Webinar: Poster winners Summer School. Examples on cancer research through computational modelling.
This webinar includes presentations from three speakers who won the poster prizes during the PerMedCoE Summer School, which run in 2023. Below there are short description for each of them:
1) Towards Tailored Cancer Therapies: Robust platform for drug testing in patient-specific Boolean models
Presenter: Viviam Bermúdez (Norwegian University of Science and Technology)
Functional precision medicine offers a crucial opportunity in oncology, allowing treatment design and testing in patient companion models. However, the key challenge lies in developing efficient computer models to select optimal candidate therapies that can be tested in limited patient-derived material. Our project aims to develop a platform that enables the customization of Boolean models into patient-specific models to simulate the action of candidate drugs. We employ bioinformatics, artificial intelligence, and machine learning approaches to integrate and harmonise datasets on available drugs, drug targets, existing drug synergy data, and precise activity states of key regulatory network proteins derived from patient omics data. The model’s predictive accuracy for drug responses enables in vitro testing and data-driven decisions for tailored treatment plans, considering a wide range of treatment designs and approved drugs, including repurposed and novel combinations. Through the fusion of diverse biological data, model simulations, and prediction analysis, the platform serves as a tool within the NTNU DrugLogics pipeline, paving the way in the field of precision oncology.
2) Computational modelling of High Grade Serous Ovarian Cancer (HGSOC)
Presenter: Marco Fariñas (Norwegian University of Science and Technology).
High Grade Serous Ovarian Cancer is a lethal gynaecological malignancy with progressively resistant metastases. To date, our results have revealed mechanisms by which the tumour micro-environment (TME) modulates the dynamical tumour behaviour. In this context, we have developed a HGSOC Boolean network that includes most relevant pathways to disease progression. By usage of multiomics data (focusing on mutation and transcriptomics-derived transcription-factor-activity data) generated upon different experimental designs, the model will be trained to capture the influence of crosstalk that takes place between tumour cells and stromal cells and the extracellular matrix. This will ultimately provide a powerful resource to uncover the molecular mechanisms behind HGSOC aggressiveness. The subsequent ability to use cohort- and patient-specific molecular profiles for treatment predictions will be of immediate clinical relevance. Methodologically, the project design will provide a user-friendly reproducible pipeline that can be easily adapted and replicated for similar and different studies.
3) Dissecting cellular communication in the tumour microenvironment through multiscale dynamical modelling
Presenter: Malvina Marku (INSERM - Toulouse Cancer Research Center)
The tumour microenvironment (TME) can be seen as a complex system containing multiple cell types interacting through contact and cytokine exchanges. In particular, immune cells play a major role in cancer development and their characterization allows a better understanding of the TME. In this context, network approaches and mathematical modelling allow studying interactions between immune and cancer cells to obtain relevant information about cellular reprogramming and state transitions, and identify novel molecular interactions and potential drug targets. This project aims to investigate how regulatory interactions between genes characterise the cellular behaviour of cancer cells in the presence of immune cells at the molecular and cellular level. Prior work in our lab has characterised the functional processes determining cancer cell – immune cell interactions at the cellular level, revealing important biological processes determining cancer cell survival. Then, following a data-driven gene regulatory network inference on cancer cells, we identified novel transcription factors involved in cancer cell – immune cell crosstalk, thus better understanding their mechanisms of survival. Integrating these two scales, we aim to recapitulate the processes that lead to the formation of pro-tumor phenotypes of immune cells, specifically highlighting their effect on cancer cell survival.
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