Identification of Spatial Proteomic Signatures of Colon Tumor Metastasis: A Digital Spatial Profiling Approach.

Autor:	Levy JJ; Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, New Hampshire; Department of Dermatology, Dartmouth Health, Lebanon, New Hampshire; Department of Epidemiology, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire; Program in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire. Electronic address: joshua.j.levy@dartmouth.edu., Zavras JP; Dartmouth College, Hanover, New Hampshire., Veziroglu EM; Dartmouth College Geisel School of Medicine, Hanover, New Hampshire., Nasir-Moin M; Harvard Medical School, Cambridge, Massachusetts., Kolling FW; Dartmouth Cancer Center, Lebanon, New Hampshire., Christensen BC; Department of Epidemiology, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire; Department of Molecular and Systems Biology, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire; Department of Community and Family Medicine, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire., Salas LA; Department of Epidemiology, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire; Department of Molecular and Systems Biology, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire; Integrative Neuroscience at Dartmouth Graduate Program, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire., Barney RE; Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, New Hampshire., Palisoul SM; Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, New Hampshire., Ren B; Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, New Hampshire., Liu X; Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, New Hampshire., Kerr DA; Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, New Hampshire., Pointer KB; Section of Radiation Oncology, Department of Medicine, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire., Tsongalis GJ; Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, New Hampshire. Electronic address: gregory.j.tsongalis@hitchcock.org., Vaickus LJ; Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, New Hampshire.
Jazyk:	angličtina
Zdroj:	The American journal of pathology [Am J Pathol] 2023 Jun; Vol. 193 (6), pp. 778-795. Date of Electronic Publication: 2023 Apr 08.
DOI:	10.1016/j.ajpath.2023.02.020
Abstrakt:	Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually >50,000 individuals are estimated to die of CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. CRC tumors are removed en bloc with surrounding vasculature and lymphatics. Examination of regional lymph nodes at the time of surgical resection is essential for prognostication. Developing alternative approaches to indirectly assess recurrence risk would have utility in cases where lymph node yield is incomplete or inadequate. Spatially dependent, immune cell-specific (eg, tumor-infiltrating lymphocytes), proteomic, and transcriptomic expression patterns inside and around the tumor-the tumor immune microenvironment-can predict nodal/distant metastasis and probe the coordinated immune response from the primary tumor site. The comprehensive characterization of tumor-infiltrating lymphocytes and other immune infiltrates is possible using highly multiplexed spatial omics technologies, such as the GeoMX Digital Spatial Profiler. In this study, machine learning and differential co-expression analyses helped identify biomarkers from Digital Spatial Profiler-assayed protein expression patterns inside, at the invasive margin, and away from the tumor, associated with extracellular matrix remodeling (eg, granzyme B and fibronectin), immune suppression (eg, forkhead box P3), exhaustion and cytotoxicity (eg, CD8), Programmed death ligand 1-expressing dendritic cells, and neutrophil proliferation, among other concomitant alterations. Further investigation of these biomarkers may reveal independent risk factors of CRC metastasis that can be formulated into low-cost, widely available assays. (Copyright © 2023 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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