Blood memory CD8 T cell phenotypes in lung cancer patients predict immune checkpoint treatment responses
Florian Schmidt1, Kan Xing Wu1, Yovita Ida Purwanti1
1ImmunoScape Pte. Ltd., Singapore, Singapore.
Background:
Immune checkpoint inhibition (ICI) has become a standard treatment to re-invigorate tumor-attacking T cell responses in multiple cancer indications, yet a patient's response is unpredictable even with a confirmed expression of the relevant targets such as PD-1 or PD-L1. Previously identified biomarkers of response have relatively low accuracy, making it difficult to reliably employ them as predictors of clinical response.
Methods:
We comprehensively phenotyped peripheral blood CD8+ T cells from patients with non-small cell lung cancer by analyzing surface marker expression, transcriptome, and TCR repertoire with single-cell sequencing technology. The cohorts were comprised of patients who (a) responded to anti-PD(L)1 treatment for a prolonged period of time (b) were new-on-treatment responders, and (c) were new-on-treatment nonresponders. Using various bioinformatics analyses, we defined the signatures of ICI response and evaluated their performance on external scRNA-seq datasets.
Results:
We identified response-specific signals in cell type and cell state proportions as well as in TCR repertoire diversity and TCR inter-donor similarity. The enrichment analysis revealed several pathways and regulatory modules enriched in different response groups. Using machine learning, we identified cell-type-specific signatures that predicted the ICI response with an accuracy between 66% and 93% at the single cell level and up to 94% at the patient level. Effector memory CD8+ T cells in long-term responders were most predictive of response, and the inferred effector memory signature could be successfully applied to two related scRNA-seq datasets. CD44, GIMAP4, CD69, and CCL4L2 were among the most relevant contributing markers defining the predictive ML signatures on lung cancer samples.
Conclusion:
Our findings suggest that CD8+ T cell subset-specific models reach an accuracy that possesses the potential to inform treatment decisions in a clinical setting.
Insights
Predicting immune checkpoint inhibitor (ICI) response in cancer is challenging. This study identified CD8+ T cell signatures using single-cell sequencing, achieving up to 94% accuracy in predicting patient response to ICI therapy.
Area of Science:
- Immunology
- Oncology
- Genomics
Background:
- Immune checkpoint inhibition (ICI) is a standard cancer therapy, but patient response is unpredictable.
- Current biomarkers for ICI response lack sufficient accuracy for reliable clinical prediction.
Purpose of the Study:
- To identify predictive biomarkers for immune checkpoint inhibitor (ICI) response in non-small cell lung cancer (NSCLC).
- To develop accurate predictive models for ICI response using single-cell sequencing data.
Main Methods:
- Comprehensive phenotyping of peripheral blood CD8+ T cells from NSCLC patients using single-cell sequencing (surface markers, transcriptome, TCR repertoire).
- Analysis of patient cohorts including long-term responders, new-on-treatment responders, and nonresponders to anti-PD(L)1 therapy.
- Bioinformatics and machine learning analyses to define and evaluate ICI response signatures on independent datasets.
Main Results:
- Identified response-specific signals in CD8+ T cell proportions, TCR diversity, and inter-donor similarity.
- Developed machine learning models predicting ICI response with 66-93% accuracy at the single-cell level and up to 94% at the patient level.
- Effector memory CD8+ T cells and specific markers (CD44, GIMAP4, CD69, CCL4L2) were highly predictive of response.
Conclusions:
- CD8+ T cell subset-specific models demonstrate high accuracy for predicting ICI response.
- These predictive models have the potential to inform clinical treatment decisions for cancer patients receiving ICI therapy.
