Tumor Evolution Reconstruction Is Heavily Influenced by Algorithmic and Experimental Choices

Tumor progression is an evolutionary process during which cells acquire distinct genetic alterations. Several cancer evolutionary studies reconstruct this evolutionary process by applying bulk DNA sequencing to a tumor sample to infer the presence of genetic alterations using various tumor evolutionary algorithms. Through a comprehensive benchmarking effort of these algorithms, a recent study by Salcedo and colleagues found that algorithmic and experimental choices are the main drivers of the accuracy of tumor evolution reconstruction, shedding new light on interpreting previous studies and suggesting a useful path forward for the research community.

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Publication Details

Journal
Cancer Research
Published
2024-10-02
DOI
https://doi.org/10.1158/0008-5472.can-24-3530
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
0.00
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article

Tumor Evolution Reconstruction Is Heavily Influenced by Algorithmic and Experimental Choices

Simone Zaccaria, Rija Zaidi
Cancer Research
Cancer Genomics and Diagnostics
article

Tumor Evolution Reconstruction Is Heavily Influenced by Algorithmic and Experimental Choices

Simone Zaccaria, Rija Zaidi
article en

Abstract

Tumor progression is an evolutionary process during which cells acquire distinct genetic alterations. Several cancer evolutionary studies reconstruct this evolutionary process by applying bulk DNA sequencing to a tumor sample to infer the presence of genetic alterations using various tumor evolutionary algorithms. Through a comprehensive benchmarking effort of these algorithms, a recent study by Salcedo and colleagues found that algorithmic and experimental choices are the main drivers of the accuracy of tumor evolution reconstruction, shedding new light on interpreting previous studies and suggesting a useful path forward for the research community.

Cancer ResearchVol. 84(23)
London Cancer (GB), CRUK Lung Cancer Centre of Excellence (GB), University College London (GB)
Openalex Percentile: Top 74%
Cancer Genomics and Diagnostics
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