Lab-On-a-Chip Biosensor Analysis of Oral Cancer Cytobrush Biopsy Specimens

There is an unmet need for a non-invasive diagnostic test that can rapidly and reliably distinguish oral premalignancies (OPM) and oral cancers (OC) from benign oral mucosal lesions. LOC biosensor technology enables high-throughput quantitative analyses morphological features and biomarker expression in single cell suspensions. Our previous study has shown that Extracellular Matrix Metalloproteinase Inducer (EMMPRIN/CD147) is an ideal molecular biomarker for OC diagnosis. AIM: To test the feasibility of using LOC biosensor to assess cellular morphology and CD147 expression in oral transepithelial brush biopsies. Methods: OC (MDA-686TU) and OPM (MSK-Leuk1) derived cell lines and oral CDX transepithelial brush biopsies of OC (n=10), OPM (n=4) and normal oral mucosa (n=2) were used for LOC analysis. Remaining cells in the cytobrush specimens were stained with PAP for cytologic examination and immunohistochemically for CD147 expression levels. Paraffin-embedded patient-matched tissue sections were concomitantly examined for CD147 expression by immunohistochemistry. Results: LOC sensor analysis demonstrated CD147 overexpression in Leuk1 and 686Tu cell lines in concordance with their immunoblotting, immunohistochemical and microarray expression data. Elevated expression levels of CD147 were noted in transepithelial cells obtained from OPM than OC. CD147 expression levels in OPM and OC cells as determined by LOC analyses correlated with its expression levels in their respective tissue sections. Conclusion: LOC biosensor system detected CD147 over-expressing cells in OPM and OC cytobrush specimens of in less than one-tenth the time of traditional methods. Our data suggest that CD147 biomarker phenotyping in combination with morphological analysis may be ideal to distinguish OPM and OC cells from non-cancerous cells with high sensitivity and specificity. Currently, further testing in a larger sample size is in progress to validate the applicability of LOC sensor based analysis.

Grant Support: NIH grant RO3 DE15723 (NV)