Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics
Typing of colon and lung adenocarcinoma by high throughput imaging mass spectrometry☆
Graphical abstract
Introduction
In advanced tumor stages diagnosis is made from metastatic tumor tissue. Distinguishing the most common metastatic adenocarcinomas (colon, kidney, breast, pancreas, lung, stomach and ovary) from each other is daily routine in clinical pathology [1]. The main metastatic localizations in the human body are liver, lung and bone. Despite histological, immunohistological (IHC) and molecular pathological techniques, the primary site of 3–5% of all epithelial tumors cannot be determined (cancer of unknown primary, CUP) [2]. Among these, 80% represent adenocarcinomas [1]. Interestingly, lung cancer is the main causative occult primary for bone metastases and, due to novel treatment options, has to be diagnosed with a high degree of certainty [3]. Adenocarcinoma is the most common histologic type in lung cancer [4]. Since therapy relies on the determination of the primary tumor site (tissue of origin), exact classification of the tumors is mandatory. Besides IHC, molecular methods have been included as a diagnostic tool [5]. Mass spectrometry and especially imaging mass spectrometry (IMS) techniques are promising candidates to define the tissue of origin since hundreds of peptides or proteins can be analyzed at the same time. Furthermore, it may be an advantage that tissue integrity is preserved [6]. IMS allows correlation of specific tissue structures with a peptide- or protein pattern. In the present large-scale study, we analyzed tissue microarrays (TMAs) to investigate whether the discrimination of colon and lung primary tumors is possible by IMS. Furthermore, we tested our algorithm on a whole tissue slide of a case of metastasized colon cancer to the lung and on a whole slide of a primary lung adenocarcinoma. We demonstrate that the differentiation between adenocarcinomas from colon and lung based on a proteomic pattern acquired by IMS on formalin-fixed paraffin embedded (FFPE) TMAs is possible with high accuracy.
Section snippets
Tissue samples and tissue microarray
We investigated a cohort of 383 FFPE tissue specimens from individual patients with primary adenocarcinoma of the colon (n = 217) and primary adenocarcinoma of the lung (n = 166). FFPE TMAs have been created from both tissue types. Lung TMA samples were provided by the Institute of Pathology, University Hospital of Heidelberg, colon TMAs were provided by the Institute of Pathology, Hospital Aschaffenburg, Germany and the Institute of Pathology, University Hospital “Carl Gustav Carus” of Dresden,
Histology of the analyzed tissue samples
Tissue cores from adenocarcinomas of the lung showed typical histology including lepidic, acinar, papillary, solid, and micropapillary patterns. Tissue cores from adenocarcinomas of the colon were characterized by cancerous glands, sometimes associated with typical dirty or garland-like necrosis.
Model classification
The LDA algorithm was generated by selecting 48 peaks (Supplementary Table S1) at specific m/z intervals (± 0.280 Da) with a leave-one-out cross validation accuracy of 84.3%. The model was applied to sort
State of the art methodology to differentiate adenocarcinomas of lung and colon
Adequate identification of the tumor of origin in metastatic cancer is mandatory since various tumors and even tumor subtypes may require a different therapeutic regimen. Currently, tumor typing is done on the basis of clinical information and conventional histology. Although many diagnostic problems may be solved by morphology alone, frequently subsequent IHC stains are mandatory to establish a definitive diagnosis. The differentiation between adenocarcinomas from different origins may be
Conclusions
In the present study, we show that differentiation between adenocarcinomas from colon and lung based on a proteomic pattern acquired by IMS on FFPE TMAs is possible with high accuracy.
The following is the supplementary data related to this article.
Transparency document
Acknowledgements
The authors acknowledge the support of the BMBF grant FKZ 131A029F as part of the Leading-Edge Cluster Ci3 (Cluster for Individualized Immune Intervention), the German Central Innovation Program for SMEs of the German Federal Ministry of Economic Affairs and Energy (BMWI-ZIM grant KF3342501SB4), and the BMBF grant 13GW0081B sponsored by the German Federal Ministry of Education and Research (“KMU-innovativ: Medizintechnik” programme. MK is supported by the Post-doc program of the medical faculty
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This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.