Periodic Reporting for period 1 - ImmunoMECH (High-performance biomechanical model of combined immunotherapy and anti-angiogenic cancer treatment)
Período documentado: 2019-06-17 hasta 2021-06-16
Tumor cells are able to evade immune responses by activating negative regulatory pathways, also known as immune checkpoints, that block cytotoxic T-cell activation and thus, the immune system cannot kill cancer. Despite the promise of immunotherapy, which boosts the patients immune response, the absence of dramatic immunotherapeutic responses has been attributed to a variety of factors, including hindering of immune cell delivery and activity in the tumor owing to the hypo-perfused tumor microenvironment (TME). Perfusion rates (i.e. blood flow), in some regions of a tumor can be significantly lower than that in the peri-tumor normal tissue, leading to inadequate delivery of medicines, which in turn, compromises the efficacy of cancer therapies, including immunotherapy. Particularly, hypo-perfusion can reduce the number of immune cells that infiltrate into the tumor and induce hypoxia (i.e. lack of oxygen). Hypoxia in turn renders TME immunosuppressive and attenuates the killing potential of immune cells.
There is evidence that improving perfusion in tumors by restoring abnormalities of the tumor vessels can improve the efficacy of cancer therapies. In ImmunoMECH, we tested experimentally and with the use of mathematical modeling the hypothesis that re-purposing safe and well tolerated drugs with the aim to repair vascular abnormalities in tumors and restore normal blood flow, namely tumor normalization strategy, can optimize the efficacy of immunotherapy in murine tumor models.
The overall objectives of the project are: the development of innovative data driven algorithms leading to realistic biomechanical modeling for immune cell/anti-angiogenic agents and tumor evolution that will harness the power of High Performance Computing environments, the experimental investigation of the normalization/immunotherapy efficiency with in vivo experiments in animal tumor models and the experimental validation and verification of model parameters to optimize the therapeutic strategy in terms of immunotherapeutic and normalization agents dosage.
A biomechanical model was extended to study the effects of immunotherapy and normalization therapy on tumor growth, based on previous work of the host Cancer Biophysics Laboratory. The MSolve Finite Element numerical solution platform, developed in the Fellow’s former laboratory at the National Technical University of Athens, was implemented to tackle the computational cost. The hypothesis of the project was tested experimentally. Orthotopic syngeneic models for sarcoma tumors were generated by implantation of MCA205 and K7M2wt cancer cells in mice. Using these two murine sarcoma models, we demonstrated that normalization of the tumor microenvironment by re-purposing an approved antihistamine drug can effectively modulate tumor stiffness and mechanical stresses, improving vascular perfusion and promote immunostimulation. Furthermore, we found the optimal dose of the normalization agent that most effectively improves perfusion and showed that normalization treatment can optimize the efficacy of immune checkpoint inhibition in both tumor models.
We concluded that modulating the tumor micro-environment to restore vascular abnormalities is an effective therapeutic strategy to improve immunotherapy.
There is evidence that improving perfusion in tumors by restoring abnormalities of the tumor vessels can improve the efficacy of cancer therapies. In ImmunoMECH, we tested experimentally and with the use of mathematical modeling the hypothesis that re-purposing safe and well tolerated drugs with the aim to repair vascular abnormalities in tumors and restore normal blood flow, namely tumor normalization strategy, can optimize the efficacy of immunotherapy in murine tumor models.
The overall objectives of the project are: the development of innovative data driven algorithms leading to realistic biomechanical modeling for immune cell/anti-angiogenic agents and tumor evolution that will harness the power of High Performance Computing environments, the experimental investigation of the normalization/immunotherapy efficiency with in vivo experiments in animal tumor models and the experimental validation and verification of model parameters to optimize the therapeutic strategy in terms of immunotherapeutic and normalization agents dosage.
A biomechanical model was extended to study the effects of immunotherapy and normalization therapy on tumor growth, based on previous work of the host Cancer Biophysics Laboratory. The MSolve Finite Element numerical solution platform, developed in the Fellow’s former laboratory at the National Technical University of Athens, was implemented to tackle the computational cost. The hypothesis of the project was tested experimentally. Orthotopic syngeneic models for sarcoma tumors were generated by implantation of MCA205 and K7M2wt cancer cells in mice. Using these two murine sarcoma models, we demonstrated that normalization of the tumor microenvironment by re-purposing an approved antihistamine drug can effectively modulate tumor stiffness and mechanical stresses, improving vascular perfusion and promote immunostimulation. Furthermore, we found the optimal dose of the normalization agent that most effectively improves perfusion and showed that normalization treatment can optimize the efficacy of immune checkpoint inhibition in both tumor models.
We concluded that modulating the tumor micro-environment to restore vascular abnormalities is an effective therapeutic strategy to improve immunotherapy.
Within the 2-years period of the project, the proposed research objectives have been addressed. The mathematical model was developed and a series of experiments in murine tumor models was performed based on model predictions to prove experimentally that the tumor normalization strategy can improve the efficacy of immune checkpoint inhibition. We firstly found that repurposing a common antihistamine drug at the proper dose can alleviate hypoxia and normalize the tumor microenvironment making the tumor less stiff. Also, the reduction of tissue stiffness that we observed allowed the decompression of the tumor vessels which improved perfusion. The newly remodeled vessels exhibited high expression of adhesion molecules facilitating attachment of cytotoxic T cells. Critically, we found that such normalization strategy improved the efficacy of doxorubicin and/or anti-PD-L1 blockade to cure tumors by offering durable remission. Overall, our findings illustrated the presence of a positive feedback loop from immune-vascular crosstalk that enhances the efficacy of immune checkpoint inhibition, leading to cancer eradication.
The mathematical model was developed using the Finite Elements software MSolve. Model equations to describe the population of cancer cells and immune cells as well as the delivery of the drugs and tumor oxygenation were developed and solved in MSolve. Along with the development of the mathematical model, experiments in murine sarcoma models were performed. Two orthotopic and syngeneic models of fibrosarcoma and bone sarcoma were employed to prove experimentally the hypothesis of the project. We found that normalization of the tumor microenvironment in these tumors could improve perfusion, reduce tumor stiffness, alleviate mechanical forces and thus, improve the efficacy of immunotherapy. Furthermore, the optimal dose of the normalization agent was identified.
The results of the experiments have been documented in a manuscript, which will be submitted for publication in a peer-reviewed scientific journal soon. A second publication that includes the mathematical model is in preparation. Results of the project were presented in inviting talks given by the Principal Investigator in academic institutes in the USA.
The mathematical model was developed using the Finite Elements software MSolve. Model equations to describe the population of cancer cells and immune cells as well as the delivery of the drugs and tumor oxygenation were developed and solved in MSolve. Along with the development of the mathematical model, experiments in murine sarcoma models were performed. Two orthotopic and syngeneic models of fibrosarcoma and bone sarcoma were employed to prove experimentally the hypothesis of the project. We found that normalization of the tumor microenvironment in these tumors could improve perfusion, reduce tumor stiffness, alleviate mechanical forces and thus, improve the efficacy of immunotherapy. Furthermore, the optimal dose of the normalization agent was identified.
The results of the experiments have been documented in a manuscript, which will be submitted for publication in a peer-reviewed scientific journal soon. A second publication that includes the mathematical model is in preparation. Results of the project were presented in inviting talks given by the Principal Investigator in academic institutes in the USA.
Failure of standard cancer therapies has dramatic effects on the health and quality of life of cancer patients and their families. Also, any cancer therapy involves high costs for the compensation of clinicians and health centers as well as for the purchase of the drugs. The successful implementation of the project led to the finding that re-purposing a common antihistamine drug to modulate the tumor microenvironment can improve the efficacy of chemotherapy, immunotherapy and their combination in preclinical models of sarcomas. This is very important given that a therapy for many advanced sarcoma tumors does not exist. Furthermore, all drugs that were employed in this research (antihistamine, chemotherapy, immunotherapy) are clinically approved and thus, the research is highly transferable to the clinic.
As far as the impact of the project on the career development of the Fellow is concern, through thorough collaboration with the Supervisor and other members of the host lab, the fellowship allowed the Fellow to substantially built upon his previous experience and strengthen his overall scientific abilities. In particular, he expanded his skills in computational mechanics by gaining experience in mathematical modeling of cancer, tumor pathophysiology and biophysics. The Fellow also obtained new skills in a wide range of in vitro and in vivo experiments, and training in computational modelling and 3D imaging techniques. Moreover, the fellowship helped the Fellow to broaden his understanding of biological processes and provided valuable insight to phenomena pertinent to cancer mechanics and how to address them effectively theoretically. These experiences helped establish the Fellow as an independent researcher, capable of designing complex research plans and drawing from a diverse set of tools to implement them, in line with the purpose of MSCA-IF and the promotion of European Excellence in research.
As far as the impact of the project on the career development of the Fellow is concern, through thorough collaboration with the Supervisor and other members of the host lab, the fellowship allowed the Fellow to substantially built upon his previous experience and strengthen his overall scientific abilities. In particular, he expanded his skills in computational mechanics by gaining experience in mathematical modeling of cancer, tumor pathophysiology and biophysics. The Fellow also obtained new skills in a wide range of in vitro and in vivo experiments, and training in computational modelling and 3D imaging techniques. Moreover, the fellowship helped the Fellow to broaden his understanding of biological processes and provided valuable insight to phenomena pertinent to cancer mechanics and how to address them effectively theoretically. These experiences helped establish the Fellow as an independent researcher, capable of designing complex research plans and drawing from a diverse set of tools to implement them, in line with the purpose of MSCA-IF and the promotion of European Excellence in research.