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Molecular Advances of Mass Spectrometry in Rapid Cancer Detection and Treatment

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 2962

Special Issue Editor

Dr. Julia Balog

E-Mail Website
Guest Editor
Waters Research Center, 1031 Budapest, Hungary
Interests: ambient ionization mass spectrometry methods

Special Issue Information

Dear Colleagues,

Routinely using molecular information in the characterization of tumors is essential for both decision-making during surgery and in the careful selection of personalized therapies. Having real-time access to rapid molecular fingerprints of cancers in the operating rooms to assist decision-making during cancer surgeries is part of our current and future vision. This information—if available in a short time frame during cancer surgery—can have a significant impact on the outcomes and re-operation rates. In the future of automated, robotic surgery, real-time molecular information is also essential in the precise guidance of surgical robots. In parallel, easily accessible molecular information for pathologists can also support the rapid, detailed subtyping of the tumors and the suggested point-of-care therapies. Modern applications of mass spectrometry have enabled the collection of rapid, in vitro, even in vivo molecular data that is easily accessible to anyone. The interpretation of the gained molecular fingerprints, including small metabolites, lipids, peptides and proteins and linking the observed molecular patterns to known cancerous cell mechanisms is essential in our understanding of the role of mass spectrometry in cancer detection. This Special Issue on ‘Molecular Advances of Mass Spectrometry in Rapid Cancer Detection and Treatment’ invites original research and reviews on all aspects of novel and rapid mass spectrometry-based molecular information collection, interpretation of known cancer mechanisms and supporting computational technologies, and machine learning in cancer detection and subtyping.

Dr. Julia Balog
Guest Editor

Manuscript Submission Information

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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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • mass spectrometry in cancer detection
  • mass spectrometry in personalized cancer therapies
  • mass spectrometry in future operating rooms
  • cancer metabolomics, lipidomics and proteomics using mass spectrometry
  • real-time tissue identification using mass spectrometry, cancer subtyping using mass spectrometry

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Published Papers (2 papers)

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Research

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11 pages, 1513 KiB  
Article
Identification of Phospholipids Relevant to Cancer Tissue Using Differential Ion Mobility Spectrometry
by Patrik Sioris, Meri Mäkelä, Anton Kontunen, Markus Karjalainen, Antti Vehkaoja, Niku Oksala and Antti Roine
Int. J. Mol. Sci. 2024, 25(20), 11002; https://doi.org/10.3390/ijms252011002 - 13 Oct 2024
Viewed by 830
Abstract
Phospholipids are the main building components of cell membranes and are also used for cell signaling and as energy storages. Cancer cells alter their lipid metabolism, which ultimately leads to an increase in phospholipids in cancer tissue. Surgical energy instruments use electrical or [...] Read more.
Phospholipids are the main building components of cell membranes and are also used for cell signaling and as energy storages. Cancer cells alter their lipid metabolism, which ultimately leads to an increase in phospholipids in cancer tissue. Surgical energy instruments use electrical or vibrational energy to heat tissues, which causes intra- and extracellular water to expand rapidly and degrade cell structures, bursting the cells, which causes the formation of a tissue aerosol or smoke depending on the amount of energy used. This gas phase analyte can then be analyzed via gas analysis methods. Differential mobility spectrometry (DMS) is a method that can be used to differentiate malignant tissue from benign tissues in real time via the analysis of surgical smoke produced by energy instruments. Previously, the DMS identification of cancer tissue was based on a ‘black box method’ by differentiating the 2D dispersion plots of samples. This study sets out to find datapoints from the DMS dispersion plots that represent relevant target molecules. We studied the ability of DMS to differentiate three subclasses of phospholipids (phosphatidylcholine, phosphatidylinositol, and phosphatidylethanolamine) from a control sample using a bovine skeletal muscle matrix with a 5 mg addition of each phospholipid subclass to the sample matrix. We trained binary classifiers using linear discriminant analysis (LDA) and support vector machines (SVM) for sample classification. We were able to identify phosphatidylcholine, -inositol, and -ethanolamine with SVM binary classification accuracies of 91%, 73%, and 66% and with LDA binary classification accuracies of 82%, 74%, and 72%, respectively. Phosphatidylcholine was detected with a reliable classification accuracy, but ion separation setups should be adjusted in future studies to reliably detect other relevant phospholipids such as phosphatidylinositol and phosphatidylethanolamine and improve DMS as a microanalysis method and identify other phospholipids relevant to cancer tissue. Full article
(This article belongs to the Special Issue Molecular Advances of Mass Spectrometry in Rapid Cancer Detection and Treatment)
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Review

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24 pages, 2106 KiB  
Review
A Workflow for Meaningful Interpretation of Classification Results from Handheld Ambient Mass Spectrometry Analysis Probes
by Alexa Fiorante, Lan Anna Ye, Alessandra Tata, Taira Kiyota, Michael Woolman, Francis Talbot, Yasamine Farahmand, Darah Vlaminck, Lauren Katz, Andrea Massaro, Howard Ginsberg, Ahmed Aman and Arash Zarrine-Afsar
Int. J. Mol. Sci. 2024, 25(6), 3491; https://doi.org/10.3390/ijms25063491 - 20 Mar 2024
Cited by 1 | Viewed by 1405
Abstract
While untargeted analysis of biological tissues with ambient mass spectrometry analysis probes has been widely reported in the literature, there are currently no guidelines to standardize the workflows for the experimental design, creation, and validation of molecular models that are utilized in these [...] Read more.
While untargeted analysis of biological tissues with ambient mass spectrometry analysis probes has been widely reported in the literature, there are currently no guidelines to standardize the workflows for the experimental design, creation, and validation of molecular models that are utilized in these methods to perform class predictions. By drawing parallels with hurdles that are faced in the field of food fraud detection with untargeted mass spectrometry, we provide a stepwise workflow for the creation, refinement, evaluation, and assessment of the robustness of molecular models, aimed at meaningful interpretation of mass spectrometry-based tissue classification results. We propose strategies to obtain a sufficient number of samples for the creation of molecular models and discuss the potential overfitting of data, emphasizing both the need for model validation using an independent cohort of test samples, as well as the use of a fully characterized feature-based approach that verifies the biological relevance of the features that are used to avoid false discoveries. We additionally highlight the need to treat molecular models as “dynamic” and “living” entities and to further refine them as new knowledge concerning disease pathways and classifier feature noise becomes apparent in large(r) population studies. Where appropriate, we have provided a discussion of the challenges that we faced in our development of a 10 s cancer classification method using picosecond infrared laser mass spectrometry (PIRL-MS) to facilitate clinical decision-making at the bedside. Full article
(This article belongs to the Special Issue Molecular Advances of Mass Spectrometry in Rapid Cancer Detection and Treatment)
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