Periodic Reporting for period 2 - ICoMICS (Individual and Collective Migration of the Immune Cellular System)
Reporting period: 2023-07-01 to 2024-12-31
Cancer remains one of the world's major health problems. In Europe, approximately 3.5 million new cases are diagnosed each year and nearly 2 million people die from cancer. It is estimated that the number of cases diagnosed annually will increase to more than 4 million in the next 10 years. More than 95% of cancers are solid tumours. Despite small advances in cancer treatment, the survival rate for solid tumours has not changed significantly in recent decades. In a new scenario where personalised medicine represents new strategies, immunotherapy has emerged as a promising field.
The immune system consists of a collection of cells with a high ability to migrate that work together to remove harmful foreign material from the body. Each immune cell can migrate between tissues, fulfilling specific functions in different microenvironments. However, this immune-surveillance response is not very effective in solid tumours, which are tissues with a high non-physiological stiffness and a significant level of residual stresses. Understanding the mechanisms that govern the cellular immune response to solid tumours is crucial to strengthen the development of novel immunotherapies.
ICoMICS aims to develop a novel predictive modelling platform to investigate how therapeutic immune cells (TICs) sense, migrate and interact with cancerous cells and with the tumor microenvironment (TME). This platform will be built on two key pillars: in-vitro 3D tumor organoids and multicellular simulations, which will be combined and integrated by means of machine learning algorithms. On the one hand, cell culture microfluidic chips will be microfabricated, allowing continuous perfusion of chemical modulators through hydrogels (including decellularized matrices) inhabited by human tumour cells arranged to recreate 3D solid tumor organoids (Objective 1). On the other hand, an agent-based model will be developed to simulate cells, including cell-cell and cell-matrix interactions, combined with a continuum approach to model matrix mechanics and chemical reactions of cells, such as reactive oxygen species (ROS) and nutrients diffusion (Objective 2). The ICoMICS platform will, therefore, provide a modelling approach capable of predicting how therapeutic immune cells will migrate and interact with the tumour microenvironment, helping to develop novel immunotherapy-based treatments (Objective 3).
All this research will be applied to three main solid tumours: lung, liver and pancreas.
The immune system consists of a collection of cells with a high ability to migrate that work together to remove harmful foreign material from the body. Each immune cell can migrate between tissues, fulfilling specific functions in different microenvironments. However, this immune-surveillance response is not very effective in solid tumours, which are tissues with a high non-physiological stiffness and a significant level of residual stresses. Understanding the mechanisms that govern the cellular immune response to solid tumours is crucial to strengthen the development of novel immunotherapies.
ICoMICS aims to develop a novel predictive modelling platform to investigate how therapeutic immune cells (TICs) sense, migrate and interact with cancerous cells and with the tumor microenvironment (TME). This platform will be built on two key pillars: in-vitro 3D tumor organoids and multicellular simulations, which will be combined and integrated by means of machine learning algorithms. On the one hand, cell culture microfluidic chips will be microfabricated, allowing continuous perfusion of chemical modulators through hydrogels (including decellularized matrices) inhabited by human tumour cells arranged to recreate 3D solid tumor organoids (Objective 1). On the other hand, an agent-based model will be developed to simulate cells, including cell-cell and cell-matrix interactions, combined with a continuum approach to model matrix mechanics and chemical reactions of cells, such as reactive oxygen species (ROS) and nutrients diffusion (Objective 2). The ICoMICS platform will, therefore, provide a modelling approach capable of predicting how therapeutic immune cells will migrate and interact with the tumour microenvironment, helping to develop novel immunotherapy-based treatments (Objective 3).
All this research will be applied to three main solid tumours: lung, liver and pancreas.
The main work developed and the main results achieved since the beginning of the project are presented and organized according to the three main objectives of the project.
OBJ 1. In-vitro recreation of solid tumours
• We have characterized the physico-chemical properties of pancreatic ductal adenocarcinoma (PDAC) coming from patient-derived PDAC xenografts in mice. We concluded that stroma of this tumour is determined by its interconnected porous architecture with very low permeability and small pores due to the contractility of the cellular components.
OBJ 2. Predictive computer-based simulations
• We have defined a new computational framework based on deep learning techniques to simulate morphogenetic patterns in organoids. In particular, we focus on the understanding of how cell functions are coordinated spatially and temporally for the formation of PDAC solid organoids and with lumen. The graphical abstract of this methodology is shown in Figure 1.
OBJ 3. Test novel immunotherapy strategies
• EGFR-targeted CAR-T cells were generated, which present a motility pattern different than immune T cells in both confined conditions and 3D migration.
OBJ 1. In-vitro recreation of solid tumours
• We have characterized the physico-chemical properties of pancreatic ductal adenocarcinoma (PDAC) coming from patient-derived PDAC xenografts in mice. We concluded that stroma of this tumour is determined by its interconnected porous architecture with very low permeability and small pores due to the contractility of the cellular components.
OBJ 2. Predictive computer-based simulations
• We have defined a new computational framework based on deep learning techniques to simulate morphogenetic patterns in organoids. In particular, we focus on the understanding of how cell functions are coordinated spatially and temporally for the formation of PDAC solid organoids and with lumen. The graphical abstract of this methodology is shown in Figure 1.
OBJ 3. Test novel immunotherapy strategies
• EGFR-targeted CAR-T cells were generated, which present a motility pattern different than immune T cells in both confined conditions and 3D migration.
The expected results with a significant progress beyond the state of the art are presented along the main objectives of the project:
OBJ 1. In-vitro recreation of solid tumours
• 3D organoid cultures of solid tumours will be generated using different novel approaches: coculture with stroma stellate cells, hydrogels of different composition mixing collagen-, Matrigel-, basement membrane extract (BME-) and decellularized matrices from animals.
• Patient-derived xenograft organoids of PDAC (Pancreatic Ductal Adenocarcinoma) will be generated on our chip
• Unravelling the relationship between nutrient availability in tumour microenvironment and cancer progression
OBJ 2. Predictive computer-based simulations
• We will extend the approach already developed for modelling the dynamics of individual cells taking into account the highly heterogeneous behaviour of the cell population.
• A hybrid approach will be presented to analyse tumour metabolism simulating cells by a discrete approach and a continuum approach for the nutrients and different chemical reactions.
• Modelling CAR-T interaction with organoids of solid tumours, taking into account infiltration in the tumour and the consequently interaction with tumour cells.
OBJ 3. Test novel immunotherapy strategies
• Currently, we have generated EGFR-targeted CART cells. We will fabricate other CARTs against other tumour antigens.
• To mechanically stimulate CARTs to improve their capacity for infiltration in solid tumours.
• To engineer cell clusters as bio-bots to attack solid tumours.
OBJ 1. In-vitro recreation of solid tumours
• 3D organoid cultures of solid tumours will be generated using different novel approaches: coculture with stroma stellate cells, hydrogels of different composition mixing collagen-, Matrigel-, basement membrane extract (BME-) and decellularized matrices from animals.
• Patient-derived xenograft organoids of PDAC (Pancreatic Ductal Adenocarcinoma) will be generated on our chip
• Unravelling the relationship between nutrient availability in tumour microenvironment and cancer progression
OBJ 2. Predictive computer-based simulations
• We will extend the approach already developed for modelling the dynamics of individual cells taking into account the highly heterogeneous behaviour of the cell population.
• A hybrid approach will be presented to analyse tumour metabolism simulating cells by a discrete approach and a continuum approach for the nutrients and different chemical reactions.
• Modelling CAR-T interaction with organoids of solid tumours, taking into account infiltration in the tumour and the consequently interaction with tumour cells.
OBJ 3. Test novel immunotherapy strategies
• Currently, we have generated EGFR-targeted CART cells. We will fabricate other CARTs against other tumour antigens.
• To mechanically stimulate CARTs to improve their capacity for infiltration in solid tumours.
• To engineer cell clusters as bio-bots to attack solid tumours.