The purpose of this study is to develop a methodology that incorporates a more accurate assessment of tissue mechanical properties compared to current mathematical modeling by use of biomechanical data from magnetic resonance elastography. The elastography data were derived from five glioblastoma patients and a healthy subject and used in a model that simulates tumor growth, vascular changes due to mechanical stresses and delivery of therapeutic agents. The model investigates the effect of tumor-specific biomechanical properties on tumor anisotropic growth, vascular density heterogeneity and chemotherapy delivery. The results showed that including elastography data provides a more realistic distribution of the mechanical stresses in the tumor and induces anisotropic tumor growth. Solid stress distribution differs among patients, which, in turn, induces a distinct functional vascular density distribution—owing to the compression of tumor vessels—and intratumoral drug distribution for each patient. In conclusion, incorporating elastography data results in a more accurate calculation of intratumoral mechanical stresses and enables a better mathematical description of subsequent events, such as the heterogeneous development of the tumor vasculature and intrapatient variations in tumor perfusion and delivery of drugs.

IntroductionImmunotherapy is changing the standard of care in cancer treatment. Immune checkpoint blockers (ICBs) that block the checkpoint programs used by tumor cells to inhibit the immune response have been approved by the US Food and Drug Administration with varying efficacy across cancer types. Given the clear benefits of ICB in some patients, there is an urgent research need to understand the determinants of ICB efficacy so more patients can benefit. To this end, there is growing interest in the interactions between immune cells and tumor cells as well as other extrinsic factors that can impact ICB therapy. Recent studies have shown that the microbiome can modulate the efficacy of immunotherapy. Specifically, clinical and therapeutic outcomes improved in a subset of ICB-refractory patients treated with fecal microbiota transplants (FMTs) from melanoma patients who responded to ICB therapy in combination with additional cycles of ICB therapy suggesting a synergistic effect of the gut microbiome. However, there is limited information and often inconsistencies among studies about the effect of bacteria on the immune system and checkpoint immunotherapy.MethodologyTo study the mechanisms by which the gut microbiome affects components of the immune system and response to ICB, we developed a mechanistic mathematical model for tumor growth in terms of immune components as features. The model simulates the temporal changes in the immune system during tumor progression and checkpoint immunotherapy. More specifically, the model simulates the tumor cells, relevant immune cells, and cytokines. The model was used for the reproduction of tumor growth curves from several preclinical FMT studies with ICB treatment. To determine the possible effect of the microbiome on the immune components, existing statistical approaches were used between experimental microbiome data and the immune components of the model.ResultsThe mathematical model was able to reproduce the experimental tumor growth curves by varying only two critical parameters. These parameters identify two possible mechanisms by which the gut microbiome can influence the immune response: i) the killing potential of tumor cells by immune cells and ii) the activation of the adaptive immune system. Furthermore, the model predictions agree with the observation that ICB treatment is only effective with FMT from responder patients. Additionally, the association analysis was able to identify specific microbes that induce positive and negative effects for ICB, and how these microbes affect various components of the immune system.ConclusionOur analysis identifies possible mechanisms by which the microbiome affects checkpoint immunotherapy and predicts not only which constituents of the microbiome have positive and negative effects but also identifies how these microbes are related to immune mechanisms. This information could be valuable for designing future targeted studies to validate these possible mechanisms at the cell and molecular levels. Citation Format: Constantinos Harkos, Andreas Hadjigeorgiou, Aditya K. Mishra, Golnaz Morad, Sarah Johnson, Nadim J. Ajami, Triantafyllos Stylianopoulos, Lance L. Munn, Jeniffer A. Wargo, Rakesh K. Jain. Modulation of cancer immunotherapy by the microbiome: Insights from computational analyses of preclinical studies [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy; 2022 Oct 21-24; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(12 Suppl):Abstract nr B39.