New drug candidates discovered for leukaemia through data analysis

Professor Merja Heinäniemi and her research team at the University of Eastern Finland use computational methods to interpret cancer samples in order to determine which cellular processes are defective and how cells behave during drug treatments.

In 2019, Heinäniemi and other researchers collected a large dataset of more than 10,000 patient samples. This data on haematological malignancies (HEMAP) will continue to be shared with researchers through the Finnish ELIXIR node, CSC – IT Center for Science. They were able to deduce more than 30 types of cancer from this dataset by computational methods. In addition, new disease biomarkers and new drug candidates were discovered when the data was combined with drug target databases.HEMAP, hosted by CSC, now enables target gene analysis of drug molecules.

Now Heinäniemi’s team has started to use the neural network models they have developed. Data is collected from different studies for this model.

“Our leukaemia project focuses on childhood cancers. It’s a very rare type of cancer: if we don’t get the data combined, the data sets will be very small. The aim is to use CSC’s infrastructure for these projects. This would allow the data to be processed and made available to the scientific community.”

Sharing data requires building trust. In practice, this means working with the patients involved in the projects.

“It’s really important that they are involved and that their data is stored securely, and that researchers are able to do their work with the data. This is what CSC is enabling at national and EU level through its involvement in the ELIXIR infrastructure.”

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