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Updated: Jun 11, 2025

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
The Importance of Predictive Biomarkers and Their Correlation with the Response to Immunotherapy in Solid
Raluca Ioana Mihaila1,2, Adelina Silvana Gheorghe1,2, Daniela Luminita Zob2
1Department of Oncology, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania.
Background/Objectives: Immunotherapy has changed the therapeutic approach for various solid tumors, especially lung tumors, malignant melanoma, renal and urogenital carcinomas, demonstrating significant antitumor activity, with tolerable safety profiles and durable responses. However, not all patients benefit from immunotherapy, underscoring the need for predictive biomarkers that can identify those most likely to respond to treatment. Methods: The integration of predictive biomarkers into clinical practice for immune checkpoint inhibitors (ICI) holds great promise for personalized cancer treatment. Programmed death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), microsatellite instability (MSI), gene expression profiles and circulating tumor DNA (ctDNA) have shown potential in predicting ICI responses across various cancers. Results: Challenges such as standardization, validation, regulatory approval, and cost-effectiveness must be addressed to realize their full potential. Predictive biomarkers are crucial for optimizing the clinical use of ICIs in cancer therapy. Conclusions: While significant progress has been made, further research and collaboration among clinicians, researchers, and regulatory institutes are essential to overcome the challenges of clinical implementation. However, little is known about the relationship between local and systemic immune responses and the correlation with response to oncological therapies and patient survival.
Background/Objectives: Immunotherapy has changed the therapeutic approach for various solid tumors, especially lung tumors, malignant melanoma, renal and urogenital carcinomas, demonstrating significant antitumor activity, with tolerable safety profiles and durable responses. However, not all patients benefit from immunotherapy, underscoring the need for predictive biomarkers that can identify those most likely to respond to treatment. Methods: The integration of predictive biomarkers into clinical practice for immune checkpoint inhibitors (ICI) holds great promise for personalized cancer treatment. Programmed death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), microsatellite instability (MSI), gene expression profiles and circulating tumor DNA (ctDNA) have shown potential in predicting ICI responses across various cancers. Results: Challenges such as standardization, validation, regulatory approval, and cost-effectiveness must be addressed to realize their full potential. Predictive biomarkers are crucial for optimizing the clinical use of ICIs in cancer therapy. Conclusions: While significant progress has been made, further research and collaboration among clinicians, researchers, and regulatory institutes are essential to overcome the challenges of clinical implementation. However, little is known about the relationship between local and systemic immune responses and the correlation with response to oncological therapies and patient survival.
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