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 associa

SUBMITTER: Arsuaga J 

PROVIDER: S-EPMC4996377 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature