The project is suitable for Honours, Masters or PhD students with a bioinformatics background.
Acute myeloid leukemia is a highly aggressive disease with the majority of patients still relapsing even after achieving remission from chemotherapy. It is hypothesized that relapse arises from residual leukaemic stem cells that are resistant to chemotherapy. To date transcriptional analysis of AML has focused on whole bone marrow or peripheral blood samples, which is mainly composed of leukaemic blasts, masking the transcriptional program of leukaemic stem cells. Data generated from AML samples using single cell RNA sequencing will enable the analysis of the leukaemic stem cell transcriptome.
The aim of this project is to analyse single cell RNA Sequencing data of AML to determine potential mechanisms of resistance in leukaemic stem cells. These findings will be correlated with previously identified genome-wide CRISPR screen hits that conferred chemotherapy resistance in AML cell lines and other datasets of relapsed/refractory AML. In addition, you will characterize leukaemic stem cells compared with leukaemic blasts. You will use dimension reduction and machine learning approaches to integrate data of AMLs with different genetic background and prognosis.
Findings from this project will inform further investigation of pathways involved in chemotherapy resistance and therapeutic strategies targeting chemoresistant leukaemic stem cells.