Identification and validation of an ERBB2 gene expression signature in breast cancers

François Bertucci1, Nathalie Borie, Christophe Ginestier

  • 1Département d'Oncologie Moléculaire, Institut Paoli-Calmettes and UMR119 Inserm, IFR57, and Faculté de Médecine, Université de la Méditerranée, Marseille, France.

Oncogene
|January 27, 2004
PubMed

Insights

Researchers identified a gene expression signature (GES) that accurately distinguishes ERBB2-positive and -negative breast cancer. This signature aids in determining ERBB2 status for targeted therapy like trastuzumab.

Area of Science:

  • Oncology
  • Genomics
  • Molecular Biology

Background:

  • ERBB2 (Erb-B2 receptor tyrosine kinase 2) gene amplification drives mammary oncogenesis.
  • Trastuzumab targets ERBB2-overexpressing breast cancers, necessitating accurate ERBB2 status determination.
  • Current ERBB2 testing involves immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH).

Purpose of the Study:

  • To identify a gene expression signature (GES) associated with ERBB2 status in breast cancer.
  • To evaluate the potential of this GES for classifying ERBB2-positive and -negative tumors.
  • To correlate gene expression findings with protein levels and established diagnostic methods.

Main Methods:

  • Gene expression profiling of 213 breast tumors and 16 cell lines using DiscoveryChip microarrays (~9000 cDNAs).
  • Analysis of differential gene expression between ERBB2-positive and ERBB2-negative samples.
  • Validation of gene expression findings at the protein level using IHC on tissue microarrays.

Main Results:

  • A 36-gene ERBB2-specific GES was identified, including ERBB2 and nearby 17q12 genes.
  • The GES comprised 29 overexpressed and 8 downregulated genes (e.g., ER).
  • The GES accurately differentiated ERBB2-negative/positive and FISH-negative/positive samples.

Conclusions:

  • The identified GES serves as a molecular marker for ERBB2 status in breast cancer.
  • This gene expression signature can complement or potentially refine existing ERBB2 diagnostic methods.
  • The findings support the use of gene expression profiling for classifying breast cancers based on ERBB2 alterations.