Feasibility of desorption electrospray ionization mass spectrometry for diagnosis of oral tongue squamous cell carcinoma
| Autor: | Michael G. Moore, Arnaud F. Bewley, Clint M. Alfaro, D. Gregory Farwell, R. Graham Cooks, Avinash V. Mantravadi, Don John Summerlin, Cedric D'Hue, Alan K. Jarmusch |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Spectrometry Mass Electrospray Ionization medicine.medical_specialty Pathology Tongue squamous cell carcinoma Desorption electrospray ionization mass spectrometry Analytical chemistry 01 natural sciences Article Analytical Chemistry 03 medical and health sciences Clinical Research medicine Carcinoma Humans Tongue Neoplasm Dental/Oral and Craniofacial Disease Spectroscopy Cancer Principal Component Analysis Chemistry Spectrometry 010401 analytical chemistry Organic Chemistry Electrospray Ionization Discriminant Analysis Mass Biological Sciences medicine.disease Epithelium 0104 chemical sciences Tongue Neoplasms Tumor Burden stomatognathic diseases 030104 developmental biology medicine.anatomical_structure Late diagnosis Squamous Cell Chemical Sciences Carcinoma Squamous Cell Fresh frozen Earth Sciences Histopathology Digestive Diseases |
| Zdroj: | Rapid communications in mass spectrometry : RCM, vol 32, iss 2 |
| Popis: | 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. Conclusion 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. |
| Databáze: | OpenAIRE |
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