D'Hue Cedric, Moore Michael, Summerlin Don-John, Jarmusch Alan, Alfaro Clint, Mantravadi Avinash, Bewley Arnaud, Gregory Farwell D, Cooks R Graham
Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, IN, 47907-2084, USA.
Comprehensive Cancer Center, Department of Otolaryngology-Head and Neck Surgery, University of California at Davis, 2521 Stockton Blvd., Suite 7200, Sacramento, CA, 95817, USA.
Rapid Commun Mass Spectrom. 2018 Jan 30;32(2):133-141. doi: 10.1002/rcm.8019.
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.
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.
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.
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.
解吸电喷雾电离质谱法（DESI-MS）已证明在区分泌尿外科和神经外科标本中的肿瘤与相邻正常组织方面具有实用性。由于目前晚期口腔鳞状细胞癌（SCC）诊断存在问题，我们试图评估该技术在区分口腔舌鳞状细胞癌与相邻正常上皮组织方面是否具有相似的准确性。
通过手术切除获取SCC和相邻正常组织的新鲜冷冻样本。有时由一名不知情的技术人员使用DESI-MS对切除样本进行分析。获取包含SCC或相邻正常上皮组织的不同区域的标准光谱。使用光谱的主成分分析和线性判别分析（PCA-LDA）来预测SCC与正常舌上皮组织。将预测结果与病理结果进行比较，以评估区分口腔SCC与相邻正常组织的准确性。
最初的PCA得分图和载荷图显示，使用DESI-MS可清晰区分SCC和正常上皮组织。PCA-LDA得出SCC与正常组织的准确率为95%，SCC、相邻正常组织与正常组织的准确率为93%。使用PCA-LDA逐像素地将额外样本预测分类为SCC或正常舌上皮组织，并与组织病理学进行比较。m/z 700 - 900预测模型的准确率为91%。
DESI-MS能准确区分口腔SCC与相邻正常上皮组织。对所有典型组织类型进行分类以及进行额外分类的像素预测应能提高验证模型的可信度。
