Scholarlink

Background <p>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.</p> Results <p>To resolve this challenge, we introduce Sapling, a method to solve two variants of the <span>Backbone Tree Inference from Reads</span> 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.</p> Conclusions <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Inferring and summarizing tumor phylogenies from bulk DNA data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月18日

查看全文
  • Yuanyuan Qi,
  • Henri Schmidt,
  • Mohammed El-Kebir

摘要

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.