Evidence map›Paper›PMID 40353246›Full record

ArticleToxicology reports2025

Immune checkpoint inhibitors: From friend to foe.

Prem Rajak

Abstract read
In one paragraph

Article in Toxicology reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Prem RajakToxicology Research Laboratory, Department of Animal Science, Kazi Nazrul University, Asansol, West Bengal, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immune checkpoints are crucial in regulating the activation of cell-mediated and humoral immune responses. However, cancer cells hijack this mechanism to evade the immune surveillance and anti-cancer response. Typically, receptors like PD-1 and CTLA4, expressed on immune cells, prevent the activation and differentiation of T cells. They also inhibit the development of autoimmune reactions. However, ligands such as PD-L1 for the receptor PD-1 are also expressed on the surface of cancer cells that help prevent the activation of anti-cancer immune responses by blocking the signalling pathways mediated by PD-1 and CTLA4. Immune checkpoint inhibitors (ICIs) have promising therapeutic efficacy for treating several cancers by activating T cells and their differentiation into effector cells against tumours. Nonetheless, hyperactivated immune cells usually contribute to detrimental issues, also known as immune-related adverse effects (IrAE). IrAEs have been observed in multiple organs, leading to neurological issues, colitis, endocrine dysfunction, renal issues, hepatitis, pneumonitis, and dermatitis. The interplay between hyperactivated T cells and Treg cells helps in orchestrating the development of autoimmunity. Moreover, the crosstalk between proinflammatory interleukins and the development of autoantibodies also mediates the multiorgan effects of ICIs in cancer patients. IrAEs are generally managed by terminating the ICI therapy, reducing the ICI dose, and by using corticosteroids to subvert inflammation. Therefore, the present review aims to delineate the impacts of ICIs on the development of autoimmune diseases and inflammatory outcomes in cancer patients. In addition, mechanistic insight involving immune cells, cytokines, and autoantibodies for ICI-mediated IrAEs will also be discussed with updated findings in this field.

Indexed as

Auto-immunityImmune checkpoint inhibitorsImmune-related adverse effectsInflammationRheumatoid arthritis

Identifiers

PMID40353246
PMCPMC12063143

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.