Methods for High-throughput Drug Combination Screening and Synergy Scoring

Part of the Methods in Molecular Biology book series (MIMB, volume 1711)


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 words

Drug 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).


  1. 1.
    Vogelstein B, Papadopoulos N, Velculescu VE et al (2013) Cancer genome landscapes. Science 339:1546–1558CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Pemovska T, Kontro M, Yadav B et al (2013) Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discov 3:1416–1429CrossRefPubMedGoogle Scholar
  3. 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
  4. 4.
    Seashore-Ludlow B, Rees MG, Cheah JH et al (2015) Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov 5:1210–1223CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Tang J, Aittokallio T (2014) Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles. Curr Pharm Des 20:20–36PubMedCentralGoogle Scholar
  6. 6.
    Gillies RJ, Verduzco D, Gatenby RA (2012) Evolutionary dynamics of carcinogenesis and why targeted therapy does not work. Nat Rev Cancer 12:487–493CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Mathews Griner LA, Guha R, Shinn P et al (2014) High-throughput combinatorial screening identifies drugs that cooperate with ibrutinib to kill activated B-cell-like diffuse large B-cell lymphoma cells. Proc Natl Acad Sci U S A 111:2349–2354CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Crystal AS, Shaw TA, Sequist VL et al (2014) Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science 346:1480–1486CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Pemovska T, Johnson E, Kontro M et al (2015) Axitinib effectively inhibits BCR-ABL1 (T315I) with a distinct binding conformation. Nature 519:102–105CrossRefPubMedGoogle Scholar
  10. 10.
    Kulesskiy E, Saarela J, Turunen L et al (2016) Precision cancer medicine in the acoustic dispensing era: ex vivo primary cell drug sensitivity testing. J Lab Autom 21:27–36CrossRefPubMedGoogle Scholar
  11. 11.
    Haltia UM, Andersson N, Yadav B et al (2017) Systematic drug sensitivity testing reveals synergistic growth inhibition by dasatinib or mTOR inhibitors with paclitaxel in ovarian granulosa cell tumor cells. Gynecol Oncol 144:621CrossRefPubMedGoogle Scholar
  12. 12.
    Saeed K, Rahkama V, Eldfors S et al (2017) Comprehensive drug testing of patient-derived conditionally reprogrammed cells from castration-resistant prostate cancer. Eur Urol 71:319. CrossRefPubMedGoogle Scholar
  13. 13.
    Berenbaum MC (1989) What is synergy. Pharmacol Rev 41:93–141PubMedGoogle Scholar
  14. 14.
    Loewe S (1953) The problem of synergism and antagonism of combined drugs. Arzneimittelforschung 3:285–290PubMedGoogle Scholar
  15. 15.
    Bliss CI (1939) The toxicity of poisons applied jointly. Ann Appl Biol 26:585–615CrossRefGoogle Scholar
  16. 16.
    Chou TC (2006) Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies. Pharmacol Rev 58:621–681CrossRefPubMedGoogle Scholar
  17. 17.
    Boik JC, Narasimhan B (2010) An R package for assessing drug synergism/antagonism. J Stat Softw 34:6CrossRefGoogle Scholar
  18. 18.
    Ritz C, Baty F, Streibig JC (2005) Bioassay analysis using R. J Stat Softw 12:5CrossRefGoogle Scholar
  19. 19.
    Greco WR, Bravo G, Parsons JC (1995) The search for synergy: a critical review from a response surface perspective. Pharmacol Rev 47:331–385PubMedGoogle Scholar
  20. 20.
    Zhao W, Sachsenmeier K, Zhang L et al (2014) A new bliss independence model to analyze drug combination data. J Biomol Screen 19:817–821CrossRefPubMedGoogle Scholar
  21. 21.
    Yadav B, Wennerberg K, Aittokallio T et al (2015) Searching for drug synergy in complex dose-response landscapes using an interaction potency model. Comput Struct Biotechnol J 13:504–513CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Szwajda A, Gautam P, Karhinen L et al (2015) Systematic mapping of kinase addiction combinations in breast cancer cells by integrating drug sensitivity and selectivity profiles. Chem Biol 22:1144–1155CrossRefPubMedGoogle Scholar
  23. 23.
    Gautam P, Karhinen L, Szwajda A et al (2016) Identification of selective cytotoxic and synthetic lethal drug responses in triple negative breast cancer cells. Mol Cancer 15:34CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Karjalainen R, Pemovska T, Majumder M et al (2017) JAK1/2 and BCL2 inhibitors synergize to counteract bone marrow stromal cell-induced protection of AML. Blood 130:789CrossRefPubMedGoogle Scholar

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© Springer Science+Business Media LLC 2018

Authors and Affiliations

  1. 1.Institute for Molecular Medicine Finland (FIMM)University of HelsinkiHelsinkiFinland
  2. 2.Department of Mathematics and StatisticsUniversity of TurkuTurkuFinland
  3. 3.Institute of BiomedicineUniversity of HelsinkiHelsinkiFinland

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