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Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling

Item Type:Article
Title:Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling
Creators Name:Yuan, Y. and Failmezger, H. and Rueda, O.M. and Ali, H.R. and Graef, S. and Chin, S.F. and Schwarz, R.F. and Curtis, C. and Dunning, M.J. and Bardwell, H. and Johnson, N. and Doyle, S. and Turashvili, G. and Provenzano, E. and Aparicio, S. and Caldas, C. and Markowetz, F.
Abstract:Solid tumors are heterogeneous tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Furthermore, tissue architecture is generally not reflected in molecular assays, rendering this rich information underused. To address these challenges, we developed a computational approach based on standard hematoxylin and eosin-stained tissue sections and demonstrated its power in a discovery and validation cohort of 323 and 241 breast tumors, respectively. To deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy number profiles between samples. We next devised a predictor for survival in estrogen receptor-negative breast cancer that integrated both image-based and gene expression analyses and significantly outperformed classifiers that use single data types, such as microarray expression signatures. Image processing also allowed us to describe and validate an independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative, image-based method could benefit any large-scale cancer study by refining and complementing molecular assays of tumor samples.
Keywords:Automation, Breast Neoplasms, Computer-Assisted Image Processing, Estrogen Receptors, Gene Dosage, Gene Expression Profiling, Genomics, Neoplastic Gene Expression Regulation, Prognosis, Stromal Cells, Survival Analysis, Tumor-Infiltrating Lymphocytes
Source:Science Translational Medicine
Publisher:American Association for the Advancement of Science
Page Range:157ra143
Date:24 October 2012
Additional Information:Erratum in: Sci Transl Med 4(157):161er6.
Official Publication:https://doi.org/10.1126/scitranslmed.3004330
PubMed:View item in PubMed

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