The project is suitable for Honours, Masters or PhD students with a bioinformatics background.
MYC is a pleotropic transcription factor with a key role in controlling cell proliferation. Deregulation of MYC through amplification or genomic rearrangement is the oncogenic driver in many cancers of different tissue origin. Novel therapies that inhibit downstream effects of MYC activation have great efficacy and improve clinical outcome.
In acute myeloid leukemia (AML) compared to other cancers, MYC is not subject to genomic amplification or rearrangement. However, it is highly expressed in majority of AMLs. To date little is known about its role in disease progression and therapy resistance.
The objective of this project is to study the effect of MYC expression in AML with different oncogenic drivers.
The project involves the use of single cell RNA-Sequencing data of human AML patients to characterize the role of MYC expression in different stages of leukaemic cells. You will use dimension reduction, machine learning and novel RNA velocity estimation techniques to integrate data from AML with different genetic backgrounds.
The results of the projects will aid to understand the combined effect of MYC expression and different oncogenic drivers on cell phenotype and differentiation and to rationalize MYC downstream effect inhibition as a treatment for AML.