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

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.

Figures

Figure 1

Images from two representative specimens. From left to right, images include a white light photograph, fluorescence after incubation with EGF-Alexa 647 to show EGFR expression, fluorescence afterincubation with 2-NBDG to show metabolic activity, and the correspondinghistopathologymap. The specimen shown in the top row hada worst diagnosis of moderate dysplasia; the specimenin the bottom row had a worst diagnosis of cancer. Scale bars, 1 cm. The color scale on the bottom shows the key for the histopathology maps.

Figure 2

A representative set of images to demonstrate the correlation between EGF-Alexa 647 fluorescence intensity and IHC for EGFR. (A) Wide-field fluorescence image of specimen following application of EGF-Alexa 647 with tissue slice selected for IHC outlined in white. (B) Histopathology map of specimen. (C) IHC staining of selected slice as graded by a pathologist is indicated on a scale of 0 to 3 along with the original IHC image. The fluorescence image from the corresponding portion of tissue is also shown, and fluorescence intensity is graphed across the slice on a scale normalized to the maximum value. The original H&E slide and corresponding histologic diagnosis are also shown for reference.

Figure 3

Intensity of IHC staining and corresponding fluorescence intensity graphs for two additional specimens with two slices shown from each: (A) specimen 2, slice M7; (B) specimen 2, slice M10; (C) specimen 6, slice H9; and (D) specimen 6, slice H4.

Figure 4

Representative example of how 50 x 50-pixel regions of interest were selected. This sample contains four normal regions, three regions of mild dysplasia, nine regions of moderate dysplasia, and three regions of severe dysplasia. Quantitative features were calculated from each region of interest. Scale bar, 1 cm.

Figure 5

Scatterplots showing the distribution of feature values within the set of 286 regions; each point represents a single region. Regions are grouped by pathologic diagnosis with green indicating normal epithelium, blue indicating mild dysplasia, pink indicating moderate dysplasia, red indicating severe dysplasia, and black indicating cancer. Horizontal black lines indicate the mean feature value and vertical black lines indicate ±1 standard deviation for each pathologic category. The scatterplots show the following features: (A) mean intensity of the 2-NBDG signal and (B) mean intensity of the EGF-Alexa 647 signal.

Figure 6

(A) Scatterplot of posterior probability by diagnosis using the algorithm. The horizontal line indicates the optimum cutoff at 0.41. The color key is the same as in Figure 5. (B) Receiver operator characteristic curve using a three-feature classificat ion algorithm. The operating point at the optimum cutoff is shown with a blue circle and results in a sensitivity of 73%, a specificity of 77%, and an AUC of 0.83.

Figure 7

(A) Superimposed grid to divide each specimen into a new set of regions of interest. Using the previously developed classification algorithm, posterior probabilities were calculated for each of these new regions of interest. (B) On the basis of two levels of posterior probability, regions identified as most likely to contain neoplasia were marked with black (posterior probability = 0.80–1.00) or gray (posterior probability = 0.60–0.79) stars. Scale bars, 1 cm.

Figure 8

All clinical specimens; regions identified by the algorithm as most likely to contain neoplasia are marked by stars. Black stars indicate the highest level of posterior probability (0.80–1.00) and gray stars indicate the next highest level of posterior probability (0.60–0.79). Regions predicted to contain neoplasia have excellent agreement with the criterion standard of histopathology. Scale bars, 1 cm.