Identification of Copy Number Aberrations in Breast Cancer Subtypes Using Persistence Topology

Abstract

DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Identification of tumor-driving CNAs (driver CNAs) however remains a challenging task, because they are frequently hidden by CNAs that are the product of random events that take place during tumor evolution. Experimental detection of CNAs is commonly accomplished through array comparative genomic hybridization (aCGH) assays followed by supervised and/or unsupervised statistical methods that combine the segmented profiles of all patients to identify driver CNAs. Here, we extend a previously-presented supervised algorithm for the identification of CNAs that is based on a topological representation of the data. Our method associates a two-dimensional (2D) point cloud with each aCGH profile and generates a sequence of simplicial complexes, mathematical objects that generalize the concept of a graph. This representation of the data permits segmenting the data at different resolutions and identifying CNAs by interrogating the topological properties of these simplicial complexes. We tested our approach on a published dataset with the goal of identifying specific breast cancer CNAs associated with specific molecular subtypes. Identification of CNAs associated with each subtype was performed by analyzing each subtype separately from the others and by taking the rest of the subtypes as the control. Our results found a new amplification in 11q at the location of the progesterone receptor in the Luminal A subtype. Aberrations in the Luminal B subtype were found only upon removal of the basal-like subtype from the control set. Under those conditions, all regions found in the original publication, except for 17q, were confirmed; all aberrations, except those in chromosome arms 8q and 12q were confirmed in the basal-like subtype. These two chromosome arms, however, were detected only upon removal of three patients with exceedingly large copy number values. More importantly, we detected 10 and 21 additional regions in the Luminal B and basal-like subtypes, respectively. Most of the additional regions were either validated on an independent dataset and/or using GISTIC. Furthermore, we found three new CNAs in the basal-like subtype: a combination of gains and losses in 1p, a gain in 2p and a loss in 14q. Based on these results, we suggest that topological approaches that incorporate multiresolution analyses and that interrogate topological properties of the data can help in the identification of copy number changes in cancer.

High DRC Levels Are Associated with Let-7b Overexpression in Women with Breast Cancer

“…Over the past decade, BC therapies have been based on the knowledge gained from breast tumor studies and clinical outcomes of patients exposed to different therapies [ 49 , 50 , 51 ]. These studies have led to recognition of the heterogenic diversity within BC diagnosis, and breast tumors have been classified into molecular subtypes based on hormone receptor status PR, ER, HER2 [ 20 ].…”

Gene Coexpression Network Comparison via Persistent Homology

“…Topological data analysis (TDA) has been applied to biological data before. Arsuaga et al [ 17 ] associate a two-dimensional (2D) point cloud with each array comparative genomic hybridization (aCGH) profile and generate a sequence of simplicial complexes. They use these mathematical objects to identify DNA copy number aberrations by interrogating the topological properties.…”

Topological data analysis reveals principles of chromosome structure throughout cellular differentiation

“…Emerging from work in applied topology and computational geometry, TDA aims to infer information about the robust structures of complex data sets [9]. These methods have already been applied to various biological contexts [3], including in studies of gene expression at the single cell level [27], viral reassortment [8], horizontal evolution [4], cancer genomics [23,1], and other complex diseases [20,18]. Similar methods have also been used in tools to enable large-scale biological database searching [31].…”