Acute myeloid leukaemia (AML) is one of the few cancers where, despite the substantial increase in our understanding the gene defects driving the disease, the mortality rate is still increasing, with the overall 5-year survival being less than 20%. The main reason for this poor prognosis is drug resistance, which causes 60-90% of AML patients to relapse, at which stage there are no effective treatment options. Drug resistance is due to leukaemia cell heterogeneity and the bone marrow microenvironment (BMM) which protects and sustains the viability of the leukemic cells.
Here, we aim to tackle this unmet clinical need via a novel approach: understanding how the tumour microenvironment interacts and protects the malignant cells that initiate the disease, i.e. the leukemia stem cells (LSC). In this project, we will identify the genetic determinants LSC subpopulations that drive drug resistance and relapse, and computationally model how the BMM and LSC interaction underpin leukemogenesis, drug resistance and relapse after treatment.
We have previously shown that some LSCs are resistant to clinically used chemotherapeutics and thus may drive relapse (O’Reilly et al., Sci Reports, 2018; Dhami et al., Br. J. Haematol., 2020). To determine which type of LSCs are resistant to drugs and why, AML patient samples will be exposed to chemotherapeutics and the genetic make-up of the LSCs will be determined using single cell transcriptomics. In parallel, LSC subpopulation heterogeneity in patient samples collected at diagnosis, after chemotherapy, and at relapse will also be determined using data from the Blood Cancer Biobank Ireland (BCBI) and the GEO public database (GSE66525, GSE35907). New computational methods for identifying distinct cell populations from mixtures will be applied to identify the drug resistant subpopulations driving relapse. Analysis of the transcriptomics data using the computational modelling will guide the design of new drug treatment combinations and their validation in our ex vivo culture models (Dhami et al., Drug Disc Today, 2016).
In order to understand the role of the BMM in AML, we will use computational methods to determine cell type-specific genetic alterations and altered cellular composition of the BMM from genomic and genetic datasets from BM samples. With this data, we aim to develop a computational model of the AML-BMM system enabling the simulation of a patient’s response to treatments.
The team’s academic partner, Dr. Szegezdi, has expertise in AML biology, relevant AML and BMM culture models, and the industry team at Celgene are experts in advanced computational analysis methods.
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- O' Reilly E, Dhami SPS, Baev DV, Ortutay C, Halpin-McCormick A, Morrell R, Santocanale C, Samali A, Quinn J, O'Dwyer ME, Szegezdi E. (2018) Repression of Mcl-1 expression by the CDC7/CDK9 inhibitor PHA-767491 overcomes bone marrow stroma-mediated drug resistance in AML. Sci Rep. 8: 15752; PMID: 30361682
- Dhami SPS, Tirincsi A, Baev D, Krawczyk J, Quinn J, Cahill MR, Zeugolis D, Szegezdi E. (2020) Theranostic drug test incorporating the bone-marrow microenvironment can predict the clinical response of acute myeloid leukaemia to chemotherapy. Br J Haematol. 189:e254-e258; PMID: 32342487.
- Dhami SPS, Kappala SS, Thompson A, Szegezdi E. (2016) Three-dimensional ex vivo co-culture models of the leukaemic bone marrow niche for functional drug testing. Drug Discov Today. 21: 1464-1471; PMID: 27130156