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Ferroptosis and Cancer Prognosis · Journal article
npj Digital Medicine · September 5, 2026
Encouraging direction, but not yet definitive.
This retrospective analysis proposes a senescence-aware filtering framework to improve ICI-response prediction by excluding biologically confounding tumor states prior to model training. The approach demonstrated improved accuracy, AUROC, precision, and specificity across multiple cancer types with consistent external validation, but lacks prospective clinical validation and hard patient outcomes.
Retrospective cohort analysis with external validation. Patients with melanoma, gastric, bladder, and lung cancers; specific eligibility criteria and treatment details not provided. Intervention: Senescence-aware filtering framework applied to tumor microenvironment data before ICI-response prediction model training. Compared with: Standard target-based ICI-response prediction models (comparator design and explicit performance metrics not detailed).
Framework improved accuracy, AUROC, precision, and specificity in within-cohort evaluations across melanoma, gastric, bladder, and lung cancer cohorts Consistent predictive performance demonstrated in external validation using independent melanoma cohorts Excluded tumors (senescence-associated non-responders) were enriched for senescence-associated markers and transcriptional features
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
If validated prospectively, this filtering approach could improve selection and prediction accuracy for ICI therapy response, potentially reducing reliance on uncertain biomarkers and enabling more personalized treatment decisions. Current evidence is insufficient to recommend adoption in clinical practice without clinical outcome validation.
A sound methodological study demonstrating improved prediction accuracy across multiple cancer cohorts using a novel filtering approach, but limited by lack of prospective validation, absence of hard clinical outcomes, and no comparison to established biomarkers.
As stated by the source record.
Quoted from the source exactly as published.
If validated prospectively, this filtering approach could improve selection and prediction accuracy for ICI therapy response, potentially reducing reliance on uncertain biomarkers and enabling more personalized treatment decisions. Current evidence is insufficient to recommend adoption in clinical practice without clinical outcome validation.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Predictive biomarkers for immune-checkpoint inhibitor (ICI) therapy response often fail to show stable predictive performance because tumor microenvironment (TME) heterogeneity can create biologically confounding tumor states, where similar molecular patterns can lead to divergent therapeutic outcomes. When analyzed together, these biologically distinct states can act as out-of-distribution (OOD) samples, complicating model training and limiting predictive consistency. Here, we present a stepwise framework that improves target-based ICI-response prediction by excluding senescence-associated tumor states prior to model training, thereby reducing biological heterogeneity that can obscure the relationship between checkpoint activity and therapeutic response. The framework leverages network-based representations of immune-checkpoint and senescence pathways to identify senescence-associated non-responders (SNRs). Across multiple melanoma, gastric, bladder, and lung cancer cohorts, this approach improved accuracy, AUROC, precision, and specificity in within-cohort evaluations, and showed consistent performance in external validation using independent melanoma cohorts. The excluded tumors were enriched for senescence-associated markers, suggesting that they exhibit senescence-related transcriptional features that may act as biological confounders limiting the predictive capacity of standard target-based models. These results suggest that confounder-aware filtering of tumor microenvironment states can provide a practical approach to improving ICI therapy response prediction.
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