The Beatson Institute Research Groups

Introduction

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For decades the genome has been hailed as the major, if not the sole, evolutionary powerhouse of all of biology. However, compelling evidence obtained from various cellular systems and organisms suggest that complex networks of non-genetic information are equally fundamental in shaping evolution. Although, during the last decade the study of non-genetically encoded networks has seen a technology driven resurgence, the underlying molecular details encompassing how the genetic and non-genetic compartments crosstalk shape phenotypic output remain largely unknown. Notably, as evidenced by numerous examples scattered across the various areas of biology, including cancer, a cell phenotype is not exclusively determined by its genotype but is rather moulded by a multitude of non-genetic mechanisms encoded in complex dynamic networks. To mention a few, we can count DNA and histone modifications, high-order chromatin architecture, gene expression dynamics and RNA-protein interactions, amongst others of equal relevance; all of them acting in concert to bequest cells with the plasticity to thrive within an ever-changing environment.

It is in that context that phenotypic plasticity, the ability of a single genotype to produce a variety of phenotypes, has been documented as a core biological process underlying numerous molecular and cellular events ranging from unicellular adaptation to multi-cellular organism development. Translating this concept onto cancer cell populations, phenotypic plasticity may lead to the establishment of co-existing genetically identical cells yet harbouring phenotypically distinct metastable states that in turn, may endow tumour cells with the capability to adapt to fast-paced environmental conditions (exposure to anti-cancer drugs, hypoxia, invasion of new niches, etc).

Given the crucial role that non-genetically encoded phenotypic states play in biology, our research aims to unravel the molecular mechanisms underlying such a phenomenon and thrives to address its role as a key determinant in cell plasticity during cancer onset, progression and evolution. To do so, our lab blends the development and use of multimodal single cell technologies with the in-depth exploration of the basic biology underlying cell plasticity and populational heterogeneity in models of cellular proliferation, epithelial-to-mesenchymal transition, oncogene-induced transformation and resistance to anticancer drugs in 2D, 3D and organoid settings.

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Following those lines, we have recently shown that in determined, fully differentiated cellular systems, non-genetic plasticity in terms of transcriptome diversity is not unlimited and/or random but is defined by the transcriptome states contained within its ancestry and their divergence, remarkably highlighting the existence of phylo(epi-)genetic lineages embedded within populations of genetically identical cells. Moreover, we have shown that the observed “restricted” plasticity correlates with the susceptibility of non-malignant cells to become tumourigenic upon oncogene activation and encompasses the adaptability of individual cancer cells to diverse extracellular challenges, including their response to anticancer therapeutic paradigms.

Given the profound relevance of our discoveries for most fields of biology, our lab is now moving forward into the decryption of the molecular devices regulating intra-populational lineage linked non-genetic plasticity and its crosstalk with genetic perturbations leading to cancer. We postulate that integrating these two crucial biological concepts – namely genetic and non-genetic information – and deciphering their interplay will drive forward our understanding of cancer evolution, which in turn would lead our discoveries into the design of more effective anticancer therapies.

 

Introduction

LeQuesne John

Vision 

Our lab is at the forefront of integrating artificial intelligence (AI) with cancer research. We aim to harness the power of AI to make biologically impactful predictions, drawing from the vast and rich datasets generated through cutting-edge cancer research. By focusing on building AI models across various biological data modalities, we seek to transform how cancer is diagnosed, understood, and treated, accelerating progress toward personalised medicine and therapeutic advancements. 

Key Research Areas 

  1. AI for Histology and Spatial Deep Phenotyping 
    We are developing AI algorithms to analyse histological slides and integrate data across spatial biology and genomics. This will allow us to map the histomorphological landscape of both mouse models and human samples, improving phenotypic assessment and accelerating discoveries in tumour characterisation and treatment response. 
  1. Large Language Models (LLMs) for Biological Sequences 
    We develop LLMs tailored to biological sequences such as proteins and RNA. These models allow us to predict the effects of genomic mutations, including rare events, providing new insights into protein-protein interactions and RNA functions in cancer biology. 
  1. Comprehensive In Silico Tumor Models 
    We are building digital twin models of mouse systems, which simulate genotype-phenotype relationships to predict treatment responses and intervention outcomes. This initiative holds the potential to revolutionise preclinical cancer research by enabling real-time, cost-effective simulations of therapeutic strategies. 

Methodology 

We develop machine learning and deep learning models tailored to make novel inferences in cancer. We utilise state-of-the-art local and national computing infrastructures, large-scale datasets, and advanced spatial biology tools to ensure the robust training and validation of our models. 

Impact and Applications 

The application of our AI models has the potential to significantly advance the fields of cancer diagnosis, prognosis, and treatment. By integrating histopathology, spatial biology, and genomic data, we aim to produce AI-driven diagnostic tools that will enhance routine clinical workflows, personalise cancer treatments, identify new biomarkers for drug development, and deliver breakthroughs in understanding cancer's molecular underpinnings.  

Collaborations 

We collaborate very closely with world-class pathologists, computational biologists, and cancer researchers from the University of Glasgow, CRUK Scotland Institute, NHS Greater Glasgow and Clyde, and international institutions. These partnerships provide us with the expertise and resources to bridge the gap between computational innovation and clinical application, ensuring that our AI tools make a tangible impact on cancer research and patient care. 

 

 

Introduction

LeQuesne John

The genomic sequencing of colorectal cancer (CRC) has identified many important oncogenic drivers, leading to the establishment of the Vogelstein genetic model for colorectal tumorigenesis. However, substantial phenotypic heterogeneity exists across and within genetically identical tumours, meaning that genetics alone cannot explain the complexity of the tumour ecosystem. To characterise the transcriptional landscape in CRC, numerous studies have developed molecular classification/subtyping systems based on gene-level data that align with genetic alterations underpinning the Vogelstein paradigm.

To move beyond these established gene-level systems, we developed a CRC classification system reflecting phenotypic landscape in CRC, based on pathway-level biological signalling. These pathway-derived subtypes (PDS) are independent of KRAS and other Vogelstein features, and reveal subtle phenotypes related to epithelial differentiation and lineage maturity, reminiscent of those proposed within Waddington’s landscape. This approach reveals how individual cells contribute to the overall phenotypic landscape observe in each human tumour, and how the selective advantage of each individual phenotype evolves during tumour development.

Using these phenotypic landscapes as the basis for biological discovery in both human tumours and genetically engineered mouse models (GEMMs), our team are using a combination of bulk, single cell and spatial transcriptomics to define a more holistic phenotypic map of the tumour landscape and cellular communication networks associated with tumour development and progression in CRC. Furthermore, while data availability has increased, molecular data rarely realises its full potential due to the programming skills required for analysis. Therefore, our team complement our biological analyses with the development of “no-programming-required” publicly available data apps for mechanistic interrogation of these cohorts. This approach facilitates new discoveries and leads to the democratisation of data analysis by removing a major bottleneck in the analysis of complex molecular profiling datasets.

Other funding: 

cruk             MRC logo                   NewAICRIlogo beige

 

Introduction

LeQuesne John

The mammalian skin is an excellent model system to functionally interrogate fundamental cell biological processes required for epithelial homeostasis. The intricate and dynamic relationship between cell adhesion, migration, and basement membrane organisation, in the context of the local immune microenvironment, is critical to normal skin development and healthy tissue function. Gaining insight into the complex interplay between these processes allows us to understand how they go awry in pathological conditions such as inflammatory skin disorders and cancer.

Our work is organized into two major research programs:

1. Epithelial-Immune Metabolic Crosstalk and Inflammatory Skin Diseases. This program focuses on understanding the crosstalk between epithelial cells, immune cells, and the extracellular matrix (ECM) in maintaining homeostasis and exploring the metabolic drivers of inflammatory skin diseases and cancer. 

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2. Stem Cell Homeostasis and Nuclear Mechanosensing. This program focuses on understanding the mechanical underpinning of the crosstalk between the ECM and cell junctions with the cytoskeleton and nucleus in maintaining stem cell quiescence and the role altered nuclear mechanotransduction in driving diseases such as metastatic cancers.  

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Subcategories

Cancer Metabolism, Growth and Survival

Invasion and Metastasis

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