ReviewCancers2026
Towards Predicting Immune-Related Adverse Events: Emerging Biomarkers in Patients Undergoing Immune Checkpoint Inhibitor Therapy.
Review in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With immune checkpoint inhibitors becoming the mainstay of systemic therapy in both metastatic and early-stage settings across many cancer types, the management of immune-related adverse events has emerged as a central priority of modern oncological supportive care. These toxicities can substantially impair the quality of life of cancer patients, including those who achieve long-term survival. Consequently, there is a pressing need to develop reliable predictive biomarkers to better tailor immune checkpoint inhibitor treatment and optimize patient selection. This review summarizes several of the most promising predictive biomarkers currently under investigation, including genetic factors; peripheral blood parameters and their ratios; autoantibodies; cytokines and chemokines; cytomegalovirus serostatus; gut microbiome characteristics; body composition metrics; molecular imaging features; and tumour- and patient-related factors such as cancer type, gender, and physical activity. Because single biomarkers have limited predictive value, multi-omics prediction models and composite immune-cell scores are increasingly demonstrating greater potential. However, none of these candidate biomarkers have yet undergone sufficient validation to support their incorporation into routine clinical practice.
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