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Updated: Mar 18, 2026

Analysis of Human T Cell Activity in an Allogeneic Co-Culture Setting of Pre-Treated Tumor Cells
Published on: March 7, 2025
Mutational Landscape and Sensitivity to Immune Checkpoint Blockers
Roman M Chabanon1, Marion Pedrero2, Céline Lefebvre1
1Faculté de Médicine, Université Paris Saclay, Université Paris-Sud, Le Kremlin Bicêtre, France. Inserm Unit U981, Gustave Roussy, Villejuif, France.
Abstract:
Immunotherapy is currently transforming cancer treatment. Notably, immune checkpoint blockers (ICB) have shown unprecedented therapeutic successes in numerous tumor types, including cancers that were traditionally considered as nonimmunogenic. However, a significant proportion of patients do not respond to these therapies. Thus, early selection of the most sensitive patients is key, and the development of predictive companion biomarkers constitutes one of the biggest challenges of ICB development. Recent publications have suggested that the tumor genomic landscape, mutational load, and tumor-specific neoantigens are potential determinants of the response to ICB and can influence patients' outcomes upon immunotherapy. Furthermore, defects in the DNA repair machinery have consistently been associated with improved survival and durable clinical benefit from ICB. Thus, closely reflecting the DNA damage repair capacity of tumor cells and their intrinsic genomic instability, the mutational load and its associated tumor-specific neoantigens appear as key predictive paths to anticipate potential clinical benefits of ICB. In the era of next-generation sequencing, while more and more patients are getting the full molecular portrait of their tumor, it is crucial to optimally exploit sequencing data for the benefit of patients. Therefore, sequencing technologies, analytic tools, and relevant criteria for mutational load and neoantigens prediction should be homogenized and combined in more integrative pipelines to fully optimize the measurement of such parameters, so that these biomarkers can ultimately reach the analytic validity and reproducibility required for a clinical implementation. Clin Cancer Res; 22(17); 4309-21. ©2016 AACR.
Insights
Predicting immunotherapy response is crucial. Tumor mutational load and neoantigens, reflecting DNA repair capacity, are key biomarkers for immune checkpoint blockers (ICB) success. Further research aims to standardize their measurement for clinical use.
Area of Science:
- Oncology
- Immunology
- Genomics
Background:
- Immunotherapy, particularly immune checkpoint blockers (ICB), has revolutionized cancer treatment, showing success even in non-immunogenic tumors.
- However, a substantial number of patients do not respond to ICB, highlighting the need for predictive biomarkers.
- Identifying patients likely to benefit from ICB is a major challenge in its development.
Purpose of the Study:
- To explore the role of tumor genomic landscape, mutational load, and neoantigens as predictive biomarkers for ICB response.
- To investigate the association between DNA repair defects and improved survival or clinical benefit from ICB.
- To emphasize the need for standardized analytical methods for genomic biomarkers in clinical implementation.
Main Methods:
- Review of recent publications linking tumor genomic features to ICB outcomes.
- Analysis of the relationship between DNA repair defects, genomic instability, mutational load, and neoantigens.
- Discussion on the integration of next-generation sequencing data for biomarker development.
Main Results:
- Tumor mutational load and neoantigens, influenced by DNA repair capacity and genomic instability, are potential determinants of ICB response.
- Defects in DNA repair machinery are associated with better survival and durable clinical benefit from ICB.
- Current next-generation sequencing data needs optimized analytical pipelines for reliable biomarker prediction.
Conclusions:
- Mutational load and neoantigens are promising predictive biomarkers for ICB therapy.
- Standardization and integration of sequencing data analysis are essential for clinical implementation of these biomarkers.
- Optimizing the measurement of these genomic parameters will enhance patient selection for immunotherapy.

