Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling
Menée sur une cohorte initiale de 323 échantillons tumoraux prélevés sur des patientes atteintes d'un cancer du sein, puis validée sur 241 échantillons complémentaires, cette étude évalue la faisabilité d'une approche de traitement automatisé d’images d’histopathologie, en combinaison avec des données génomiques, pour le diagnostic du cancer du sein
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.
Science Translational Medicine , résumé, 2012