Immunotherapy has come of age as a crucial pillar in cancer treatment, however responses are limited to some tumour types, and even in these, to subsets of patients. Furthermore, some patients experience significant systemic toxicity and organ inflammation. Therefore, a better understanding of correlates of response is needed to match each patient with the most appropriate (neo)adjuvant treatment.
Breast cancer is the second leading cause of death in women, and the most common cancer in the UK, with around 57000 new cases every year. Breast cancer is classified into four main molecular subtypes, with different response to treatment. While hormone receptor positive breast cancers respond to hormone therapy and HER2+ cancers can be treated with targeted therapies, the triple negative subset is the subtype most responsive to immunotherapy. Immunotherapy trials are ongoing to improve response to chemotherapy or to overcome resistance to targeted therapies. To improve response rate and identify patients most likely to benefit from immunotherapy, a better understanding of the molecular heterogeneity and cellular phenotype(s) of breast cancer’s tumour microenvironment (TME) and discovery of novel targets is required.
The student will apply advanced computational methodologies to integrate multiomic datasets from a well annotated cohort of a large-scale breast cancer cohort, ranging from spatial transcriptomics dataset to map in detail interactions between morphological and molecular local ecotypes in the TME and their prognostic significance, to innovative computational pathology approaches, to analyse and interpret multiplexed histopathology images, with a focus on mechanisms of immune escape. Expression of surface antigens commonly associated with breast cancer will be investigated in the context of the identified spatial ecotypes, to provide a rationale for novel target combinations for T cell engagers.
At Immunocore, the student will integrate the spatial analysis with mass spectrometry proteomic and immunopeptidomic data to quantify expression of immunotherapy targets of interest. The student will then leverage machine learning algorithms to further classify response to therapy of different TME ecotypes.
Year 1: PhD will set up analytical pipelines for spatial transcriptomics and digital pathology. The student will benefit from a large team of computer scientists in Prof Grigoriadis’ team, with links to the Alan Turing Institute and Francis Crick. In parallel, Dr Yin Wu (early career researcher), an immuno-oncologist, will guide the immunological and clinical aspects of this study.
Year 2: Analyse large scale multimodal breast cancer data.
Year 3: Analyse mass spectrometry data of breast cancer cohorts.
Year 4: Integrative multimodal AI agent analyses and writing up thesis.