Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression

TL;DR

This paper introduces PiCnIc, a modular pipeline that leverages machine learning and genomic data to model cancer progression, addressing heterogeneity and enabling the discovery of both known and novel progression pathways.

Contribution

The paper presents PiCnIc, a new versatile pipeline for inferring cancer progression models from cross-sectional genomic data, integrating advanced techniques for stratification and driver mutation analysis.

Findings

Successfully reproduces known colorectal cancer progression pathways.

Suggests novel hypotheses for experimental validation.

Demonstrates robustness across heterogeneous cancer datasets.

Abstract

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next generation sequencing (NGS) data, and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent works on "selective advantage" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications as it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression, as well as to suggest novel experimentally verifiable hypotheses.