Dataset Information

Ancestral inference in tumors: how much can we know?

ABSTRACT: A tumor is thought to start from a single cell and genome. Yet genomes in the final tumor are typically heterogeneous. The mystery of this intratumoral heterogeneity (ITH) has not yet been uncovered, but much of this ITH may be secondary to replication errors. Methylation of cytosine bases often exhibits ITH and therefore may encode the ancestry of the tumor. In this study, we measure the passenger methylation patterns of a specific CpG region in 9 colorectal tumors by bisulfite sequencing and apply a tumor development model. Based on our model, we are able to retrieve information regarding the ancestry of each tumor using approximate Bayesian computation. With a large simulation study we explore the conditions under which we can estimate the model parameters, and the initial state of the first transformed cell. Finally we apply our analysis to clinical data to gain insight into the dynamics of tumor formation.

SUBMITTER: Zhao J

PROVIDER: S-EPMC4138290 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Ancestral inference in tumors: how much can we know?

Zhao Junsong J Siegmund Kimberly D KD Shibata Darryl D Marjoram Paul P

Journal of theoretical biology 20140605

A tumor is thought to start from a single cell and genome. Yet genomes in the final tumor are typically heterogeneous. The mystery of this intratumoral heterogeneity (ITH) has not yet been uncovered, but much of this ITH may be secondary to replication errors. Methylation of cytosine bases often exhibits ITH and therefore may encode the ancestry of the tumor. In this study, we measure the passenger methylation patterns of a specific CpG region in 9 colorectal tumors by bisulfite sequencing and a ...[more]

PMID: 24907673

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Project description:The complex correlation structure of a collection of orthologous DNA sequences is uniquely captured by the "ancestral recombination graph" (ARG), a complete record of coalescence and recombination events in the history of the sample. However, existing methods for ARG inference are computationally intensive, highly approximate, or limited to small numbers of sequences, and, as a consequence, explicit ARG inference is rarely used in applied population genomics. Here, we introduce a new algorithm for ARG inference that is efficient enough to apply to dozens of complete mammalian genomes. The key idea of our approach is to sample an ARG of [Formula: see text] chromosomes conditional on an ARG of [Formula: see text] chromosomes, an operation we call "threading." Using techniques based on hidden Markov models, we can perform this threading operation exactly, up to the assumptions of the sequentially Markov coalescent and a discretization of time. An extension allows for threading of subtrees instead of individual sequences. Repeated application of these threading operations results in highly efficient Markov chain Monte Carlo samplers for ARGs. We have implemented these methods in a computer program called ARGweaver. Experiments with simulated data indicate that ARGweaver converges rapidly to the posterior distribution over ARGs and is effective in recovering various features of the ARG for dozens of sequences generated under realistic parameters for human populations. In applications of ARGweaver to 54 human genome sequences from Complete Genomics, we find clear signatures of natural selection, including regions of unusually ancient ancestry associated with balancing selection and reductions in allele age in sites under directional selection. The patterns we observe near protein-coding genes are consistent with a primary influence from background selection rather than hitchhiking, although we cannot rule out a contribution from recurrent selective sweeps.

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Dataset Information

Ancestral inference in tumors: how much can we know?

Publications

Ancestral inference in tumors: how much can we know?

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