Department of Surgery and Cancer, Imperial College London, 10th Floor QEQM Wing, St Mary's Hospital, London, United Kingdom.
Department of Surgery and Cancer, Imperial College London, 6th Floor Sir Alexander Fleming Building, South Kensington Campus, London, United Kingdom.
Cancer Res. 2016 Oct 1;76(19):5647-5656. doi: 10.1158/0008-5472.CAN-16-0699. Epub 2016 Jun 30.
Histopathological assessment of lymph node metastases (LNM) depends on subjective analysis of cellular morphology with inter-/intraobserver variability. In this study, LNM from esophageal adenocarcinoma was objectively detected using desorption electrospray ionization-mass spectrometry imaging (DESI-MSI). Ninety lymph nodes (LN) and their primary tumor biopsies from 11 esophago-gastrectomy specimens were examined and analyzed by DESI-MSI. Images from mass spectrometry and corresponding histology were coregistered and analyzed using multivariate statistical tools. The MSIs revealed consistent lipidomic profiles of individual tissue types found within LNs. Spatial mapping of the profiles showed identical distribution patterns as per the tissue types in matched IHC images. Lipidomic profile comparisons of LNM versus the primary tumor revealed a close association in contrast to benign LN tissue types. This similarity was used for the objective prediction of LNM in mass spectrometry images utilizing the average lipidomic profile of esophageal adenocarcinoma. The multivariate statistical algorithm developed for LNM identification demonstrated a sensitivity, specificity, positive predictive value, and negative predictive value of 89.5%, 100%, 100%, and 97.2%, respectively, when compared with gold-standard IHC. DESI-MSI has the potential to be a diagnostic tool for perioperative identification of LNM and compares favorably with techniques currently used by histopathology experts. Cancer Res; 76(19); 5647-56. ©2016 AACR.
淋巴结转移(LNM)的组织病理学评估取决于对细胞形态的主观分析,存在观察者内和观察者间的变异性。在这项研究中,使用解吸电喷雾电离-质谱成像(DESI-MSI)对食管腺癌的 LNM 进行了客观检测。对 11 例食管胃切除术标本的 90 个淋巴结(LN)及其原发肿瘤活检进行了检查和分析。使用多元统计工具对质谱和相应组织学的图像进行了共定位和分析。MSI 显示了 LN 内单个组织类型的一致脂质组谱。基于组织类型的图谱空间映射显示了与匹配 IHC 图像相同的分布模式。与良性 LN 组织类型相比,LNM 与原发肿瘤的脂质组谱比较显示出密切的关联。利用食管腺癌的平均脂质组谱,在质谱图像中对 LNM 进行客观预测时,这种相似性被用于对 LNM 进行客观预测。用于 LNM 识别的多元统计算法与金标准 IHC 相比,其灵敏度、特异性、阳性预测值和阴性预测值分别为 89.5%、100%、100%和 97.2%。DESI-MSI 有可能成为围手术期识别 LNM 的诊断工具,与组织病理学专家目前使用的技术相比具有优势。Cancer Res; 76(19); 5647-56. ©2016 AACR.