Work

Inferring and summarizing tumor phylogenies from bulk DNA data

Year: 2026

Type: article

Abstract: BACKGROUND: Cancer phylogenies are key to understanding tumor evolution. However, due to the uncertainty in phylogenetic estimation, one typically infers many, equally-plausible phylogenies from bulk DNA sequencing data of tumors, hindering downstream analysis that relies on correct phylogenies. RESULTS: To resolve this challenge, we introduce Sapling, a method to solve two variants of the BACKBONE TREE INFERENCE FROM READS problem, which seeks a small set of backbone trees on a subset of mutations that collectively summarize the space of plausible cancer phylogenies. We prove that the problems are NP-hard. CONCLUSIONS: On simulated and real data, we demonstrate that Sapling is capable of inferring high-quality backbone trees that adequately summarize the space of plausible cancer phylogenies. In addition, we demonstrate that Sapling is able to infer full-size trees with higher likelihoods than state-of-the-art methods.

Source: Algorithms for Molecular Biology

DOI: 10.1186/s13015-025-00295-5

Authors: Yuanyuan Qi, Henri Schmidt, Mohammed El-Kebir

Institutions: University of Illinois Urbana-Champaign, Princeton University, Cancer Center at Illinois

Language: English

Topic: Cancer Genomics and Diagnostics

Subfield: Cancer Research

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,490

Funder: Division of Computing and Communication Foundations