Reliable Entity Subtyping in Non-small Cell Lung Cancer by Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry on Formalin-fixed Paraffin-embedded Tissue Specimens

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100

Purpose To facilitate the transition of MALDI–MS Imaging (MALDI–MSI) from basic science to clinical application, it is necessary to analyze formalin‐fixed paraffin‐embedded (FFPE) tissues. The aim is to improve in situ tryptic digestion for MALDI–MSI of FFPE samples and determine if similar results would be reproducible if obtained from different sites. Experimental Design FFPE tissues (mouse intestine, human ovarian teratoma, tissue microarray of tumor entities sampled from three different sites) are prepared for MALDI–MSI. Samples are coated with trypsin using an automated sprayer then incubated using deliquescence to maintain a stable humid environment. After digestion, samples are sprayed with CHCA using the same spraying device and analyzed with a rapifleX MALDI Tissuetyper at 50 µm spatial resolution. Data are analyzed using flexImaging, SCiLS, and R. Results Trypsin application and digestion are identified as sources of variation and loss of spatial resolution in the MALDI–MSI of FFPE samples. Using the described workflow, it is possible to discriminate discrete histological features in different tissues and enabled different sites to generate images of similar quality when assessed by spatial segmentation and PCA. Conclusions and Clinical Relevance Spatial resolution and site‐to‐site reproducibility can be maintained by adhering to a standardized MALDI–MSI workflow.

Section: Introductionmentioning

confidence: 99%

Site‐to‐Site Reproducibility and Spatial Resolution in MALDI–MSI of Peptides from Formalin‐Fixed Paraffin‐Embedded Samples

Longuespée

Casadonte³

et al. 2019

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100

“…So far, most imaging mass spectrometry studies using classification attempts have addressed tumors in different organs or of major different histologic types, for example, squamous and adenocarcinoma …”

Section: Discussionmentioning

confidence: 99%

MALDI‐Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods

Klein

Kanter

Kulbe

et al. 2018

Purpose Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study is to examine the potential of matrix‐assisted laser desorption/ionization (MALDI) imaging mass spectrometry in combination with machine learning methods to classify EOC histological subtypes from tissue microarray. Experimental design Formalin‐fixed‐paraffin‐embedded tissue of 20 patients with ovarian clear‐cell, 14 low‐grade serous, 19 high‐grade serous ovarian carcinomas, and 14 serous borderline tumors are analyzed using MALDI‐Imaging. Classifications are computed by linear discriminant analysis (LDA), support vector machines with linear (SVM‐lin) and radial basis function kernels (SVM‐rbf), a neural network (NN), and a convolutional neural network (CNN). Results MALDI‐Imaging and machine learning methods result in classification of EOC histotypes with mean accuracy of 80% for LDA, 80% SVM‐lin, 74% SVM‐rbf, 83% NN, and 85% CNN. Based on sensitivity (69–100%) and specificity (90–99%), CCN and NN are most suited to EOC classification. Conclusion and clinical relevance The pilot study demonstrates the potential of MALDI‐Imaging derived proteomic classifiers in combination with machine learning algorithms to discriminate EOC histotypes. Applications may support the development of new prognostic parameters in the assessment of EOC.

“…Using this method, formalin‐fixed and paraffin‐embedded tissue sections can be investigated on the proteomic level using bottom‐up approaches without the need for target‐specific reagents . MSI has been applied for diagnostic subtyping and can also provide prognostic information, which has been demonstrated in various entities including for example lung, breast, pancreatic, colon, and skin cancer . Usually, tissue slides analyzed by MSI are subsequently stained by Hematoxylin and Eosin (H&E) in order to identify particular regions of interest (ROI) .…”

Section: Introductionmentioning

confidence: 99%

Combined Immunohistochemistry after Mass Spectrometry Imaging for Superior Spatial Information

Kriegsmann

Longuespée

Hundemer

et al. 2018

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