Computational Models for Cancer Phylogenetics

Schwartz, Russell

doi:10.1007/978-3-030-10837-3_11

Cited by 5 publications

(12 citation statements)

References 83 publications

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“…For example, phylogenies have been used to predict evolution of human influenza A [15,123]; to understand the relationships between the virulence and evolution of HIV [17,115,124,133]; to identify emerging viruses such as SARS [2,67,104]; to recreate and investigate ancestral proteins [33,160]; to design neuropeptides causing smooth muscle contraction [5]; to relate geographic patterns to ecological and macro-evolutionary processes [69,90,96,107]; or to uncover similarities in evolution of a number of human languages [12,79]. Phylogenies have also been used to study the evolutionary processes underlying the genetic factors involved in common human diseases [24,29,109,122,147,148] as well as those at the core of the progression of carcinomas over time [30,35,91,110,129,130,142,152]. In particular, in the cancer context, phylogenies allowed the remarkable classification of tumor cells of given pathologies in subfamilies characterized by specific evolutionary traits [142].…”

Section: Phylogeniesmentioning

confidence: 99%

“…Phylogenies have also been used to study the evolutionary processes underlying the genetic factors involved in common human diseases [24,29,109,122,147,148] as well as those at the core of the progression of carcinomas over time [30,35,91,110,129,130,142,152]. In particular, in the cancer context, phylogenies allowed the remarkable classification of tumor cells of given pathologies in subfamilies characterized by specific evolutionary traits [142]. A shared hope is that this classification could enable a better understanding of cellular atypia over time and, in the long run, suggest new therapeutic targets [6,91,110,142].…”

Section: Phylogeniesmentioning

confidence: 99%

“…In particular, in the cancer context, phylogenies allowed the remarkable classification of tumor cells of given pathologies in subfamilies characterized by specific evolutionary traits [142]. A shared hope is that this classification could enable a better understanding of cellular atypia over time and, in the long run, suggest new therapeutic targets [6,91,110,142]. Hemorrhagic Fever (CCHF) orthonairovirus, Ebolavirus, the Lassa mammarenavirus, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the Human Immunodeficiency Viruses (HIVs) 1 and 2, and the Nipah virus.…”

Section: Phylogeniesmentioning

confidence: 99%

See 2 more Smart Citations

A tutorial on the balanced minimum evolution problem

Catanzaro

Frohn

Gascuel

et al. 2022

European Journal of Operational Research

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Section: Phylogeniesmentioning

confidence: 99%

Section: Phylogeniesmentioning

confidence: 99%

Section: Phylogeniesmentioning

confidence: 99%

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A tutorial on the balanced minimum evolution problem

Catanzaro

Frohn

Gascuel

et al. 2022

European Journal of Operational Research

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“…CNAs play an important role in driving cancer development ( Burrell et al , 2013 ; McGranahan and Swanton, 2015 ), and thus characterization of these events is essential for disease diagnosis, prognosis and treatment ( Fisher et al , 2013 ). Moreover, CNAs provide important information for reconstructing tumor evolution ( Beerenwinkel et al , 2015 ; Schwartz, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…Modeling copy number evolution using CNPs is challenging because, unlike single-nucleotide mutations, CNAs often overlap, and therefore the copy numbers of different segments are not independent ( Beerenwinkel et al , 2015 ; Schwartz, 2019 ). Recently, several methods have been introduced to describe the evolution of CNPs.…”

Section: Introductionmentioning

confidence: 99%

Copy number evolution with weighted aberrations in cancer

Zeira

Raphael

2020

Bioinformatics

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Motivation Copy number aberrations (CNAs), which delete or amplify large contiguous segments of the genome, are a common type of somatic mutation in cancer. Copy number profiles, representing the number of copies of each region of a genome, are readily obtained from whole-genome sequencing or microarrays. However, modeling copy number evolution is a substantial challenge, because different CNAs may overlap with one another on the genome. A recent popular model for copy number evolution is the copy number distance (CND), defined as the length of a shortest sequence of deletions and amplifications of contiguous segments that transforms one profile into the other. In the CND, all events contribute equally; however, it is well known that rates of CNAs vary by length, genomic position and type (amplification versus deletion). Results We introduce a weighted CND that allows events to have varying weights, or probabilities, based on their length, position and type. We derive an efficient algorithm to compute the weighted CND as well as the associated transformation. This algorithm is based on the observation that the constraint matrix of the underlying optimization problem is totally unimodular. We show that the weighted CND improves phylogenetic reconstruction on simulated data where CNAs occur with varying probabilities, aids in the derivation of phylogenies from ultra-low-coverage single-cell DNA sequencing data and helps estimate CNA rates in a large pan-cancer dataset. Availability and implementation Code is available at https://github.com/raphael-group/WCND. Supplementary information Supplementary data are available at Bioinformatics online.

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An evolution strategy approach for the balanced minimum evolution problem

Gasparin,

Camerota Verdù,

Catanzaro

et al. 2023

Bioinformatics

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Motivation The Balanced Minimum Evolution (BME) is a powerful distance based phylogenetic estimation model introduced by Desper and Gascuel and nowadays implemented in popular tools for phylogenetic analyses. It was proven to be computationally less demanding than more sophisticated estimation methods, e.g. maximum likelihood or Bayesian inference while preserving the statistical consistency and the ability to run with almost any kind of data for which a dissimilarity measure is available. BME can be stated in terms of a nonlinear non-convex combinatorial optimisation problem, usually referred to as the Balanced Minimum Evolution Problem (BMEP). Currently, the state-of-the-art among approximate methods for the BMEP is represented by FastME (version 2.0), a software which implements several deterministic phylogenetic construction heuristics combined with a local search on specific neighbourhoods derived by classical topological tree rearrangements. These combinations, however, may not guarantee convergence to close-to-optimal solutions to the problem due to the lack of solution space exploration, a phenomenon which is exacerbated when tackling molecular datasets characterised by a large number of taxa. Results To overcome such convergence issues, in this article we propose a novel metaheuristic, named PhyloES, which exploits the combination of an exploration phase based on Evolution Strategies, a special type of evolutionary algorithm, with a refinement phase based on two local search algorithms. Extensive computational experiments show that PhyloES consistently outperforms FastME, especially when tackling larger datasets, providing solutions characterised by a shorter tree length but also significantly different from the topological perspective. Availability The software is available at https://github.com/andygaspar/PHYLOES Supplementary information Supplementary data are available at Bioinformatics online.

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Computational Models for Cancer Phylogenetics

Cited by 5 publications

References 83 publications

A tutorial on the balanced minimum evolution problem

A tutorial on the balanced minimum evolution problem

Copy number evolution with weighted aberrations in cancer

An evolution strategy approach for the balanced minimum evolution problem

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