Topological Data Analysis of copy number alterations in cancer

TL;DR

This paper introduces a topology-based method using persistence diagrams to analyze cancer genomic data, enabling the extraction of meaningful features for identifying subgroups and comparing cancer types.

Contribution

It presents a novel topological approach for analyzing copy number alterations in cancer, capturing complex data structures in a low-dimensional form.

Findings

Topology-based features can distinguish cancer subgroups.

Persistence diagrams effectively compare different cancer types.

Method shows promise for personalized cancer diagnosis.

Abstract

Identifying subgroups and properties of cancer biopsy samples is a crucial step towards obtaining precise diagnoses and being able to perform personalized treatment of cancer patients. Recent data collections provide a comprehensive characterization of cancer cell data, including genetic data on copy number alterations (CNAs). We explore the potential to capture information contained in cancer genomic information using a novel topology-based approach that encodes each cancer sample as a persistence diagram of topological features, i.e., high-dimensional voids represented in the data. We find that this technique has the potential to extract meaningful low-dimensional representations in cancer somatic genetic data and demonstrate the viability of some applications on finding substructures in cancer data as well as comparing similarity of cancer types.