Project: Understanding and controlling resistance mutations in leukaemias

The long term objective of this project is to reduce the risk for resistance to targeted therapies in cancers. Our theory is that you may use existing drugs in a way that reduces the risk of resistance mutations.

Project information

Project manager
Ran Friedman
Cancerfonden (The Swedish Cancer Society)
1 Jan 2019-31 Dec 2021
Biomedical sciences (Department of chemistry and biomedical sciences, Faculty of health and life sciences)

More about the project

The long term objective of this project is to reduce the risk for resistance to targeted therapies in cancers. We use chronic myeloid leukaemia (CML) and acute myeloid leukaemia (AML) with activating mutations in FLT3 as models for diseases where targeted treatments exist, but their effects are limited by the emergence of resistance. FLT3 is a human protein which is overly active in around 35 % of the AML patients, and plays an important role in maintenance of the disease.

Resistance to targeted therapy is a significant problem in many cancers and most often occurs due to the emergence of mutations in the molecular drug target that limit the efficacy of the drug. The underlying hypothesis of our studies is that, given two or more drugs whose efficacy is limited by non-overlapping resistance mutations, it is possible to prolong the time until resistance emerges by switching periodically between the drugs. A computational model has been developed to study drug resistance in tumour cells, and we were able to suggest treatment protocols that will delay the emergence of drug resistance.

In this project, we wish to expand our studies to a new target (FLT3 in AML, where the clinical problem due to resistance mutations is acute), and a new drug (axitinib for treatment of CML). To this aim, we will analyse experimental data on resistance, build a knowledge-based model, run computer simulations, develop new rotation-therapy protocols and test them. In addition, we will develop and apply a model for combination therapy in AML, where two kinases are targeted at once in the hope to minimise the risk for resistance.

Resistance to cancer therapy limits its success. To combat drug resistance we must first understand it. Computational models have been proven instrumental for this, since it is not possible to follow on the development of resistance in individual cells within a tumour. Here we will use a combined computational/experimental approach to shed light on new possibilities to overcome drug resistance in leukaemias, which will also bear consequences for other types of cancer.

The project is part of the research in the Computational Chemistry and Biochemistry Group research group.

Read the news item about the project: Linnaeus University gets SEK 1.8 million for cancer research.