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Edited by David O. Siegmund, Stanford University, Stanford, CA, and approved July 12, 2016 (received for review November 10, 2015)
Significance
Cancer is a disease driven by rounds of genetic and epigenetic mutations that follow Darwinian evolution. The tumor for a given patient is often a mixture of multiple genotypically and phenotypically distinct cell populations. This contributes to failures of targeted therapies and to drug resistance, and thus it is important to study intratumor heterogeneity. Here, we propose Canopy, a statistical framework to reconstruct tumor phylogeny by next-generation sequencing data from temporally and/or spatially separated tumor resections from the same patient. We show that such analyses lead to the identification of potentially useful prognostic/diagnostic biomarkers and successfully recover the tumor’s evolutionary history, validated by single-cell sequencing. Canopy provides a rigorous foundation for statistical analysis of repeated sequencing data from evolving populations.
Abstract
Cancer is a disease driven by evolutionary selection on somatic genetic and epigenetic alterations. Here, we propose Canopy, a method for inferring the evolutionary phylogeny of a tumor using both somatic copy number alterations and single-nucleotide alterations from one or more samples derived from a single patient. Canopy is applied to bulk sequencing datasets of both longitudinal and spatial experimental designs and to a transplantable metastasis model derived from human cancer cell line MDA-MB-231. Canopy successfully identifies cell populations and infers phylogenies that are in concordance with existing knowledge and ground truth. Through simulations, we explore the effects of key parameters on deconvolution accuracy and compare against existing methods. Canopy is an open-source R package available at https://cran.r-project.org/web/packages/Canopy/.
Footnotes
- ↵1To whom correspondence should be addressed. Email: nzh{at}wharton.upenn.edu.
Author contributions: Y.J. and N.R.Z. formulated the model; Y.J. developed the algorithm and methods; Y.J. and N.R.Z. planned and executed the simulations studies and the analysis of the human breast cancer xenograft dataset; Y.J., Y.Q., A.J.M., and N.R.Z. generated and analyzed the breast cancer cell line dataset; and Y.J. and N.R.Z. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Data deposition: The whole-exome sequencing data of the transplantable metastasis model derived from MDA-MB-231 have been deposited in the BioProject database, www.ncbi.nlm.nih.gov/bioproject (accession no. PRJNA315318).
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1522203113/-/DCSupplemental.
Freely available online through the PNAS open access option.
Intratumor heterogeneity and phylogeny inference
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