Therapeutic Potential of Bacteria against Solid Tumors
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Updated: Mar 7, 2026
Bioluminescent Bacterial Imaging In Vivo
Published on: November 4, 2012
Haralampos Hatzikirou1,2, Juan Carlos López Alfonso1,2, Sara Leschner3
1Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany.
Abstract:
Intentional bacterial infections can produce efficacious antitumor responses in mice, rats, dogs, and humans. However, low overall success rates and intense side effects prevent such approaches from being employed clinically. In this work, we titered bacteria and/or the proinflammatory cytokine TNFα in a set of established murine models of cancer. To interpret the experiments conducted, we considered and calibrated a tumor-effector cell recruitment model under the influence of functional tumor-associated vasculature. In this model, bacterial infections and TNFα enhanced immune activity and altered vascularization in the tumor bed. Information to predict bacterial therapy outcomes was provided by pretreatment tumor size and the underlying immune recruitment dynamics. Notably, increasing bacterial loads did not necessarily produce better long-term tumor control, suggesting that tumor sizes affected optimal bacterial loads. Short-term treatment responses were favored by high concentrations of effector cells postinjection, such as induced by higher bacterial loads, but in the longer term did not correlate with an effective restoration of immune surveillance. Overall, our findings suggested that a combination of intermediate bacterial loads with low levels TNFα administration could enable more favorable outcomes elicited by bacterial infections in tumor-bearing subjects. Cancer Res; 77(7); 1553-63. ©2017 AACR.
Insights
Optimizing bacterial infection therapy for cancer involves balancing bacterial loads and tumor necrosis factor alpha (TNFα) levels. Intermediate bacterial doses with low TNFα show promise for better antitumor outcomes in preclinical models.
Area of Science:
- Oncology
- Immunology
- Microbiology
Background:
- Bacterial infections show potential for antitumor responses but face clinical limitations due to low success rates and side effects.
- Existing approaches require optimization for efficacy and safety in cancer treatment.
Purpose of the Study:
- To investigate the impact of varying bacterial loads and tumor necrosis factor alpha (TNFα) on antitumor responses in murine cancer models.
- To develop a predictive model for bacterial therapy outcomes based on tumor characteristics and immune dynamics.
Main Methods:
- Titration of bacteria and/or TNFα in established murine cancer models.
- Utilized a calibrated tumor-effector cell recruitment model incorporating tumor vasculature.
- Analyzed pretreatment tumor size and immune cell recruitment dynamics to predict outcomes.
Main Results:
- Bacterial infections and TNFα modulated immune activity and tumor vascularization.
- Optimal bacterial loads were influenced by tumor size, with higher loads not always yielding better long-term control.
- Short-term responses correlated with high effector cell concentrations, but not necessarily with sustained immune surveillance restoration.
Conclusions:
- A combination of intermediate bacterial loads and low-level TNFα administration may offer improved outcomes for bacterial-based cancer therapies.
- Tumor size and immune recruitment dynamics are critical factors in predicting and optimizing bacterial therapy efficacy.
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