumors
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Summary: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 high accuracy, in most cases, and compares favorably with existing methods. Moreover, we demonstrated the validity of our method through analyzing real tumor samples from patients from multiple cancer types (breast, prostate, and lung). Our results reveal driver mutation events specific to cancer types, and indicate clonal expansion by manual phylogenetic analysis. MATLAB code and datasets are available to download at: https://github.com/moyanre/tumor_clones.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0211213