Development of a cell line based gene classifier to predict therapeutic response of head and neck cancer patients
Authors: Seema Gupta, Sana Ghafoor, Craig Wood, Kathryn Masker, Robert Brown, Amy Law, Phillip Pellitteri, Ping Zhang, David Carey, Mansoor Ahmed
Journal: Clinical Cancer Research (2007)

C57  A major goal of cancer biology is the development of a successful predictive assay to measure therapeutic response in solid tumors. We have developed a classifier that predicts the clinical response to chemotherapy and radiation therapy. In this study, we developed NFκB targeted gene classifier based on targeted signaling pathway gene expression profiles and surviving fraction values from four squamous cell carcinoma of head and neck (SCCHN) tumor cell lines for which the response to radiation or chemotherapy or the combination of chemo- and radiotherapy is well documented. SQ20B and T409 cells were both radio- and chemotherapy resistant compared to SCC61 and T167 cells that were sensitive to these treatments. The gene selection step identified eight novel NFκB dependent genes of which expression values correlated with resistance to radiation and chemotherapy. The clinical validation of this classifier was achieved by using tumor specimens from 22 untreated SCCHN cancer patients. In particular, from these tumor specimens, we isolated mRNA to test the expression levels of 84 genes involved in NFκB signaling pathway using real time RT PCR. In this approach, we considered therapy-based killing of tumor cells as a continuous variable, significance analysis of targeted gene arrays is used for gene selection, and a multivariate linear regression model is used for response prediction. These data were stratified using the classifier and response was predicted.Eighteen genes with more than 4-fold difference (14 up-and 4 down-regulated) were identified in radioresistant cells compared to radiosensitive cells. Two down-regulated genes were classified as NFκB responsive genes (ICAM1 and CSF2) and one was for activation of NFkB pathway (IL8). Based on the p value of 0.1, out of 4 down-regulated genes in radioresistant cells, 2 could be eliminated. Similarly, out of 14 up-regulated genes, only 4 had good significance. Three belong to positive regulation of IκB kinase and the NFκB cascade (TBK1, HTR2B and BIRC2) and one to activation of NFκB pathway (TLR6). The predicted response was correlated with actual clinical outcome data of these patients using Epic database. The classifier was able to predict the response correctly in 90% of the cases. However, using McNemar9s statistical method (p-value=0.001) only 31% of the assayed genes agreed between cell lines and patient samples in their expression pattern. While, 39% of the genes responsible for resistance were down-regulated in cell lines, only 7% of the genes were down-regulated in patient samples. In conclusion, we establish that therapeutic sensitivity can be predicted based on gene expression profiles and we introduce an approach to the identification of novel molecular markers of treatment sensitivity. Although the classifier predicted truly the clinical outcome in these patients, which has significant implications, analysis of more patient samples as well as other pro-survival signaling pathways is warranted for its universal application.