Research Opportunities

Tissue multiplexing is a new imaging method that allows to visualise a large number of protein targets in tissues. This exciting new technology allows for new approaches to phenotyping cells and to decode more complex patters of communication between different tissue compartments. The goal of this project is to develop the required image analysis and inference methods using advanced machine learning and AI in 2D and 3D. As a result, you will be advancing our understanding of the tumour environment and find novel ways of identifying sub-populations of cells that play a critical role in disease progression.

You will be working side by side with world leading cancer researchers at NIH NCI and the University of Oxford. At both sites you will have access to unique patient cohorts. Together with David Wink and Stephen Lockett (both NCI) you will be working on aspects if breast cancer. In Oxford, Richard Bryant and Ian Mills will lead on work in prostate cancer, which is the commonest non-cutaneous cancer in men, and often progresses to incurable metastatic disease. Your work will also be supported by expert pathologists and you will be working towards improving current practice in cellular pathology.

The broader group has already established a very active collaboration and you will be expected to work in both locations. In Oxford, you will be embedded in the Quantitative Biomedical Image Analysis group led by Prof. Rittscher. Part of your role will be to accelerate the exchange of technology and software between the two locations. This project provides a unique opportunity to study mechanisms that are common to different cancer types.

T cell recognition of a cognate peptide-MHC (pMHC) complex presented by infected/malignant antigen-presenting cells are of utmost importance for mediating a robust and long-term immune response. The recognition is mediated by specific molecular interactions between heterodimeric T Cell Receptors (TCRs) and pMHC ligands and instructs the nature of ensuing adaptive immune response. A better understanding of TCR:pMHC interaction would allow further harnessing of the adaptive T cell immunity and may lead to the development of  vaccines and therapeutics  both in the context of personalized cancer immunotherapies and infectious diseases such as COVID19. The research interests in the Koohy group are focused on the development of machine-learning and Bayesian statistical models to help us better understand two key components of this interaction: A) architecture of the immune repertoire and its dynamics upon exposure to antigens, B) processing and presentation of antigens by MHC molecules to their cognate T cells.

Cancer is usually characterized by accumulation of genetic alterations. Tumour-specific somatic mutations may generate small mutated proteins known as neoantigens that are presented on the surface of cancer cell as ‘cancerous flags’ in association with class I and II HLA molecules.  Neoantigens can be recognized by autologous T cells as foreign and therefore are considered as targets for improved cancer vaccines and adaptive T cell therapies. Almost similar mechanisms are applied to infectious diseases with the difference that the immunogenic epitopes on the surface of infected cells originate from invasive pathogens. Prediction of both immunogenic viral epitopes and cancer neoantigens has been at the centre of extensive research around the globe over the past couple of decades but remains unsolved.   We have been developing various statistical models such as Bayesian Hidden Markov Models to predict immunogenic epitopes that can be used as targets for vaccines for personalized cancer immunotherapy as well as infectious diseases such as COVID1,2.

Over the past decade we have witnessed unprecedented achievements on various cancer immunotherapies in which patients’ own immune system is modulated to find and kill cancer cells. This is evident by the 2018 Nobel Prize for development of Immune Checkpoint Blocked ICB that has greatly improved patients care. However, not all patients respond the same way, besides, some patients develop immune related adverse events such as checkpoint colitis. 
Multiple factors affect immune response to treatment including mutation burden rate, cytotoxic T cell infiltration, antigen processing and presentation defects, mutation-driven clonal signature and the composition of intestinal microbiota. Owing to advances in high throughput sequencing technologies, in particular recent single cell advancements, these features can now be measured from patients’ samples at single cell level at multiple time points including before, during and after the treatment.  We take readouts of these experiments in the form of high throughput sequencing data including genomics, transcriptomics, T cell receptor repertoire, and epigenomics data to train  statistical and machine learning models to study the mechanisms underlying heterogeneity of the response.

Undertake genomic and epigenomic studies into the mechanisms of tumorigenesis in individuals with inherited predisposition to neuroendocrine tumor syndromes, especially pheochromocytoma/paraganglioma associated with mutations in the Krebs cycle. Such discoveries can lead to understanding of developmental and other mechanisms in these tumors related to the same syndrome but behaving in a different way and occurring in different tissue of origin. Such data can be paramount to study novel therapeutic approaches for these tumors based on the discovery on novel tumor-specific targets as well as biomarkers.

The BL research is organized into four focus areas: a) epidemiology; b) infections; c) genetics, and d) tumor studies. The epidemiological studies seek to characterize the macro- and micro-geographical and spatial-temporal  patterns of endemic Burkitt lymphoma to generate new hypotheses about environmental risk factors. The infection focus seek to discover infection-related biomarkers of risk, focusing on unique serological profiles or discovery of high-risk genetic variants for EBV or Pf infection associated with eBL risk. The genetic studies (GWAS, exome, HLA) provide a powerful approach to complement questionnaire and serological methods with less concern for measurement error, reverse causality, and imperfect correlation with biology to disentangle the genetic architecture of eBL risk.  Finally, BL is a molecular disease with identifiable molecular sub-groups. The EMBLEM study provides an opportunity to collaborate with others on studies to develop a blood-based assay for BL diagnosis and molecular characterization. Students will be given the opportunity to spend time in East Africa with collaborating partners to be involved with data and sample collections.

The primary goals of EMBLEM are to investigate:

a) risk factors of BL in endemic populations in East Africa;

b) EBV and Pf immuno-profiles and other biomarkers associated with BL;

c) molecular characteristics of BL tumor genomes, B-cell receptor, and EBV variants; and

d) germline risk factors of BL using genome-wide association studies (GWAS) and exome sequencing

e) the association between BL and human leukocyte antigen (HLA) class I and II loci.

The tumour suppressor p53 is encoded by the most mutated gene in human cancers and there is extensive knowledge of its vital role in tumour suppression. However, the contribution of p53 to immune surveillance is less well understood. Cancer initiation and progression is influenced by inflammation, and it is increasingly important to understand interactions between inflammatory and tumourigenic pathways to improve cancer prevention and patient responses to immunotherapy. p53 activity is known to intersect with key inflammatory signalling pathways, including NFB, AP1, MAPK and JAK/STAT, suggesting p53 could have a pivotal role in immune surveillance. To expand knowledge in this important area, this project will investigate crosstalk between the p53 pathway and inflammation.

The project will use cutting edge technologies, including ex vivo 3D organoid co-culture models, to study interactions between cancer-initiating epithelial cells and immune cells. It will also harness recent advances in RNA-sequencing, single cell analysis and ChIP-sequencing, as well as a broad range of molecular cell biology techniques, to address the crosstalk between p53 and inflammation. The student will be able to leverage access to expertise and clinical samples in chronic inflammatory conditions – such as Barrett’s Oesophagus – that predispose patients to cancer. Oesophageal cancer and stomach cancer may be used as exemplar cancer types; these cancers have important unmet clinical needs and strong links to inflammation. This project may also extend to crosstalk of p53 with the immune system, such as in the context of immunotherapy for cancer: an emerging therapeutic strategy that is showing great success in some patients. The study will offer exciting opportunities to understand the details of the relationship between p53 and inflammation, which will be crucial for developing new approaches for early intervention to prevent cancer progression and for understanding responses to therapy.