Data mining tools for personalized cancer treatment

Jing Tang, a researcher working in FIMM, wants to integrate drug screening and genomic profiling data in order to find personalized treatment options for leukemia.

"Then, based on the patient's genetic background, we would know what might be the best drug combination," Tang clarifies.

With their idea, Tang and a cross-disciplinary team of researchers in medicine, biology and informatics from University of Helsinki and University of Turku were chosen as one of the semifinals of Helsinki Challenge, science-based idea competition and accelerator programme organised by University of Helsinki and several Finnish partner universities.

After several years of experiments the bottleneck in the project is data integration. Therefore it is integral to include informatics.

"We will develope a series of computational methods for drug combination prediction, modeling and data analysis. These methods will offer an improved efficiency to identify more effective combinatorial treatments for personalized medicine," Tang says.

In the future, it may be possible to apply the same model for personalized treatment of other cancer types and illnesses as well.

In order to make personalized medicine more accessible and applicable Tang and his team intend to take Helsinki Challenge as an opportunity to build a larger community around the topic.

"For this we need people from various fields with the same aim -- we want to give the patient the right drug the right time. That will also save costs."