Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees
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Date
2023
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Journal Article
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yes
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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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14 (1)
Pages / Article No.
3676
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Nature
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179518 - Using single-cell sequencing data to analyse tumour evolution (SNF)
951970 - Fostering Computational Biology Research and Innovation in Lisbon (EC)
951970 - Fostering Computational Biology Research and Innovation in Lisbon (EC)