ArticleNature communications2023
Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- A guide to understanding tumour evolution through the lens of population genetics.Nature reviews. Cancer · 2026Review
- Review
- Quantifying uncertainty of predictions from cancer progression models.Bioinformatics (Oxford, England) · 2026Article
- Evolutionary inference reveals global natural histories and predicted pathways of antimicrobial resistance in Klebsiella pneumoniae.PLoS biology · 2026Article
- General-purpose topology-aware embedding of tumor phylogenetic trees with graph neural networks.Bioinformatics advances · 2026Article
- Bayesian inference of fitness landscapes via tree-structured branching processes.Bioinformatics (Oxford, England) · 2025Article
- Review
- CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.Genome biology · 2025Article
- A hypercubic Mk model framework for capturing reversibility in disease, cancer, and evolutionary accumulation modelling.Bioinformatics (Oxford, England) · 2024Article
- A regression based approach to phylogenetic reconstruction from multi-sample bulk DNA sequencing of tumors.PLoS computational biology · 2024Article
- HyperTraPS-CT: Inference and prediction for accumulation pathways with flexible data and model structures.PLoS computational biology · 2024Article
- Modeling metastatic progression from cross-sectional cancer genomics data.Bioinformatics (Oxford, England) · 2024Article
- Oncotree2vec - a method for embedding and clustering of tumor mutation trees.Bioinformatics (Oxford, England) · 2024Article
- A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer.bioRxiv : the preprint server for biology · 2024Article
- Prediction uncertainty estimates elucidate the limitation of current NSCLC subtype classification in representing mutational heterogeneity.Scientific reports · 2024Article
- Modeling and predicting cancer clonal evolution with reinforcement learning.Genome research · 2023Article
- Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees.Nature communications · 2023Article
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Authors and funding
3 authors.
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Abstract
Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors enable high-resolution reconstruction of the mutational history of each tumor and highlight the extensive diversity across tumors and patients. Resolving the interactions among mutations and recovering recurrent evolutionary processes may offer greater opportunities for successful therapeutic strategies. To this end, we present a novel probabilistic framework, called TreeMHN, for the joint inference of exclusivity patterns and recurrent trajectories from a cohort of intra-tumor phylogenetic trees. Through simulations, we show that TreeMHN outperforms existing alternatives that can only focus on one aspect of the task. By analyzing datasets of blood, lung, and breast cancers, we find the most likely evolutionary trajectories and mutational patterns, consistent with and enriching our current understanding of tumorigenesis. Moreover, TreeMHN facilitates the prediction of tumor evolution and provides probabilistic measures on the next mutational events given a tumor tree, a prerequisite for evolution-guided treatment strategies.
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