A study of cellular diversity in health and disease using mass cytometry and computational approaches

The project is supported by the University of Liverpool Doctoral Network in Future Digital Health, which is directed at creating and maintaining a community of AI health care professionals that can realise the benefits that AI can bring to Health Care. The vision is that of a world-class centre providing high-quality doctoral training within the domain of AI for Future Digital Health. Each available PhD project has been carefully co-created in collaboration with a health provider and/or a healthcare commercial interest so that the outcomes of the PhD research will be of immediate benefit. The network will be providing doctoral training, culminating in a PhD, in a collaborative environment that features, amongst other things, peer-to-peer and cohort-to-cohort based learning. On completion students will be well-placed to take up rewarding careers within the domain of AI and Digital Health.

Blood cancers such as leukemias, that are characterised by circulating cancer cells, affect 35% of the UK/Global population. The cancer cells can be detected within blood samples from patients with leukemia. Despite significant advances in treatments relapse/refractory disease remains an obstacle to improving patient outcomes. Although blood cancers originate from a single cell that has undergone malignant transformation, the cancer cells exhibit significant diversity in gene expression that is responsible for clonal evolution and emergence of resistant cells. Recent advances in automated technologies, such as mass cytometry, enable better understanding of cellular diversity. Traditional analytical software are restricted in their ability to perform thorough analyses. Deep learning/Machine learning/Artificial Intelligence can identify complex features within vast amounts of data to build highly accurate recognition models. Such advanced computational analyses can help in deciphering complexity within mass cytometry data annotated with clinical information.

Aims of the PhD project
1.To better understand cellular diversity of gene expression and subclonal evolution within cancer cell populations from patients with ‘leukemic’ haematological cancers such as chronic lymphocytic leukemia (CLL) through advanced computational analysis of data. The University of Liverpool is host to a unique leukemia biobank that contains patient derived blood samples. This resource has been used to examine gene expression profiles within individual cancer cells from each patient at different stages of disease using mass cytometry. We have already generated vast amounts of data that has been analysed by traditional less refined software and is now ready to be analysed by Deep learning/ML/AI techniques.
2. To use advanced computational approaches to eliminate ‘batch-effects’. Samples analysed in different experiments can lead to time and user variability that can lead to spurious results that need to be eliminated.
3. To apply deep learning/AI analyses to characterise the emergence, expansion or disappearance of specific blood cell types (cancerous and normal) in serial patient samples. Such insights will allow us to predict disease behaviour, treatment responses and disease recurrence within individual patients.

For application enquires please contact Dr Vitaliy Kurlin on [Email Address Removed]

To apply, please visit: https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/