Tumor-associated antigens, stress proteins, and danger-associated molecular patterns are endogenous immune adjuvants that can both initiate and continually stimulate an immune response against a tumor. In retaliation, tumors can hijack intrinsic immune regulatory programs, thereby facilitating continued growth despite an activated antitumor immune response. Clinically apparent tumors have co-evolved with the patient?s immune system and form a complex Tumor-Immune EcoSystem (TIES). The success of radiotherapy (RT) may be the result of radiation shifting the relative proportions of tumor and immune cells such that surviving cancer cells are subject to elimination by the immune system. However, current RT fractionation has not specifically focused on enhancing immune responses, nor has immune cell infiltration into the tumor as biomarker been considered to predict treatment response. We hypothesize that patients with a TIES such that radiation debulks the tumor and induces a robust immune response may be cured. A TIES with weak antitumor-immunity or strong immune suppression may not be sufficiently perturbed by current RT dose fractionation to fully harness radiation-immune synergy and provide tumor control. The goal of the project is to combine experimental studies and clinical data to calibrate and rigorously validate the in silico framework that simulates the influence of different TIES compositions on the response to different radiation doses and dose fractionations. We will focus on oropharyngeal cancer, one of the few cancer types increasing in incidence. In vivo tumors with and without tumor specific T cells provide radiation dose and fractionation-dependent changes in immune infiltration to derive in silico model parameters. For clinical analysis we will use a retrospective cohort of 51 oropharyngeal cancer (OPC) tissue samples as training cohort. We will prospectively collect radiosensitivity and immune infiltration data from 105 OPC patients that undergo radiation therapy with different total doses, dependent on their intrinsic radiosensitivity index (RSI). These data serve as a test cohort to validate model outcome predictions against clinical assessment of complete response at 3 months. Our overall aims are to determine radiation dose and fractionation that optimize radiation-induced immunity, and to identify how to use RT to shift a patient-specific TIES toward immune-modulated tumor elimination.
These aims will motivate profound changes to how we conceive of and clinically prescribe RT. Radiation could be understood as immunotherapy. For patients with unfavorable TIES, RT fractionation protocols should focus on the radical perturbation of the TIES toward immune-modulated tumor control. For favorable TIES, dose could be de-escalated with focus on immune activation. Integrating our interdisciplinary expertise allows us to predict RT response and guide decision-making for individual patients, which holds the promise of leading to better outcomes. Successful project completion motivates an in silico model framework-aided clinical trial.
Tumors have co-evolved with the patient?s immune system and build a complex Tumor-Immune EcoSystem (TIES). Cytotoxic and immunologic consequences of radiotherapy perturb this dynamic system, and we propose to calibrate and validate an in silico model of treatment response to decipher how to best harness synergy with the immune system and personalize radiation dose and dose fractionation.