Cedric D'Hue1, Michael Moore2, Don-John Summerlin3, Alan Jarmusch1, Clint Alfaro1, Avinash Mantravadi4, Arnaud Bewley2, D Gregory Farwell2, R Graham Cooks1. 1. Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN, 47907-2084, USA. 2. Comprehensive Cancer Center, Department of Otolaryngology-Head and Neck Surgery, University of California at Davis, 2521 Stockton Blvd., Suite 7200, Sacramento, CA, 95817, USA. 3. Indiana University Health Pathology Laboratory, 350 West 11th Street, Room 4022, Indianapolis, IN, 46202-4108, USA. 4. Indiana University Department of Otolaryngology-Head and Neck Surgery, 550 N. University Blvd., Rm 3170, Indianapolis, IN, 46202, USA.
Abstract
RATIONALE: Desorption electrospray ionization mass spectrometry (DESI-MS) has demonstrated utility in differentiating tumor from adjacent normal tissue in both urologic and neurosurgical specimens. We sought to evaluate if this technique had similar accuracy in differentiating oral tongue squamous cell carcinoma (SCC) from adjacent normal epithelium due to current issues with late diagnosis of SCC in advanced stages. METHODS: Fresh frozen samples of SCC and adjacent normal tissue were obtained by surgical resection. Resections were analyzed using DESI-MS sometimes by a blinded technologist. Normative spectra were obtained for separate regions containing SCC or adjacent normal epithelium. Principal Component Analysis and Linear Discriminant Analysis (PCA-LDA) of spectra were used to predict SCC versus normal tongue epithelium. Predictions were compared with pathology to assess accuracy in differentiating oral SCC from adjacent normal tissue. RESULTS: Initial PCA score and loading plots showed clear separation of SCC and normal epithelial tissue using DESI-MS. PCA-LDA resulted in accuracy rates of 95% for SCC versus normal and 93% for SCC, adjacent normal and normal. Additional samples were blindly analyzed with PCA-LDA pixel-by-pixel predicted classifications as SCC or normal tongue epithelial tissue and compared against histopathology. The m/z 700-900 prediction model showed a 91% accuracy rate. CONCLUSIONS: DESI-MS accurately differentiated oral SCC from adjacent normal epithelium. Classification of all typical tissue types and pixel predictions with additional classifications should increase confidence in the validation model.
RATIONALE: Desorption electrospray ionization mass spectrometry (DESI-MS) has demonstrated utility in differentiating tumor from adjacent normal tissue in both urologic and neurosurgical specimens. We sought to evaluate if this technique had similar accuracy in differentiating oral tongue squamous cell carcinoma (SCC) from adjacent normal epithelium due to current issues with late diagnosis of SCC in advanced stages. METHODS: Fresh frozen samples of SCC and adjacent normal tissue were obtained by surgical resection. Resections were analyzed using DESI-MS sometimes by a blinded technologist. Normative spectra were obtained for separate regions containing SCC or adjacent normal epithelium. Principal Component Analysis and Linear Discriminant Analysis (PCA-LDA) of spectra were used to predict SCC versus normal tongue epithelium. Predictions were compared with pathology to assess accuracy in differentiating oral SCC from adjacent normal tissue. RESULTS: Initial PCA score and loading plots showed clear separation of SCC and normal epithelial tissue using DESI-MS. PCA-LDA resulted in accuracy rates of 95% for SCC versus normal and 93% for SCC, adjacent normal and normal. Additional samples were blindly analyzed with PCA-LDA pixel-by-pixel predicted classifications as SCC or normal tongue epithelial tissue and compared against histopathology. The m/z 700-900 prediction model showed a 91% accuracy rate. CONCLUSIONS: DESI-MS accurately differentiated oral SCC from adjacent normal epithelium. Classification of all typical tissue types and pixel predictions with additional classifications should increase confidence in the validation model.
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