ArticleComputational and systems oncology2025
Optimization of Immune Checkpoint Blockade via a Multiscale Model System.
Article in Computational and systems oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Optimization of Immune Checkpoint Blockade via a Multiscale Model System.Computational and systems oncology · 2025Article
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2 authors.
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Abstract
Cancer progresses when cancer cells selectively bind to inhibitory receptors on a T cell surface, downregulating tumor immune response. One standard-of-care strategy to combat this process is immune checkpoint blockade. Immune checkpoint blockade occurs when a therapeutic agent binds to, and inhibits, inhibitory receptors on a T cell surface, such that immune stimulation is favored when T cells and cancer cells interact. However, many cancers fail to respond to immune checkpoint blockade treatments. Here we explore a whole-tumor and an individual cell-focused model system to test expected outcomes of blockade perturbations in tumor-immune interactions. We first observe a transition point at which patients become more likely to reach "remission" or "stable disease" as a terminal state, and a "progressive disease" state is less likely. We propose a physical, agent-based framework for testing blockade strategies at the cellular level. This offers valuable guidance for blockade efficacy optimization in future development and design of therapeutic antibodies.
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