Volume 7 Supplement 1
The role of gene expression profiling by microarray analysis for prognostic classification of breast cancer
- MJ van de Vijver1
© BioMed Central 2005
Published: 27 May 2005
Prognostic and predictive factors play important roles in the treatment of breast cancer. Genome-wide monitoring of gene expression using DNA microarrays makes it possible to study thousands of genes in a tumour sample in a single experiment. By looking for an association between the gene expression pattern and tumour behaviour, it should be possible to identify new prognostic and predictive factors.
We used gene expression profiling using two different microarray platforms: one containing 25,000 oligonucleotide probes and one containing 18,000 cDNA probes. To obtain prognostic gene expression profiles, we isolated RNA from tumours from a series of 295 patients younger than 53 years presenting with stage I and II breast cancer treated at our institute between 1984 and 1993. The expression of 25,000 genes was assessed, and using various statistical approaches correlation of gene expression with distant metastasis-free probability and overall survival was assessed [1–3]. In addition, we started studies to obtain gene expression profiles predicting response to specific chemotherapy regimens. Within a single-institution, randomized phase II trial, patients with locally advanced breast cancer received six courses of either AC (n = 24) or AD (n = 24) containing neoadjuvant chemotherapy. Gene expression profiles for 18,000 genes were generated from core needle biopsies obtained before treatment and correlated with the response of the primary tumour to the chemotherapy administered . Additionally, pretreatment gene expression profiles were compared with those in tumours remaining after chemotherapy.
We previously identified a 70-gene expression profile associated with increased risk for developing distant metastases within 5 years [1, 2]. More recently, we studied a Wound Signature in these same tumors . By combining the 70-gene expression profile to subdivide the tumours into 'good prognosis' and 'poor prognosis' tumours, and the Wound signature to subdivide tumours into 'activated' and 'quiescent' tumours, subgroups of patients with markedly different prognosis can be identified. Additional gene expression signatures are being tested in this series of tumours to arrive at an optimal prognostic classifier and to obtain improved insight into breast cancer biology.
In the study to identify predictive profiles, 10 (20%) of the 48 patients showed (near) pathological complete remission of the primary tumour after treatment . No gene expression pattern correlating with response could be identified for all patients, or for the AC or AD treated groups separately.
Various gene expression profiles in breast cancer are associated with the propensity of the tumour to develop distant metastases. Gene expression profile predicting the response of primary breast carcinomas to AC or AD based neoadjuvant chemotherapy are most likely to be very subtle and cannot be detected when small series of patients are studied. Genetic tests derived from gene expression profiling studies are likely to become useful as prognostic and predictive tests to guide clinical decision making in the treatment of primary breast cancer.
- van't Veer LJ, Dai HY, van de Vijver MJ, et al: Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002, 415: 530-536. 10.1038/415530a.View ArticleGoogle Scholar
- van de Vijver MJ, He YD, van't Veer LJ, et al: A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002, 347: 1999-2009. 10.1056/NEJMoa021967.View ArticlePubMedGoogle Scholar
- Chang HY, Nuyten DSA, Sneddon JB, et al: Robustness, scalability, and integration of a wound-like gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci USA. 2005, 102: 3738-3743. 10.1073/pnas.0409462102.View ArticlePubMedPubMed CentralGoogle Scholar
- Hannemann J, Oosterkamp HM, Bosch CAJ, et al: Changes in gene expression associated with response to neoadjuvant chemotherapy. J Clin Oncol. 2005.Google Scholar