BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies

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Abstract

Cancer has long been understood as a somatic evolutionary process, but many details of tumor progression remain elusive. Here, we present BitPhylogeny, a probabilistic framework to reconstruct intra-tumor evolutionary pathways. Using a full Bayesian approach, we jointly estimate the number and composition of clones in the sample as well as the most likely tree connecting them. We validate our approach in the controlled setting of a simulation study and compare it against several competing methods. In two case studies, we demonstrate how BitPhylogeny reconstructs tumor phylogenies from methylation patterns in colon cancer and from single-cell exomes in myeloproliferative neoplasm.

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Most cited references 39

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The clonal evolution of tumor cell populations.

P C Nowell (1976)

It is proposed that most neoplasms arise from a single cell of origin, and tumor progression results from acquired genetic variability within the original clone allowing sequential selection of more aggressive sublines. Tumor cell populations are apparently more genetically unstable than normal cells, perhaps from activation of specific gene loci in the neoplasm, continued presence of carcinogen, or even nutritional deficiencies within the tumor. The acquired genetic insta0ility and associated selection process, most readily recognized cytogenetically, results in advanced human malignancies being highly individual karyotypically and biologically. Hence, each patient's cancer may require individual specific therapy, and even this may be thwarted by emergence of a genetically variant subline resistant to the treatment. More research should be directed toward understanding and controlling the evolutionary process in tumors before it reaches the late stage usually seen in clinical cancer.

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Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE

Peng-Fei Qiu, Erin Simonds, Sean Bendall … (2011)

Multiparametric single-cell analysis is critical for understanding cellular heterogeneity. Despite recent technological advances in single-cell measurements, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system under investigation. To objectively uncover cellular heterogeneity from single-cell measurements, we present a novel computational approach, Spanning-tree Progression Analysis of Density-normalized Events (SPADE). We applied SPADE to cytometry data of mouse and human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. In addition, SPADE produced a map of intracellular signal activation across the landscape of human hematopoietic development. SPADE revealed a functionally distinct cell population, natural killer (NK) cells, without using any NK-specific parameters. SPADE is a versatile method that facilitates the analysis of cellular heterogeneity, the identification of cell types, and comparison of functional markers in response to perturbations.

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Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor.

Xun Xu, Yong Hou, Xuyang Yin … (2012)

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and has very few mutations that are shared between different patients. To better understand the intratumoral genetics underlying mutations of ccRCC, we carried out single-cell exome sequencing on a ccRCC tumor and its adjacent kidney tissue. Our data indicate that this tumor was unlikely to have resulted from mutations in VHL and PBRM1. Quantitative population genetic analysis indicates that the tumor did not contain any significant clonal subpopulations and also showed that mutations that had different allele frequencies within the population also had different mutation spectrums. Analyses of these data allowed us to delineate a detailed intratumoral genetic landscape at a single-cell level. Our pilot study demonstrates that ccRCC may be more genetically complex than previously thought and provides information that can lead to new ways to investigate individual tumors, with the aim of developing more effective cellular targeted therapies. Copyright Â© 2012 Elsevier Inc. All rights reserved.

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Author and article information

Contributors

Ke Yuan: ke.yuan@cruk.cam.ac.uk

Thomas Sakoparnig: thomas.sakoparnig@unibas.ch

Florian Markowetz: florian.markowetz@cruk.cam.ac.uk

Niko Beerenwinkel: niko.beerenwinkel@bsse.ethz.ch

Journal

Journal ID (nlm-ta): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1465-6906

ISSN (Electronic): 1465-6914

Publication date (Electronic): 13 February 2015

Publication date PMC-release: 13 February 2015

Publication date (Print): 2015

Volume: 16

Issue: 1

Electronic Location Identifier: 36

Affiliations

[ ]University of Cambridge, Cancer Research UK Cambridge Institute, Cambridge, UK

[ ]Department of Biosystems Science and Engineering, ETH Zurich, Basel Switzerland

[ ]SIB Swiss Institute of Bioinformatics, Basel, Switzerland

[ ]Current address: Biozentrum, University of Basel, Basel, Switzerland

Article

Publisher ID: 592

DOI: 10.1186/s13059-015-0592-6

PMC ID: 4359483

PubMed ID: 25786108

SO-VID: 3a9941e9-1017-4503-9662-f497920e69c0

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 12 November 2014

Date accepted : 21 January 2015

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ScienceOpen disciplines: Genetics

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BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies

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Evolutionary Cell Biology

Most cited references 39

The clonal evolution of tumor cell populations.

Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE

Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor.

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Cited by 52

Most referenced authors 827