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Updated: Oct 23, 2025
Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
Published on: February 8, 2018
Interpretable systems biomarkers predict response to immune-checkpoint inhibitors
Óscar Lapuente-Santana1, Maisa van Genderen1, Peter A J Hilbers1
1Department of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands.
Abstract:
Cancer cells can leverage several cell-intrinsic and -extrinsic mechanisms to escape immune system recognition. The inherent complexity of the tumor microenvironment, with its multicellular and dynamic nature, poses great challenges for the extraction of biomarkers of immune response and immunotherapy efficacy. Here, we use RNA-sequencing (RNA-seq) data combined with different sources of prior knowledge to derive system-based signatures of the tumor microenvironment, quantifying immune-cell composition and intra- and intercellular communications. We applied multi-task learning to these signatures to predict different hallmarks of immune responses and derive cancer-type-specific models based on interpretable systems biomarkers. By applying our models to independent RNA-seq data from cancer patients treated with PD-1/PD-L1 inhibitors, we demonstrated that our method to Estimate Systems Immune Response (EaSIeR) accurately predicts therapeutic outcome. We anticipate that EaSIeR will be a valuable tool to provide a holistic description of immune responses in complex and dynamic systems such as tumors using available RNA-seq data.
Insights
This study introduces EaSIeR, a novel RNA-sequencing analysis method. EaSIeR quantifies tumor immune microenvironments to predict immunotherapy outcomes, aiding cancer treatment strategies.
Area of Science:
- Computational biology
- Immunology
- Cancer research
Background:
- Tumor cells evade immune detection through complex mechanisms.
- The tumor microenvironment's complexity hinders biomarker discovery for immunotherapy.
- Existing methods struggle to capture the dynamic immune landscape within tumors.
Purpose of the Study:
- To develop a systems-based approach for analyzing the tumor immune microenvironment.
- To quantify immune cell composition and intercellular communication from RNA-seq data.
- To predict immunotherapy efficacy using interpretable biomarkers.
Main Methods:
- Utilized RNA-sequencing (RNA-seq) data integrated with prior knowledge.
- Applied multi-task learning to derive system-based signatures of the tumor microenvironment.
- Developed a method named Estimate Systems Immune Response (EaSIeR) for predicting immune response hallmarks.
Main Results:
- EaSIeR accurately quantifies immune cell composition and communication.
- Cancer-type-specific models were generated based on interpretable systems biomarkers.
- The EaSIeR method successfully predicted therapeutic outcomes in patients treated with PD-1/PD-L1 inhibitors.
Conclusions:
- EaSIeR provides a holistic description of immune responses in tumors using RNA-seq data.
- This method offers a valuable tool for understanding tumor-immune interactions.
- EaSIeR has the potential to improve patient stratification and guide immunotherapy decisions.
