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Data · dataset · 2026

Image 2_Spatial immune ecosystems and therapy resistance in large B-cell lymphoma: from single-cell multi-omics to immunotherapy-matched validation.tiff

Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.3389/fimmu.2026.1916056.s003

<p>Large B-cell lymphomas, particularly diffuse large B-cell lymphoma (DLBCL), provide a clinically important model in which tumor-intrinsic programs, immune recognition, effector-cell fitness, and tissue architecture jointly shape response and resistance to immunotherapy.

Description

Although molecular classification has refined the understanding of lymphoma biology, relapse after chemoimmunotherapy, chimeric antigen receptor (CAR) T-cell therapy, CD20×CD3 bispecific antibodies, antibody-drug conjugates (ADCs), and immune-combination strategies cannot be explained by genetics or target-antigen expression alone.

Single-cell and spatial multi-omics now allow lymphoma to be interpreted as a spatial immune ecosystem composed of malignant B-cell states, exhausted or regulatory T-cell programs, myeloid suppressive hubs, stromal and vascular compartmentalization, metabolic gradients, and local communication networks. This Review synthesizes recent DLBCL and large B-cell lymphoma ecosystem studies from a cancer-immunology perspective, emphasizing how spatial immune organization may influence effector access, antigen engagement, macrophage-mediated suppression, antibody exposure, and treatment-associated spatial change.

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We propose a treatment-matched validation framework that connects high-dimensional discovery to formalin-fixed paraffin-embedded (FFPE)-compatible pathology assays, including reduced marker panels for CAR T-cell therapy, bispecific antibodies, ADC-containing therapy, and immune-combination trials. Throughout, we distinguish observed tissue organization from computationally inferred communication and experimentally validated mechanisms.

The resulting framework is intended as a trial-design and biomarker-development roadmap, not as a current clinical allocation algorithm.</p>

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Image 75% · Oncology and carcinogenesis 73%
Provenance · 3 source records, 16 field assertions
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