ArticleMolecular & cellular proteomics : MCP2016
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.
Article in Molecular & cellular proteomics : MCP, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed, 88 citations in OpenAlex.
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- Matrix-Assisted Laser Desorption/Ionization Imaging Mass Spectrometry.Nature reviews. Methods primers · 2026Article
- Mass Spectrometry Imaging.Analytical chemistry · 2025Review
- Reproducible Sectioning and Sample Preparation for Mass Spectrometry Imaging of Cancer Metabolism in Frozen Lung Tissue.Methods in molecular biology (Clifton, N.J.) · 2025Article
- [Research progress of deep learning applications in mass spectrometry imaging data analysis].Se pu = Chinese journal of chromatography · 2024Review
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Identification of Diagnostic Metabolic Signatures in Thyroid Tumors Using Mass Spectrometry Imaging.Molecules (Basel, Switzerland) · 2023Article
- Lung adenocarcinoma and squamous cell carcinoma difficult for immunohistochemical diagnosis can be distinguished by lipid profile.Scientific reports · 2023Article
- Supervised topological data analysis for MALDI mass spectrometry imaging applications.BMC bioinformatics · 2023Article
- MALDI Imaging Mass Spectrometry of High-Grade Gliomas: A Review of Recent Progress and Future Perspective.Current issues in molecular biology · 2023Review
- A new deep learning framework to process Matrix-assisted Laser Desorption/Ionisation Mass Spectrometry Imaging (MALDI-MSI) data of Tissue Microarrays (TMAs).AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI.Cancers · 2022Article
- Emerging Computational Methods in Mass Spectrometry Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- Multi-Class Cancer Subtyping in Salivary Gland Carcinomas with MALDI Imaging and Deep Learning.Cancers · 2022Article
- Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology.Cancers · 2022Review
- MALDI Mass Spectrometry Imaging for the Distinction of Adenocarcinomas of the Pancreas and Biliary Tree.Molecules (Basel, Switzerland) · 2022Article
- Proteomic Analysis of Lung Cancer Types-A Pilot Study.Cancers · 2022Article
- Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: a case study of an urothelial cancer cohort analyzed in the Galaxy framework.Clinical proteomics · 2022Article
- Comparing DESI-MSI and MALDI-MSI Mediated Spatial Metabolomics and Their Applications in Cancer Studies.Frontiers in oncology · 2022Review
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Histopathological subtyping of non-small cell lung cancer (NSCLC) into adenocarcinoma (ADC), and squamous cell carcinoma (SqCC) is of utmost relevance for treatment stratification. However, current immunohistochemistry (IHC) based typing approaches on biopsies are imperfect, therefore novel analytical methods for reliable subtyping are needed. We analyzed formalin-fixed paraffin-embedded tissue cores of NSCLC by Matrix-assisted laser desorption/ionization (MALDI) imaging on tissue microarrays to identify and validate discriminating MALDI imaging profiles for NSCLC subtyping. 110 ADC and 98 SqCC were used to train a Linear Discriminant Analysis (LDA) model. Results were validated on a separate set of 58 ADC and 60 SqCC. Selected differentially expressed proteins were identified by tandem mass spectrometry and validated by IHC. The LDA classification model incorporated 339 m/z values. In the validation cohort, in 117 cases (99.1%) MALDI classification on tissue cores was in accordance with the pathological diagnosis made on resection specimen. Overall, three cases in the combined cohorts were discordant, after reevaluation two were initially misclassified by pathology whereas one was classified incorrectly by MALDI. Identification of differentially expressed peptides detected well-known IHC discriminators (CK5, CK7), but also less well known differentially expressed proteins (CK15, HSP27). In conclusion, MALDI imaging on NSCLC tissue cores as small biopsy equivalents is capable to discriminate lung ADC and SqCC with a very high accuracy. In addition, replacing multislide IHC by an one-slide MALDI approach may also save tissue for subsequent predictive molecular testing. We therefore advocate to pursue routine diagnostic implementation strategies for MALDI imaging in solid tumor typing.
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