Introduction

Liver cancer is the third most common cause of cancer-related death worldwide. It is becoming an increasing problem, particularly in the Western world, and its rates have trebled in Scotland in the last 20 years. Despite some improvements in outcomes for those patients in whom the disease is detected early, there remains a limited range of only minimally effective treatment options for the overwhelming majority of patients who have their disease detected at a later stage. Precision medicine offers the potential to target more effective therapies to individuals with different forms of this disease, across this highly heterogeneous cancer.

My group has been interested in studying the regenerative responses to injury and aberrant proliferative responses in cancer of hepatocytes, the principle functional cell of the liver. These cells show immense regenerative capacity but are also the source of the most common primary liver cancer, hepatocellular carcinoma (HCC).

We have described how these cells enter a state of shock, named senescence, in response to injury, and how preventing them from doing so can promote liver regeneration. We are also investigating how this same senescent state occurs during early cancer formation as an anti- cancer therapeutic target.

To further understand these processes, we have developed a state-of-the-art suite of genetically engineered models of HCC, which mimics key features of the human condition. This suite is based upon the range of genetic mutations which drive HCC across the spectrum of human disease. Cross comparing between the models and patients we are able to identify novel pathways for therapy and some drug combinations which are highly effective in our cancer models. Working with academic and industrial collaborators, we are using these avatar-like models to uncover and test novel therapies, which could be used to target precision medicine dependent on the underlying characteristics of tumours in different patients.


Exposing liver cancer

Read about Tom's collaboration with the University of Edinburgh's Centre for Statistics to improve early detection of liver cancer here.


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Introduction

Blyth 23 266 Beatson Lab Groups 000046

In vivo models are an important tool to recapitulate human cancer and interrogate aspects of the disease within a biological context. Validating in vitro discoveries in physiologically relevant models in this way will expedite novel therapeutic approaches for patient benefit. The group has expertise in modelling different cancer types but has a specific interest in breast cancer, and how metabolic pathways and certain signalling nodes such as the RUNX/CBFβ transcriptional complex and pro-survival factor MCL-1, contribute to tumour progression and metastasis.

The RUNX genes are essential regulators in mammalian development, most notably for bone and blood cell lineages. Like many genes important for normal development, the RUNX genes are linked to human cancer, but interestingly have been found to both promote and suppress tumour formation, a paradox we are exploring. We have shown that high expression of RUNX1 and RUNX2 in breast cancer correlates with specific subtypes of the disease and with poorer patient prognosis. We are now investigating the functionality of these genes in epithelial cancers and have shown that RUNX2 has a role in mammary stem/progenitor cells.


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Introduction

Understanding how cancer spreads from its primary site of origin to distant organs is one of the major challenges in cancer research. What has become evident in recent years is that mutations in cancer cells are not sufficient to drive metastasis formation – cancer cells need assistance from surrounding healthy cells. Among these various healthy cells, immune cells have emerged as powerful instigators of metastasis formation but, at the same time, immune cells can also prevent cancer cells from spreading.

Our lab focuses on these dichotomous roles of immune cells and how tumours control immune cell behaviour. We study these concepts in the context of breast, pancreatic and colorectal cancers. We are particularly interested in γδ T cells, a rare population of T cells with properties that are distinct from conventional CD4 and CD8 T cells, as γδ T cells can be both pro-tumourigenic and anti-tumourigenic. Our ultimate goal is to understand how γδ T cells and other immune cells participate in the metastatic process and to develop new immunotherapies that counteract metastatic lesions.


Researchers discover key to 'supercharging' breast cancer treatment

Find out more about Seth's recent research in this article by STV News here.

Young Glasgow ovarian cancer survivor speaks out about life after the disease

Read the story from the Evening Times here.

Glasgow doctor reveals why he has taken on the city's fight against cancer

Read about Seth's background and move to Glasgow here.

 
 
 
 
 

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Introduction

Morton head 2020 092

Every year around 340,000 people die of pancreatic cancer worldwide, and by 2030 pancreatic cancer is predicted to be the second commonest cause of cancer death in the western world. Most patients present too late for surgery, with aggressive invasive or metastatic disease. Furthermore, current chemotherapies offer little benefit, so we urgently need to better understand the disease and identify better options for clinicians and patients.

To do this, we model different genetic subsets of the disease in genetically engineered models. These models mimic human tumours in terms of the genes altered, and also because they develop a dense desmoplastic stroma of fibroblasts, stellate cells, immune cells, and extracellular matrix proteins such as collagen.

By studying our models, we can determine the importance of specific genetic and transcriptomic changes identified in human tumours, identify novel targets for therapy, both in tumour cells and in the microenvironment, and test new therapies pre-clinically.


See the following CRUK blogs for more details about Prof Morton's work:


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Introduction

Our lab focuses on a fundamental, yet largely unanswered question: How is the normal organisation of tissue disrupted to allow cells to form disarrayed tumours? Cells of many tissues are polarised. That is that cells collectively form configurations tailored to the needs of a tissue. During tumourigenesis this exquisite organisation is lost. Despite the loss of tissue polarity being an obligate event in cancer progression, we know little about this basic process.

 

bryant model of polarity

We use mini ‘avatars’ of tumours ex vivo to understand how cells collectively organise. Our efforts are focused on colorectal and prostate cancers. We take two approaches to unravel the complexity of this process: 
1) building new tools to analyse tumour avatars ex vivo, and 
2) identifying the signalling processes that drive collective tumour invasion and metastasis. We collaborate extensively for a multi-disciplinary approach to understand tumour metastasis (Sansom, Zanivan, Blyth, Leung, Miller Labs @ CRUK Scotland Institute).

To develop better tools to understand how tumour cells loose polarity, we have developed cutting-edge, high-content microscopy and computational image analysis of tumour spheroids and organoids, live-imaging how normal cells become tumours. Molecularly, we focus on two pathways: phosphoinositide signalling (including the kinases and phosphatases that produce them, and master regulators of their function: the ARF GTPases), and the role of the metastasis-promoting sialomucin, Podocalyxin. We are particularly interested in how these participate in metastasis.

Our ultimate aim is to investigate such changes in cell polarity as potential future biomarkers of cancer in patients, and possible targets for future therapeutic interventions.


Cell scientist to watch − David Bryant

Click here to read David's interview with the Journal of Cell Science.

Click here to read David’s perspective article on LGBT+ visibility in leadership.

Click here to read David’s essay on using your voice to stand up for diversity  


University of Glasgow webpage

Google Scholar Account


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