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Project Information
The extracellular matrix (ECM) is one of the major compartments in many healthy and malignant tissues. Loss of homeostasis during cell transformation and tumour arising is linked to changes in the ECM. ECM levels, composition, and biophysical properties such as stiffness and viscoelasticity, along with matrix topographies, are fundamental changes that have been studied in several solid tumours. This research project is dedicated to uncovering the intricate role of the ECM in tumour progression, particularly at the crucial juncture where precancerous tissues transform into malignant lesions. Our objective is to understand how the ECM’s organization, including its composition and physical properties, can significantly influence the onset and advancement of cancer. Furthermore, we are keenly interested in the dynamic interplay between preneoplastic or neoplastic cells, the ECM, and the rest of the tumour microenvironment (TME).
Utilizing cutting-edge spatial biology tools, we will analyse an extensive array of samples from both human and murine origins, focusing on solid tumours and precancerous lesions. By applying machine learning algorithms to digital pathology and multiplex imaging data, our goal is to create detailed maps of tissue architecture. These maps will help us visualize and quantify the interactions between stromal, tumour or precancerous, and immune cells within the ECM, providing insights into the dynamics of tissue architecture.
The project will validate our findings through state-of-the-art 3D in vitro models using decellularized matrices and fine-tuned models that manipulate ECM composition and architecture. This will enable us to experimentally manipulate the ECM and assess its impact on tumour behaviour in a controlled environment. Through this multifaceted approach, we aim to uncover the mechanistic underpinnings of how the ECM contributes to tumour progression and to identify important histopathological features and biomarkers in the tumour microenvironment that can accelerate early diagnosis.
As a PhD student you will acquire skills in:
1. Data and Image Analysis:
2. Experimental Work:
3. Training, Interdisciplinary Collaboration and Communication:
4. Project Management:
Your Profile
We are seeking a highly motivated PhD candidate to join our dynamic, cross-disciplinary team.
1. Educational Background:
2. Technical Skills:
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