Methods for High-throughput Drug Combination Screening and Synergy Scoring
Gene products or pathways that are aberrantly activated in cancer but not in normal tissue hold great promises for being effective and safe anticancer therapeutic targets. Many targeted drugs have entered clinical trials but so far showed limited efficacy mostly due to variability in treatment responses and often rapidly emerging resistance. Toward more effective treatment options, we will need multi-targeted drugs or drug combinations, which selectively inhibit the viability and growth of cancer cells and block distinct escape mechanisms for the cells to become resistant. Functional profiling of drug combinations requires careful experimental design and robust data analysis approaches. At the Institute for Molecular Medicine Finland (FIMM), we have developed an experimental-computational pipeline for high-throughput screening of drug combination effects in cancer cells. The integration of automated screening techniques with advanced synergy scoring tools allows for efficient and reliable detection of synergistic drug interactions within a specific window of concentrations, hence accelerating the identification of potential drug combinations for further confirmatory studies.
Key wordsDrug combinations High-throughput screening Experimental design Synergy scoring Computational modeling
This work was supported by the Academy of Finland (grants 272437, 269862, 279163, 295504, and 292611 for TA, 272577 and 277293 for KW); the Integrative Life Science Doctoral Program at the University of Helsinki (LH), the Sigrid Jusélius Foundation (KW) and the Cancer Society of Finland (JT, TA, and KW). This project has received funding from the European Union’s Horizon 2020 research and innovation program 2014–2020 under Grant Agreement No 634143 (MedBioinformatics).
- 3.Yang W, Soares J, Greninger P et al (2013) Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res D41:D955–D961Google Scholar