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Topological Characterization of Cancer Driver Genes Using Reactome Super Pathways Networks

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Advances in Bioinformatics and Computational Biology (BSB 2021)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13063))

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Abstract

Cancer is a complex disease caused by genetic mutations categorized into two groups: passenger and driver. Contrary to passenger, drivers mutations directly impact oncogenesis. Drivers identification is a challenge in cancer genomics, frequently supported by statistical and computational methods. These methods utilize the increasing volume of molecular data related to cancer, gene interactions networks, and pathways. Reactome recently defined 26 Super Pathways that group genes responsible for essential biological processes. Pathways networks carry topological information relative to their biological functions that emerge from genes interactions. Since some pathways are more associated with cancer than others and all have distinct structures, this work aims to characterize cancer driver genes’ topological role in Super Pathways networks. We combine data from three different databases to create Super Pathways networks enriched with cancer driver genes information. Results show that Super Pathways networks have distinct topologies and particular roles for drivers. Drivers have significant differences in clustering, betweenness, and closeness centralities when compared to others genes. Attacks using random and intentional removal reveal a remarkable resilience in some Super Pathways networks. Attacks also reveal that drivers in the Programmed Cell Death pathway are more critical than hubs in keeping the network integrity. These distinguishable patterns associated with drivers can support the task of identifying and validate unknown drivers. In addition, recognize the topological role of drivers helps understand the impact mutations in these genes have on pathways structure.

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Notes

  1. 1.

    http://ncg.kcl.ac.uk/ and https://www.intogen.org/.

  2. 2.

    https://reactome.org/.

  3. 3.

    https://reactome.org/what-is-reactome.

  4. 4.

    https://www.centiserver.org/centrality/list/.

References

  1. Albert, R., Jeong, H., Barabási, A.L.: Error and attack tolerance of complex networks. Nature 406(6794), 378–382 (2000)

    Google Scholar 

  2. Bhatlekar, S., Fields, J.Z., Boman, B.M.: Role of hox genes in stem cell differentiation and cancer. Stem Cells Int. 2018 (2018)

    Google Scholar 

  3. Cutigi, J.F., et al.: Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery. In: Setubal, J.C., Silva, W.M. (eds.) BSB 2020. LNCS, vol. 12558, pp. 81–92. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65775-8_8

    Chapter  Google Scholar 

  4. Daum, H., Peretz, T., Laufer, N.: BRCA mutations and reproduction. Fertil. Steril. 109(1), 33–38 (2018)

    Article  CAS  Google Scholar 

  5. García-Campos, M.A., Espinal-Enríquez, J., Hernández-Lemus, E.: Pathway analysis: state of the art. Front. Physiol. 6, 383 (2015)

    Article  Google Scholar 

  6. Hanahan, D., Weinberg, R.A.: The hallmarks of cancer. Cell 100(1), 57–70 (2000)

    Article  CAS  Google Scholar 

  7. Jalili, M., et al.: CentiServer: a comprehensive resource, web-based application and R package for centrality analysis. PLoS ONE 10(11), e0143111 (2015)

    Google Scholar 

  8. Jassal, B., et al.: The reactome pathway knowledgebase. Nucl. Acids Res. 48(D1), D498–D503 (2020)

    CAS  PubMed  Google Scholar 

  9. Jin, M.H., Oh, D.Y.: ATM in DNA repair in cancer. Pharmacol. Ther. 203, 107391 (2019)

    Google Scholar 

  10. Khatri, P., Sirota, M., Butte, A.J.: Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput. Biol. 8(2), e1002375 (2012)

    Google Scholar 

  11. Laham-Karam, N., Pinto, G.P., Poso, A., Kokkonen, P.: Transcription and translation inhibitors in cancer treatment. Front. Chem. 8, 276 (2020)

    Article  CAS  Google Scholar 

  12. Martinez-Jimenez, F., et al.: A compendium of mutational cancer driver genes. Nat. Rev. Cancer 20(10), 555–572 (2020)

    Article  CAS  Google Scholar 

  13. de Mello Pessoa, V.H., Ferreira, C.d.O.L.: Resilience and structure of metabolic networks. Proc. Ser. Braz. Soc. Comput. Appl. Math. 6(2) (2018)

    Google Scholar 

  14. Milenković, T., Memišević, V., Bonato, A., Pržulj, N.: Dominating biological networks. PLoS ONE 6(8), 1–12 (2011). https://doi.org/10.1371/journal.pone.0023016

  15. Mishra, A.P., et al.: Programmed cell death, from a cancer perspective: an overview. Mol. Diagn. Ther. 22(3), 281–295 (2018)

    Article  CAS  Google Scholar 

  16. Nussinov, R., Jang, H., Tsai, C.J., Cheng, F.: Precision medicine and driver mutations: computational methods, functional assays and conformational principles for interpreting cancer drivers. PLoS Comput. Biol. 15(3), e1006658 (2019)

    Google Scholar 

  17. Oldham, S., Fulcher, B., Parkes, L., Arnatkevicute, A., Suo, C., Fornito, A.: Consistency and differences between centrality measures across distinct classes of networks. PLoS ONE 14(7), e0220061 (2019)

    Google Scholar 

  18. Ozturk, K., Dow, M., Carlin, D.E., Bejar, R., Carter, H.: The emerging potential for network analysis to inform precision cancer medicine. J. Mol. Biol. 430(18), 2875–2899 (2018)

    Article  CAS  Google Scholar 

  19. Reactome: Reproduction (2006). https://reactome.org/content/detail/R-HSA-1474165

  20. Reactome: Chromatin organization (2011). https://reactome.org/content/detail/R-HSA-4839726

  21. Reactome: Developmental biology (2011), https://reactome.org/content/detail/R-HSA-1266738

  22. Repana, D., et al.: The network of cancer genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens. Genome Biol. 20(1), 1–12 (2019)

    Article  Google Scholar 

  23. Schuster-Böckler, B., Lehner, B.: Chromatin organization is a major influence on regional mutation rates in human cancer cells. Nature 488(7412), 504–507 (2012)

    Google Scholar 

  24. Shafi, A.A., Knudsen, K.E.: Cancer and the circadian clock. Cancer Res. 79(15), 3806–3814 (2019)

    Article  CAS  Google Scholar 

  25. Stratton, M.R., Campbell, P.J., Futreal, P.A.: The cancer genome. Nature 458(7239), 719–724 (2009)

    Article  CAS  Google Scholar 

  26. Wu, G., Dawson, E., Duong, A., Haw, R., Stein, L.: Reactomefiviz: a cytoscape app for pathway and network-based data analysis. F1000Research 3 (2014)

    Google Scholar 

  27. Wu, G., Feng, X., Stein, L.: A human functional protein interaction network and its application to cancer data analysis. Genome Biol. 11(5), 1–23 (2010)

    Article  Google Scholar 

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Authors and Affiliations

  1. Federal Institute of Sao Paulo, Sao Carlos, SP, Brazil

    Rodrigo Henrique Ramos & Jorge Francisco Cutigi

  2. University of Sao Paulo, Sao Carlos, SP, Brazil

    Rodrigo Henrique Ramos, Jorge Francisco Cutigi, Cynthia de Oliveira Lage Ferreira & Adenilso Simao

Authors
  1. Rodrigo Henrique Ramos
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  2. Jorge Francisco Cutigi
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  3. Cynthia de Oliveira Lage Ferreira
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  4. Adenilso Simao
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Corresponding author

Correspondence to Rodrigo Henrique Ramos .

Editor information

Editors and Affiliations

  1. Leipzig University, Leipzig, Sachsen, Germany

    Peter F. Stadler

  2. University of BrasĂ­lia, Brasilia, Brazil

    Maria Emilia M. T. Walter

  3. CINVESTAV, Irapuato, Mexico

    Maribel Hernandez-Rosales

  4. Universidade de Brasilia, Brasilia, Brazil

    Marcelo M. Brigido

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Ramos, R.H., Cutigi, J.F., de Oliveira Lage Ferreira, C., Simao, A. (2021). Topological Characterization of Cancer Driver Genes Using Reactome Super Pathways Networks. In: Stadler, P.F., Walter, M.E.M.T., Hernandez-Rosales, M., Brigido, M.M. (eds) Advances in Bioinformatics and Computational Biology. BSB 2021. Lecture Notes in Computer Science(), vol 13063. Springer, Cham. https://doi.org/10.1007/978-3-030-91814-9_3

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  • DOI: https://doi.org/10.1007/978-3-030-91814-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91813-2

  • Online ISBN: 978-3-030-91814-9

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