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

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  • Perreard, Laurent
    • Other Affiliation: The ARUP Institute for Clinical and Experimental Pathology, SLC, Utah, USA
  • Fan, Cheng
    • Affiliation: N.C. Cancer Hospital, UNC Lineberger Comprehensive Cancer Center, School of Medicine, Department of Genetics
  • Quackenbush, John F
    • Other Affiliation: Department of Pathology, University of Utah School of Medicine, SLC, Utah, USA
  • Mullins, Michael
    • Other Affiliation: Department of Pathology, University of Utah School of Medicine, SLC, Utah, USA
  • Gauthier, Nicholas P
    • Other Affiliation: Department of Pathology, University of Utah School of Medicine, SLC, Utah, USA
  • Nelson, Edward
    • Other Affiliation: Department of Surgery, University of Utah School of Medicine, SLC, Utah, USA
  • Mone, Mary
    • Other Affiliation: Department of Surgery, University of Utah School of Medicine, SLC, Utah, USA
  • Hansen, Heidi
    • Other Affiliation: Department of Surgery, University of Utah School of Medicine, SLC, Utah, USA
  • Buys, Saundra S
    • Other Affiliation: Department of Internal Medicine, University of Utah School of Medicine, SLC, Utah, USA
  • Rasmussen, Karen
    • Other Affiliation: Department of Clinical Genetics, Maine Center for Cancer Medicine, Scarborough, Maine, USA
  • Orrico, Alejandra
    • Other Affiliation: Department of Pathology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
  • Dreher, Donna
    • Other Affiliation: Department of Pathology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
  • Walters, Rhonda
    • Other Affiliation: Department of Pathology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
  • Parker, Joel
    • Other Affiliation: Constella Health Sciences, Durham, North Carolina, USA
  • Hu, Zhiyuan
    • Affiliation: N.C. Cancer Hospital, UNC Lineberger Comprehensive Cancer Center, School of Medicine, Department of Genetics
  • He, Xiaping
    • Affiliation: N.C. Cancer Hospital, UNC Lineberger Comprehensive Cancer Center, School of Medicine, Department of Genetics
  • Palazzo, Juan P
    • Other Affiliation: Department of Pathology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
  • Olopade, Olufunmilayo I
    • Other Affiliation: Department of Medicine, University of Chicago, Illinois, USA
  • Szabo, Aniko
    • Other Affiliation: Department of Oncological Sciences, Huntsman Cancer Institute, SLC, Utah, USA
  • Perou, Charles
    • Affiliation: N.C. Cancer Hospital, UNC Lineberger Comprehensive Cancer Center, School of Medicine, Department of Genetics
  • Bernard, Philip S
    • Other Affiliation: The ARUP Institute for Clinical and Experimental Pathology, SLC, Utah, USA; Department of Pathology, University of Utah School of Medicine, SLC, Utah, USA
Abstract
  • 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 qRTPCR 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 metagene (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 × 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.
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  • Article
Rights statement
  • In Copyright
Rights holder
  • Laurent Perreard et al.; licensee BioMed Central Ltd.
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Journal title
  • Breast Cancer Research
Journal volume
  • 8
Journal issue
  • 2
Page start
  • R23
Language
  • English
Is the article or chapter peer-reviewed?
  • Yes
ISSN
  • 1465-5411
Bibliographic citation
  • Breast Cancer Research. 2006 Apr 20;8(2):R23
Publisher
  • BioMed Central Ltd
Access right
  • Open Access
Date uploaded
  • January 20, 2017

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