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Agent-Based Modelling of Gene Networks to model clonal selection in the tumour microenvironment and predict therapeutic resistance

Periodic Reporting for period 4 - MicroC (Agent-Based Modelling of Gene Networks to model clonal selection in the tumour microenvironment and predict therapeutic resistance)

Okres sprawozdawczy: 2022-02-01 do 2023-07-31

The occurrence of therapeutic resistance is a major cause for the small effect on overall survival showed by targeted cancer therapies. Whilst experimental strategies to evaluate available treatments have been faced by an ever increasing number of possible combinations, computational approaches have been challenged by the lack of a framework able to model the multiple interactions encompassed by the three major factors affecting therapeutic resistance: selection of resistant clones, adaptability of gene signalling networks, and a protective and hypoxic tumour microenvironment.

Here I propose a novel modelling framework, Agent-Based Modelling of Gene Networks, which brings together powerful computational modelling techniques and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict therapeutic resistance and guide effective treatment selection.

Using triple negative breast cancer (TNBC) as our initial testing case for this framework (15% of breast cancers, lacks validated), I propose to:

1. Develop a computational model of the TNBC tumour microenvironment using in-vitro and in-vivo, including patient-derived, models and data from clinical samples.
2. Validate the ability of the model to predict driver genes conferring a survival advantage to cancer cells under different microenvironmental conditions, focusing on those found in TNBC.
3. Predict combinations of druggable targets to tackle TNBC therapeutic resistance.
4. Select most effective drug combinations and proceed to pre-clinical validation in TNBC models.
During the first two years of the project we have A) generated and analysed multi-omics data from cell lines and clinical cohorts, and B) developed and implemented our first implementation of our novel microenviroment-aware cancer modelling framework (microC). The results from these two activities contribute mainly to the first to objectives as described above, but they build the models, tools and methods for the last two objectives of the project.

A) Generation, and analysis of, multi-omics data from in-vitro models and clinical cohorts.

Analysis of omics data in panels of cell lines and large cancer cohorts is helping us to understand how gene networks are required in cancer. Firstly, we have developed robust protocols so the such analses can be performed in a standardize manner. An example is our published sigQC protocol, where we provide guidelines for systematic evaluation of gene signatures in independent transcriptomics datasets. Next, we have used gene signatures of phenotypic hallmarks of cancer, together with machine learning methods, to characterize, in a comprehensive manner, across multiple epithelial cancer types comprising thousands of clinical samples (e.g. those from The Tumour Cancer Atlas), the association of non-coding and coding transcriptomic, methylation and mutational profiles. We have also mined data from healthy individual (e.g. those from the UK BioBank), and from other disease cohorts, to shed light into the functional implications of our findings. Results so far have highlighted a complex map of interactions underlying the relationship of transcriptional regulation and the hallmarks of cancer, which constitutes our starting point for modelling of TNBC.

B) microC development and implementation.

The microC modelling framework brings together Agent Based Modelling, a powerful computational modelling technique, and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict the evolution of complex multi-cellular dynamics. Importantly, this enables modelling co-occurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as nutrients availability, and their interactions. The currently implementation of microC enables in-silico biological experimentation and generation or testing of new hypotheses via a web interface. The development of our 3D cancer model is on-going, but it may already be used to study the effects of mutations and cell-cell or cell-microenvironment interactions on the dynamics of cell growth. Almost all features of microC (networks, cell microenvironment, cell-cell interaction, mutations) can be customised by the user. This enables to extend the study of the link between genotype, signalling networks and cell behaviour in a 3D physical environment to experimental conditions beyond this initial project.
Via the above stated objectives, this project will deliver new candidate therapeutic targets and drug combinations for TNBC, and enable us to develop and validate a novel microenviroment-aware cancer modelling framework (microC).

Complementing and empowering experimental models and assays, this work will offer a new powerful tool to study the evolution of individual tumours and predict therapeutic resistance.
Conceptual representation our environment and simulation environment.