Computational Systems Medicine in Cancer

Tero Aittokallio
Group leader
Introducing the group's aims (video)

Our group has expertise in integrating multi-omics profiling and clinical information from cancer patients using mathematical and statistical approaches such as machine learning and network modeling. The medical aim is to optimize treatment outcomes for individual patients using maximally predictive models and minimal biomarker signatures that enable real-time and cost-effective routine diagnostics and prognosis. We believe that combining functional, molecular and genomic profiling information is critical for next-generation precision medicine applications, where integrative modeling and clever use of big data will pinpoint effective and selective targets for personalized therapies.


Selected Publications

  1. Ianevski A, Lahtela J, Javarappa KK, Sergeev P, Ghimire BR, Gautam P, Vähä-Koskela M, Turunen L, Linnavirta N, Kuusanmäki H, Kontro M, Porkka K, Heckman CA, Mattila P, Wennerberg K, Giri AK, Aittokallio T.  Patient-tailored design for selective co-inhibition of leukemic cell subpopulations. Science Advances 2020; 7: eabe4038, DOI: 10.1126 / sciadv.abe4038
  2. Julkunen H, Cichonska A, Gautam P, Szedmak S, Douat J, Pahikkala T, Aittokallio T, Rousu J.  Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects.  Nature Communications 2020. doi: 10.1038 / s41467-020-19950-z.
  3. Akimov Y, Bulanova D, Timonen S, Wennerberg K, Aittokallio T. Improved detection of differentially represented DNA barcodes for high-throughput clonal phenomics. Molecular Systems Biology 2020; 16: e9195.
  4. Ianevski A, Giri AK, Gautam P, Kononov A, Potdar S, Saarela J, Wennerberg K, Aittokallio T. Prediction of drug combination effects with a minimal set of experiments. Nature Machine Intelligence 2019; 1: 568–577. PDF copy
  5. He L, Tang J, Andersson EI, Timonen S, Koschmieder S, Wennerberg K, Mustjoki S, Aittokallio T. Patient-customized drug combination prediction and testing for T-cell prolymphocytic leukemia patients. Cancer Research 2018; 78 (9): 2407-2418. doi: 10.1158 / 0008-5472.CAN-17-3644.

We are funded by the following organizations