Loss of BRCA2 correlates with reduced long-term survival of sporadic breast cancer patients.
BACKGROUND
The present study was undertaken to analyze the prognostic value of loss of heterozygosity (LOH) at 13q12-13, 17q21 and 17p13, harboring BRCA2, BRCA1 and p53 to predict the clinical course of sporadic breast cancer patients.


METHODS
LOH analysis was performed by PCR amplification of genomic DNA using nine microsatellite markers. Fifty-three sporadic breast cancer patients were followed clinically for a median of 55 months. Disease-free and overall survival was documented as the endpoint for statistical evaluation.


RESULTS
Patients presenting with LOH in their tumor samples at at least one of the loci examined were found to have a reduced overall survival time compared to those retaining heterozygosity (61% versus 48%). Focusing on the three target regions, patients with LOH at the BRCA2 locus died earlier compared to patients retaining heterozygosity (69% versus 50%) and, in addition, BRCA2 LOH-positive patients showed a shorter metastasis-free interval (30 versus 37 months). In a multivariate analysis, LOH at the 13q12-13 locus was found to be a significant predictor for reduced long-term survival (risk ratio 2.33, 95% C.I., 1.0-5.3; p<0.05) and earlier metastases manifestation (risk ratio 2.32, 95% C.I., 1.0-5.3, p<0.05).


CONCLUSIONS
Allelic loss at the BRCA2 locus may be of use as a negative predictor for metastases-free and overall survival.
Hampl
J A
JA
Department of Neurosurgery, University of Cologne, 50931 Cologne, Germany. juergen.hampl@medizin.uni-koeln.de
Hampl
M
M
Reiss
G
G
Koch
R
R
Saeger
H D
HD
Schackert
H K
HK
D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000235:genetics*; D002882:Chromosomes, Human, Pair 13 / Q000235:genetics; D002886:Chromosomes, Human, Pair 17 / Q000235:genetics; D018572:Disease-Free Survival; D005260:Female; D024522:Genes, BRCA2; D006801:Humans; D019656:Loss of Heterozygosity; D018895:Microsatellite Repeats / Q000235:genetics; D008875:Middle Aged; D015999:Multivariate Analysis