Advertisement

Integrated Analysis of Drug Sensitivity and Selectivity to Predict Synergistic Drug Combinations and Target Coaddictions in Cancer

  • Alok Jaiswal
  • Bhagwan Yadav
  • Krister Wennerberg
  • Tero Aittokallio
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1888)

Abstract

High-throughput drug sensitivity testing provides a powerful phenotypic profiling approach to identify effective drug candidates for individual cell lines or patient-derived samples. Here, we describe an experimental-computational pipeline, named target addiction scoring (TAS), which mathematically transforms the drug response profiles into target addiction signatures, and thereby provides a ranking of potential therapeutic targets according to their functional importance in a particular cancer sample. The TAS pipeline makes use of drug polypharmacology to integrate the drug sensitivity and selectivity profiles through systems-wide interconnection networks between drugs and their targets, including both primary protein targets as well as secondary off-targets. We show how the TAS pipeline enables one to identify not only single-target addictions but also combinatorial coaddictions among targets that often underlie synergistic drug combinations.

Key words

Precision oncology Drug sensitivity testing Drug polypharmacology Drug–target interactions Target addictions Target deconvolution Drug combinations 

Notes

Acknowledgments

This work was supported by the Academy of Finland (grants 272437, 269862, 279163, 292611, 295504, 310507); the Cancer Society of Finland (TA, KW); the Integrative Life Science Doctoral Program at the University of Helsinki (AJ).

References

  1. 1.
    Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Kinzler KW (2013) Cancer genome landscapes. Science 339(6127):1546CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Garraway Levi A, Lander Eric S (2013) Lessons from the cancer genome. Cell 153(1):17–37CrossRefGoogle Scholar
  3. 3.
    The Cancer Genome Atlas Research N (2017) Integrated genomic and molecular characterization of cervical cancer. Nature 543(7645):378–384CrossRefGoogle Scholar
  4. 4.
    The Cancer Genome Atlas Research N (2017) Integrated genomic characterization of oesophageal carcinoma. Nature 541(7636):169–175CrossRefGoogle Scholar
  5. 5.
    Marusyk A, Almendro V, Polyak K (2012) Intra-tumour heterogeneity: a looking glass for cancer? Nat Rev Cancer 12(5):323–334CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Yi S, Lin S, Li Y, Zhao W, Mills GB, Sahni N (2017) Functional variomics and network perturbation: connecting genotype to phenotype in cancer. Nat Rev Genet 18(7):395–410CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Weinstein IB, Joe A (2008) Oncogene addiction. Cancer Res 68(9):3077CrossRefPubMedGoogle Scholar
  8. 8.
    Pemovska T, Kontro M, Yadav B, Edgren H, Eldfors S, Szwajda A, Almusa H, Bespalov MM, Ellonen P, Elonen E, Gjertsen BT, Karjalainen R, Kulesskiy E, Lagström S, Lehto A, Lepistö M, Lundán T, Majumder MM, Lopez Marti JM, Mattila P, Murumägi A, Mustjoki S, Palva A, Parsons A, Pirttinen T, Rämet ME, Suvela M, Turunen L, Västrik I, Wolf M, Knowles J, Aittokallio T, Heckman CA, Porkka K, Kallioniemi O, Wennerberg K (2013) Individualized systems medicine (ISM) strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discov 3(12):1416–1429CrossRefPubMedGoogle Scholar
  9. 9.
    Pemovska T, Johnson E, Kontro M, Repasky GA, Chen J, Wells P, Cronin CN, McTigue M, Kallioniemi O, Porkka K, Murray BW, Wennerberg K (2015) Axitinib effectively inhibits BCR-ABL1(T315I) with a distinct binding conformation. Nature 519(7541):102–105CrossRefPubMedGoogle Scholar
  10. 10.
    Tyner JW, Yang WF, Bankhead A, Fan G, Fletcher LB, Bryant J, Glover JM, Chang BH, Spurgeon SE, Fleming WH, Kovacsovics T, Gotlib JR, Oh ST, Deininger MW, Zwaan CM, Den Boer ML, van den Heuvel-Eibrink MM, Hare T, Druker BJ, Loriaux MM (2013) Kinase pathway dependence in primary human leukemias determined by rapid inhibitor screening. Cancer Res 73(1):285CrossRefGoogle Scholar
  11. 11.
    Friedman AA, Letai A, Fisher DE, Flaherty KT (2015) Precision medicine for cancer with next-generation functional diagnostics. Nat Rev Cancer 15(12):747–756CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Malani D, Murumagi A, Yadav B, Kontro M, Eldfors S, Kumar A, Karjalainen R, Majumder MM, Ojamies P, Pemovska T, Wennerberg K, Heckman C, Porkka K, Wolf M, Aittokallio T, Kallioniemi O (2017) Enhanced sensitivity to glucocorticoids in cytarabine-resistant AML. Leukemia 31(5):1187–1195CrossRefGoogle Scholar
  13. 13.
    Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehar J, Kryukov GV, Sonkin D, Reddy A, Liu M, Murray L, Berger MF, Monahan JE, Morais P, Meltzer J, Korejwa A, Jane-Valbuena J, Mapa FA, Thibault J, Bric-Furlong E, Raman P, Shipway A, Engels IH, Cheng J, Yu GK, Yu J, Aspesi P, de Silva M, Jagtap K, Jones MD, Wang L, Hatton C, Palescandolo E, Gupta S, Mahan S, Sougnez C, Onofrio RC, Liefeld T, MacConaill L, Winckler W, Reich M, Li N, Mesirov JP, Gabriel SB, Getz G, Ardlie K, Chan V, Myer VE, Weber BL, Porter J, Warmuth M, Finan P, Harris JL, Meyerson M, Golub TR, Morrissey MP, Sellers WR, Schlegel R, Garraway LA (2012) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483(7391):603–307CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, Greninger P, Thompson IR, Luo X, Soares J, Liu Q, Iorio F, Surdez D, Chen L, Milano RJ, Bignell GR, Tam AT, Davies H, Stevenson JA, Barthorpe S, Lutz SR, Kogera F, Lawrence K, McLaren-Douglas A, Mitropoulos X, Mironenko T, Thi H, Richardson L, Zhou W, Jewitt F, Zhang T, O/'Brien P, Boisvert JL, Price S, Hur W, Yang W, Deng X, Butler A, Choi HG, Chang JW, Baselga J, Stamenkovic I, Engelman JA, Sharma SV, Delattre O, Saez-Rodriguez J, Gray NS, Settleman J, Futreal PA, Haber DA, Stratton MR, Ramaswamy S, McDermott U, Benes CH (2012) Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483(7391):570–575CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, Aben N, Gonçalves E, Barthorpe S, Lightfoot H, Cokelaer T, Greninger P, van Dyk E, Chang H, de Silva H, Heyn H, Deng X, Egan RK, Liu Q, Mironenko T, Mitropoulos X, Richardson L, Wang J, Zhang T, Moran S, Sayols S, Soleimani M, Tamborero D, Lopez-Bigas N, Ross-Macdonald P, Esteller M, Gray NS, Haber DA, Stratton MR, Benes CH, Wessels LFA, Saez-Rodriguez J, McDermott U, Garnett MJ (2016) A landscape of pharmacogenomic interactions in cancer. Cell 166(3):740–754CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Basu A, Bodycombe Nicole E, Cheah Jaime H, Price Edmund V, Liu K, Schaefer Giannina I, Ebright Richard Y, Stewart Michelle L, Ito D, Wang S, Bracha Abigail L, Liefeld T, Wawer M, Gilbert Joshua C, Wilson Andrew J, Stransky N, Kryukov Gregory V, Dancik V, Barretina J, Garraway Levi A, Hon CS-Y, Munoz B, Bittker Joshua A, Stockwell Brent R, Khabele D, Stern Andrew M, Clemons Paul A, Shamji Alykhan F, Schreiber Stuart L (2013) An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154(5):1151–1161CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Rees MG, Seashore-Ludlow B, Cheah JH, Adams DJ, Price EV, Gill S, Javaid S, Coletti ME, Jones VL, Bodycombe NE, Soule CK, Alexander B, Li A, Montgomery P, Kotz JD, Hon CS-Y, Munoz B, Liefeld T, Dancik V, Haber DA, Clish CB, Bittker JA, Palmer M, Wagner BK, Clemons PA, Shamji AF, Schreiber SL (2016) Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat Chem Biol 12(2):109–116CrossRefGoogle Scholar
  18. 18.
    Seashore-Ludlow B, Rees MG, Cheah JH, Cokol M, Price EV, Coletti ME, Jones V, Bodycombe NE, Soule CK, Gould J, Alexander B, Li A, Montgomery P, Wawer MJ, Kuru N, Kotz JD, Hon CS-Y, Munoz B, Liefeld T, Dančík V, Bittker JA, Palmer M, Bradner JE, Shamji AF, Clemons PA, Schreiber SL (2015) Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov 5(11):1210–1223CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4(11):682–690CrossRefGoogle Scholar
  20. 20.
    Szwajda A, Gautam P, Karhinen L, Jha Sawan K, Saarela J, Shakyawar S, Turunen L, Yadav B, Tang J, Wennerberg K, Aittokallio T (2015) Systematic mapping of kinase addiction combinations in breast cancer cells by integrating drug sensitivity and selectivity profiles. Chem Biol 22(8):1144–1155CrossRefPubMedGoogle Scholar
  21. 21.
    Yadav B, Gopalacharyulu P, Pemovska T, Khan SA, Szwajda A, Tang J, Wennerberg K, Aittokallio T (2015) From drug response profiling to target addiction scoring in cancer cell models. Dis Model Mech 8(10):1255–1264CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Gautam P, Karhinen L, Szwajda A, Jha SK, Yadav B, Aittokallio T, Wennerberg K (2016) Identification of selective cytotoxic and synthetic lethal drug responses in triple negative breast cancer cells. Mol Cancer 15(1):34CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Hersey A, Chambers J, Bellis L, Patrícia Bento A, Gaulton A, Overington JP (2015) Chemical databases: curation or integration by user-defined equivalence? Drug Discov Today Technol 14:17–24CrossRefGoogle Scholar
  24. 24.
    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 (2014) Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Sci Rep 4:5193CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Davis MI, Hunt JP, Herrgard S, Ciceri P, Wodicka LM, Pallares G, Hocker M, Treiber DK, Zarrinkar PP (2011) Comprehensive analysis of kinase inhibitor selectivity. Nat Biotechnol 29(11):1046–1051CrossRefPubMedGoogle Scholar
  26. 26.
    Metz JT, Johnson EF, Soni NB, Merta PJ, Kifle L, Hajduk PJ (2011) Navigating the kinome. Nat Chem Biol 7(4):200–202CrossRefPubMedGoogle Scholar
  27. 27.
    Knapp S, Arruda P, Blagg J, Burley S, Drewry DH, Edwards A, Fabbro D, Gillespie P, Gray NS, Kuster B, Lackey KE, Mazzafera P, Tomkinson NCO, Willson TM, Workman P, Zuercher WJ (2013) A public-private partnership to unlock the untargeted kinome. Nat Chem Biol 9(1):3–6CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M, Krüger FA, Light Y, Mak L, McGlinchey S, Nowotka M, Papadatos G, Santos R, Overington JP (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 42(D1):D1083–D1090CrossRefGoogle Scholar
  29. 29.
    Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40(D1):D1100–D1107CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    He L, Kulesskiy E, Saarela J, Turunen L, Wennerberg K, Aittokallio T, Tang J (2016) Methods for high-throughput drug combination screening and synergy scoring. bioRxiv.  https://doi.org/10.1101/051698
  31. 31.
    Bliss CI (1939) The toxicity of poisons applied jointly. Ann Appl Biol 26(3):585–615CrossRefGoogle Scholar
  32. 32.
    Ianevski A, He L, Aittokallio T, Tang J (2017) SynergyFinder: a web application for analyzing drug combination dose–response matrix data. Bioinformatics 33(15):2413–2415.  https://doi.org/10.1093/bioinformatics/btx168CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Mpindi JP, Yadav B, Östling P, Gautam P, Malani D, Murumägi A, Hirasawa A, Kangaspeska S, Wennerberg K, Kallioniemi O, Aittokallio T (2016) Consistency in drug response profiling. Nature 540(7631):E5–E6CrossRefGoogle Scholar
  34. 34.
    Santos R, Ursu O, Gaulton A, Bento AP, Donadi RS, Bologa CG, Karlsson A, Al-Lazikani B, Hersey A, Oprea TI, Overington JP (2017) A comprehensive map of molecular drug targets. Nat Rev Drug Discov 16(1):19–34CrossRefGoogle Scholar
  35. 35.
    Tang J, Wennerberg K, Aittokallio T (2015) What is synergy? The Saariselkä agreement revisited. Front Pharmacol 6:181CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Alok Jaiswal
    • 1
  • Bhagwan Yadav
    • 2
  • Krister Wennerberg
    • 1
  • Tero Aittokallio
    • 1
    • 3
  1. 1.Institute for Molecular Medicine Finland (FIMM)University of HelsinkiHelsinkiFinland
  2. 2.Hematology Research Unit Helsinki (HRUH)University of HelsinkiHelsinkiFinland
  3. 3.Department of Mathematics and StatisticsUniversity of TurkuTurkuFinland

Personalised recommendations