Dataset Information

Biological Classification of Breast Cancer by Real-Time Quantitative RTPCR: Comparisons to Microarray and Histopathology

ABSTRACT: Microarrays have shown that gene expression patterns can be used to molecularly classify breast cancers into distinct and clinically significant groups. In order to translate these profiles into routine diagnostics, we have recapitulated a microarray breast cancer classification using real-time quantitative (q)RT-PCR. We performed statistical analyses on multiple independent microarray datasets to select an “intrinsic” gene set that can classify breast tumors into four different subtypes designated as Luminal, Normal-like, HER2+/ER-, and Basal-like. A minimal gene set from the microarray “intrinsic” list, and additional genes important for outcome (e.g., proliferation genes), were used to develop a real-time qRT-PCR assay comprised of 53 classifiers and 3 housekeepers. We prospectively compared the expression data and classifications from microarray and real-time qRT-PCR using 123 unique breast samples (117 invasive carcinomas, 1 fibroadenoma and 5 normal tissues) and 3 cells lines. The overall correlation for the 50 genes in common between microarray and qRT-PCR was 0.76. There was 91% (114/126) concordance in the hierarchical clustering classification of the real-time qRT-PCR minimal “intrinsic” gene set (37 genes) and the larger (402 genes) microarray gene set from which the PCR list was derived. As expected, the Luminal tumors (ER+) had a significantly better outcome than the HER2+/ER- (p=0.043) and Basal-like tumors (p=0.001). High expression of the proliferation genes GTBP4 (p=0.011), HSPA14 (p=0.023), and STK6 (p=0.027) were significant predictors of relapse free survival (RFS) independent of grade and stage. Our study shows that genomic microarray data can be translated into a qRT-PCR diagnostic assay that enhances the ability to predict outcome and may therefore improve the standard of care in breast cancer. Keywords: other

ORGANISM(S): Homo sapiens

PROVIDER: GSE2607 | GEO | 2006/01/31

SECONDARY ACCESSION(S): PRJNA92079

REPOSITORIES: GEO

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Project description:Ductal carcinoma in situ (DCIS) is a non-invasive form of breast cancer where cells restricted to the ducts exhibit an atypical phenotype. Some DCIS lesions are believed to rapidly transit to invasive ductal carcinomas (IDCs), while others remain unchanged. Existing classification systems for DCIS fail to identify those lesions that transit to IDC. We studied gene expression patterns of 31 pure DCIS, 36 pure invasive cancers and 42 cases of mixed diagnosis (invasive cancer with an in situ component) using Agilent Whole Human Genome Oligo Microarrays 44k. Six normal breast tissue samples were also included as controls. qRT-PCR was used for validation. All DCIS and invasive samples could be classified into the intrinsic molecular subtypes defined for invasive breast cancer. Hierarchical clustering establishes that samples group by intrinsic subtype, and not by diagnosis. We observed heterogeneity in the transcriptomes among DCIS of high histological grade and identified a distinct subgroup containing seven of the 31 DCIS samples with gene expression characteristics more similar to advanced tumours. A set of genes independent of grade, ER-status and HER2-status was identified by logistic regression that univariately classified a sample as belonging to this distinct DCIS subgroup. qRT-PCR of single markers clearly separated this DCIS subgroup from the other DCIS, and contains samples from several histopathological and intrinsic molecular subtypes. The genes that differentiate between these two types of DCIS suggest several processes related to the re-organisation of the microenvironment. This raises interesting possibilities for identification of DCIS lesions both with and without invasive characteristics, which potentially could be used in clinical assessment of a woman's risk of progression, and lead to improved management that would avoid the current over- and under-treatment of patients. Breast cancer samples, 31 pure DCIS patients, 36 IDC patients, 42 mixed and 6 normal.

Dataset Information

Biological Classification of Breast Cancer by Real-Time Quantitative RTPCR: Comparisons to Microarray and Histopathology

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