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Updated: Oct 21, 2025

Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
Published on: February 8, 2018
Refining patient selection for breast cancer immunotherapy: beyond PD-L1
M Kossai1, N Radosevic-Robin1, F Penault-Llorca1
1Department of Pathology, University Clermont Auvergne, INSERM U1240, Centre Jean Perrin, Clermont-Ferrand, France.
Abstract:
Therapies that modulate immune response to cancer, such as immune checkpoint inhibitors, began an intense development a few years ago; however, in breast cancer (BC), the results have been relatively disappointing so far. Finding biomarkers for better selection of BC patients for various immunotherapies remains a significant unmet medical need. At present, only tumour tissue programmed death-ligand 1 (PD-L1) and mismatch repair deficiency status are approved as theranostic biomarkers for programmed cell death-1 (PD-1)/PD-L1 inhibitors in BC. However, due to the complexity of tumour microenvironment (TME) and cancer response to immunomodulators, none of them is a perfect selector. Therefore, an intense quest is ongoing for complementary tumour- or host-related predictive biomarkers in breast immuno-oncology. Among the upcoming biomarkers, quantity, immunophenotype and spatial distribution of tumour-infiltrating lymphocytes and other TME cells as well as immune gene signatures emerge as most promising and are being increasingly tested in clinical trials. Biomarkers or strategies allowing dynamic assessment of BC response to immunotherapy, such as circulating/exosomal PD-L1, quantity of white/immune blood cell subpopulations and molecular imaging are particularly suitable for immunotreatment monitoring. Finally, host-related factors, such as microbiome and lifestyle, should also be taken into account when planning integration of immunomodulating therapies into BC management. As none of the biomarkers taken separately is accurate enough, the solution could come from composite biomarkers, which would combine clinical, molecular and immunological features of the disease, possibly powered by artificial intelligence.
Insights
Finding better biomarkers is crucial for improving breast cancer immunotherapy. New biomarkers, including immune cell characteristics and host factors, are being explored to enhance patient selection and treatment monitoring.
Area of Science:
- Oncology
- Immunology
- Biomarker Discovery
Background:
- Breast cancer (BC) immunotherapies, like immune checkpoint inhibitors, show limited efficacy.
- Current biomarkers (PD-L1, MMR) have imperfect predictive power for BC treatment.
- A significant unmet need exists for improved patient selection in BC immunotherapy.
Purpose of the Study:
- To identify and evaluate novel predictive biomarkers for breast cancer immunotherapy.
- To explore complementary biomarkers beyond current standards for BC patient selection.
- To investigate dynamic assessment strategies for monitoring immunotherapy response in BC.
Main Methods:
- Review of emerging biomarkers including tumor-infiltrating lymphocytes, immune gene signatures, and circulating biomarkers.
- Assessment of host-related factors such as microbiome and lifestyle.
- Exploration of composite biomarkers integrating clinical, molecular, and immunological data, potentially using AI.
Main Results:
- Tumor microenvironment (TME) features like immune cell quantity, phenotype, and spatial distribution are promising.
- Immune gene signatures and dynamic monitoring tools (circulating PD-L1, imaging) show potential.
- Host factors and composite biomarkers may offer improved predictive accuracy.
Conclusions:
- No single biomarker is sufficient for accurate BC immunotherapy selection.
- Composite biomarkers combining diverse features, potentially enhanced by AI, represent a promising future direction.
- Integrating TME, host factors, and dynamic monitoring is key to advancing BC immuno-oncology.
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