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In acute lymphoblastic leukemia (ALL), hypermethylation of the asparagine synthetase (ASNS) gene promoter, leading to low levels of ASNS in tumor cells, is recognized as a prognostic biomarker, and l-asparaginase-based treatments (e.g., Asparlas) are frequently administered to these patients. In these cancers, tumor cells rely on external asparagine, and its depletion in the bloodstream results in tumor cell apoptosis. A multiomics (imaging) workflow is required to evaluate key molecular changes and characterize solid tumors to explore the potential efficacy of Asparlas in solid tumors. This study introduces a multiomics imaging workflow applicable to solid tumor specimens for the comprehensive molecular profiling of Asparlas treatment effects. The workflow integrates matrix-assisted laser desorption-ionization mass spectrometry imaging (MALDI-MSI), liquid chromatography coupled with high-resolution mass spectrometry, and histopathological staining on consecutive tumor tissue sections. It enables the detection and analysis of metabolites, lipids, and proteins. Tumor characterization was achieved through histology and clustering analysis based on lipid signatures, yielding consistent annotations. On-tissue chemical derivatization followed by MALDI-MSI was performed to assess metabolic alterations, with a focus on amino acids. ASNS distribution was mapped utilizing targeted MALDI-immunohistochemistry, followed by untargeted (spatial) proteomics on adjacent tissue sections. This study established a multiomics imaging approach and demonstrated its applicability in elucidating the metabolic changes in tumor tissue consequent to Asparlas treatment. Furthermore, it highlights the added value of multiomics imaging in pharmaceutical research and development.
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http://dx.doi.org/10.1021/acs.analchem.5c01503 | DOI Listing |
J Hepatol
July 2025
Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, 01307 Dresden, Germany; Medical Oncology, National Center for Tumor Disease
Artificial intelligence (AI) methods in hepatology have proliferated since the mid-2010s, with numerous publications and some regulatory approvals. Yet, adoption of AI methods in real-world clinical practice and clinical research remains limited. Despite clear benefits of using AI to analyze complex data types in hepatology, such as histopathology, radiology images, multi-omics and more recently, natural language patient data, there are still substantial barriers and challenges to its integration into routine clinical workflows.
View Article and Find Full Text PDFJ Pharm Anal
September 2025
School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, 110016, China.
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View Article and Find Full Text PDFNEJM AI
September 2025
Department of Biomedical Informatics, Harvard Medical School, Boston.
Over the past two decades, network medicine (NM) has evolved to help define disease mechanisms, identify drug targets, and guide increasingly precise therapies. In recent years, the integration of NM with artificial intelligence (AI), particularly deep learning techniques, has evolved with increasing applications. AI techniques help elucidate complex disease mechanisms and define precise therapies.
View Article and Find Full Text PDFBioinform Adv
August 2025
IBM Research, Yorktown Heights, NY, 10598, United States.
Motivation: Due to the intricate etiology of neurological disorders, finding interpretable associations between multiomics features can be challenging using standard approaches.
Results: We propose COMICAL, a contrastive learning approach using multiomics data to generate associations between genetic markers and brain imaging-derived phenotypes. COMICAL jointly learns omics representations utilizing transformer-based encoders with custom tokenizers.
J Allergy Clin Immunol
September 2025
National Heart and Lung Institute, Imperial College London, London, United Kingdom; Frankland and Kay Allergy Centre, UK NIHR Imperial Biomedical Research Centre, United Kingdom.
Recent advancements in genomics and "omic" technologies have ushered in a transformative era referred to as personalized or precision medicine. This innovative approach considers the unique genetic profiles of individuals, along with a range of variability factors, to devise tailored disease treatments and prevention strategies that cater to the distinct needs of each patient. Although the terms personalized medicine and precision medicine are frequently utilized interchangeably, it is essential to delineate the subtle distinctions between them.
View Article and Find Full Text PDF