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Agreement in breast cancer classification between microarray and quantitative reverse transcription PCR from fresh-frozen and formalin-fixed, paraffin-embedded tissues. Clin Chem 2007 Jul;53(7):1273-9 PMID: 17525107

Pubmed ID

17525107

Abstract

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.

Author List

Mullins M, Perreard L, Quackenbush JF, Gauthier N, Bayer S, Ellis M, Parker J, Perou CM, Szabo A, Bernard PS

Author

Aniko Szabo PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin




Scopus

2-s2.0-34347388416   52 Citations

MESH terms used to index this publication - Major topics in bold

Algorithms
Breast Neoplasms
Cryopreservation
Female
Fixatives
Formaldehyde
Humans
Oligonucleotide Array Sequence Analysis
Paraffin Embedding
Predictive Value of Tests
Reverse Transcriptase Polymerase Chain Reaction
Specimen Handling
jenkins-FCD Prod-299 9ef562391eceb2b8f95265c767fbba1ce5a52fd6