# Inferring and summarizing tumor phylogenies from bulk DNA data

## TL;DR

This paper introduces Sapling, a method to summarize many possible cancer phylogenies into a few representative trees, improving analysis of tumor evolution.

## Contribution

Sapling introduces a novel approach to infer backbone trees that summarize the space of plausible cancer phylogenies.

## Key findings

- Sapling infers high-quality backbone trees that summarize plausible cancer phylogenies.
- Sapling outperforms state-of-the-art methods in inferring full-size trees with higher likelihoods.
- The method is tested on both simulated and real data.

## Abstract

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.

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.

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.