Gene expression profiles can predict panitumumab monotherapy responsiveness in human tumor xenograft models.

BACKGROUND: Epidermal growth factor receptor (EGFR)-targeted agents have demonstrated clinical benefit in patients with cancer. Identifying tissue-of-origin-independent predictive biomarkers is important to optimally treat patients. We sought to identify a gene array profile that could predict responsiveness to panitumumab, a fully human EGFR-binding antibody, using preclinical models of human cancer.
METHODS: Mice bearing 25 different xenograft models were treated twice weekly with panitumumab or immunoglobulin G2 control to determine their responsiveness to panitumumab. Samples from these xenografts and untreated xenografts were arrayed on the Affymetrix human U133A gene chip to identify gene sets predicting responsiveness to panitumumab using univariate and multivariate analyses. The predictive models were validated using the leave-one-group-out (LOO) method.
RESULTS: Of the 25 xenograft models tested, 12 were responsive and 13 were resistant to panitumumab. Unsupervised analysis demonstrated that the xenograft models clustered by tissue type rather than responsiveness to panitumumab. After normalizing for tissue effects, samples clustered by responsiveness using an unsupervised multidimensional scaling. A multivariate selection algorithm was used to select 13 genes that could stratify xenograft models based on responsiveness after adjustment for tissue effects. The method was validated using the LOO method on a training set of 22 models and confirmed independently on three new models. In contrast, a univariate gene selection method resulted in higher misclassification rates.
CONCLUSION: A model was constructed from microarray data that prospectively predict responsiveness to panitumumab in xenograft models. This approach may help identify patients, independent of disease origin, likely to benefit from panitumumab.