Gene expression profiling to predict outcome after chemoradiation in head and neck cancer.

Abstract

PURPOSE

The goal of the present study was to improve prediction of outcome after chemoradiation in advanced head and neck cancer using gene expression analysis.

RESULTS

Using supervised classification on the whole series, gene sets separating good and poor outcome could be found for all end points. However, when splitting tumors into training and validation groups, no robust classifiers could be found. Using Gene Set Enrichment analysis, several gene sets were found to be enriched in locoregional recurrences, although with high false-discovery rates. Previously published signatures for radiosensitivity, hypoxia, proliferation, "wound," stem cells, and chromosomal instability were not significantly correlated with outcome. However, a recently published signature for HNSCC defining a "high-risk" group was shown to be predictive for locoregional control in our dataset.

MATERIALS AND METHODS

We collected 92 biopsies from untreated head and neck cancer patients subsequently given cisplatin-based chemoradiation (RADPLAT) for advanced squamous cell carcinomas (HNSCC). After RNA extraction and labeling, we performed dye swap experiments using 35k oligo-microarrays. Supervised analyses were performed to create classifiers to predict locoregional control and disease recurrence. Published gene sets with prognostic value in other studies were also tested.

CONCLUSION

Gene sets can be found with predictive potential for locoregional control after combined radiation and chemotherapy in HNSCC. How treatment-specific these gene sets are needs further study.