Abstract A19: Systems biology approach predicts the diagnostic value of T effector: T regulatory cell ratio in clinical response to combined radiation/immunotherapy of high-risk soft tissue sarcoma
Authors: Prokopiou Sotiris , Poleszczuk Jan , Robertson-Tessi Mark , Luddy Kimberly A. , Fishman Mayer , Moros Eduardo , Djeu Julie Y. , Enderling Heiko 
Published: October 2015

Abstract
Background: The combination of radiotherapy and immunotherapy for cancer treatment has regained momentum since the recent remarkable clinical success in immune checkpoint strategies. The attraction comes from the concept that radiation-induced tumor cell death will release tumor antigens capable of activating dendritic cells to establish long term adaptive T cell immunity against the tumor. However, such combination trials are difficult to interpret, especially when proper markers are not available that predict clinical response. Using a set of data published on patients with high risk soft tissue sarcoma who were treated with intratumoral administration of autologous dendritic cells and local fractionated external beam radiation, we developed a mathematical model examining the levels of T effector cells and T regulatory cells that were reported in each patient who did or did not demonstrate a clinical response.
Materials and Methods: The Phase II clinical trial included 15 Grade 2 high-risk soft tissue sarcoma patients who received radiation/dendritic cell therapy, of which there were 5 responders and 10 non-responders. With the T cell phenotypes that were found in the patients and the accompanying clinical outcome, we designed a mathematical model of tolerogenic and immunogenic tumor subpopulations and their interactions with the host immune system, comprised of T effector and T regulatory cells. Tumor response to the investigational radio-immunotherapy protocol is simulated. The model is calibrated to fit patient-specific pre- and post-treatment response dynamics using computational genetic algorithms. Cell kinetics that separate responder and non-responder cohorts are also categorized.
Results: The mathematical model can be calibrated to reproduce patient-specific tumor volume and immune cell number evolution during treatment. Comparison of responder and non-responder cohorts reveals that immune T effector cell recruitment and efficacy are determinants of treatment response. In contrast, tumor growth dynamics were indistinguishable between individual patients and patient cohorts. Increased T effector to T regulatory cell ratio at diagnosis (2.74 in responders vs. 1.88 in non-responders) emerges as a prognostic marker for treatment response.
Conclusion: A calibrated quantitative tumor model may help to identify mechanisms that determine treatment response. Current clinical practice predicts treatment outcome predominantly based on tumor characteristics. Our initial work indicates that tumor growth dynamics are indistinguishable between high-risk soft tissue sarcoma patients that respond to radio-immunotherapy and those who do not. Instead, our preliminary studies suggest that the host response to the growing tumor – the quantity and quality of the immune response in particular – may serve exclusively as a prognostic marker. This suggests a departure from the current paradigm to derive prognostic factors from genetic characterization of tumor biopsy samples.
Citation Format: Sotiris Prokopiou, Jan Poleszczuk, Mark Robertson-Tessi, Kimberly A. Luddy, Mayer Fishman, Eduardo Moros, Julie Y. Djeu, Heiko Enderling. Systems biology approach predicts the diagnostic value of T effector: T regulatory cell ratio in clinical response to combined radiation/immunotherapy of high-risk soft tissue sarcoma. [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy: A New Chapter; December 1-4, 2014; Orlando, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2015;3(10 Suppl):Abstract nr A19.