Characterization of Breast Cancer Preclinical Models Reveals a Specific Pattern of Macrophage Polarization.

Drug discovery efforts have focused on the tumor microenvironment in recent years. However, few studies have characterized the stroma component in patient-derived xenografts (PDXs) and genetically engineered mouse models (GEMs). In this study, we characterized the stroma in various models of breast...

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Main Authors: David Vallerand, Gérald Massonnet, Fatima Kébir, David Gentien, Zofia Maciorowski, Pierre De la Grange, Brigitte Sigal-Zafrani, Marion Richardson, Sandrine Humbert, Aurélie Thuleau, Franck Assayag, Ludmilla de Plater, André Nicolas, Suzy Scholl, Elisabetta Marangoni, Stefan Weigand, Sergio Roman-Roman, Ariel Savina, Didier Decaudin
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2016-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4936680?pdf=render
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Summary:Drug discovery efforts have focused on the tumor microenvironment in recent years. However, few studies have characterized the stroma component in patient-derived xenografts (PDXs) and genetically engineered mouse models (GEMs). In this study, we characterized the stroma in various models of breast cancer tumors in mice. We performed transcriptomic and flow cytometry analyses on murine populations for a series of 25 PDXs and the two most commonly used GEMs (MMTV-PyMT and MMTV-erBb2). We sorted macrophages from five models. We then profiled gene expression in these cells, which were also subjected to flow cytometry for phenotypic characterization. Hematopoietic cell composition, mostly macrophages and granulocytes, differed between tumors. Macrophages had a specific polarization phenotype related to their M1/M2 classification and associated with the expression of genes involved in the recruitment, invasion and metastasis processes. The heterogeneity of the stroma component of the models studied suggests that tumor cells modify their microenvironment to satisfy their needs. Our observations suggest that such models are of relevance for preclinical studies.
ISSN:1932-6203