Development of an assay to predict oxaliplatin sensitivity from formalin-fixed, paraffin-embedded (FFPE) colorectal cancer tissues.

Abstract

429 Background: Genomic profiling has improved our understanding of the underlying biology of tumors, accuracy of diagnosing disease, predictions of the courses of disease, and ability to determine the therapeutic agents that will be most effective in the treatment of particular tumors. However, in order for assays involving microarray data to be useful in the clinical setting, the ability to generate reliable and consistent data from FFPE tissues is essential. Methods: Cancer cell lines from the NCI-60 collection exhibiting greatest sensitivity or resistance to oxaliplatin were identified. These cells were grown in culture, fixed for 24 hours in formalin, and paraffin-embedded. RNA from the FFPE cells was isolated, amplified, and hybridized to Affymetrix arrays. A Bayesian binary regression analysis was used to generate a predictor of oxaliplatin sensitivity from the gene expression data. Metastatic derived xenografts (MDXs) from resected colorectal tumors were established and treated with oxaliplatin. Samples from tumors prior to treatment were paraffin-embedded and used for RNA extraction, amplification, and hybridization to Affymetrix arrays. The gene expression signature predicting sensitivity to oxaliplatin was then validated with response data from MDXs treated with oxaliplatin. Results: A predictor consisting of 300 genes that could predict sensitivity to oxaliplatin was generated using FFPE samples. Significant correlation was observed between the predicted probability of oxaliplatin sensitivity and the tumor growth inhibition measurement for a given MDX (p=0.0012). Conclusions: Reliable and consistent predictions of oxaliplatin sensitivity can be obtained from gene expression data from FFPE tissues. This method has potential utility in the clinical setting. The ability to predict response to a therapeutic in a FFPE sample has the potential to guide the choice of therapeutics, resulting in an option that could be most effective in treating an individual with metastatic colorectal cancer. No significant financial relationships to disclose.