Back

Local Delivery of SBRT and IL12 by mRNA Technology Overcomes Immunosuppressive Barriers to Eliminate Pancreatic Cancer

Hughson, A. L.; Hannon, G.; Salama, N. A.; Vrooman, T. G.; Stockwell, C. A.; Mills, B. N.; Garrett-Larsen, J.; Qui, H.; Katerji, R.; Benoodt, L.; Johnston, C. J.; Murphy, J. D.; Kruger, E.; Ye, J.; Gavras, N. W.; Keeley, D. C.; Qin, S. S.; Lesch, M. L.; Muhitch, J. B.; Love, T. M. T.; Calvi, L. M.; Lord, E. M.; Luheshi, N.; Elyes, J.; Linehan, D.; Gerber, S. A.

2023-11-02 cancer biology
10.1101/2023.10.30.564833 bioRxiv
Show abstract

The immunosuppressive milieu in pancreatic cancer (PC) is a significant hurdle to treatments, resulting in survival statistics that have barely changed in 5 decades. Here we present a combination treatment consisting of stereotactic body radiation therapy (SBRT) and IL-12 mRNA lipid nanoparticles delivered directly to pancreatic murine tumors. This treatment was effective against primary and metastatic models, achieving cures in both settings. IL-12 protein concentrations were transient and localized primarily to the tumor. Depleting CD4 and CD8 T cells abrogated treatment efficacy, confirming they were essential to treatment response. Single cell RNA sequencing from SBRT/IL-12 mRNA treated tumors demonstrated not only a complete loss of T cell exhaustion, but also an abundance of highly proliferative and effector T cell subtypes. SBRT elicited T cell receptor clonal expansion, whereas IL-12 licensed these cells with effector function. This is the first report demonstrating the utility of SBRT and IL-12 mRNA in PC. Statement of significanceThis study demonstrates the use of a novel combination treatment consisting of radiation and immunotherapy in murine pancreatic tumors. This treatment could effectively treat local and metastatic disease, suggesting it may have the potential to treat a cancer that has not seen a meaningful increase in survival in 5 decades.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.