Topological Data Analysis of copy number alterations in cancer
This research addresses the problem of identifying cancer subgroups and comparing cancer types for personalized treatment, offering a new analytical tool for cancer researchers.
This paper explores using a topology-based approach to analyze copy number alterations (CNAs) in cancer biopsy samples. The method encodes each cancer sample as a persistence diagram, demonstrating its ability to extract meaningful low-dimensional representations and identify substructures and similarities among cancer types.
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.