Final Report Summary - MODICELL (Targeted Modulation of Immune-System Responses in Cell Therapies)
MODICELL sets out to improve the understanding of the complexity of immune cell responses by studying cells designed for therapies in the settings of cancer or transplantation and implement bioinformatics and in silico immune modeling to better understand the dynamics and plasticity of immune cell populations.
The ultimate goal is to identify molecular factors, patterns and pathways which leave immune cells in a robust state to mediate either anti-tumor activity or transplantation tolerance and thus can be used with high efficacy in future medical intervention.
So far, the success of cell therapies explored for their efficacy in promoting anti-tumor immune responses in cancer or promoting tolerogenic responses in transplantation has been limited because of their often unpredictable performance. For example, immune cells endowed with strong immune effector activities turn into cells with tolerogenic activities and vice versa. This unpredictable behaviour results from the complex regulation of immune cell function. Indeed, immune reactions mediated by transferred immune cells are dynamic and plastic, which means that immune cells can lose or enhance their activity by time or can convert their activity from immune activation to tolerance and vice versa.
Participants of project MODICELL actively cooperate to cover clinical entities including cancer and transplantation, in which immune cell therapies are considered.
The main scientific objectives within MODICELL are:
• The identification of factors critical to the generation of robust effector immune system responses, to eradicate cancer cells; or tolerogenic immune system responses, to improve tolerance towards transplanted organs.
• The generation of computer-based predictive models to aid in the identification of specific factors and/or pathways critical for induction of the desired types of immune responses.
Achievements of MODICELL
The consortium has defined strategies to test that may induce allo-specific tolerance in vitro and also established strategies for potent dendritic cell generation that could be useful for tumor vaccination. Currently, project partners are heavily engaged in running wet-lab experiments and gathering high-throughput data for bio-informatics analysis.
Certain combinations of the most promising tolerizing approaches defined so far (e.g. CB, DC stimulation, hypoxia) have been tested and experimental conditions that induce a robust regulatory phenotype have been identified.
In the course of RNA-Seq analysis the consortium was able to i) confirm experimental in vitro results; ii) gain deeper insight on the role of hypoxia in shaping immune responses; and iii) establish a fully automated RNA-Seq analysis pipeline that is readily available for the other projects.
Tumor-derived T cells from 12 glioblastoma (GBM) patients were subjected to next generation sequencing of the T cell receptor (TCR). Analysis of the TCR sequencing data identified a differential pattern between different GBM patients that is associated with higher survival and response to cancer immune-therapy (CIT). With this study we have the first indications that deep sequencing the TCR can provide key information how to generate potent and durable anti-tumour immune responses with dendritic cell-CIT approach.
Partner Bar Ilan University has restructured the infrastructure behind the modelling Reactive Animation to facilitate 3D cells and cellular surfaces. The restructuring has been done using a gaming platform, thus making use of the most advances GPU technology, and enabling visualization combined with simulation. To allow input of user (biologist) data, a new interface has been built from the ground up, that allows the specifications of scenarios and cells.
The consortium has achieved key deliverables by reporting research results on i) the effect of oxygen tension in immune decision; ii) intra-tumoural and systemic immunity in patients and mouse models undergoing CIT; iii) factors determining the stimulatory capacity of DCs; iv) T cell characteristics stimulated by differently activated DCs; and v) Reactive Animation Predictions to test in biologic experiments.
To accomplish the network’s complementary training strategy, MODICELL fellows had the privilege to participate in training workshops within the frame of a work package entirely dedicated to career development, which included: Bioinformatics and Data Analysis; Automated analysis pipelines; Soft Skills in the context of oral communications; Lecture Series on Regulatory Issues; Introduction to Clinical Trials; and Entrepreneurship and Business Development.
With the creation of a first prototype, MODICELL seeks to develop a computational approach (Reactive Animation) for simulating more complex biological systems involved in Dendritic Cell-T cell interactions. Reactive Animation aims at integrating the dynamic characteristics of these biological interactions based on evidence collected from relevant scientific literature and from in vitro experiments. Upon data integration, the computational tool should allow the simulation of biological interactions to be visualized through a user-friendly system.
This platform could soon become available for the generation of predictions describing the process of immunological “decision-making”. In silico-generated predictions should then be tested in wet lab experiments in order to validate the biological simulation system. Reactive Animation could offer a unique opportunity for scientists and clinicians to test hypotheses in the manipulation the immune system for transplantation as well as cancer therapy. Due to the complexity and ambition of the topic more resources should be allocated and collaborative networks established to drive further the achievements of this project. However, MODICELL sets out to be an example of how researchers from different fields such and biological, computational and mathematical sciences, can team up to solve complex and urgent medical needs.
Project website: http://modicell.eu/