Cancer phylogenies are key to understanding tumor evolution. There exist many important downstream analyses that take as input a single or a small number of trees. However, due to uncertainty, one typically infers many, equally-plausible phylogenies from bulk DNA sequencing data of tumors. We introduce Sapling, a heuristic method to solve the Backbone Tree Inference from Reads problem, which seeks a small set of backbone trees on a smaller subset of mutations that collectively summarize the entire solution space. Sapling also includes a greedy algorithm to solve the Backbone Tree Expansion from Reads problem, which aims to expand an inferred backbone tree into a full tree. We prove that both problems are NP-hard. On simulated and real data, we demonstrate that Sapling is capable of inferring high-quality backbone trees that adequately summarize the solution space and that can be expanded into full trees.
@InProceedings{qi_et_al:LIPIcs.WABI.2024.7, author = {Qi, Yuanyuan and El-Kebir, Mohammed}, title = {{Sapling: Inferring and Summarizing Tumor Phylogenies from Bulk Data Using Backbone Trees}}, booktitle = {24th International Workshop on Algorithms in Bioinformatics (WABI 2024)}, pages = {7:1--7:19}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-340-9}, ISSN = {1868-8969}, year = {2024}, volume = {312}, editor = {Pissis, Solon P. and Sung, Wing-Kin}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2024.7}, URN = {urn:nbn:de:0030-drops-206518}, doi = {10.4230/LIPIcs.WABI.2024.7}, annote = {Keywords: Cancer, intra-tumor heterogeneity, consensus, maximum agreement} }
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