Research Opportunities

Our research group aims to understand hypertensive disease progression of women and their children following pregnancy complications, such as hypertensive pregnancy and preterm birth, to identify optimal approaches to reduce long term risk. This includes development of new clinical tools to identify, track, and slow the disease progression as well as novel interventions.

This project will apply computational modelling and machine learning to large scale imaging datasets to study disease progression related to a hypertensive pregnancy across multiple modalities and organs. The insights into key structural and functional changes at the organ-level that describe stages of disease will be used to identify potential intervention targets.

Furthermore, we will use imaging data collected within our ongoing clinical trials to help us understand how interventions modify the underlying disease development and how this could be incorporated in clinical practice to transform long-term patient outcomes after a hypertensive pregnancy.

We are interested in understanding the clonality of drug resistance in cancer (primarily in a haematological cancer called Multiple Myeloma (MM)). To achieve our goals, we have developed state of the art long-read single-cell sequencing approaches (termed scCOLOR-seq) that allow us to measure single clones within patient samples. This method allows for the simultaneous measurement of gene expression, exon mutations, exon SNPs and translocations. We apply this technology and develop cutting edge computational analysis solutions to understand the relationship between clonality and drug resistance in oncology. Furthermore, our lab is also working as part of the Human Cell Atlas (HCA) project and we have several international collaborations with immunologists, cancer biologists to support our work.

The aim of this project is to develop computational analysis strategies aimed at better defining specific MM clones within patients that are resistant to first line therapeutics. The work will involve combining long-read (Oxford Nanopore Technology) multi-modal datasets and performing machine learning approaches to better define high risk patients. This work is important to better understand drug resistance mechanisms in MM and identify patients that may respond less well to therapy.

There has been increasing interest in applying computational methods in medicine, to make sense of cancer’s ‘big data’ problem by exploiting recent advances in data-processing and machine learning to capture and integrate clinical, genomic, and image data collated from hundreds of cancer patients in real-time. Such methods can be applied to digital clinical images to extract image information about patterns of pixels that are not perceivable to the human eye, allowing characterisation of tumour.  Prostate cancer is the 2nd commonest male cancer worldwide, and MRI is the diagnostic tool of choice, however, MRI can miss 10% of significant tumours and leads to unnecessary (invasive) biopsy in around 1/3rd patients who do not have cancer.  

We will use a prototype AI system (Pi) developed with Lucida Medical on retrospective data, in a prospective clinical study. We plan to link histological data to imaging features derived from MRI (including texture analysis) to identify predictors of lesion aggressiveness and need for sampling, using biopsy cores and surgical specimens from the prospective cohort. Further work will link biopsy tissue to MRI data to identify radiogenomic markers of disease aggressiveness. The project presents an opportunity for AI to answer key clinical questions at the intersection of interpretation, imaging and biopsy.  

The project will involve working with an established interdisciplinary programme of researchers and help in the assessment of cross-cutting “multi-omic” approaches to cancer assessment, involving integration of advanced image analysis, transcriptomic, genomic, tissue, and patient outcomes to inform the design of diagnostic strategies.

Since 2018, several studies have reported “germline-somatic interactions” across cancer types. However, such studies demonstrate extensive heterogeneity in defining the germline and somatic features involved or the specific mechanism of interaction (including predicted protein changes, cell lines/CRISPR screens, and immune system-associated phenotypes), and effectively none evaluate results clinically. We hypothesize that pan-cancer or breast-cancer-specific germline-somatic interactions will demonstrate associations with therapy in the metastatic setting. To test this, we will compile reported (a) germline-somatic variant interactions from papers and (b) datasets with individual-level treatment information and test the associations between these interactions and treatment response in metastatic breast cancer.

We are interested in a variety of topics related to “Deciphering cellular heterogeneity in tumor microenvironment using single cell data”, “Functional characterization of tumor infiltrating T cells”, “Links between embryonic development and cancer”, “Functional characterization of non-coding somatic mutations”, and “Methods for single cell omics”. These projects involve non-trivial  methods development as well as sophisticated data analysis but the focus is always on the biological question. Our lab is involved in several collaborations with immunologists and cancer biologists.