Accounting for Uncertainty in Clonal Phylogeny Reconstruction

Maitena Tellaetxe-Abete, Borja Calvo

Keywords: Tumor Phylogeny Reconstruction, Iterated Local Search, Uncertainty Modeling

TL;DR: In this project we analyze how leveraging uncertainty across an instance enhances metaheuristic approaches to clonal phylogeny reconstruction.

Abstract: Intratumoral heterogeneity in cancer arises from the evolutionary accumulation of genetic mutations, leading to multiple clones within a single tumor. The Clonal Deconvolution and Evolution Problem addresses the reconstruction of these distinct clonal subpopulations and their ancestral relationships using mutation frequency estimates with varying levels of reliability. In this project, we propose an Iterated Local Search algorithm with two objective functions: one that treats all information uniformly and another that accounts for the uncertainty associated with each instance element by giving greater weight to more reliable positions. Our ultimate goal is to determine the conditions under which leveraging uncertainty enhances the performance of metaheuristic algorithms that address this problem.

Submission Number: 11