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Improved Treatments of Acute Myeloid Leukaemias by Personalised Medicine


Description:

Acute myeloid leukaemia (AML) is a highly aggressive cancer with few treatment options for the majority of patients. Since alterations in cell signalling are a hallmark of the cancer, drugs targeting signalling pathways are being developed. A major challenge is the inter- and intra-tumour heterogeneity of AML patients. The dynamic development of cancer subclones makes it difficult to provide appropriate treatment options. To tackle these challenges, we initially focus on a rare AML subtype, pure erythroleukaemia (PEL) to develop a pipeline to elucidate pathological mechanisms and subsequently adapt it to other AML subtypes. Our translational approach with access to AML biobanks as well as longitudinal data from clinical trials provides a high quality source for big data analytics and mathematical modelling and encompasses the implementation of data management to ensure data interoperability, reusability, patient security and outreach to patient organisations and public. We pursue an integrative approach for early identification of responders to novel signalling- targeted drugs. The subclonal architecture of individual AML patients and their signalling status are mapped by mass and flow cytometry and validated by quantitative proteomics. The quantitative multiomics and clinical data are combined with methods for machine learning to develop AML classifiers. To predict clonal evolution, longitudinal cell population and single cell data are utilised to establish mechanism-based dynamic pathway models. With these models it is possible to improve our AML classifiers and to predict optimal treatment for individual patients, performing proof-of-concept validation in in vitro and in vivo models. AML_PM develops pipelines for clinical decision-making that enable next generation diagnostics for AML and tailoring treatment for individual AML patients. This personalised medicine approach promises to transform aggressive leukaemia into chronic disease and contribute to cure.


Project leader: Inge Jonassen

Started: 2019

Ends: 2022

Category: Universiteter

Sector: UoH-sektor

Budget: 3886000

Institution: Institutt for informatikk

Address: Bergen