Mapping genomic and transcriptomic alterations spatially in epithelial cells adjacent to human breast carcinoma. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Abdalla, Moustafa; Tran-Thanh, Danh; Moreno, Juan; Iakovlev, Vladimir; Nair, Ranju; Kanwar, Nisha; Abdalla, Mohamed; Lee, Jennifer; Kwan, Jennifer; Cawthorn, Thomas; Warren, Keisha; Arneson, Nona; Wang, Dong-Yu; Fox, Natalie; Youngson, Bruce; Miller, Naomi; Easson, Alexandra; McCready, David; Leong, Wey; Boutros, Paul; Done, Susan
• Is Part Of: Nature communications. Volume 8:Issue 1(2017)
• Page Start: 1
• Page End: 11
• Publication Date: 2017-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-017-01357-y
• Languages: English
• ISSNs: 2041-1723

Abstract

Abstract Almost all genomic studies of breast cancer have focused on well-established tumours because it is technically challenging to study the earliest mutational events occurring in human breast epithelial cells. To address this we created a unique dataset of epithelial samples ductoscopically obtained from ducts leading to breast carcinomas and matched samples from ducts on the opposite side of the nipple. Here, we demonstrate that perturbations in mRNA abundance, with increasing proximity to tumour, cannot be explained by copy number aberrations. Rather, we find a possibility of field cancerization surrounding the primary tumour by constructing a classifier that evaluates where epithelial samples were obtained relative to a tumour (cross-validated micro-averaged AUC = 0.74). We implement a spectral co-clustering algorithm to define biclusters. Relating to over-represented bicluster pathways, we further validate two genes with tissue microarrays and in vitro experiments. We highlight evidence suggesting that bicluster perturbation occurs early in tumour development. Studying the spatial mutational and gene expression alterations in breast cancer could impact our understanding of breast cancer development. Here, the authors analyse a unique dataset of epithelial samples that highlight potential field cancerisation surrounding the primary tumour.