Received on December 5, 2006
Accepted on May 3, 2007

Cancer Diagnostics

Agreement in Breast Cancer Classification between Microarray and Quantitative Reverse Transcription PCR from Fresh-Frozen and Formalin-Fixed, Paraffin-Embedded Tissues

¹ Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT
² The ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT
³ Siteman Cancer Center, Washington University, St. Louis, MO
⁴ Constella Group, Durham, NC
⁵ Department of Genetics and Pathology and Laboratory Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
⁶ Department of Oncological Sciences, Huntsman Cancer Institute, Salt Lake City, UT
⁷ Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT, and The ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT

^* To whom correspondence should be addressed. E-mail: phil.bernard{at}hci.utah.edu.

Background: Microarray studies have identified different molecular subtypes of breast cancer with prognostic significance. To transition these classifications into the clinical laboratory, we have developed a real-time quantitative reverse transcription (qRT)-PCR assay to diagnose the biological subtypes of breast cancer from fresh-frozen (FF) and formalin-fixed, paraffin-embedded (FFPE) tissues.

Methods: We used microarray data from 124 breast samples as a training set for classifying tumors into 4 previously defined molecular subtypes: Luminal, HER2⁺/ER^-, Basal-like, and Normal-like. We used the training set data in 2 different centroid-based algorithms to predict sample class on 35 breast tumors (test set) procured as FF and FFPE tissues (70 samples). We classified samples on the basis of large and minimized gene sets. We used the minimized gene set in a real-time qRT-PCR assay to predict sample subtype from the FF and FFPE tissues. We evaluated primer set performance between procurement methods by use of several measures of agreement.

Results: The centroid-based algorithms were in complete agreement in classification from FFPE tissues by use of qRT-PCR and the minimized "intrinsic" gene set (40 classifiers). There was 94% (33 of 35) concordance between the diagnostic algorithms when comparing subtype classification from FF tissue by use of microarray (large and minimized gene set) and qRT-PCR data. We found that the ratio of the diagonal SD to the dynamic range was the best method for assessing agreement on a gene-by-gene basis.

Conclusions: Centroid-based algorithms are robust classifiers for breast cancer subtype assignment across platforms and procurement conditions.