Combining microenvironment normalization strategies to improve cancer immunotherapy

Abstract

Advances in immunotherapy have revolutionized the treatment of multiple cancers. Unfortunately, tumors usually have impaired blood perfusion, which limits the delivery of therapeutics and cytotoxic immune cells to tumors and also results in hypoxia-a hallmark of the abnormal tumor microenvironment (TME)-that causes immunosuppression. We proposed that normalization of TME using antiangiogenic drugs and/or mechanotherapeutics can overcome these challenges. Recently, immunotherapy with checkpoint blockers was shown to effectively induce vascular normalization in some types of cancer. Although these therapeutic approaches have been used in combination in preclinical and clinical studies, their combined effects on TME are not fully understood. To identify strategies for improved immunotherapy, we have developed a mathematical framework that incorporates complex interactions among various types of cancer cells, immune cells, stroma, angiogenic molecules, and the vasculature. Model predictions were compared with the data from five previously reported experimental studies. We found that low doses of antiangiogenic treatment improve immunotherapy when the two treatments are administered sequentially, but that high doses are less efficacious because of excessive vessel pruning and hypoxia. Stroma normalization can further increase the efficacy of immunotherapy, and the benefit is additive when combined with vascular normalization. We conclude that vessel functionality dictates the efficacy of immunotherapy, and thus increased tumor perfusion should be investigated as a predictive biomarker of response to immunotherapy.immunotherapy | vascular function | normalization | anti-angiogenic therapy | mechanotherapeutics

Therapy-Induced Modulation of the Tumor Microenvironment: New Opportunities for Cancer Therapies

“…Restoring vessel function by normalizing tumor stroma has been evaluated in preclinical models through targeting angiotensin signaling with anti-hypertensive agents (125) or inhibiting SDF-1/CXCR4 (86) which can target CAFs and collagen/hyaluronan to decompress tumor vessels and improve perfusion and effect of the ICB. Vascular normalization can be achieved with antiangiogenic agents to improve tumor perfusion and treatment delivery, but it is dose-and time-dependent, making outcome predictions for combinations of antiangiogenics, stroma normalization and immune therapies difficult to optimize (126) (Figure 5).…”

Immunoactivating the tumor microenvironment enhances immunotherapy as predicted by integrative computational model

“…It is not possible without the advent of modern systems biology, specifically computational systems biology and quantitative systems pharmacology, which is being recognized as a necessary methodology in academia and pharmaceutical industry (14)(15)(16)(17). In PNAS, Mpekris et al (18) formulate an integrative computational model of tumor that includes multiple elements described above and explore the behavior of the system under different conditions. The model is based on several animal experiments from the authors' laboratory as well as data from the literature.…”

“…However, if used judiciously and intelligently, computational models could be of enormous value in gaining quantitative and mechanistic understanding of the system. The model of Mpekris et al (18) is built on solid foundation of fundamental principles of chemical kinetics, biological transport and tissue mechanics, and modern knowledge and understanding of vascular biology and tumor immunology. Therefore, its predictions have the potential to guide clinical trials and drug design.…”

“…In summary, Mpekris et al (18) describe a computational model that builds on the previous studies from this group of coauthors that focus on descriptions of the tumor microenvironment, including its immune and vascular components, and intratumoral mechanical stress. The study provides a broad coverage of these important phenomena and their cross talks.…”

“…This and other models of tumor growth and cancer progression need to be thoroughly validated against clinical data using rigorous statistical tests and the arsenal of methodologies developed for calibration and validation of multiscale computational models, such as global sensitivity analysis against parameters of the model, uncertainty quantification, and parameter identifiability. Eventually, these developments should lead to in silico virtual clinical trials and contribute to the emerging field of personalized or precision medicine; the paper of Mpekris et al (18) is an important step in this direction.…”