Characterization and Clinical Evaluation of CD10 þ Stroma Cells in the Breast Cancer Microenvironment

1. Breast Cancer Translational Research Laboratory JC Heuson, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium; 2. Computational Biology and Functional Genomics Laboratory, Center for Cancer Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health; 3. Laboratory of Oncology and Experimental Surgery, Institut J Bordet, Centre des Tumeurs de l’Université Libre de Bruxelles, Brussels, Belgium; 4. Department of flow cytometry, Institut Jules Bordet; Université Libre de Bruxelles, Brussels, Belgium; 5. Interdisciplinary Research Institute (IRIBHM), Université Libre de Bruxelles (ULB), Brussels, Belgium 6. Department of Experimental Hematology, Institut Jules Bordet, Brussels, Belgium; 7. Département de formation et recherche, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland; 8. Department of Pathology, Institut Jules Bordet, Brussels, Belgium; 9. Medical oncology, Institut Jules Bordet, Brussels, Belgium;

[1]  Yuan Qi,et al.  Multifactorial approach to predicting resistance to anthracyclines. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[2]  P. Garnero,et al.  Increased expression and serum levels of the stromal cell‐secreted protein periostin in breast cancer bone metastases , 2011, International journal of cancer.

[3]  Aedín C. Culhane,et al.  GeneSigDB—a curated database of gene expression signatures , 2009, Nucleic Acids Res..

[4]  A. Friedl,et al.  Heterogeneity of Gene Expression in Stromal Fibroblasts of Human Breast Carcinomas and Normal Breast , 2009, Oncogene.

[5]  W. Gerald,et al.  Genes that mediate breast cancer metastasis to the brain , 2009, Nature.

[6]  S. Bao,et al.  The multifaceted role of periostin in tumorigenesis , 2009, Cellular and Molecular Life Sciences.

[7]  Timothy Hunter,et al.  Molecular signatures suggest a major role for stromal cells in development of invasive breast cancer , 2009, Breast Cancer Research and Treatment.

[8]  S. Schnitt The transition from ductal carcinoma in situ to invasive breast cancer: the other side of the coin , 2009, Breast Cancer Research.

[9]  Xiao-Jun Ma,et al.  Gene expression profiling of the tumor microenvironment during breast cancer progression , 2009, Breast Cancer Research.

[10]  Gianluca Bontempi,et al.  Comparison of prognostic gene expression signatures for breast cancer , 2008, BMC Genomics.

[11]  Gianluca Bontempi,et al.  Biological Processes Associated with Breast Cancer Clinical Outcome Depend on the Molecular Subtypes , 2008, Clinical Cancer Research.

[12]  H. Kölbl,et al.  The humoral immune system has a key prognostic impact in node-negative breast cancer. , 2008, Cancer research.

[13]  F. Pépin,et al.  Stromal gene expression predicts clinical outcome in breast cancer , 2008, Nature Medicine.

[14]  J. Bergh,et al.  Strong Time Dependence of the 76-Gene Prognostic Signature for Node-Negative Breast Cancer Patients in the TRANSBIG Multicenter Independent Validation Series , 2007, Clinical Cancer Research.

[15]  H. Ishwaran,et al.  Lung metastasis genes couple breast tumor size and metastatic spread , 2007, Proceedings of the National Academy of Sciences.

[16]  Wen-Lin Kuo,et al.  A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. , 2006, Cancer cell.

[17]  Greg Finak,et al.  Gene expression signatures of morphologically normal breast tissue identify basal-like tumors , 2006, Breast Cancer Research.

[18]  T. Fehm,et al.  Progression-specific Genes Identified by Expression Profiling of Matched Ductal Carcinomas in situ and Invasive Breast Tumors, Combining Laser Capture Microdissection and Oligonucleotide Microarray Analysis , 2006 .

[19]  L. Lagneaux,et al.  Human marrow mesenchymal stem cell culture: serum‐free medium allows better expansion than classical α‐MEM medium , 2006, European journal of haematology.

[20]  P. Hall,et al.  An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Rameen Beroukhim,et al.  Molecular characterization of the tumor microenvironment in breast cancer. , 2004, Cancer cell.

[22]  Benjamin M. Bolstad,et al.  affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..

[23]  Yudong D. He,et al.  A Gene-Expression Signature as a Predictor of Survival in Breast Cancer , 2002 .

[24]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[25]  R. Jain,et al.  Role of extracellular matrix assembly in interstitial transport in solid tumors. , 2000, Cancer research.

[26]  F. Harrell,et al.  Evaluating the yield of medical tests. , 1982, JAMA.

[27]  Maqc Consortium The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements , 2006, Nature Biotechnology.

[28]  M. J. van de Vijver,et al.  Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. , 2006, Journal of the National Cancer Institute.

[29]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[30]  W. G. Cochran Problems arising in the analysis of a series of similar experiments , 1937 .