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TLR5 agonists enhance anti-tumor immunity and overcome resistance to immune checkpoint therapy

Caleb Gonzalez1, Sarah Williamson1, Seth T Gammon1

  • 1Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.

Communications Biology
|January 12, 2023
PubMed

Insights

Combining immune checkpoint therapies (ICT) with toll-like receptor 5 (TLR5) agonists like flagellin or CBLB502 improved survival in refractory triple-negative breast cancer and melanoma models. This strategy may overcome resistance to current cancer immunotherapies.

Area of Science:

  • Immunology
  • Oncology
  • Pharmacology

Background:

  • Primary and adaptive resistance to immune checkpoint therapies (ICT) limit patient survival.
  • Innate immune activators offer potential for enhancing anti-tumor immunity.

Purpose of the Study:

  • To investigate the efficacy of combining TLR5 agonists with ICT in refractory cancer models.
  • To identify potential biomarkers for predicting response to combination therapy.

Main Methods:

  • Utilized a syngeneic 4T1 mammary carcinoma murine model for triple-negative breast cancer and a B16-F10 melanoma model.
  • Administered intra-tumoral flagellin or CBLB502 in combination with anti-CTLA-4 and anti-PD-1 antibodies.
  • Analyzed survival, immunologic memory, cytokine profiles, and serum biomarker levels (G-CSF, CXCL5, IL-15).

Main Results:

  • Combination therapy significantly enhanced survival in both 4T1 and B16-F10 models.
  • Long-term survivors exhibited immunologic memory and a distinct cytokine profile activating innate and adaptive immunity.
  • Low G-CSF and CXCL5, along with high IL-15, were identified as candidate predictive biomarkers for enhanced survival.

Conclusions:

  • Combination of TLR5 agonists with ICT represents a promising therapeutic strategy for ICT-refractory solid tumors.
  • This approach engages both innate and adaptive immune responses for improved anti-tumor activity.
  • Biomarker analysis suggests potential for personalized treatment selection in future clinical applications.

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