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Huang J, Bai X, Qiu Y, He X. Application of AI on cholangiocarcinoma. Front Oncol 2024; 14:1324222. [PMID: 38347839 PMCID: PMC10859478 DOI: 10.3389/fonc.2024.1324222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.
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Affiliation(s)
| | | | | | - Xiaodong He
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
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Rajbhandari P, Neelakantan TV, Hosny N, Stockwell BR. Spatial pharmacology using mass spectrometry imaging. Trends Pharmacol Sci 2024; 45:67-80. [PMID: 38103980 PMCID: PMC10842749 DOI: 10.1016/j.tips.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/07/2023] [Accepted: 11/11/2023] [Indexed: 12/19/2023]
Abstract
The emerging and powerful field of spatial pharmacology can map the spatial distribution of drugs and their metabolites, as well as their effects on endogenous biomolecules including metabolites, lipids, proteins, peptides, and glycans, without the need for labeling. This is enabled by mass spectrometry imaging (MSI) that provides previously inaccessible information in diverse phases of drug discovery and development. We provide a perspective on how MSI technologies and computational tools can be implemented to reveal quantitative spatial drug pharmacokinetics and toxicology, tissue subtyping, and associated biomarkers. We also highlight the emerging potential of comprehensive spatial pharmacology through integration of multimodal MSI data with other spatial technologies. Finally, we describe how to overcome challenges including improving reproducibility and compound annotation to generate robust conclusions that will improve drug discovery and development processes.
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Affiliation(s)
- Presha Rajbhandari
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | | | - Noreen Hosny
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA; Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Brent R Stockwell
- Department of Biological Sciences, Columbia University, New York, NY, USA; Department of Chemistry, Columbia University, New York, NY, USA; Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA; Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA; Department of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA.
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Djambazova KV, van Ardenne JM, Spraggins JM. Advances in Imaging Mass Spectrometry for Biomedical and Clinical Research. Trends Analyt Chem 2023; 169:117344. [PMID: 38045023 PMCID: PMC10688507 DOI: 10.1016/j.trac.2023.117344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Imaging mass spectrometry (IMS) allows for the untargeted mapping of biomolecules directly from tissue sections. This technology is increasingly integrated into biomedical and clinical research environments to supplement traditional microscopy and provide molecular context for tissue imaging. IMS has widespread clinical applicability in the fields of oncology, dermatology, microbiology, and others. This review summarizes the two most widely employed IMS technologies, matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI), and covers technological advancements, including efforts to increase spatial resolution, specificity, and throughput. We also highlight recent biomedical applications of IMS, primarily focusing on disease diagnosis, classification, and subtyping.
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Affiliation(s)
- Katerina V. Djambazova
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA
| | - Jacqueline M. van Ardenne
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Jeffrey M. Spraggins
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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Gonçalves JPL, Bollwein C, Noske A, Jacob A, Jank P, Loibl S, Nekljudova V, Fasching PA, Karn T, Marmé F, Müller V, Schem C, Sinn BV, Stickeler E, van Mackelenbergh M, Schmitt WD, Denkert C, Weichert W, Schwamborn K. Characterization of Hormone Receptor and HER2 Status in Breast Cancer Using Mass Spectrometry Imaging. Int J Mol Sci 2023; 24:ijms24032860. [PMID: 36769215 PMCID: PMC9918176 DOI: 10.3390/ijms24032860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. Mass spectrometry imaging transcribes the molecular content of tissue specimens without requiring additional tags or preliminary analysis of the samples, being therefore an excellent methodology for an unbiased determination of tissue constituents, in particular tumor markers. In this study, the proteomic content of 1191 human breast cancer samples was characterized by mass spectrometry imaging and the epithelial regions were employed to train and test machine-learning models to characterize the individual receptor status and to classify TNBC. The classification models presented yielded high accuracies for estrogen and progesterone receptors and over 95% accuracy for classification of TNBC. Analysis of the molecular features revealed that vimentin overexpression is associated with TNBC, supported by immunohistochemistry validation, revealing a new potential target for diagnosis and treatment.
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Affiliation(s)
- Juliana Pereira Lopes Gonçalves
- Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstraße 18, 81675 Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, 80336 Munich, Germany
| | - Christine Bollwein
- Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstraße 18, 81675 Munich, Germany
| | - Aurelia Noske
- Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstraße 18, 81675 Munich, Germany
| | - Anne Jacob
- Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstraße 18, 81675 Munich, Germany
| | - Paul Jank
- Institute of Pathology, Philipps-University Marburg and University Hospital Marburg (UKGM), 35043 Marburg, Germany
| | - Sibylle Loibl
- German Breast Group (GBG), 63263 Neu-Isenburg, Germany
| | | | - Peter A. Fasching
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), 91054 Erlangen, Germany
| | - Thomas Karn
- Department of Gynecology and Obstetrics, Goethe-University Frankfurt, 60590 Frankfurt, Germany
| | - Frederik Marmé
- Department of Obstetrics and Gynecology, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
| | - Volkmar Müller
- Department of Gynecology, Universitätsklinikum Hamburg-Eppendorf, 20251 Hamburg, Germany
| | | | | | - Elmar Stickeler
- Department of Obstetrics and Gynecology, University Hospital Aachen, 52074 Aachen, Germany
| | - Marion van Mackelenbergh
- Klinik für Gynäkologie und Geburtshilfe, Universitätsklinikum Schleswig-Holstein, 24105 Kiel, Germany
| | | | - Carsten Denkert
- Institute of Pathology, Philipps-University Marburg and University Hospital Marburg (UKGM), 35043 Marburg, Germany
| | - Wilko Weichert
- Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstraße 18, 81675 Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, 80336 Munich, Germany
| | - Kristina Schwamborn
- Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstraße 18, 81675 Munich, Germany
- Correspondence:
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Kanter F, Lellmann J, Thiele H, Kalloger S, Schaeffer DF, Wellmann A, Klein O. Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks. Cancers (Basel) 2023; 15:cancers15030686. [PMID: 36765644 PMCID: PMC9913229 DOI: 10.3390/cancers15030686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/17/2023] [Accepted: 01/20/2023] [Indexed: 01/25/2023] Open
Abstract
Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.
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Affiliation(s)
- Frederic Kanter
- Institute of Mathematics and Image Computing, Universität zu Lübeck, 23562 Luebeck, Germany
| | - Jan Lellmann
- Institute of Mathematics and Image Computing, Universität zu Lübeck, 23562 Luebeck, Germany
- Correspondence: (J.L.); (O.K.)
| | - Herbert Thiele
- Fraunhofer Institute for Digital Medicine MEVIS, 23562 Luebeck, Germany
| | - Steve Kalloger
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - David F. Schaeffer
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Pancreas Centre BC, Vancouver, BC V5Z 1G1, Canada
- Division of Anatomic Pathology, Vancouver General Hospital, Vancouver, BC V5Z 1M9, Canada
| | - Axel Wellmann
- Institute of Pathology, Wittinger Strasse 14, 29223 Celle, Germany
| | - Oliver Klein
- BIH Center for Regenerative Therapies, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
- Correspondence: (J.L.); (O.K.)
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Gonçalves JPL, Bollwein C, Schlitter AM, Kriegsmann M, Jacob A, Weichert W, Schwamborn K. MALDI-MSI: A Powerful Approach to Understand Primary Pancreatic Ductal Adenocarcinoma and Metastases. Molecules 2022; 27:4811. [PMID: 35956764 PMCID: PMC9369872 DOI: 10.3390/molecules27154811] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 07/25/2022] [Accepted: 07/25/2022] [Indexed: 11/17/2022] Open
Abstract
Cancer-related deaths are very commonly attributed to complications from metastases to neighboring as well as distant organs. Dissociate response in the treatment of pancreatic adenocarcinoma is one of the main causes of low treatment success and low survival rates. This behavior could not be explained by transcriptomics or genomics; however, differences in the composition at the protein level could be observed. We have characterized the proteomic composition of primary pancreatic adenocarcinoma and distant metastasis directly in human tissue samples, utilizing mass spectrometry imaging. The mass spectrometry data was used to train and validate machine learning models that could distinguish both tissue entities with an accuracy above 90%. Model validation on samples from another collection yielded a correct classification of both entities. Tentative identification of the discriminative molecular features showed that collagen fragments (COL1A1, COL1A2, and COL3A1) play a fundamental role in tumor development. From the analysis of the receiver operating characteristic, we could further advance some potential targets, such as histone and histone variations, that could provide a better understanding of tumor development, and consequently, more effective treatments.
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Gonçalves JPL, Bollwein C, Schwamborn K. Mass Spectrometry Imaging Spatial Tissue Analysis toward Personalized Medicine. Life (Basel) 2022; 12:1037. [PMID: 35888125 DOI: 10.3390/life12071037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/04/2022] [Accepted: 07/10/2022] [Indexed: 12/19/2022]
Abstract
Novel profiling methodologies are redefining the diagnostic capabilities and therapeutic approaches towards more precise and personalized healthcare. Complementary information can be obtained from different omic approaches in combination with the traditional macro- and microscopic analysis of the tissue, providing a more complete assessment of the disease. Mass spectrometry imaging, as a tissue typing approach, provides information on the molecular level directly measured from the tissue. Lipids, metabolites, glycans, and proteins can be used for better understanding imbalances in the DNA to RNA to protein translation, which leads to aberrant cellular behavior. Several studies have explored the capabilities of this technology to be applied to tumor subtyping, patient prognosis, and tissue profiling for intraoperative tissue evaluation. In the future, intercenter studies may provide the needed confirmation on the reproducibility, robustness, and applicability of the developed classification models for tissue characterization to assist in disease management.
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