Histo-molecular differentiation of renal cancer subtypes by mass spectrometry imaging and rapid proteome profiling of formalin-fixed paraffin-embedded tumor tissue sections

Möginger, Uwe; Marcussen, Niels; Jensen, Ole N.

doi:10.18632/oncotarget.27787

Cited by 15 publications

(13 citation statements)

References 66 publications

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“…Whereas more heterogeneous tumor/tissue types showed an increase in classification accuracy of more than 20%, more homogeneous tumor/tissue types only show a slight improvement in classification accuracy . Other researchers conducting tissue classifications with MALDI-MSI have used a variety of strategies to overcome this problem. ,,, We therefore restricted all further analyses to regions that had been identified by a pathologist as tumor.…”

Section: Resultsmentioning

confidence: 99%

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Multicenter Evaluation of Tissue Classification by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Deininger

Bollwein

Casadonte³

et al. 2022

Anal. Chem.

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Many studies have demonstrated that tissue phenotyping (tissue typing) based on mass spectrometric imaging data is possible; however, comprehensive studies assessing variation and classifier transferability are largely lacking. This study evaluated the generalization of tissue classification based on Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometric imaging (MSI) across measurements performed at different sites. Sections of a tissue microarray (TMA) consisting of different formalin-fixed and paraffin-embedded (FFPE) human tissue samples from different tumor entities (leiomyoma, seminoma, mantle cell lymphoma, melanoma, breast cancer, and squamous cell carcinoma of the lung) were prepared and measured by MALDI-MSI at different sites using a standard protocol (SOP). Technical variation was deliberately introduced on two separate measurements via a different sample preparation protocol and a MALDI Time of Flight mass spectrometer that was not tuned to optimal performance. Using standard data preprocessing, a classification accuracy of 91.4% per pixel was achieved for intrasite classifications. When applying a leave-one-site-out cross-validation strategy, accuracy per pixel over sites was 78.6% for the SOP-compliant data sets and as low as 36.1% for the mistuned instrument data set. Data preprocessing designed to remove technical variation while retaining biological information substantially increased classification accuracy for all data sets with SOP-compliant data sets improved to 94.3%. In particular, classification accuracy of the mistuned instrument data set improved to 81.3% and from 67.0% to 87.8% per pixel for the non-SOP-compliant data set. We demonstrate that MALDI-MSI-based tissue classification is possible across sites when applying histological annotation and an optimized data preprocessing pipeline to improve generalization of classifications over technical variation and increasing overall robustness.

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Section: Resultsmentioning

confidence: 99%

“…32 Other researchers conducting tissue classifications with MALDI-MSI have used a variety of strategies to overcome this problem. 15,16,33,34 We therefore restricted all further analyses to regions that had been identified by a pathologist as tumor.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Multicenter Evaluation of Tissue Classification by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Deininger

Bollwein

Casadonte³

et al. 2022

Anal. Chem.

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“…Metabolites are found to be largely conserved in FFPE tissue samples, and thus, the data acquired with this protocol can be used in research and clinical practice, making full use to mining data in traditional FFPE tissue. Recent research applied MALDI-MS to FFPE tumor tissue sections and enabled cancer subtype classification, providing a promising complementary approach to current pathological technologies for precise digitized diagnosis of diseases (Möginger et al, 2020).…”

Section: Formalin-fixed and Paraffin-embedded Tissuesmentioning

confidence: 99%

“…Especially in cancer research, spatial protein characterization of tissue and biomarker identification will lead to better diagnosis and individual predictive patterns of therapy response. Now, MALDI-MSI combined with machine learning has been used to classify various cancers including renal oncocytoma, clear cell renal cell carcinoma, and chromophobe renal cell carcinoma, and results showed that MSI correctly classified 87% of patients (Möginger et al, 2020). In addition, the major advantages of the method classifying cancer subtypes also simultaneously reveal the molecular features of cancer cells.…”

Section: Pathological Classificationmentioning

confidence: 99%

Advances in MALDI Mass Spectrometry Imaging Single Cell and Tissues

Zhu

Chen

et al. 2022

Front. Chem.

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Compared with conventional optical microscopy techniques, mass spectrometry imaging (MSI) or imaging mass spectrometry (IMS) is a powerful, label-free analytical technique, which can sensitively and simultaneously detect, quantify, and map hundreds of biomolecules, such as peptides, proteins, lipid, and other organic compounds in cells and tissues. So far, although several soft ionization techniques, such as desorption electrospray ionization (DESI) and secondary ion mass spectrometry (SIMS) have been used for imaging biomolecules, matrix-assisted laser desorption/ionization (MALDI) is still the most widespread MSI scanning method. Here, we aim to provide a comprehensive review of MALDI-MSI with an emphasis on its advances of the instrumentation, methods, application, and future directions in single cell and biological tissues.

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“…In particular, the analysis of tumor tissues with pronounced cellular and morphological heterogeneity benefits from the spatially resolved MSI technology [ 3 , 4 ]. Common applications for MSI in cancer studies include tumor typing and subtyping [ 5 – 7 ], studying resection margins and tumor heterogeneity [ 8 , 9 ], and finding biomarkers for tumor diagnosis, prognosis or prediction [ 10 – 12 ].…”

Section: Introductionmentioning

confidence: 99%

Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: a case study of an urothelial cancer cohort analyzed in the Galaxy framework

et al. 2022

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Background Mass spectrometry imaging (MSI) derives spatial molecular distribution maps directly from clinical tissue specimens and thus bears great potential for assisting pathologists with diagnostic decisions or personalized treatments. Unfortunately, progress in translational MSI is often hindered by insufficient quality control and lack of reproducible data analysis. Raw data and analysis scripts are rarely publicly shared. Here, we demonstrate the application of the Galaxy MSI tool set for the reproducible analysis of a urothelial carcinoma dataset. Methods Tryptic peptides were imaged in a cohort of 39 formalin-fixed, paraffin-embedded human urothelial cancer tissue cores with a MALDI-TOF/TOF device. The complete data analysis was performed in a fully transparent and reproducible manner on the European Galaxy Server. Annotations of tumor and stroma were performed by a pathologist and transferred to the MSI data to allow for supervised classifications of tumor vs. stroma tissue areas as well as for muscle-infiltrating and non-muscle infiltrating urothelial carcinomas. For putative peptide identifications, m/z features were matched to the MSiMass list. Results Rigorous quality control in combination with careful pre-processing enabled reduction of m/z shifts and intensity batch effects. High classification accuracy was found for both, tumor vs. stroma and muscle-infiltrating vs. non-muscle infiltrating urothelial tumors. Some of the most discriminative m/z features for each condition could be assigned a putative identity: stromal tissue was characterized by collagen peptides and tumor tissue by histone peptides. Immunohistochemistry confirmed an increased histone H2A abundance in the tumor compared to the stroma tissues. The muscle-infiltration status was distinguished via MSI by peptides from intermediate filaments such as cytokeratin 7 in non-muscle infiltrating carcinomas and vimentin in muscle-infiltrating urothelial carcinomas, which was confirmed by immunohistochemistry. To make the study fully reproducible and to advocate the criteria of FAIR (findability, accessibility, interoperability, and reusability) research data, we share the raw data, spectra annotations as well as all Galaxy histories and workflows. Data are available via ProteomeXchange with identifier PXD026459 and Galaxy results via https://github.com/foellmelanie/Bladder_MSI_Manuscript_Galaxy_links. Conclusion Here, we show that translational MSI data analysis in a fully transparent and reproducible manner is possible and we would like to encourage the community to join our efforts.

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Histo-molecular differentiation of renal cancer subtypes by mass spectrometry imaging and rapid proteome profiling of formalin-fixed paraffin-embedded tumor tissue sections

Cited by 15 publications

References 66 publications

Multicenter Evaluation of Tissue Classification by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Multicenter Evaluation of Tissue Classification by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Advances in MALDI Mass Spectrometry Imaging Single Cell and Tissues

Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: a case study of an urothelial cancer cohort analyzed in the Galaxy framework

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