A small number of patients with the chronic blood disorder myeloproliferative neoplasia (MPN) will see their disease transform into an aggressive, treatment-resistant leukaemia, and researchers want to know why. The problem is that doctors cannot reliably predict which MPN patients will undergo this transformation to secondary acute myeloid leukaemia (sAML). The biological mechanisms driving the change are poorly understood, and no effective therapies exist once it occurs. This project will analyse preserved bone marrow samples taken from eight patients at different stages of their disease—chronic phase, accelerated phase, and blast phase—using spatial transcriptomics to map gene activity within the tissue. By comparing samples from patients who progressed with those who did not, the team aims to identify specific features of the bone marrow microenvironment that signal impending leukaemic transformation. If successful, this work could produce a molecular signature that flags high-risk MPN patients before leukaemia develops, enabling closer monitoring or earlier intervention. It may also reveal new cellular interactions or signalling pathways that could be targeted with drugs, potentially preventing transformation altogether. This is fundamental science with a clear clinical endpoint: better risk stratification and, eventually, new treatment strategies for a condition that currently offers few options.
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Myeloproliferative neoplasia (MPN) is a clonal neoplastic disorder characterised by well-defined driver mutations and excess production of myeloid cells. Whilst most patients experience chronic disease, a small proportion of patients will undergo transformation to secondary acute myeloid leukaemia (sAML) with poor clinical prognosis and few therapeutic options. Whilst single cell sequencing approaches have demonstrated the role of chronic inflammation in promoting survival of a malignant haematopoietic stem cell clone, mechanisms that underpin leukaemic transformation are not well understood. We need to develop better ways of identifying patients with MPN at high-risk of leukemic transformation, and to define biological microenvironmental mechanisms that drive leukaemic transformation. The aim of this project is to identify microenvironmental features of the high-risk pre-leukaemic bone marrow and to identify features of early transformation. We will use spatial transcriptomic (ST) analysis of serial FFPE bone marrow trephines (BMTs) from patients with MPN who progress from chronic phase to either accelerated phase (MPN-AP), or blast phase (MPN-BP), a type of sAML (n=8), using the 10x Xenium platform. We will apply our in-house pipeline for analysing ST BMT data, integrating our existing computational BM image analysis tools to improve descriptions of the BM microenvironment. We compare the pre-leukaemic BM microenvironment with matched samples from chronic-phase MPN non-progressors. We will identify microenvironmental features of early transformation in MPN-AP samples. This approach provides comprehensive cellular phenotyping from which we will infer novel features that underpin leukaemic transformation (e.g. cell-cell interactions, pathway activation) and identify spatial microenvironmental features characteristic of leukaemic transformation.
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