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http://dx.doi.org/10.1038/d41573-020-00166-1 | DOI Listing |
Am J Psychiatry
January 2025
Department of Neuroscience, Medical University of South Carolina, Charleston (Kuhn, Crow, Walterhouse, Chalhoub, Dereschewitz, Roberts, Kalivas); School of Pharmacy, Center for Neuroscience, Pharmacology Unit, University of Camerino, Camerino, Italy (Cannella, Lunerti, Ciccocioppo); Interdisciplinary Ph.D. Program in Biostatistics (Gupta) and Department of Biomedical Informatics (Gupta, Allen, Chung), and Pelotonia Institute for Immuno-Oncology, James Comprehensive Cancer Center, Ohio State University, Columbus (Gupta, Allen, Chung); Department of Internal Medicine, Wake Forest University, Winston-Salem, NC (Cockerham, Beeson, Solberg Woods); Department of Psychology, Jacksonville State University, Jacksonville, AL (Nall); Institute for Genomic Medicine, University of California San Diego, La Jolla (Palmer); School of Biological Sciences, Queen's University Belfast, Belfast, Northern Ireland (Hardiman).
Objective: The behavioral and diagnostic heterogeneity within the opioid use disorder (OUD) diagnosis is not readily captured in current animal models, limiting the translational relevance of the mechanistic research that is conducted in experimental animals. The authors hypothesized that a nonlinear clustering of OUD-like behavioral traits would capture population heterogeneity and yield subpopulations of OUD vulnerable rats with distinct behavioral and neurocircuit profiles.
Methods: Over 900 male and female heterogeneous stock rats, a line capturing genetic and behavioral heterogeneity present in humans, were assessed for several measures of heroin use and rewarded and non-rewarded seeking behaviors.
Cancer Cell Int
January 2025
Department of Immuno-Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, 510080, China.
Background: Patients with lung adenocarcinoma (LUAD) receiving drug treatment often have an unpredictive response and there is a lack of effective methods to predict treatment outcome for patients. Dendritic cells (DCs) play a significant role in the tumor microenvironment and the DCs-related gene signature may be used to predict treatment outcome. Here, we screened for DC-related genes to construct a prognostic signature to predict prognosis and response to immunotherapy in LUAD patients.
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January 2025
Department of Medicine, Institut Bergonié, Bordeaux, France; Faculty of Medicine, University of Bordeaux, Bordeaux, France. Electronic address:
Tertiary lymphoid structures (TLSs) are organized ectopic lymphoid aggregates within the tumor microenvironment that serve as crucial sites for the development of adaptive antitumor cellular and humoral immunity. TLSs have been consistently documented in numerous cancer types, correlating with improved prognosis and enhanced responses to immunotherapy, especially immune-checkpoint blockade (ICB). Given the potential role of TLSs as predictive biomarkers for the efficacy of ICB in cancer patients, the therapeutic manipulation of TLSs is gaining significant attention as a promising avenue for cancer treatment.
View Article and Find Full Text PDFCancers (Basel)
December 2024
Division of Hematology/Oncology, Department of Internal Medicine, University of California Davis School of Medicine, University of California Davis Comprehensive Cancer Center, Sacramento, CA 95817, USA.
Patient-centered precision oncology strives to deliver individualized cancer care. In lung cancer, preclinical models and technological innovations have become critical in advancing this approach. Preclinical models enable deeper insights into tumor biology and enhance the selection of appropriate systemic therapies across chemotherapy, targeted therapies, immunotherapies, antibody-drug conjugates, and emerging investigational treatments.
View Article and Find Full Text PDFCells
December 2024
BIH Center for Regenerative Therapies (BCRT), Therapy-Induced Remodeling in Immuno-Oncology, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany.
Antibody-dependent cell-mediated cytotoxicity (ADCC) by NK cells is a key mechanism in anti-cancer therapies with monoclonal antibodies, including cetuximab (EGFR-targeting) and avelumab (PDL1-targeting). Fc gamma receptor IIIa (FcγRIIIa) polymorphisms impact ADCC, yet their clinical relevance in NK cell functionality remains debated. We developed two complementary flow cytometry assays: one to predict the FcγRIIIa-V158F polymorphism using a machine learning model, and a 15-color flow cytometry panel to assess antibody-induced NK cell functionality and cancer-immune cell interactions.
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