Phenotypic Characterization of Breast Invasive Carcinoma via Transferable Tissue Morphometric Patterns learned from Glioblastoma Multiforme.

TitlePhenotypic Characterization of Breast Invasive Carcinoma via Transferable Tissue Morphometric Patterns learned from Glioblastoma Multiforme.
Publication TypeJournal Article
Year of Publication2016
AuthorsHan J, Fontenay GV, Wang Y, Mao J-H, Chang H
JournalProc IEEE Int Symp Biomed Imaging
Volume2016
Pagination1025-1028
Date Published2016 Apr
ISSN1945-7928
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.

DOI10.1109/ISBI.2016.7493440
Alternate JournalProc IEEE Int Symp Biomed Imaging
PubMed ID27390615
PubMed Central IDPMC4932846
Grant ListR01 CA184476 / CA / NCI NIH HHS / United States
Attachment
Main Document: 
Related Projects