Related Experiment Video
Updated: Jun 26, 2026
Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
Published on: February 21, 2015
Molecular characterization of breast cancer with high-resolution oligonucleotide comparative genomic hybridization
Fabrice Andre1, Bastien Job, Philippe Dessen
1Translational Research Unit, UPRES03535, Paris Sud University, Orsay, France. fandre@igr.fr
Purpose:
We used high-resolution oligonucleotide comparative genomic hybridization (CGH) arrays and matching gene expression array data to identify dysregulated genes and to classify breast cancers according to gene copy number anomalies.
Experimental Design:
DNA was extracted from 106 pretreatment fine needle aspirations of stage II-III breast cancers that received preoperative chemotherapy. CGH was done using Agilent Human 4 x 44K arrays. Gene expression data generated with Affymetrix U133A gene chips was also available on 103 patients. All P values were adjusted for multiple comparisons.
Results:
The average number of copy number abnormalities in individual tumors was 76 (range 1-318). Eleven and 37 distinct minimal common regions were gained or lost in >20% of samples, respectively. Several potential therapeutic targets were identified, including FGFR1 that showed high-level amplification in 10% of cases. Close correlation between DNA copy number and mRNA expression levels was detected. Nonnegative matrix factorization (NMF) clustering of DNA copy number aberrations revealed three distinct molecular classes in this data set. NMF class I was characterized by a high rate of triple-negative cancers (64%) and gains of 6p21. VEGFA, E2F3, and NOTCH4 were also gained in 29% to 34% of triple-negative tumors. A gain of ERBB2 gene was observed in 52% of NMF class II and class III was characterized by a high rate of estrogen receptor-positive tumors (73%) and a low rate of pathologic complete response to preoperative chemotherapy (3%).
Conclusion:
The present study identified dysregulated genes that could classify breast cancer and may represent novel therapeutic targets for molecular subsets of cancers.
