Optical molecular imaging of multiple biomarkers of epithelial neoplasia: epidermal growth factor receptor expression and metabolic activity in oral mucosa

Transl Oncol. 2012 Jun;5(3):160-71. doi: 10.1593/tlo.11310. Epub 2012 Jun 1.

Abstract

Biomarkers of cancer can indicate the presence of disease and serve as therapeutic targets. Our goal is to develop an optical imaging approach using molecularly targeted contrast agents to assess several centimeters of mucosal surface for mapping expression of multiple biomarkers simultaneously with high spatial resolution. The ability to image biomarker expression level and heterogeneity in vivo would be extremely useful for clinical cancer research, patient selection of personalized medicine, and monitoring therapy. In this proof-of-concept ex vivo study, we examined correlation of neoplasia with two clinically relevant biomarkers: epidermal growth factor receptor (EGFR) and metabolic activity. Two hundred eighty-six unique locations in nine samples of freshly resected oral mucosa were imaged after topically applying optical imaging agents EGF-Alexa 647 (to target EGFR) and 2-(N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)amino)-2-deoxyglucose (to target metabolic activity). Quantitative features were calculated from resulting fluorescence images and compared with tissue histopathology maps. The EGF-Alexa 647 signal correlated well with EGFR expression as indicated by immunohistochemistry. A classification algorithm for presence of neoplasia based on the signal from both contrast agents resulted in an area under the curve of 0.83. Regions with a posterior probability from 0.80 to 1.00 contained more than 50% neoplasia 99% (84/85) of the time. This study demonstrates a proof-of-concept of how noninvasive optical imaging can be used as a tool to study expression levels of multiple biomarkers and their heterogeneity across a large mucosal surface and how biomarker characteristics correlate with presence of neoplasia. Applications of this approach include predicting regions with the highest likelihood of disease, elucidating the role of biomarker heterogeneity in cancer biology, and identifying patients who will respond to targeted therapy.