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Updated: Dec 8, 2025
Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
Published on: February 8, 2018
Immune profiling before treatment is predictive of TLR9-induced antitumor efficacy
Qun Xu1, Chengli Dai1, Jun Kong1
1Department of Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100082, China.
Abstract:
TLR9 targeting has been a dynamic research field with promising potential in tumor immunotherapy. However, why most patients do not respond to TLR9 agonists remains unknown. In our attempt to resolve this issue, we observed that anti-tumor response to our TLR9-targeting cancer nanomedicines varied according to the initial immune profile of the animals. Speculating that immune profiling before treatment, including the measurement of IFN-α, IL-12, IL-6, TNF, tumor-infiltrating lymphocytes and spleen-residing lymphocytes, could be used to predictively distinguish responders from non-responders, we performed experiments in two different tumor models 4T1-BALB/c and B16-C57BL/6, to validate the hypothesis. Results confirmed that antitumor efficacy with respect to tumor growth, immune cell infiltration, and cytokines release, correlated with the different condition of individuals, as well as the categorization of the animals. This work suggests that immune profiling before treatment might be able to predict the antitumor efficacy of TLR9 agonists in vivo.
Insights
Predicting cancer immunotherapy response is key. Immune profiling before treatment may predict patient outcomes with Toll-like receptor 9 (TLR9) agonists, improving tumor immunotherapy efficacy.
Area of Science:
- Immunology
- Oncology
- Nanomedicine
Background:
- Toll-like receptor 9 (TLR9) agonists show promise in tumor immunotherapy.
- Understanding non-response to TLR9 agonists is crucial for clinical success.
Purpose of the Study:
- To investigate if pre-treatment immune profiling can predict response to TLR9-targeting cancer nanomedicines.
- To identify key immune markers for stratifying patients into responders and non-responders.
Main Methods:
- Utilized two distinct tumor models (4T1-BALB/c and B16-C57BL/6).
- Measured baseline immune profiles including cytokines (IFN-α, IL-12, IL-6, TNF) and lymphocytes (tumor-infiltrating and spleen-residing).
- Assessed antitumor efficacy, immune cell infiltration, and cytokine release post-treatment.
Main Results:
- Anti-tumor response to TLR9 agonists varied based on initial immune profiles.
- Immune profiling successfully categorized animals into responders and non-responders.
- Efficacy correlated with individual immune status and categorization.
Conclusions:
- Pre-treatment immune profiling may predict in vivo antitumor efficacy of TLR9 agonists.
- Stratifying patients based on immune status could optimize cancer immunotherapy strategies.
- This approach may enhance the effectiveness of TLR9-targeting nanomedicines.
