Classification and risk stratification of invasive breast carcinomas using a real-time quantitative RT-PCR assay.

Authors

Laurent Perreard, The ARUP Institute for Clinical and Experimental Pathology, SLC, Utah, USAFollow
Cheng Fan, Department of Genetics and Department of Pathology & Laboratory Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USAFollow
John F Quackenbush, Department of Pathology, University of Utah School of Medicine, SLC, Utah, USAFollow
Michael Mullins, Department of Pathology, University of Utah School of Medicine, SLC, Utah, USAFollow
Nicholas P Gauthier, Department of Pathology, University of Utah School of Medicine, SLC, Utah, USA
Edward Nelson, Department of Surgery, University of Utah School of Medicine, SLC, Utah, USAFollow
Mary Mone, Department of Surgery, University of Utah School of Medicine, SLC, Utah, USAFollow
Heidi Hansen, Department of Surgery, University of Utah School of Medicine, SLC, Utah, USAFollow
Saundra S Buys, Department of Internal Medicine, University of Utah School of Medicine, SLC, Utah, USA
Karen Rasmussen, Department of Clinical Genetics, Maine Center for Cancer Medicine, Scarborough, Maine, USAFollow
Alejandra Ruiz Orrico, Department of Pathology, Thomas Jefferson University, Philadelphia, Pennsylvania, USAFollow
Donna Dreher, Department of Pathology, Thomas Jefferson University, Philadelphia, Pennsylvania, USAFollow
Rhonda Walters, Department of Pathology, Thomas Jefferson University, Philadelphia, Pennsylvania, USAFollow
Joel Parker, Constella Health Sciences, Durham, North Carolina, USAFollow
Zhiyuan Hu, Department of Genetics and Department of Pathology & Laboratory Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USAFollow
Xiaping He, Department of Genetics and Department of Pathology & Laboratory Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USAFollow
Juan P Palazzo, Department of Pathology, Thomas Jefferson University, Philadelphia, Pennsylvania, USAFollow
Olufunmilayo I Olopade, Department of Medicine, University of Chicago, Illinois, USAFollow
Aniko Szabo, Department of Oncological Sciences, Huntsman Cancer Institute, SLC, Utah, USA
Charles M Perou, Department of Genetics and Department of Pathology & Laboratory Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USAFollow
Philip S Bernard, The ARUP Institute for Clinical and Experimental Pathology, SLC, Utah, USA, Department of Pathology, University of Utah School of Medicine, SLC, Utah, USAFollow

Document Type

Article

Publication Date

1-1-2006

Comments

This article has been peer reviewed and is published in BMC Breast Cancer Research Volume 7, 27 April 2006, Article number 96. The published version is available at DOI: 10.1186/1471-2164-7-96. Copyright © BioMed Central Ltd.

Abstract

INTRODUCTION: Predicting the clinical course of breast cancer is often difficult because it is a diverse disease comprised of many biological subtypes. Gene expression profiling by microarray analysis has identified breast cancer signatures that are important for prognosis and treatment. In the current article, we use microarray analysis and a real-time quantitative reverse-transcription (qRT)-PCR assay to risk-stratify breast cancers based on biological 'intrinsic' subtypes and proliferation. METHODS: Gene sets were selected from microarray data to assess proliferation and to classify breast cancers into four different molecular subtypes, designated Luminal, Normal-like, HER2+/ER-, and Basal-like. One-hundred and twenty-three breast samples (117 invasive carcinomas, one fibroadenoma and five normal tissues) and three breast cancer cell lines were prospectively analyzed using a microarray (Agilent) and a qRT-PCR assay comprised of 53 genes. Biological subtypes were assigned from the microarray and qRT-PCR data by hierarchical clustering. A proliferation signature was used as a single meta-gene (log2 average of 14 genes) to predict outcome within the context of estrogen receptor status and biological 'intrinsic' subtype. RESULTS: We found that the qRT-PCR assay could determine the intrinsic subtype (93% concordance with microarray-based assignments) and that the intrinsic subtypes were predictive of outcome. The proliferation meta-gene provided additional prognostic information for patients with the Luminal subtype (P = 0.0012), and for patients with estrogen receptor-positive tumors (P = 3.4 x 10-6). High proliferation in the Luminal subtype conferred a 19-fold relative risk of relapse (confidence interval = 95%) compared with Luminal tumors with low proliferation. CONCLUSION: A real-time qRT-PCR assay can recapitulate microarray classifications of breast cancer and can risk-stratify patients using the intrinsic subtype and proliferation. The proliferation meta-gene offers an objective and quantitative measurement for grade and adds significant prognostic information to the biological subtypes.

Recommended Citation

Perreard, Laurent; Fan, Cheng; Quackenbush, John F; Mullins, Michael; Gauthier, Nicholas P; Nelson, Edward; Mone, Mary; Hansen, Heidi; Buys, Saundra S; Rasmussen, Karen; Orrico, Alejandra Ruiz; Dreher, Donna; Walters, Rhonda; Parker, Joel; Hu, Zhiyuan; He, Xiaping; Palazzo, Juan P; Olopade, Olufunmilayo I; Szabo, Aniko; Perou, Charles M; and Bernard, Philip S, "Classification and risk stratification of invasive breast carcinomas using a real-time quantitative RT-PCR assay." (2006). Department of Pathology, Anatomy, and Cell Biology Faculty Papers. Paper 51.
https://jdc.jefferson.edu/pacbfp/51

PubMed ID

16626501