A longitudinal multi-omic approach to understanding the evolution of myelodysplastic syndrome (MDS) and the factors that drive or impede clonal evolution towards acute myeloid leukaemia (AML)

Year of award: 2023


  • Dr Caroline Watson

    University of Cambridge , United Kingdom

Project summary

A major challenge for early cancer detection is predicting which pre-cancerous clones will progress to cancer.  In blood, pre-leukemic mutation acquisition is increasingly common with age.  In most, this so-called ‘clonal haematopoiesis’ does not progress to cancer, but if clonal expansion and further mutation acquisition occurs, acute myeloid leukaemia (AML) can develop.  Clonal haematopoiesis can also progress to AML via myelodysplastic syndrome (MDS), a course associated with particularly poor prognosis.  Approximately 30% of individuals with MDS will progress to AML and so, not only is a better understanding of MDS important, but its high rate of transformation, compared to clonal haematopoiesis, makes MDS a model system to better understand factors that drive clonal evolution towards or away from AML. 

Harnessing the power of longitudinal samples and multi-omics, this project aims to answer:
How does clonal evolution differ in individuals with MDS who progress to AML?
What effect do health and lifestyle factors have on driving/ impeding clonal evolution?
Can longitudinal multi-omic profiling in MDS patients improve AML early detection and reveal clinically relevant molecular pathways and potential therapeutic targets?
Answering these questions will be invaluable for accelerating development of AML early detection tools and therapies to prevent progression to AML.