Determining the role of the adaptive immune system in controlling breast cancer metastasis and evolution.

Immune responses involve a balanced interplay between the evolution of a target alongside the evolution of the immune system. The immune system prevents cancer by evolving to recognise and eliminate cancer cells. When this fails, cancer cells grow and metastasise.

This is particularly important in an aggressive subtype of breast cancer called triple-negative breast cancer (TNBC; negative for oestrogen, progesterone and HER2 receptors), which is frequently diagnosed in younger women and is characterised by high rates of lymph node metastasis, limited therapy options, and poor prognosis. TNBC is treated with chemo-immunotherapy, however around 40% of patients show a poor response to this (Schmid et al., NEJM, 2020) and have a poor prognosis. The mechanisms behind resistance and response to immunotherapy in TNBC are unknown, and therefore immunotherapies continue to be given in a one size fits all approach.

In this project, you will leverage the laboratory’s expertise in translational cancer medicine as well as the generation and analysis of large-scale molecular profiling data from women with breast cancer, and its integration using machine learning (Sammut et al., Nature 2022), to generate a single-cell atlas of primary TNBCs and metastatic lymph nodes. By developing computational methods that combine this data, you will elucidate how the interaction between tumour cells, their microenvironment and the adaptive immune response enables immunoevasion during treatment with chemo-immunotherapy, resulting in therapy failure. You will determine biological mechanisms associated with response and resistance to immunotherapy, and use this knowledge to identify new targets for which novel immunotherapies can be developed.

This is a highly interdisciplinary project, and you will be working closely with experimental scientists, functional and basic immunologists, experts in tumour evolution, as well as members of the oncology multidisciplinary team at the Royal Marsden Hospital.

This PhD will equip you with a deep knowledge of breast cancer genomics and translational cancer medicine, next-generation sequencing technologies, molecular pathology and molecular diagnostics. You will develop quantitative skills in bulk and single cell molecular data analysis, as well skills in the application of machine learning to biomedical data.

Candidates must have, or be on track to receive, a First- or Upper Second- class Honours degree (or a Masters) in computational biology, quantitative biology, computer science, statistics, or a related discipline and have experience in quantitative data analysis using R and/or Python, and must have a basic knowledge of cancer genomics and/or immunology.