Bioinformatics is an essential part of the high throughput operations. On the one hand, all experimental data need to be aggregated, organised, statistically evaluated, and visualized. On the other hand, bioinformatic tools can be used to explore the vast drug response data accumulated over the years to generate new knowledge and formulate new hypotheses.
Bioinformaticians and computer scientists at FIMM use various programming instruments (R, Python, etc.) to process the experimental data. Several computational tools have been built in house to facilitate data processing.
Dr. Tero Aittokallio's group developed a "Drug Sensitivity Score" (DSS) - a single number describing cellular response to a particular drug, making it possible to compare responses of different cells to different drugs between each other (1).
Breeze2 open access tool has been built for automated analysis of drug sensitivity and resistance (DSRT) data (2).
Several computationsl pipelines (SynergyFinder (3,4), Decrease (5)) have been developed to explore potential synergy between drugs and identify useful drug combinations for particular molecular targets (3).
Dr. Imre Västrik developed an in-house data analysis platform TheDB for processing Individualised Systems Medicine clinical samples and combining DSRT results and patient mutations, thus greatly facilitating the communications between researchers and clinicians.
1. Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R, Majumder MM, Malani D, Murumägi A, Knowles J, Porkka K, Heckman C, Kallioniemi O, Wennerberg K, Aittokallio T. Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Sci Rep. 2014 Jun 5;4:5193. doi: 10.1038/srep05193.PMID: 24898935
2. Potdar S, Ianevski A, Mpindi JP, Bychkov D, Fiere C, Ianevski P, Yadav B, Wennerberg K, Aittokallio T, Kallioniemi O, Saarela J, Östling P. Breeze: an integrated quality control and data analysis application for high-throughput drug screening. Bioinformatics, 2020, 36(11): 3602–3604. doi: 10.1093/bioinformatics/btaa138
3. Ianevski A, He L, Aittokallio T, Tang J. SynergyFinder: a web application for analyzing drug combination dose-response matrix data. Bioinformatics. 2017, 33(15):2413-2415. doi: 10.1093/bioinformatics/btx162.
4. Ianevski A, Giri AK, Aittokallio T. SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples. Nucleic Acids Research, 2022, gkac382, https://doi.org/10.1093/nar/gkac382.
5. 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. Nat Mach Intell 2019, 1, 568–577. doi.org/10.1038/s42256-019-0122-4.