ArticleBioinformatics (Oxford, England)2019
Estimating the predictability of cancer evolution.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
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- Translation of Epigenetics in Cell-Free DNA Liquid Biopsy Technology and Precision Oncology.Current issues in molecular biology · 2024Review
- Graph 'texture' features as novel metrics that can summarize complex biological graphs.Physics in medicine and biology · 2023Article
- Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees.Nature communications · 2023Article
- EvAM-Tools: tools for evolutionary accumulation and cancer progression models.Bioinformatics (Oxford, England) · 2022Article
- Attribution of Cancer Origins to Endogenous, Exogenous, and Preventable Mutational Processes.Molecular biology and evolution · 2022Article
- Bayesian networks elucidate complex genomic landscapes in cancer.Communications biology · 2022Article
- Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?PLoS computational biology · 2021Article
- Inference of mutability landscapes of tumors from single cell sequencing data.PLoS computational biology · 2020Article
- When, why and how tumour clonal diversity predicts survival.Evolutionary applications · 2020Article
- Identification of conserved evolutionary trajectories in tumors.Bioinformatics (Oxford, England) · 2020Article
- Article
- Every which way? On predicting tumor evolution using cancer progression models.PLoS computational biology · 2019Article
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Authors and funding
4 authors at 4 institutions in 3 countries.
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Abstract
motivationHow predictable is the evolution of cancer? This fundamental question is of immense relevance for the diagnosis, prognosis and treatment of cancer. Evolutionary biologists have approached the question of predictability based on the underlying fitness landscape. However, empirical fitness landscapes of tumor cells are impossible to determine in vivo. Thus, in order to quantify the predictability of cancer evolution, alternative approaches are required that circumvent the need for fitness landscapes.
resultsWe developed a computational method based on conjunctive Bayesian networks (CBNs) to quantify the predictability of cancer evolution directly from mutational data, without the need for measuring or estimating fitness. Using simulated data derived from >200 different fitness landscapes, we show that our CBN-based notion of evolutionary predictability strongly correlates with the classical notion of predictability based on fitness landscapes under the strong selection weak mutation assumption. The statistical framework enables robust and scalable quantification of evolutionary predictability. We applied our approach to driver mutation data from the TCGA and the MSK-IMPACT clinical cohorts to systematically compare the predictability of 15 different cancer types. We found that cancer evolution is remarkably predictable as only a small fraction of evolutionary trajectories are feasible during cancer progression. AVAILABILITY AND IMPLEMENTATION: https://github.com/cbg-ethz/predictability\_of\_cancer\_evolution. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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