Integrating Open Data on Cancer in Support to Tumor Growth Analysis

  • Fleur Jeanquartier
  • Claire Jean-Quartier
  • Tobias Schreck
  • David Cemernek
  • Andreas Holzinger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9832)

Abstract

The general disease group of malignant neoplasms depicts one of the leading and increasing causes for death. The underlying complexity of cancer demands for abstractions to disclose an exclusive subset of information related to the disease. Our idea is to create a user interface for linking a simulation on cancer modeling to relevant additional publicly and freely available data. We are not only providing a categorized list of open datasets and queryable databases for the different types of cancer and related information, we also identify a certain subset of temporal and spatial data related to tumor growth. Furthermore, we describe the integration possibilities into a simulation tool on tumor growth that incorporates the tumor’s kinetics.

Keywords

Open data Data integration Cancer Tumor growth Data Visualization Simulation 

References

  1. 1.
    Holzinger, A., Dehmer, M., Jurisica, I.: Knowledge discovery and interactive data mining in bioinformatics - State-of-the-Art, future challenges and research directions. BMC Bioinform. 15(Suppl. 6), I1 (2014)CrossRefGoogle Scholar
  2. 2.
    Jeanquartier, F., Jean-Quartier, C., Cemernek, D., Holzinger, A.: In silico modeling for tumor growth visualization. BMC Syst. Biol. (2016)Google Scholar
  3. 3.
    Hey, T., Tansley, S., Tolle, K.: The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research (2009)Google Scholar
  4. 4.
    Ward, M.O., Grinstein, G., Keim, D.: Interactive Data Visualization: Foundations, Techniques, and Applications. CRC Press, Natick (2010)MATHGoogle Scholar
  5. 5.
    Turkay, C., Jeanquartier, F., Holzinger, A., Hauser, H.: On computationally-enhanced visual analysis of heterogeneous data and its application in biomedical informatics. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 117–140. Springer, Heidelberg (2014)Google Scholar
  6. 6.
    Unger, A., Schumann, H.: Visual support for the understanding of simulation processes. In: IEEE Pacific Visualization Symposium, PacificVis 2009, pp. 57–64. IEEE (2009)Google Scholar
  7. 7.
    Bernard, J., Daberkow, D., Fellner, D., Fischer, K., Koepler, O., Kohlhammer, J., Runnwerth, M., Ruppert, T., Schreck, T., Sens, I.: VisInfo: a digital library system for time series research data based on exploratory search - a user-centered design approach. Int. J. Digit. Libr. 1, 37–59 (2015). SpringerCrossRefGoogle Scholar
  8. 8.
    Bernard, J., Ruppert, T., Scherer, M., Kohlhammer, J., Schreck, T.: Content-based layouts for exploratory metadata search in scientific research data. In: Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 139–148. ACM, June 2012Google Scholar
  9. 9.
    Scherer, M., von Landesberger, T., Schreck, T.: Visual-interactive querying for multivariate research data repositories using bag-of-words. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries, pp. 285–294 (2013)Google Scholar
  10. 10.
    Shao, L., Behrisch, M., Schreck, T., von Landesberger, T., Scherer, M., Bremm, S., Keim, D.: Guided sketching for visual search and exploration in large scatter plot spaces. In: Proceedings of EuroVA International Workshop on Visual Analytics, pp. 19–23 (2014)Google Scholar
  11. 11.
    Kandel, S., Paepcke, A., Hellerstein, J., Wrangler, J.H.: Interactive visual specification of data transformation scripts. In: ACM Human Factors in Computing Systems (CHI) (2011)Google Scholar
  12. 12.
    Jeanquartier, F., Jean-Quartier, C., Holzinger, A.: Integrated Web visualizations for protein-protein interaction databases. BMC Bioinform. 16(1), 195 (2015). doi:10.1186/s12859-015-0615-z
  13. 13.
    Gomez-Cabrero, D., Abugessaisa, I., Maier, D., Teschendorff, A., Merkenschlager, M., Gisel, A., Tegnér, J.: Data integration in the era of omics: current and future challenges. BMC Syst. Biol. 8(Suppl. 2), I1 (2014)Google Scholar
  14. 14.
    Angrist, M., Cook-Deegan, R.: Distributing the future: the weak justifications for keeping human genomic databases secret and the challenges and opportunities in reverse engineering them. Appl. Transl. Genomics 3(4), 124–127 (2014)CrossRefGoogle Scholar
  15. 15.
    Cerami, E., Gao, J., Dogrusoz, U., Gross, B.E., Sumer, S.O., Aksoy, B.A., Antipin, Y.: The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2(5), 401–404 (2012)CrossRefGoogle Scholar
  16. 16.
    Cline, M.S., Craft, B., Swatloski, T., Goldman, M., Ma, S., Haussler, D., Zhu, J.: Exploring TCGA pan-cancer data at the UCSC cancer genomics browser. Sci. Rep. 3, 2652 (2013)Google Scholar
  17. 17.
    Beroukhim, R., Mermel, C.H., Porter, D., Wei, G., Raychaudhuri, S., Donovan, J., Mc Henry, K.T.: The landscape of somatic copy-number alteration across human cancers. Nature 463(7283), 899–905 (2010)CrossRefGoogle Scholar
  18. 18.
    Forbes, S.A., Beare, D., Gunasekaran, P., Leung, K., Bindal, N., Boutselakis, H., Kok, C.Y.: COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43(D1), D805–D811 (2015)CrossRefGoogle Scholar
  19. 19.
    Zhang, J., Baran, J., Cros, A., Guberman, J.M., Haider, S., Hsu, J., Wong-Erasmus, M.: International Cancer Genome Consortium Data Portala one-stop shop for cancer genomics data. Database (Oxford) (2011) bar026Google Scholar
  20. 20.
    Rubio-Perez, C., Tamborero, D., Schroeder, M.P., Antoln, A.A., Deu-Pons, J., Perez-Llamas, C., Lopez-Bigas, N.: In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities. Cancer Cell 27(3), 382–396 (2015)CrossRefGoogle Scholar
  21. 21.
    Thorvaldsdttir, H., Robinson, J.T., Mesirov, J.P.: Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Briefings Bioinform. 14(2), 178–192 (2013)CrossRefGoogle Scholar
  22. 22.
    Dietmann, S., Lee, W., Wong, P., Rodchenkov, I., Antonov, A.V.: CCancer: a birds eye view on gene lists reported in cancer-related studies. Nucleic Acids Res. 38(Suppl. 2), W118–W123 (2010)CrossRefGoogle Scholar
  23. 23.
    Jiang, G., Sohn, S., Zimmermann, M.T., Wang, C., Liu, H., Chute, C.G.: Drug normalization for cancer therapeutic and druggable genome target discovery. AMIA Summits Transl. Sci. Proc. 2015, 72 (2015)Google Scholar
  24. 24.
    Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., Prior, F.: The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)CrossRefGoogle Scholar
  25. 25.
    Ongenaert, M., Van Neste, L., De Meyer, T., Menschaert, G., Bekaert, S., Van Criekinge, W.: PubMeth: a cancer methylation database combining text mining and expert annotation. Nucleic Acids Res. 36(Suppl. 1), D842–D846 (2008)Google Scholar
  26. 26.
    Zhu, F., Patumcharoenpol, P., Zhang, C., Yang, Y., Chan, J., Meechai, A., Shen, B.: Biomedical text mining and its applications in cancer research. J. Biomed. Inform. 46(2), 200–211 (2013)CrossRefGoogle Scholar
  27. 27.
    Pletscher-Frankild, S., Pallej, A., Tsafou, K., Binder, J.X., Jensen, L.J.: DISEASES: text mining and data integration of diseasegene associations. Methods 74, 83–89 (2015)CrossRefGoogle Scholar
  28. 28.
    Holzinger, A., Schantl, J., Schroettner, M., Seifert, C., Verspoor, K.: Biomedical text mining: state-of-the-art, open problems and future challenges. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 271–300. Springer, Heidelberg (2014)Google Scholar
  29. 29.
    Torre, L.A., Siegel, R.L., Ward, E.M., Jemal, A.: Global cancer incidence and mortality rates and trendsan update. Cancer Epidemiol. Biomark. Prev. 25(1), 16–27 (2016)CrossRefGoogle Scholar
  30. 30.
    Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2016. CA: A Cancer J. Clin. 66(1), 7–30 (2015)Google Scholar
  31. 31.
    Bray, F., Ferlay, J., Laversanne, M., Brewster, D.H., Gombe Mbalawa, C., Kohler, B., Soerjomataram, I.: Cancer incidence in five continents: inclusion criteria, highlights from Volume X and the global status of cancer registration. Int. J. Cancer 137(9), 2060–2071 (2015)Google Scholar
  32. 32.
    Europe PMC Consortium: Europe PMC: a full-text literature database for the life sciences and platform for innovation. Nucleic Acids Res. 43(D1), D1042–D1048 (2015)Google Scholar
  33. 33.
    Holzinger, A., Jurisica, I.: Knowledge discovery and data mining in biomedical informatics: the future is in integrative, interactive machine learning solutions. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 1–18. Springer, Heidelberg (2014)Google Scholar
  34. 34.
    Kieseberg, P., Weippl, E., Holzinger, A.: Trust for the doctor-in-the-loop. In: European Research Consortium for Informatics and Mathematics (ERCIM) News: Tackling Big Data in the Life Sciences, vol. 104(1), pp. 32–33 (2016)Google Scholar
  35. 35.
    Greiling, D.A., Jacquez, G.M., Kaufmann, A.M., Rommel, R.G.: Space-time visualization and analysis in the Cancer Atlas Viewer. J. Geogr. Syst. 7(1), 67–84 (2005)CrossRefGoogle Scholar
  36. 36.
    Wei, Y.: Integrative analyses of cancer data: a review from a statistical perspective. Cancer Inform. 14(Suppl. 2), 173 (2015)Google Scholar
  37. 37.
    Wu, T.J., Schriml, L.M., Chen, Q.R., Colbert, M., Crichton, D.J., Finney, R., Mitraka, E.: Generating a focused view of disease ontology cancer terms for pan-cancer data integration and analysis. Database (2015) bav032Google Scholar
  38. 38.
    Sioutos, N., de Coronado, S., Haber, M.W., Hartel, F.W., Shaiu, W.L., Wright, L.W.: NCI Thesaurus: a semantic model integrating cancer-related clinical and molecular information. J. Biomed. Inform. 40(1), 30–43 (2007)CrossRefGoogle Scholar
  39. 39.
    Drake, J.W., Charlesworth, B., Charlesworth, D., Crow, J.F.: Rates of spontaneous mutation. Genetics 148(4), 1667–1686 (1998)Google Scholar
  40. 40.
    Lodish, H., Berk, A., Zipursky, S.L., et al.: Molecular Cell Biology, 4th edn. W.H. Freeman, New York (2000)Google Scholar
  41. 41.
    Yang, Y., Dong, X., Xie, B., Ding, N., Chen, J., Li, Y., Fang, X.: Databases and web tools for cancer genomics study. Genomics Proteomics Bioinform. 13(1), 46–50 (2015)CrossRefGoogle Scholar
  42. 42.
    Müller, H.M., Kenny, E.E., Sternberg, P.W.: Textpresso: an ontology-based information retrieval and extraction system for biological literature. PLoS Biol. 2(11), e309 (2004)Google Scholar
  43. 43.
    Schaefer, C., Grouse, L., Buetow, K., Strausberg, R.L.: A new cancer genome anatomy project web resource for the community. Cancer J. 7(1), 52–60 (2001)Google Scholar
  44. 44.
    Bult, C.J., Krupke, D.M., Begley, D.A., Richardson, J.E., Neuhauser, S.B., Sundberg, J.P., Eppig, J.T.: Mouse Tumor Biology (MTB): a database of mouse models for human cancer. Nucleic Acids Res. 43(D1), D818–D824 (2015)CrossRefGoogle Scholar
  45. 45.
    Roelofs, E., Dekker, A., Meldolesi, E., van Stiphout, R.G., Valentini, V., Lambin, P.: International data-sharing for radiotherapy research: an open-source based infrastructure for multicentric clinical data mining. Radiother. Oncol. 110(2), 370–374 (2014)CrossRefGoogle Scholar
  46. 46.
    WHO cancer mortality database (IARC). http://www-dep.iarc.fr/WHOdb/WHOdb.htm. Accessed 01 May 2016
  47. 47.
    Eyler, C.E., et al.: Glioma stem cell proliferation and tumor growth are promoted by nitric oxide synthase-2. Cell 146(1), 53–66 (2011)Google Scholar
  48. 48.
    Herman, A.B., Savage, V.M., West, G.B.: A quantitative theory of solid tumor growth, metabolic rate and vascularization. PLOS One 6, e22973 (2011)Google Scholar
  49. 49.
    Kisker, O., Becker, C.M., Prox, D., Fannon, M., D’Amato, R., Flynn, E., Fogler, W.E., Kim Lee Sim, B., Allred, E.N., Pirie-Shepherd, S.R., Folkman, J.: Continuous administration of endostatin by intraperitoneally implanted osmotic pump improves the efficacy and potency of therapy in a mouse xenograft tumor model. Cancer Res. 61, 7669 (2001)Google Scholar
  50. 50.
    Mroz, E.A., Tward, A.M., Hammon, R.J., Ren, Y., Rocco, J.W.: Intra-tumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the cancer genome atlas. PLoS Med. 12(2), e1001786 (2015)Google Scholar
  51. 51.
    Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-oriented Data. Springer Science & Business Media, New York (2011)Google Scholar
  52. 52.
    Holzinger, A.: Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inform. 3(2), 119–131 (2016). SpringerCrossRefGoogle Scholar
  53. 53.
    Jean-Quartier, C., Jeanquartier, F., Cemernek, D., Holzinger, A.: Tumor growth simulation profiling. In: Renda, M.E., Bursa, M., Holzinger, A., Khuri, S. (eds.) ITBAM 2016. LNCS, vol. 9832, pp. 208–213. Springer, Heidelberg (2016)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Fleur Jeanquartier
    • 1
  • Claire Jean-Quartier
    • 1
  • Tobias Schreck
    • 3
  • David Cemernek
    • 1
  • Andreas Holzinger
    • 1
    • 2
  1. 1.Holzinger Group, Institute for Medical Informatics, Statistics and DocumentationMedical University GrazGrazAustria
  2. 2.Institute of Information Systems and Computer MediaGraz University of TechnologyGrazAustria
  3. 3.Institute of Computer Graphics and Knowledge Visualisation GrazUniversity of TechnologyGrazAustria

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