Layla Oesper

THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data

Tumor samples are typically heterogeneous, containing admixture by normal, non-cancerous cells and one or more subpopulations of cancerous cells. Whole-genome sequencing of a tumor sample yields reads from this mixture, but does not directly reveal the cell of origin for each read. We introduce THetA (Tumor Heterogeneity Analysis), an algorithm that infers the most likely collection of genomes and their proportions in a sample, for the case where copy number aberrations distinguish subpopulations. THetA successfully estimates normal admixture and recovers clonal and subclonal copy number aberrations in real and simulated sequencing data. THetA is available at http://compbio.cs.brown.edu/software/

Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine

High-throughput DNA sequencing is revolutionizing the study of cancer and enabling the measurement of the somatic mutations that drive cancer development. However, the resulting sequencing datasets are large and complex, obscuring the clinically important mutations in a background of errors, noise, and random mutations. Here, we review computational approaches to identify somatic mutations in cancer genome sequences and to distinguish the driver mutations that are responsible for cancer from random, passenger mutations. First, we describe approaches to detect somatic mutations from high-throughput DNA sequencing data, particularly for tumor samples that comprise heterogeneous populations of cells. Next, we review computational approaches that aim to predict driver mutations according to their frequency of occurrence in a cohort of samples, or according to their predicted functional impact on protein sequence or structure. Finally, we review techniques to identify recurrent combinations of somatic mutations, including approaches that examine mutations in known pathways or protein-interaction networks, as well as de novo approaches that identify combinations of mutations according to statistical patterns of mutual exclusivity. These techniques, coupled with advances in high-throughput DNA sequencing, are enabling precision medicine approaches to the diagnosis and treatment of cancer.

Quantifying tumor heterogeneity in whole-genome and whole-exome sequencing data

MOTIVATION Most tumor samples are a heterogeneous mixture of cells, including admixture by normal (non-cancerous) cells and subpopulations of cancerous cells with different complements of somatic aberrations. This intra-tumor heterogeneity complicates the analysis of somatic aberrations in DNA sequencing data from tumor samples. RESULTS We describe an algorithm called THetA2 that infers the composition of a tumor sample-including not only tumor purity but also the number and content of tumor subpopulations-directly from both whole-genome (WGS) and whole-exome (WXS) high-throughput DNA sequencing data. This algorithm builds on our earlier Tumor Heterogeneity Analysis (THetA) algorithm in several important directions. These include improved ability to analyze highly rearranged genomes using a variety of data types: both WGS sequencing (including low ∼7× coverage) and WXS sequencing. We apply our improved THetA2 algorithm to WGS (including low-pass) and WXS sequence data from 18 samples from The Cancer Genome Atlas (TCGA). We find that the improved algorithm is substantially faster and identifies numerous tumor samples containing subclonal populations in the TCGA data, including in one highly rearranged sample for which other tumor purity estimation algorithms were unable to estimate tumor purity.

Reconstruction of clonal trees and tumor composition from multi-sample sequencing data

Motivation: DNA sequencing of multiple samples from the same tumor provides data to analyze the process of clonal evolution in the population of cells that give rise to a tumor. Results: We formalize the problem of reconstructing the clonal evolution of a tumor using single-nucleotide mutations as the variant allele frequency (VAF) factorization problem. We derive a combinatorial characterization of the solutions to this problem and show that the problem is NP-complete. We derive an integer linear programming solution to the VAF factorization problem in the case of error-free data and extend this solution to real data with a probabilistic model for errors. The resulting AncesTree algorithm is better able to identify ancestral relationships between individual mutations than existing approaches, particularly in ultra-deep sequencing data when high read counts for mutations yield high confidence VAFs. Availability and implementation: An implementation of AncesTree is available at: http://compbio.cs.brown.edu/software. Contact: braphael@brown.edu Supplementary information: Supplementary data are available at Bioinformatics online.

WordCloud: a Cytoscape plugin to create a visual semantic summary of networks

BackgroundWhen biological networks are studied, it is common to look for clusters, i.e. sets of nodes that are highly inter-connected. To understand the biological meaning of a cluster, the user usually has to sift through many textual annotations that are associated with biological entities.FindingsThe WordCloud Cytoscape plugin generates a visual summary of these annotations by displaying them as a tag cloud, where more frequent words are displayed using a larger font size. Word co-occurrence in a phrase can be visualized by arranging words in clusters or as a network.ConclusionsWordCloud provides a concise visual summary of annotations which is helpful for network analysis and interpretation. WordCloud is freely available at http://baderlab.org/Software/WordCloudPlugin

Inferring intra-tumor heterogeneity from high-throughput DNA sequencing data

Cancer is a disease driven in part by somatic mutations that accumulate during the lifetime of an individual. The clonal theory [1] posits that the cancerous cells in a tumor are descended from a single founder cell and that descendants of this cell acquired multiple mutations beneficial for tumor growth through rounds of selection and clonal expansion. A tumor is thus a heterogeneous population of cells, with different subpopulations of cells containing both clonal mutations from the founder cell or early rounds of clonal expansion, and subclonal mutations that occurred after the most recent clonal expansion. Most cancer sequencing projects sequence a mixture of cells from a tumor sample including admixture by normal (non-cancerous) cells and different subpopulations of cancerous cells. In addition most solid tumors exhibit extensive aneuploidy and copy number aberrations. Intra-tumor heterogeneity and aneuploidy conspire to complicate analysis of somatic mutations in sequenced tumor samples.

Open adjacencies and k-breaks: detecting simultaneous rearrangements in cancer genomes

BackgroundThe evolution of a cancer genome has traditionally been described as a sequential accumulation of mutations - including chromosomal rearrangements - over a period of time. Recent research suggests, however, that numerous rearrangements may be acquired simultaneously during a single cataclysmic event, leading to the proposal of new mechanisms of rearrangement such as chromothripsis and chromoplexy.ResultsWe introduce two measures, open adjacency rate (OAR) and copy-number asymmetry enrichment (CAE), that assess the prevalence of simultaneously formed breakpoints, or k-breaks with k >2, compared to the sequential accumulation of standard rearrangements, or 2-breaks. We apply the OAR and the CAE to genome sequencing data from 121 cancer genomes from two different studies.ConclusionsWe find that the OAR and CAE correlate well with previous analyses of chromothripsis/chromoplexy but make differing predictions on a small subset of genomes. These results lend support to the existence of simultaneous rearrangements, but also demonstrate the difficulty of characterizing such rearrangements using different criterion.

Identifying simultaneous rearrangements in cancer genomes

Abstract Motivation The traditional view of cancer evolution states that a cancer genome accumulates a sequential ordering of mutations over a long period of time. However, in recent years it has been suggested that a cancer genome may instead undergo a one-time catastrophic event, such as chromothripsis, where a large number of mutations instead occur simultaneously. A number of potential signatures of chromothripsis have been proposed. In this work, we provide a rigorous formulation and analysis of the ‘ability to walk the derivative chromosome’ signature originally proposed by Korbel and Campbell. In particular, we show that this signature, as originally envisioned, may not always be present in a chromothripsis genome and we provide a precise quantification of under what circumstances it would be present. We also propose a variation on this signature, the H/T alternating fraction, which allows us to overcome some of the limitations of the original signature. Results We apply our measure to both simulated data and a previously analyzed real cancer dataset and find that the H/T alternating fraction may provide useful signal for distinguishing genomes having acquired mutations simultaneously from those acquired in a sequential fashion. Availability and implementation An implementation of the H/T alternating fraction is available at https://bitbucket.org/oesperlab/ht-altfrac. Supplementary information Supplementary data are available at Bioinformatics online.

A Consensus Approach to Infer Tumor Evolutionary Histories

Inspired by recent efforts to model cancer evolution with phylogenetic trees, we consider the problem of finding a consensus tumor evolution tree from a set of conflicting input trees. In contrast to traditional phylogenetic trees, the tumor trees we consider contain features such as mutation labels on internal vertices (in addition to the leaves) and allow multiple mutations to label a single vertex. We describe several distance measures between these tumor trees and present an algorithm to solve the consensus problem called GraPhyC. Our approach uses a weighted directed graph where vertices are sets of mutations and edges are weighted using a function that depends on the number of times a parental relationship is observed between their constituent mutations in the set of input trees. We find a minimum weight spanning arborescence in this graph and prove that the resulting tree minimizes the total distance to all input trees for one of our presented distance measures. We evaluate our GraPhyC method using both simulated and real data. On simulated data we show that our method outperforms a baseline method at finding an appropriate representative tree. Using a set of tumor trees derived from both whole-genome and deep sequencing data from a Chronic Lymphocytic Leukemia patient we find that our approach identifies a tree not included in the set of input trees, but that contains characteristics supported by other reported evolutionary reconstructions of this tumor.

Workshop: Reconstructing the organization of cancer genomes

We apply our algorithms to multiple datasets. We apply PREGO to ovarian carcinoma samples from The Cancer Genome Atlas (TCGA) [3] and and identify rearrangements consistent with known mechanisms of duplication such as tandem duplications and breakage/fusion/bridge (B/F/B) cycles.