title: PHENOTYPIC CHARACTERIZATION OF BREAST INVASIVE CARCINOMA VIA TRANSFERABLE TISSUE MORPHOMETRIC PATTERNS LEARNED FROM GLIOBLASTOMA MULTIFORME.
abstract: Quantitative analysis of whole slide images (WSIs) in a large cohort may provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. Although unsupervised feature learning provides a promising way in learning pertinent features without human intervention, its capability can be greatly limited due to the lack of well-curated examples. In this paper, we explored the transferability of knowledge acquired from a well-curated Glioblastoma Multiforme (GBM) dataset through its application to the representation and characterization of tissue histology from the Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) cohort. Our experimental results reveals two major phenotypic subtypes with statistically significantly different survival curves. Further differential expression analysis of these two subtypes indicates enrichment of genes regulated by NF-kB in response to TNF and genes up-regulated in response to IFNG.
authors.affiliation: Department of Electrical and Biomedical Engineering, University of Nevada, Reno, Nevada, USA.
authors.forename: Ju
authors.initials: J
authors.lastname: Han
authors.affiliation: Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
authors.forename: Gerald V
authors.initials: GV
authors.lastname: Fontenay
authors.affiliation: Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA; Department of Neurology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
authors.forename: Yunfu
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
authors.forename: Jian-Hua
authors.initials: JH
authors.lastname: Mao
authors.affiliation: Department of Electrical and Biomedical Engineering, University of Nevada, Reno, Nevada, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
authors.forename: Hang
authors.initials: H
authors.lastname: Chang
keywords: Breast invasive carcinoma; consensus clustering; enrichment analysis; knowledge sharing; predictive sparse decomposition; survival analysis; unsupervised feature learning