Gene expression of vascular endothelial growth factor A, thymidylate synthase, and tissue inhibitor of metalloproteinase 3 in prediction of response to bevacizumab treatment in colorectal cancer patients

BACKGROUND: Regimens containing bevacizumab and 5-fluorouracil have achieved substantial progress in the treatment of colorectal cancer. However, individual responses to bevacizumab vary widely in regard to efficacy and toxicity. OBJECTIVE: To be able to select patients who would benefit from bevacizumab, we aimed to establish a predictor model for response to bevacizumab therapy based on gene expression profiles. DESIGN AND SETTING: Retrospective analysis of tumor samples in the laboratory. PATIENTS: The patient population comprised 25 patients with metastatic colorectal cancer treated with bevacizumab with either modified FOLFOX6 or FOLFIRI, from whom tumor samples were available for gene expression analysis. MAIN OUTCOME MEASURES: Response Evaluation Criteria in Solid Tumors were used to classify patients as responders or nonresponders to chemotherapy. Geneexpression profiles were determined with both microarray analysis and quantitative, real-time reverse-transcriptase polymerase chain reaction, and responders were compared with nonresponders, correcting for multiple comparisons. Genes that discriminated between groups on both analyses with the greatest accuracy were selected for the predictive model. Between-group differences in protein expression were confirmed with polymerase chain reaction and immunohistochemical staining. RESULTS: From 19 probes that differentiated between responders and nonresponders on microarray analyses, we identified 13 genes that were differentially expressed between responders and nonresponders on both microarray and real-time reverse-transcriptase polymerase chain reaction. A model using the genes for vascular endothelial growth factor-A, thymidylate synthase, and tissue inhibitor of metalloproteinase 3 predicted response to bevacizumab therapy with an accuracy of 96%, sensitivity of 90.9% (10/11), specificity of 100% (14/14), positive predictive value of 100% (10/10), and negative predictive value of 93.3% (14/15). The protein expression of vascular endothelial growth factor-A, thymidylate synthase, and tissue inhibitor of metalloproteinase 3 correlated with the findings of mRNA expression analyses. LIMITATIONS: Validation of the model in a different cohort of patients is necessary. CONCLUSIONS: The present predictive model based on quantitative, real-time, reverse-transcriptase polymerase chain reaction assessment of vascular endothelial growth factor-A, thymidylate synthase, and tissue inhibitor of metalloproteinase 3 may enable selection of colorectal cancer patients who would benefit from bevacizumab therapy.