NEOADJUVANT CHEMORADIOTHERAPY RESPONSE PREDICTION USING DNA MICROARRAYS TECHNOLOGY IN OESOPHAGEAL CANCER

Background

The aim of this pilot study was to identify predictive factors of response to neoadjuvant chemoradiotherapy in patients with oesophageal cancer using modern molecular-biological methods. The method of DNA microarrays can determine gene expression level of the whole human genome, nowadays about 23 thousand genes. The method also allows expressed genes variants detection, can also analyze gene polymorphisms and chromosomal rearrangement. In oesophageal cancer, the neoadjuvant therapy is widely used. However, there are no clear predictive factors of therapy response today.

 Patients and methods

Tumour samples were collected during diagnostic endoscopy and fixed in RNA Later before neoadjuvant therapy. Total RNA was isolated from samples using phenol-chloroform method. After RNA isolation, DNA microarray analysis using the GeneChip Human Gene 1.0 ST Array (Affymetrix) was performed Subsequently, a statistical analysis using “R” software and Bioconductor package was done.

 Results

Microarray analysis was performed on a total of 33 patients with oesophageal cancer, diagnosed and treated at The University Hospital Olomouc in the years 2006-2009. Based on the microarray analysis data, unsupervised clustering revealed clustering of adenocarcinomas with good and bad therapeutic response. In supervised clustering analysis, we naturally identified different gene expression profiles (205 genes, p < 0.001, logFC < -2 or > 2) in squamous cell carcinomas and adenocarcinomas. Finally, we identified gene expression profiles that can predict clinical response to neoadjuvant chemoradiotherapy in patients with oesophageal cancer. In total, we found 156 differentially expressed genes (p < 0.001 or logFC < -2 or logFC > 2) identifying patients with good or bad response to the therapy.

 Conclusion

The DNA microarray method can identify gene expression profiles related to predicting therapeutic response. Identification of patients who are likely to benefit from the treatment, will contribute to the individualization of therapy and the rational use of resources. In this pilot study, we identified gene expression profiles based on the differential expression of 156 genes, allowing prediction of neoadjuvant therapy results in oesophageal cancer patients. More validation studies with a larger sample of patients are necessary to confirm our results.

 Acknowledgments of research support This study was supported by grants MSMT CR 6198959216 and LC07017. Infrastructural part of this project (Institute of Molecular and Translational Medicine) was supported from the Operational Programme Research and Development for Innovations (project CZ.1.05/2.1.00/01.0030).

e-mail: josef.srovnal@seznam.cz