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Updated: Aug 3, 2025
Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
Published on: February 8, 2018
Neoepitope load, T cell signatures and PD-L2 as combined biomarker strategy for response to checkpoint inhibition
Annie Borch1, Anne-Mette Bjerregaard1,2, Vinicius Araujo Barbosa de Lima3
1Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.
Abstract:
Immune checkpoint inhibition for the treatment of cancer has provided a breakthrough in oncology, and several new checkpoint inhibition pathways are currently being investigated regarding their potential to provide additional clinical benefit. However, only a fraction of patients respond to such treatment modalities, and there is an urgent need to identify biomarkers to rationally select patients that will benefit from treatment. In this study, we explore different tumor associated characteristics for their association with favorable clinical outcome in a diverse cohort of cancer patients treated with checkpoint inhibitors. We studied 29 patients in a basket trial comprising 12 different tumor types, treated with 10 different checkpoint inhibition regimens. Our analysis revealed that even across this diverse cohort, patients achieving clinical benefit had significantly higher neoepitope load, higher expression of T cell signatures, and higher PD-L2 expression, which also correlated with improved progression-free and overall survival. Importantly, the combination of biomarkers serves as a better predictor than each of the biomarkers alone. Basket trials are frequently used in modern immunotherapy trial design, and here we identify a set of biomarkers of potential relevance across multiple cancer types, allowing for the selection of patients that most likely will benefit from immune checkpoint inhibition.
Insights
Identifying biomarkers for cancer immunotherapy is crucial. This study found that higher neoepitope load, T cell signatures, and PD-L2 expression predict better outcomes in patients treated with immune checkpoint inhibitors.
Area of Science:
- Oncology
- Immunotherapy
- Biomarker Discovery
Background:
- Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment.
- Patient response to ICIs varies significantly, necessitating predictive biomarkers.
- Identifying patients likely to benefit from ICIs is an unmet clinical need.
Purpose of the Study:
- To identify tumor-associated characteristics predictive of clinical benefit from ICI therapy.
- To explore biomarkers across a diverse range of cancer types in a basket trial setting.
- To evaluate the predictive power of individual and combined biomarkers for ICI response.
Main Methods:
- Analysis of 29 patients across 12 tumor types in a basket trial.
- Treatment with 10 different immune checkpoint inhibition regimens.
- Assessment of tumor-associated characteristics including neoepitope load, T cell signatures, and PD-L2 expression.
Main Results:
- Patients with clinical benefit showed significantly higher neoepitope load, T cell signatures, and PD-L2 expression.
- These biomarkers correlated with improved progression-free survival and overall survival.
- A combination of biomarkers demonstrated superior predictive capacity compared to individual markers.
Conclusions:
- Neoepitope load, T cell signatures, and PD-L2 expression are promising pan-cancer biomarkers for ICI treatment selection.
- Combined biomarker analysis enhances prediction of clinical benefit in immunotherapy.
- These findings support the use of basket trials for identifying broad-acting predictive biomarkers in oncology.
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