Deciphering Pan-Cancer Tumour Microenvironment Ecosystems Using Single-Cell Atlases and Patient-Derived Explant Models

College of Life Sciences, University of Leicester

Dr Gareth Miles, Dr Giuditta Viticchie, Dr Constantinos Demetriou

Applications accepted all year round

Self-Funded PhD Students Only

About the Project

Background/project proposal:

Cancer is characterized by multi-cellular communities with complex interactions that shape disease progression, treatment responses, and patient survival [1]. Previous studies have revealed broad tumour microenvironment (TME) phenotypes across cancer types, such as T cell inflamed (“hot”) and T cell depleted (“cold”) subtypes [2]. Although these classifications can guide the use of immune-checkpoint blockage (ICB) and other targeted therapies, they oversimplify the extensive cell type and cell state heterogeneity present in the TME. Consequently, this may hinder effective clinical decision-making as well as contribute to both over- and undertreatment. Therefore, a more comprehensive understanding of the TME heterogeneity is essential for improving clinical outcomes and guiding the development of targeted therapeutic strategies.

Recent advancements in single-cell RNA sequencing (scRNA-seq) technologies have expanded our ability to profile individual cells at unprecedented resolution, leading to the identification of distinct cellular ecosystems, or “ecotypes”, across cancer types [1] [3]. Nevertheless, most scRNA-seq studies are limited by small number of samples, and their variable experimental protocols and cell type annotations lead to inconsistent findings [4]. To overcome these limitations, integrated single-cell atlases capture broad population diversity and enable holistic cell type annotations, thereby serving as universal references [4]. What is more, recent reference atlases have led to the discovery of unknown cell types, and patient stratification for cancer TME subtypes [5][6]. Despite these recent advances and the publications of several cancer-specific reference atlases, comprehensive pan-cancer reference atlases remain scarce. Such atlases may be crucial for uncovering recurrent cellular ecosystems that span multiple cell types, ultimately enabling more accurate prediction of immunotherapy responses and patient outcomes.

Validation of the transcriptomic-defined cellular ecosystems in preclinical models is essential for predicting treatment responses and identifying potential combination therapies. Current pre-clinical models, including patient-derived cell lines, xenografts (PDX), genetically engineered mouse models (GEMMs), and patient-derived organoids (PDOs) fail to capture the genomic complexity and TME heterogeneity of native tumours. To overcome these limitations, our patient-derived explant (PDE) model provides a platform which tumour, stromal, and immune infiltrates can be assessed in their native architecture [7]. Moreover, we have shown that PDEs preserve inter-patient heterogeneity and can predict immunotherapeutic responses in a clinically relevant timeframe across multiple cancer types [7][8][9]. Thus, our extensive collection of archived cross-tumour PDE slices, treated with diverse chemotherapies and ICBs, provides a platform to validate the identified cellular ecosystems. Once validated, prospective PDEs could be used to advance therapeutic discovery and support novel precision medicine approaches.

Techniques to be learned:

As part of this project, the PhD student will gain experience in curating and processing scRNA-seq datasets using R and Python programming languages. By leveraging the University’s high-performance computing (HPC) resources, the student will perform large-scale integration of scRNA-seq datasets using state-of-the-art integration tools. Next, machine learning, high-dimensional analyses, and consensus clustering will be applied to identify recurrent cellular ecosystems. Moreover, the student will gain experience in antibody optimization and the development of novel marker panels required for multiplexed immunofluorescence (mIF) staining in PDE slices. This project may also include processing prospective PDE specimens and evaluating their responses to combination therapies.

Outputs and impact:

This project will generate an integrated pan-cancer single-cell atlas, uncovering novel cellular ecosystems across diverse tumour types in high resolution. Validation using the unique PDE model system, will link transcriptomic-defined ecotypes to patient-derived therapeutic responses. These findings will advance our understanding of the TME heterogeneity, and may inform rational combination therapies, thus advancing precision oncology. Furthermore, the project will provide the PhD student with comprehensive computational, experimental, and analytical training, equipping them with the skills necessary to drive future discoveries in cancer research and translational medicine.

Apply at:

https://le.ac.uk/study/research-degrees/research-subjects/cancer-studies

PhD entry requirements: https://le.ac.uk/study/research-degrees/entry-reqs

Supervisor contact details:

Dr Gareth Miles - gjm14@leicester.ac.uk

Dr Giuditta Viticchie - Gv51@leicester.ac.uk

Dr Constantinos Demetriou - cd317@le.ac.uk