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Predictive Immune Modeling of Solid Tumors

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Predicting Response to Immune Checkpoint Inhibitors in Melanoma: Emerging Approaches in Digital Pathology, Spatial

Jakub Banaszek¹, Dawid Bąk¹, Kinga Barańska¹

¹Chair and Department of Clinical Pathomorphology, Medical University of Lublin, Jaczewskiego 8b, 20-090 Lublin, Poland.

International Journal of Molecular Sciences

|June 26, 2026

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Predicting immunotherapy response in melanoma patients is crucial. Advanced digital pathology and machine learning offer superior predictive value over current biomarkers by analyzing immune cell interactions within the tumor microenvironment.

Area of Science:

Oncology
Immunology
Computational Pathology

Background:

Immune checkpoint inhibitors (ICIs) have improved melanoma survival but lack universal efficacy.
Predictive biomarkers for ICI response are needed due to variable patient benefit and adverse events.
Current biomarkers like PD-L1 and TILs show limited predictive power.

Purpose of the Study:

To critically review digital pathology, multiplex, and machine learning techniques for predicting ICI response in melanoma.
To assess the potential of advanced computational methods in identifying patients likely to benefit from immunotherapy.

Main Methods:

Narrative review of studies utilizing digital pathology, multiplex immunofluorescence, imaging mass cytometry, and digital spatial profiling.
Appraisal of machine learning algorithms applied to melanoma ICI response prediction.

Keywords:

digital pathology immune checkpoint inhibitors (ICIs)machine learning melanoma multiplex methods response predictors

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Focus on quantitative and spatial analysis of the tumor immune microenvironment.

Main Results:

Integrating quantitative immune infiltration with spatial distribution and cellular interactions shows high predictive potential.
Phenotypic classification (immune-inflamed, excluded, desert), tertiary lymphoid structures, and immune niches are relevant predictive features.
Current approaches face limitations including lack of standardization, data heterogeneity, and insufficient validation.

Conclusions:

Advanced digital and computational pathology methods hold promise for predicting ICI response in melanoma.
Future research must address standardization and validation for clinical implementation.
Spatial and quantitative analysis of the tumor immune microenvironment is key for personalized immunotherapy.