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Updated: Jun 16, 2025
Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
Published on: February 8, 2018
Quantitatively defined stromal B cell aggregates are associated with response to checkpoint inhibitors in
James W Smithy1, Xiyu Peng2, Fiona D Ehrich3
1Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Insights
Multiplex immunofluorescence aids immunotherapy biomarker discovery. Higher stromal B cells in melanoma predict better response to immune checkpoint inhibitors, identified using machine learning.
Area of Science:
- Oncology
- Immunology
- Computational Biology
Background:
- Multiplex immunofluorescence (mIF) offers detailed spatial and phenotypic insights into the tumor microenvironment.
- Analyzing complex mIF data requires automated pipelines for efficient biomarker discovery.
- Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy, necessitating predictive biomarkers.
Purpose of the Study:
- To investigate the utility of mIF for identifying immunotherapy biomarkers in melanoma.
- To develop and validate automated analysis methods for mIF data.
- To explore the relationship between immune cell populations and clinical response to ICIs.
Main Methods:
- Utilized multiplex immunofluorescence on pre-treatment melanoma samples from 50 patients receiving ICIs.
- Applied DBSCAN, a machine learning algorithm, for automated detection of B cell aggregates.
- Analyzed spatial relationships between T cell subpopulations (TCF1+, LAG3-) and stromal B cells.
Main Results:
- A higher percentage of stromal B cells correlated with improved clinical benefit from ICI therapy.
- Automated DBSCAN detection of B cell aggregates showed potential for higher accuracy than manual pathologist assessment.
- TCF1+ and LAG3- T cell subpopulations were found to be enriched near stromal B cells.
Conclusions:
- Multiplex immunofluorescence is a valuable tool for discovering spatial immunotherapy biomarkers in melanoma.
- Automated analysis of mIF data, using machine learning, can enhance biomarker discovery accuracy.
- Stromal B cells and associated T cell subpopulations represent potential predictive biomarkers for ICI therapy response.
Abstract:
Multiplex immunofluorescence (mIF) is a promising tool for immunotherapy biomarker discovery in melanoma and other solid tumors. mIF captures detailed phenotypic information of immune cells in the tumor microenvironment, as well as spatial data that can reveal biologically relevant interactions among cell types. Given the complexity of mIF data, the development of automated analysis pipelines is crucial for advancing biomarker discovery. In pre-treatment melanoma samples from 50 patients treated with immune checkpoint inhibitors (ICIs), a higher stromal B cell percentage is associated with the clinical benefit of ICI therapy. The automatic detection of B cell aggregates with DBSCAN, a novel application of a computer-aided machine learning algorithm, demonstrates the potential for enhanced accuracy compared to pathologist assessment of lymphoid aggregates. TCF1+ and LAG3- T cell subpopulations are enriched near stromal B cells, suggesting potential functional interactions. These analyses provide a roadmap for the further development of spatial immunotherapy biomarkers in melanoma and other diseases.
