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Health & Medicine

Adapting the MasSpec Pen for noninvasive detection of oral cavity squamous cell carcinoma

| Source: PNAS

Oral Cavity Squamous Cell Carcinoma (OCSCC) is a common and deadly form of head and neck cancer which disproportionately afflicts low- and middle-income communities. Since the majority of OCSCC lesions are visible within the oral cavity, visual exam-guided biopsy remains the standard method for detection. However, visual examination can be subjective, biopsy can be painful, and pathological evaluation can be time consuming. With the goal of developing an accessible and noninvasive platform for O

Oral Cavity Squamous Cell Carcinoma (OCSCC) is a common and deadly form of head and neck cancer which disproportionately afflicts low- and middle-income communities. Since the majority of OCSCC lesions are visible within the oral cavity, visual exam-guided biopsy remains the standard method for detection. However, visual examination can be subjective, biopsy can be painful, and pathological evaluation can be time consuming. With the goal of developing an accessible and noninvasive platform for OCSCC detection, we adapted the MSPen technology as a modular system for direct molecular analysis of OCSCC in vivo. The modular MSPen (Mod-MSPen) system consists of a controller unit and a disposable handheld device that directly contacts tissues, performs gentle liquid extraction of molecules, and collects the sample in a vial for subsequent mass spectrometric analysis. We have used the Mod-MSPen to analyze OCSCC and normal oral tissues spanning various anatomical areas: tongue, buccal mucosa, gingiva, and floor of mouth. The molecular data obtained presented distinct trends in the abundances of metabolites such as glutamate, which was consistently observed in higher relative abundances in OCSCC tissues when compared to normal oral tissues. These trends were corroborated using mass spectrometry imaging, which revealed clear spatio-molecular differences in OCSCC and adjacent normal oral mucosa tissues. Statistical classifiers built from the data provided high performance for disease detection, with >95% agreement with standard histopathology. To evaluate clinical suitability, we also deployed the Mod-MSPen for in vivo analysis of OCSCC and normal oral tissues alongside clinical procedures.

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