Computational Cancer Biology: An Evolutionary Perspective

ISSN:1553-734XISSN:1553-735

Inference of Tumor Phylogenies from Genomic Assays on Heterogeneous Samples

Tumorigenesis can in principle result from many combinations of mutations, but only a few roughly equivalent sequences of mutations, or “progression pathways,” seem to account for most human tumors. Phylogenetics provides a promising way to identify common progression pathways and markers of those pathways. This approach, however, can be confounded by the high heterogeneity within and between tumors, which makes it difficult to identify conserved progression stages or organize them into robust progression pathways. To tackle this problem, we previously developed methods for inferring progression stages from heterogeneous tumor profiles through computational unmixing. In this paper, we develop a novel pipeline for building trees of tumor evolution from the unmixed tumor data. The pipeline implements a statistical approach for identifying robust progression markers from unmixed tumor data and calling those markers in inferred cell states. The result is a set of phylogenetic characters and their assignments in progression states to which we apply maximum parsimony phylogenetic inference to infer tumor progression pathways. We demonstrate the full pipeline on simulated and real comparative genomic hybridization (CGH) data, validating its effectiveness and making novel predictions of major progression pathways and ancestral cell states in breast cancers

Analysis of the copy number profiles of several tumor samples from the same patient reveals the successive steps in tumorigenesis

We present a computational method, TuMult, for reconstructing the sequence of copy number changes driving carcinogenesis, based on the analysis of several tumor samples from the same patient. We demonstrate the reliability of the method with simulated data, and describe applications to three different cancers, showing that TuMult is a valuable tool for the establishment of clonal relationships between tumor samples and the identification of chromosome aberrations occurring at crucial steps in cancer progression

Cancer evolution: mathematical models and computational inference.

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.FM would like to acknowledge the support of The University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited.This is the final published version. It first appeared at http://sysbio.oxfordjournals.org/content/early/2014/10/07/sysbio.syu081.short?rss=1

Inferring progression models for CGH data

Motivation: One of the mutational processes that has been monitored genome-wide is the occurrence of regional DNA copy number alterations (CNAs), which may lead to deletion or over-expression of tumor suppressors or oncogenes, respectively. Understanding the relationship between CNAs and different cancer types is a fundamental problem in cancer studies. Results: This article develops an efficient method that can accurately model the progression of the cancer markers and reconstruct evolutionary relationship between multiple types of cancers using comparative genomic hybridization (CGH) data. Such modeling can lead to better understanding of the commonalities and differences between multiple cancer types and potential therapies. We have developed an automatic method to infer a graph model for the markers of multiple cancers from a large population of CGH data. Our method identifies highly related markers across different cancer types. It then builds a directed acyclic graph that shows the evolutionary history of these markers based on how common each marker is in different cancer types. We demonstrated the use of this model in determining the importance of markers in cancer evolution. We have also developed a new method to measure the evolutionary distance between different cancers based on their markers. This method employs the graph model we developed for the individual markers to measure the distance between pairs of cancers. We used this measure to create an evolutionary tree for multiple cancers. Our experiments on Progenetix database show that our markers are largely consistent to the reported hot-spot imbalances and most frequent imbalances. The results show that our distance measure can accurately reconstruct the evolutionary relationship between multiple cancer types. Availability: All the code developed in this article are available at http://bioinformatics.cise.ufl.edu/phylogeny.html. Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

TrAp: a Tree Approach for Fingerprinting Subclonal Tumor Composition

Revealing the clonal composition of a single tumor is essential for identifying cell subpopulations with metastatic potential in primary tumors or with resistance to therapies in metastatic tumors. Sequencing technologies provide an overview of an aggregate of numerous cells, rather than subclonal-specific quantification of aberrations such as single nucleotide variants (SNVs). Computational approaches to de-mix a single collective signal from the mixed cell population of a tumor sample into its individual components are currently not available. Herein we propose a framework for deconvolving data from a single genome-wide experiment to infer the composition, abundance and evolutionary paths of the underlying cell subpopulations of a tumor. The method is based on the plausible biological assumption that tumor progression is an evolutionary process where each individual aberration event stems from a unique subclone and is present in all its descendants subclones. We have developed an efficient algorithm (TrAp) for solving this mixture problem. In silico analyses show that TrAp correctly deconvolves mixed subpopulations when the number of subpopulations and the measurement errors are moderate. We demonstrate the applicability of the method using tumor karyotypes and somatic hypermutation datasets. We applied TrAp to SNV frequency profile from Exome-Seq experiment of a renal cell carcinoma tumor sample and compared the mutational profile of the inferred subpopulations to the mutational profiles of twenty single cells of the same tumor. Despite the large experimental noise, specific co-occurring mutations found in clones inferred by TrAp are also present in some of these single cells. Finally, we deconvolve Exome-Seq data from three distinct metastases from different body compartments of one melanoma patient and exhibit the evolutionary relationships of their subpopulations