Mapping the Matrix in Tumour Progression: Integrating Digital Pathology and 3D Biological Models

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:

• Utilize machine learning algorithms and statistical analysis tools to process and interpret digital pathology, multiplex imaging data, and combine several multi-omic modalities.
• Gain proficiency in Image analysis platforms such as ImageJ, QuPath, MATLAB, Python, and R to support analysis and modelling efforts.
• Gain proficiency in computer vision: Python and Pytorch in digital pathology to develop (Generative Adversarial Networks) and segmentation tools.

2. Experimental Work:

• Acquire foundational knowledge in oncologic pathology, learning to interpret cancer-related pathological findings within a clinical setting.
• Design, execute, and troubleshoot in vitro experiments using 3D cell culture models, including techniques for decellularization, alongside potential in vivo studies for comprehensive validation.
• Develop hands-on expertise in brightfield, confocal and multiphoton microscopy. To perform histopathology stainings such as IHC, multiplex IHC and special stainings (Masson’s Trichrome, Picrosirius red) and Second-Harmonic Generation.
• To gain experience in molecular biology and cell culture techniques, enhancing technical proficiency in a laboratory setting.

3. Training, Interdisciplinary Collaboration and Communication:

• The PhD candidate will attend specialized courses and training programs to boost their expertise in specific areas crucial for advancing the project.
• Work closely with an interdisciplinary team, integrating findings from diverse areas like biology, computer vision, data science and clinical research to foster a holistic understanding of tumour biology.
• Enhance communication skills through writing scientific papers, contributing to grant applications, and presenting findings at conferences, ensuring the ability to articulate complex scientific ideas clearly and effectively.

4. Project Management:

• Planning and execution of data analysis, developing strong project management and organizational skills.

Your Profile

We are seeking a highly motivated PhD candidate to join our dynamic, cross-disciplinary team.

1. Educational Background:

• BSc (First or 2:1) or MSc Degree in a Relevant Field: Computational Biology, Bioinformatics, Cancer Biology or a related field.
• Strong Foundation in Biological Sciences: A solid understanding of cellular biology, oncology, and pathology is essential.
• Familiarity with Histopathology: Experience with histopathological techniques and analysis would be highly beneficial.

2. Technical Skills:

• Proficiency in Data Analysis: Candidates should have experience with statistical software and tools used in data analysis, particularly related to biological data.
• Image Analysis Expertise: Experience with digital pathology and imaging software such as ImageJ, QuPath, MATLAB, or similar platforms.
• Programming Skills: Knowledge in programming languages such as Python or R, especially as applied to machine learning and data modelling.
• Laboratory Techniques: Hands-on experience with cell culture, particularly 3D culture systems, and basic molecular biology techniques.

We offer

• This 4-year PhD studentship is funded by Barts Charity and comes with a tax-free starting stipend of £24,278. It is open to all students. Those who are not UK Nationals or non-UK nationals with indefinite leave to remain in the UK will need to acquire a visa ahead of the start of the studentship. University tuition fees (at UK levels) will be met by the funding body. 
• The opportunity to work on a project with significant societal implications, directly impacting the lives of cancer patients. 
• Access to state-of-the-art computing resources, including high-performance GPUs and a dedicated research computing cluster. 
• Opportunities to develop your professional network through collaborations with leading researchers and institutions.