The Beatson Institute Research Groups

Introduction

Portal 23 266 Beatson Lab Groups 000034

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.

Portal CPE Themes Final

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. 

Epithelial Immune Metabolic Crosstalk and Inflammatory Skin Diseases Image

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.  

Stem Cell Homeostasis and Nuclear Mechanosensing Image

 

 

Introduction

Johan Vande Voorde

Cancer is a multifactorial disease with widespread effects on patients’ health. Cancer cells undergo metabolic rewiring to sustain continued proliferation and to survive in hostile environments. This includes alterations in the uptake and utilization of nutrients and metabolites. As such, the tumour microenvironment is important for metabolite supply to cancer cells and the presence of a tumour affects the normal function of its host organ. In addition, cancer is associated with systemic metabolic changes that can dramatically impact quality of life for patients and their fitness to undergo treatments. Research in our laboratory focuses on metabolic crosstalk between the host and tumours, ultimately aiming to develop new, more efficient therapies.

Our current areas of focus are:

  1. Metabolic implications of the gut microbiome in cancer

Gut microbiome dysbiosis is associated with various malignancies and this has implications for cancer onset, progression and therapy sensitivity. We study metabolic interactions between microbiota and host cells using preclinical cancer models and patient samples. Because of its unique association with the gut microbiome, we have a particular interest in colorectal cancer.

  1. Metabolic determinants of cancer-associated cachexia

Cancer cachexia is a wasting syndrome defined by ongoing loss of skeletal muscle mass, with or without loss of fat mass, which cannot be restored by conventional nutritional support. At present, there is no cure and the underlying mechanisms of this debilitating condition are poorly understood. We use advanced preclinical models to study cachexia and identify underlying metabolic mechanisms.

 

 

Introduction

Ram DasGupta

Unlocking the Developmental Origins of Cancer

Cancer remains one of the most complex, and devastating diseases of our time, characterized by its ability to evade treatment and adapt to an ever-changing biological landscape with diverse selective pressures. At the forefront of this challenge, our laboratory is pioneering the use of next-generation single-cell and spatial biology tools, paired with functional genomic technologies and relevant murine models, to unravel the developmental origins of cancer. Specifically, we investigate how damage-associated regenerative programs, activated in the context of chronic inflammatory diseases (fatty liver/MASLD, viral hepatis, colitis, IBD) contribute to cancer initiation and progression.

The Origins of Cancer: A Crossroads of Regeneration and Dysfunction

Recent work from our laboratory and others has elucidated remarkable foetal-like, developmental remodelling of the tumour microenvironment (TME) in human hepatocellular carcinoma (HCC) (Sharma et al., 2020, Cell; Nguyen et al., 2022, Nature Communications). Our current research is rooted in understanding how chronic inflammation, a hallmark of diseases such as colitis, chronic hepatitis, or pancreatitis, can activate similar developmental programs to establish a “pro-tumourigenic niche” or a fertile soil for tumourigenesis (Balakrishnan et al., 2024, Journal of Hepatology; Cappellesso et al., 2022, Nature Cancer; Scolaro et al., 2024, Nature Cancer). Using advanced single-cell and spatial biology tools (single cell RNA-seq, scATAC-seq, spatial transcriptomics, proteomics and metabolomics, and multi-parametric flow cytometry), we study the cellular and molecular mechanisms underlying this transition from chronic inflammatory “non-healing wounds” to cancer. Finally, our understanding of the molecular mechanisms of cross-regulatory interactions between tumour cells and their ecosystem is paving the way for innovative therapeutic approaches that we, in close collaborations with clinicians, are implementing as part of clinical trials aimed at interrogating the efficacy of combining drugs targeting the tumour microenvironment or tumour stroma (eg. anti-angiogenics) along with immune checkpoint inhibitors (Chong et al., 2025, Lancet Oncology). 

legendlocalizing cell types and cell states to their spatial coordinates in NPC
Cells to location: localizing cell types and cell states to their spatial coordinates in NPC

A Focus on Co-Evolutionary Mechanisms in the Tumour Microenvironment

Cancers do not evolve in isolation. It emerges and thrives in the context of its microenvironment, engaging in dynamic cross-regulatory interactions with surrounding cells. Our work centres on uncovering these co-evolutionary mechanisms that shape tumour initiation, tissue remodelling, and progression.

Key areas of focus include:

  • Endothelial Dysfunction: Endothelial cells play a critical role in maintaining vascular integrity and regulating tissue homeostasis. In chronic inflammation, persistent endothelial dysfunction can promote abnormal angiogenesis, hypoxia, and endothelial anergy, fostering tumour growth and metastasis.
  • Fibrosis: Fibroblasts, activated during tissue repair, can become cancer-associated fibroblasts (CAFs) that contribute to a fibrotic tumour microenvironment. This fibrosis not only supports tumour growth but also serves as a barrier to effective drug delivery, or immune-cell infiltration, making tumours more resistant to treatment.
  • Immune Dysregulation: Chronic inflammation disrupts immune homeostasis, leading to immune suppression and the recruitment of tumour-promoting immune cells, such as pro-remodelling macrophages and regulatory T cells (Tregs). By studying how tumours exploit immune-stromal interactions, we aim to identify new therapeutic strategies targeting the tumour ecosystem to restore immune balance.

Approach: Bridging Technologies to Decode Tumour Evolution

Our laboratory employs an integrated approach, leveraging cutting-edge single cell and spatial multi-Omics technologies to study mechanisms of damage-associated chronic disease progression to cancer:

  • Single-Cell Analysis: Tools like single-cell RNA sequencing (scRNA-seq, ATAC-seq) allow us to dissect cellular heterogeneity of disease-associated cell states (DACs) within tumours and their microenvironments, as well as gene regulatory networks and signalling pathways that specify the transcriptomic signatures of DACs
  • Spatial Biology: Advanced imaging technologies enable us to visualize the geo-spatial organization and cellular interactions within tissue architecture, capturing the spatial dynamics of DACs or tumour cells with their ecosystem, including endothelial cells, fibroblasts and immune cells.
  • Functional Genomics: CRISPR-based screens and drug libraries in relevant murine and patient-derived cell line/organoid models enable the identification of genes and pathways that drive tumour evolution, offering insights into potential therapeutic vulnerabilities.

image005

Spatial organization of individual cell type within a tumour (left); spatial distribution of distinct neighbourhoods of interacting cells (niches) identified within tumour biopsies

From Discovery to Clinical Translation

Our ultimate goal is to translate these discoveries into tangible clinical benefits. By understanding the developmental origins of cancer and the role of co-evolutionary mechanisms, we aim to:

  • Identify early biomarkers that predict tumour initiation and progression.
  • Develop preventative therapies targeting the regenerative programs (especially those targeting the tissue/tumour ecosystem) that are hijacked during chronic inflammation.
  • Create strategies to disrupt tumour-immune-stromal cross-regulatory interactions in order to prevent tumour progression into treatment-resistant, metastatic disease.

A Vision for the Future

The intersection of inflammation, chronic disease and cancer provides a unique opportunity to address some of the most pressing questions in oncology. By integrating next-generation single-cell and spatial biology tools with functional genomics/validation in preclinical models, we are not only uncovering the fundamental mechanisms driving cancer but also paving the way for innovative therapeutic approaches. Our laboratory remains committed to advancing this field, with the hope that our work will lead to earlier interventions and improved outcomes for patients battling cancer. Stay tuned as we continue to push the boundaries of cancer research and move closer to a future where precision medicine transforms the landscape of oncology.

 

 

 

Subcategories

Cancer Metabolism, Growth and Survival

Invasion and Metastasis

What's Happening Now

Focal adhesion Jamie Whitelaw

See our most recent publications

Melanoma cells 2

Click here to see current job vacancies at the Scotland Institute

Cancer Research UK Scotland Institute

The Beatson Institute for Cancer Research, a Registered Scottish Charity No SC006106 & Company Limited by Guarantee (Registered in Scotland No 84170) [trading as the Cancer Research UK Scotland Institute]

Get in Touch

call +44(0) 141 330 3953

reception@crukscotlandinstitute.ac.uk

Garscube Estate Switchback Road Bearsden Glasgow, G61 1BD

See our Wikipedia page