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Cancers
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Advances in Mass Spectrometry Imaging-Based Cancer Research
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Advances in Mass Spectrometry Imaging-Based Cancer Research

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Pathophysiology".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 12941

Special Issue Editors

Dr. Rita Casadonte

E-Mail Website
Guest Editor
Proteopath GmbH, Max-Planck-Str. 17, 54296 Trier, Germany
Interests: cancer; diagnostics; imaging mass spectrometry; molecular biology; proteomics
Special Issues, Collections and Topics in MDPI journals
Dr. Kristina Schwamborn

E-Mail Website
Guest Editor
Institute of Pathology, School of Medicine, Technical University of Munich, Trogerstr. 18, 81675 Munich, Germany
Interests: immunohistochemistry; hemato-oncology; uro-oncology; mass spectrometry
Special Issues, Collections and Topics in MDPI journals
Dr. Mark Kriegsmann

E-Mail Website
Guest Editor
Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
Interests: molecular pathology; thoracic tumors; mass spectrometry; artificial intelligence

Special Issue Information

Dear Colleagues,

Discovering molecular signatures/pathways in cells, tissues, organs, and body fluids is of great relevance in cancer research as it may lead to a better understanding of tumor biology. The science of Omics such as genomics, proteomics, lipidomics, and metabolomics has provided new opportunities in the molecular analysis of cancer facilitating and improving diagnosis and treatment. For example, studying the proteome in cells, tissues, organs, and body fluids is of great relevance in cancer research, as differential forms of proteins are expressed in response to specific signals. Mass spectrometry (MS) technology have made the “-omic” revolution possible, facing a series of challenging tasks such as high sensitivity, specificity, throughput, robustness, flexibility, and quantification of complex biological samples. MS-based technologies offer an ever-increasing number of outstanding contributions in the field of cancer and have entered in many clinical settings. One of the recent areas of interest in MS is tissue imaging, i.e., MS imaging (MSI). This allows simultaneous visualization of the spatial distribution of hundreds of a broad variety of biomolecules ranging from peptides, to glycans, lipids, and even metabolites, known to play important roles in cancer, directly on tissue specimen. Additionally, this technology does not require any prior labeling. MSI may begin to replace traditional histology techniques, to provide substantial new information to pathologists and clinicians. This Special Issue looks to highlight the major advantages of MSI in the clinic and provide an insight into the potential applications for cancer diagnosis and prognosis but also for the development of new treatments in the context of personalized disease management and medicine. The articles to be submitted are by international teams of experts in these technology and cover advances in the field of mass spectrometry imaging with special emphasis on translational pathology.

Dr. Rita Casadonte
Dr. Kristina Schwamborn
Dr. Mark Kriegsmann
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biomarkers
  • diagnostics
  • mass spectrometry
  • mass spectrometry imaging
  • proteomics
  • tumor typing

Published Papers (6 papers)

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Research

20 pages, 2400 KiB  
Article
Spatial Omics Imaging of Fresh-Frozen Tissue and Routine FFPE Histopathology of a Single Cancer Needle Core Biopsy: A Freezing Device and Multimodal Workflow
by Miriam F. Rittel, Stefan Schmidt, Cleo-Aron Weis, Emrullah Birgin, Björn van Marwick, Matthias Rädle, Steffen J. Diehl, Nuh N. Rahbari, Alexander Marx and Carsten Hopf
Cancers 2023, 15(10), 2676; https://doi.org/10.3390/cancers15102676 - 10 May 2023
Viewed by 2301
Abstract
The complex molecular alterations that underlie cancer pathophysiology are studied in depth with omics methods using bulk tissue extracts. For spatially resolved tissue diagnostics using needle biopsy cores, however, histopathological analysis using stained FFPE tissue and the immunohistochemistry (IHC) of a few marker [...] Read more.
The complex molecular alterations that underlie cancer pathophysiology are studied in depth with omics methods using bulk tissue extracts. For spatially resolved tissue diagnostics using needle biopsy cores, however, histopathological analysis using stained FFPE tissue and the immunohistochemistry (IHC) of a few marker proteins is currently the main clinical focus. Today, spatial omics imaging using MSI or IRI is an emerging diagnostic technology for the identification and classification of various cancer types. However, to conserve tissue-specific metabolomic states, fast, reliable, and precise methods for the preparation of fresh-frozen (FF) tissue sections are crucial. Such methods are often incompatible with clinical practice, since spatial metabolomics and the routine histopathology of needle biopsies currently require two biopsies for FF and FFPE sampling, respectively. Therefore, we developed a device and corresponding laboratory and computational workflows for the multimodal spatial omics analysis of fresh-frozen, longitudinally sectioned needle biopsies to accompany standard FFPE histopathology of the same biopsy core. As a proof-of-concept, we analyzed surgical human liver cancer specimens using IRI and MSI with precise co-registration and, following FFPE processing, by sequential clinical pathology analysis of the same biopsy core. This workflow allowed for a spatial comparison between different spectral profiles and alterations in tissue histology, as well as a direct comparison for histological diagnosis without the need for an extra biopsy. Full article
(This article belongs to the Special Issue Advances in Mass Spectrometry Imaging-Based Cancer Research)
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12 pages, 9986 KiB  
Article
MALDI Imaging, a Powerful Multiplex Approach to Decipher Intratumoral Heterogeneity: Combined Hepato-Cholangiocarcinomas as Proof of Concept
by Elia Gigante, Hélène Cazier, Miguel Albuquerque, Samira Laouirem, Aurélie Beaufrère and Valérie Paradis
Cancers 2023, 15(7), 2143; https://doi.org/10.3390/cancers15072143 - 04 Apr 2023
Cited by 2 | Viewed by 1470
Abstract
Combined hepato-cholangiocarcinomas (cHCC-CCA) belong to the spectrum of primary liver carcinomas, which include hepatocellular carcinomas (HCC) and intrahepatic cholangiocarcinomas (iCCA) at both ends of the spectrum. Mainly due to the high intratumor heterogeneity of cHCC-CCA, its diagnosis and pathological description remain challenging. Taking [...] Read more.
Combined hepato-cholangiocarcinomas (cHCC-CCA) belong to the spectrum of primary liver carcinomas, which include hepatocellular carcinomas (HCC) and intrahepatic cholangiocarcinomas (iCCA) at both ends of the spectrum. Mainly due to the high intratumor heterogeneity of cHCC-CCA, its diagnosis and pathological description remain challenging. Taking advantage of in situ non-targeted molecular mapping provided by MALDI (Matrix Assisted Laser Desorption Ionization) imaging, we sought to develop a multiscale and multiparametric morphological approach, integrating molecular and conventional pathological analysis. MALDI imaging was applied to five representative cases of resected cHCC-CCA. Principal component analysis and segmentations with MALDI imaging techniques identified areas related to either iCCA or HCC and also hidden tumor areas not visible microscopically. In addition, the overlap between MALDI segmentation and immunostaining provided a comprehensive description of cHCC-CCA tumor heterogeneity by identifying transitional and micro-metastatic areas. Moreover, a list of peptides derived from in silico digestion was obtained for each immunohistochemical marker and was matched within the peptide peak list acquired by MALDI. Comparison of immunostaining images with ions from in silico digestion revealed an accurate identification of iCCA and HCC areas. Our study provides further evidence on the performance of MALDI imaging in exploring intratumor heterogeneity and offering virtual multiplex immunostaining through a single acquisition. Full article
(This article belongs to the Special Issue Advances in Mass Spectrometry Imaging-Based Cancer Research)
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16 pages, 2033 KiB  
Article
In Situ Imaging of O-Linked β-N-Acetylglucosamine Using On-Tissue Hydrolysis and MALDI Mass Spectrometry
by Edwin E. Escobar, Erin H. Seeley, Jesús E. Serrano-Negrón, David J. Vocadlo and Jennifer S. Brodbelt
Cancers 2023, 15(4), 1224; https://doi.org/10.3390/cancers15041224 - 15 Feb 2023
Cited by 2 | Viewed by 2085
Abstract
Post-translational O-glycosylation of proteins via the addition of N-acetylglucosamine (O-GlcNAc) is a regulator of many aspects of cellular physiology. Processes driven by perturbed dynamics of O-GlcNAcylation modification have been implicated in cancer development. Variability in O-GlcNAcylation is emerging as a metabolic biomarker of [...] Read more.
Post-translational O-glycosylation of proteins via the addition of N-acetylglucosamine (O-GlcNAc) is a regulator of many aspects of cellular physiology. Processes driven by perturbed dynamics of O-GlcNAcylation modification have been implicated in cancer development. Variability in O-GlcNAcylation is emerging as a metabolic biomarker of many cancers. Here, we evaluate the use of MALDI-mass spectrometry imaging (MSI) to visualize the location of O-GlcNAcylated proteins in tissue sections by mapping GlcNAc that has been released by the enzymatic hydrolysis of glycoproteins using an O-GlcNAc hydrolase. We use this strategy to monitor O-GlcNAc within hepatic VX2 tumor tissue. We show that increased O-GlcNAc is found within both viable tumor and tumor margin regions, implicating GlcNAc in tumor progression. Full article
(This article belongs to the Special Issue Advances in Mass Spectrometry Imaging-Based Cancer Research)
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13 pages, 2369 KiB  
Communication
A Comparison of Different Sample Processing Protocols for MALDI Imaging Mass Spectrometry Analysis of Formalin-Fixed Multiple Myeloma Cells
by Rita Casadonte, Jörg Kriegsmann, Mark Kriegsmann, Katharina Kriegsmann, Roberta Torcasio, Maria Eugenia Gallo Cantafio, Giuseppe Viglietto and Nicola Amodio
Cancers 2023, 15(3), 974; https://doi.org/10.3390/cancers15030974 - 03 Feb 2023
Cited by 1 | Viewed by 1944
Abstract
Sample processing of formalin-fixed specimens constitutes a major challenge in molecular profiling efforts. Pre-analytical factors such as fixative temperature, dehydration, and embedding media affect downstream analysis, generating data dependent on technical processing rather than disease state. In this study, we investigated two different [...] Read more.
Sample processing of formalin-fixed specimens constitutes a major challenge in molecular profiling efforts. Pre-analytical factors such as fixative temperature, dehydration, and embedding media affect downstream analysis, generating data dependent on technical processing rather than disease state. In this study, we investigated two different sample processing methods, including the use of the cytospin sample preparation and automated sample processing apparatuses for proteomic analysis of multiple myeloma (MM) cell lines using imaging mass spectrometry (IMS). In addition, two sample-embedding instruments using different reagents and processing times were considered. Three MM cell lines fixed in 4% paraformaldehyde were either directly centrifuged onto glass slides using cytospin preparation techniques or processed to create paraffin-embedded specimens with an automatic tissue processor, and further cut onto glass slides for IMS analysis. The number of peaks obtained from paraffin-embedded samples was comparable between the two different sample processing instruments. Interestingly, spectra profiles showed enhanced ion yield in cytospin compared to paraffin-embedded samples along with high reproducibility compared to the sample replicate. Full article
(This article belongs to the Special Issue Advances in Mass Spectrometry Imaging-Based Cancer Research)
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16 pages, 3527 KiB  
Article
Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks
by Frederic Kanter, Jan Lellmann, Herbert Thiele, Steve Kalloger, David F. Schaeffer, Axel Wellmann and Oliver Klein
Cancers 2023, 15(3), 686; https://doi.org/10.3390/cancers15030686 - 22 Jan 2023
Cited by 1 | Viewed by 2283
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Mass Spectrometry Imaging-Based Cancer Research)
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21 pages, 170025 KiB  
Article
Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI
by Charlotte Janßen, Tobias Boskamp, Jean Le’Clerc Arrastia, Daniel Otero Baguer, Lena Hauberg-Lotte, Mark Kriegsmann, Katharina Kriegsmann, Georg Steinbuß, Rita Casadonte, Jörg Kriegsmann and Peter Maaß
Cancers 2022, 14(24), 6181; https://doi.org/10.3390/cancers14246181 - 14 Dec 2022
Cited by 2 | Viewed by 1964
Abstract
Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), is crucial for the development of an effective treatment [...] Read more.
Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), is crucial for the development of an effective treatment plan. This task, however, may still present challenges in clinical routine. We propose a two-modality, AI-based classification algorithm to detect and subtype tumor areas, which combines information from matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) data and digital microscopy whole slide images (WSIs) of lung tissue sections. The method consists of first detecting areas with high tumor cell content by performing a segmentation of the hematoxylin and eosin-stained (H&E-stained) WSIs, and subsequently classifying the tumor areas based on the corresponding MALDI MSI data. We trained the algorithm on six tissue microarrays (TMAs) with tumor samples from N = 232 patients and used 14 additional whole sections for validation and model selection. Classification accuracy was evaluated on a test dataset with another 16 whole sections. The algorithm accurately detected and classified tumor areas, yielding a test accuracy of 94.7% on spectrum level, and correctly classified 15 of 16 test sections. When an additional quality control criterion was introduced, a 100% test accuracy was achieved on sections that passed the quality control (14 of 16). The presented method provides a step further towards the inclusion of AI and MALDI MSI data into clinical routine and has the potential to reduce the pathologist’s work load. A careful analysis of the results revealed specific challenges to be considered when training neural networks on data from lung cancer tissue. Full article
(This article belongs to the Special Issue Advances in Mass Spectrometry Imaging-Based Cancer Research)
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