Biology subjects

Oesper, L.

Publications and source records attributed to Oesper, L..

4 recordsLinked to original sources

bioRxiv2025

Analyzing and Visualizing Distance Measures for Clonal Trees

Cancer is an evolutionary process, in that a tumor results from a series of genomic mutations that are acquired over time. The history of how a tumor evolved is often represented using a clonal tree: a particular type of rooted tree where vertices represent tumor cell clones and edges represent ancestral relationships between them. In recent years, numerous algorithms have been developed that infer clonal trees from sequencing data. Correspondingly, researchers have also developed distance measures to compare clonal trees. While such distance measures have proven valuable, they are limited in a variety of ways. First off, they usually only utilize one or a few types of specific information when being computed - making it challenging to assess how well they capture other common types of differences between clonal trees. Secondly, their final output is just a single numerical value - meaning that information on what parts of the trees contributed to the distance is not being readily conveyed. In this work, we address both of these issues. We define a set of four classes of tree differences that describe common ways that clonal trees might differ from each other and analyze these classes using three different clonal tree distance measures. We then introduce a visualization tool called VECTr (Visual Encodings for Clonal Trees) that provides an enhanced pairwise comparison of clonal trees using the same three distance measures. In particular, VECTr contains three types of visualizations that allow a user to easily identify what parts of a tree contribute to its distance, including information relevant to when the trees differ in a manner that corresponds to one of our four classes of tree differences. We demonstrate the utility of our visualization tool using a variety of different use cases, including application to both simulated and real clonal trees. Code associated with the project is freely available at: https://github.com/quocodile/visualize-clonal-trees CCS ConceptsApplied computing Molecular evolution; Computational genomics; Computational biology.

bioinformatics↗

bioRxiv2025

Enumerating and Exploring the Space of Clonal Trees

Tumor growth is a complex evolutionary process initiated by an abnormal ancestor cell progressively gaining mutations, which eventually results in uncontrolled cell division. Researchers use a structure called a clonal tree-which depicts ancestral relationships between mutated cell populations-to represent a tumors evolutionary history. Numerous methods have been developed to reconstruct a clonal tree from tumor sequencing data. To evaluate the accuracy of such tree inference methods, researchers typically use simulated data where the true evolutionary history is known. However, previous research has not thoroughly analyzed the space of clonal trees under different evolutionary models. Such exploration would help to better understand the underlying structure and characteristics of these spaces of trees and would aid in creation of more appropriate simulated datasets. We analyzed four different categories of clonal trees, each with their own set of assumptions. For each category, we designed and implemented enumeration algorithms that provably generate all such clonal trees with a specified number of mutations. We then used our algorithms to generate several datasets and analyzed the generated trees to discover patterns in the data across different assumptions. We also investigated two tree sampling methods to compare their output with our fully enumerated trees and found that one of the methods does a good job of representing the entire space of trees, while the other does quite poorly. Our findings have important implications for the creation of simulated data commonly used to assess new clonal tree inference methods. Code associated with the project is freely available at: https://bitbucket.org/oesperlab/tree-space/src/main/

bioinformatics↗

bioRxiv2019

Distance Measures for Tumor Evolutionary Trees

In recent years, there has been increased interest in studying cancer by using algorithmic methods to infer the evolutionary tree underlying a tumors developmental history. Quantitative measures that compare such trees are then vital to benchmarking these algorithmic tree inference methods, understanding the structure of the space of possible trees for a given dataset, and clustering together similar trees in order to evaluate inheritance patterns. However, few appropriate distance measures exist, and those that do exist have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and how the mutations that label that topology are inherited. Here we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to two simulated and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures. Implementations of CASet and DISC are available at: https://bitbucket.org/oesperlab/stereodist.

bioinformatics↗

bioRxiv2017

Identifying Simultaneous Rearrangements in Cancer Genomes

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 (2013). 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. 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. An implementation of the H/T alternating fraction is available at https://bitbucket.org/oesperlab/ht-altfrac.

bioinformatics↗