Imaging mass spectrometry (IMS) to discriminate metastasis of breast from pancreatic cancer in FFPE tissues. MALDI Imaging zur Differenzierung von Mamma- und Pankreaskarzinomen in FFPE Gewebsproben

Aim: Personalized cancer therapy of metastatic disease relies on accurate classification of the tumor. The primary site of cancer usually dictates the treatment, expected outcome, and overall prognosis. At present, histopathological diagnosis is based on clinical information, morphology, immunohistochemistry, and may include molecular methods. This process is complex, expensive, requires an experienced pathologist and may be time consuming. The purpose of this study is to establish IMS as a new tool to classify metastases in regard to the tumor of origin in FFPE tissues. Methods: 96 breast and 92 pancreatic biopsies in a tissue microarray from different patients, were included in our study. Additionally, six liver metastases, including three breast and three pancreatic carcinomas were used for proteomic classification. All samples were subjected to trypsin and matrix deposition using the ImagePrep device (Bruker, Bremen), and were subsequently analyzed by MALDI IMS using a Bruker Autoflex Speed mass spectrometer. Statistical analysis was performed by ClinProTools 3.0. Results: 29 breast and 32 pancreas tumor samples were used as training set to build a classification model using a support vector machine algorithm (SVM). 17 m/z peptide species allowed to establish SVM classifiers, determined through statistical comparison by means of a Wilcoxon/Kruskal-Wallis test (PWKW< 0.05),which could discriminate breast from pancreas carcinomas, with high recognition capability (100%) and an overall cross validation of 99.21%. These classifiers were applied to two test sets including 67 breast and 60 pancreas primary carcinoma patients. SVM model classified breast from pancreas carcinoma with an overall accuracy of 83.38%, a sensitivity of 85.95% and a specificity of 76.96%. We applied our model to classify three breast and three pancreatic individual liver metastasis samples. Classification results were concordant to the histopathological diagnosis made by pathologists. Conclusion: We have developed an IMS classification model using FFPE breast and pancreatic tumor specimens and found proteomic signatures that accurately could discern breast from pancreatic carcinomas. Furthermore, this classification model was applied to metastasis of breast and pancreatic carcinoma with correct identification of the adequate tumor type.