Investigation of the performance of driver mutation identification methods using biological networks and enriched biological networks

• Alfredo Guilherme da Silva Souza USP
• Adenilso Simao USP

DOI: https://doi.org/10.5753/sbcas.2022.222457

Resumo

Several computational methods allow identifying genes related to cancer (driver mutation) through patient mutation data and biological networks. Usually, networks are not built focusing on biological activities associated with cancer because they are designed for general use. In this study, we investigate the performance of methods for identifying driver mutations using biological networks and enriched biological networks, applying a gene prioritization method to classify genes associated with cancer understudy in the biological network. The results indicated that employing the enrichment method helped identify different driver genes in all cases.

Referências

Abeshouse, A., Ahn, J., Akbani, R., Ally, A., Amin, S., Andry, C. D., Annala, M., Aprikian, A., Armenia, J., Arora, A., et al. (2015). The molecular taxonomy of primary prostate cancer. Cell, 163(4):1011-1025.

Armenia, J., Wankowicz, S. A., Liu, D., Gao, J., Kundra, R., Reznik, E., Chatila, W. K., Chakravarty, D., Han, G. C., Coleman, I., et al. (2018). The long tail of oncogenic drivers in prostate cancer. Nature genetics, 50(5):645-651.

Bass, A. J., Thorsson, V., Shmulevich, I., Reynolds, S. M., Miller, M., Bernard, B., Hinoue, T., Laird, P. W., Curtis, C., Shen, H., et al. (2014). Comprehensive molecular characterization of gastric adenocarcinoma. Nature, 513(7517):202.

Chatr-Aryamontri, A., Oughtred, R., Boucher, L., Rust, J., Chang, C., Kolas, N. K., O'Donnell, L., Oster, S., Theesfeld, C., Sellam, A., et al. (2017). The biogrid interaction database: 2017 update. Nucleic acids research, 45(D1):D369-D379.

Chen, J., Bardes, E. E., Aronow, B. J., and Jegga, A. G. (2009). Toppgene suite for gene list enrichment analysis and candidate gene prioritization. Nucleic acids research, 37(suppl 2):W305-W311.

Ciriello, G., Gatza, M. L., Beck, A. H., Wilkerson, M. D., Rhie, S. K., Pastore, A., Zhang, H., McLellan, M., Yau, C., Kandoth, C., et al. (2015). Comprehensive molecular portraits of invasive lobular breast cancer. Cell, 163(2):506-519.

Cornish, A. J., David, A., and Sternberg, M. J. (2018). Phenorank: reducing study bias in gene prioritization through simulation. Bioinformatics, 34(12):2087-2095.

Croft, D., Mundo, A. F., Haw, R., Milacic, M., Weiser, J., Wu, G., Caudy, M., Garapati, P., Gillespie, M., Kamdar, M. R., et al. (2013). The reactome pathway knowledgebase. Nucleic acids research, 42(D1):D472-D477.

Cutigi, J. F., Evangelista, A. F., and Simao, A. (2020a). Genwemme: a network-based computational method for prioritizing groups of significant related genes in cancer. In Advances in Bioinformatics and Computational Biology: 12th Brazilian Symposium on Bioinformatics, BSB 2019, Fortaleza, Brazil, October 7-10, 2019, Revised Selected Papers, volume 11347, page 29. Springer Nature.

Cutigi, J. F., Evangelista, R. F., Ramos, R. H., Ferreira, C. d. O. L., Evangelista, A. F., de Carvalho, A. C., and Simao, A. (2020b). Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery. In Brazilian Symposium on Bioinformatics, pages 81-92. Springer.

Das, J. and Yu, H. (2012). Hint: High-quality protein interactomes and their applications in understanding human disease. BMC systems biology, 6(1):92.

Gao, J., Aksoy, B. A., Dogrusoz, U., Dresdner, G., Gross, B., Sumer, S. O., Sun, Y., Jacobsen, A., Sinha, R., Larsson, E., et al. (2013). Integrative analysis of complex cancer genomics and clinical profiles using the cbioportal. Science signaling, 6(269):pl1-pl1.

Guala, D., Sjölund, E., and Sonnhammer, E. L. (2014). Maxlink: network-based prioritization of genes tightly linked to a disease seed set. Bioinformatics, 30(18):2689-2690.

Hristov, B. H. and Singh, M. (2017). Network-based coverage of mutational profiles reveals cancer genes. Cell systems, 5(3):221-229.

Joshi-Tope, G., Gillespie, M., Vastrik, I., D'Eustachio, P., Schmidt, E., de Bono, B., Jassal, B., Gopinath, G., Wu, G., Matthews, L., et al. (2005). Reactome: a knowledgebase of biological pathways. Nucleic acids research, 33(suppl 1):D428-D432.

Jourquin, J., Duncan, D., Shi, Z., and Zhang, B. (2012). Glad4u: deriving and prioritizing gene lists from pubmed literature. BMC genomics, 13(8):1-12.

Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., and Hirakawa, M. (2009). Kegg for representation and analysis of molecular networks involving diseases and drugs. Nucleic acids research, 38(suppl 1):D355-D360.

Keshava Prasad, T., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, B., Venugopal, A., et al. (2008). Human protein reference database-2009 update. Nucleic acids research, 37(suppl 1):D767-D772.

Koscielny, G., An, P., Carvalho-Silva, D., Cham, J. A., Fumis, L., Gasparyan, R., Hasan, S., Karamanis, N., Maguire, M., Papa, E., et al. (2017). Open targets: a platform for therapeutic target identification and validation. Nucleic acids research, 45(D1):D985-D994.

Kumar, A. A., Van Laer, L., Alaerts, M., Ardeshirdavani, A., Moreau, Y., Laukens, K., Loeys, B., and Vandeweyer, G. (2018). pbrit: gene prioritization by correlating functional and phenotypic annotations through integrative data fusion. Bioinformatics, 34(13):2254-2262.

Liu, Y., Liang, Y., and Wishart, D. (2015). Polysearch2: a significantly improved textmining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more. Nucleic acids research, 43(W1):W535-W542.

Meshkin, A., Shakery, A., and Masoudi-Nejad, A. (2019). Gps: Identification of disease genes by rank aggregation of multi-genomic scoring schemes. Genomics, 111(4):612-618.

Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C., and Morris, Q. (2008). Genemania: a real-time multiple association network integration algorithm for predicting gene function. Genome biology, 9(1):1-15.

Network, C. G. A. T. R. et al. (2008). Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 455(7216):1061.

Nussbaum, R. L., McInnes, R. R., and Willard, H. F. (2015). Thompson & Thompson genetics in medicine e-book. Elsevier Health Sciences.

Ozturk, K., Dow, M., Carlin, D. E., Bejar, R., and Carter, H. (2018). The emerging potential for network analysis to inform precision cancer medicine. Journal of molecular biology, 430(18):2875-2899.

Piñero, J., Bravo, À., Queralt-Rosinach, N., Gutiérrez-Sacristán, A., Deu-Pons, J., Centeno, E., García-García, J., Sanz, F., and Furlong, L. I. (2016). Disgenet: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic acids research, page gkw943.

Raj, M. R. and Sreeja, A. (2018). Analysis of computational gene prioritization approaches. Procedia computer science, 143:395-410.

Robertson, A. G., Kim, J., Al-Ahmadie, H., Bellmunt, J., Guo, G., Cherniack, A. D., Hinoue, T., Laird, P. W., Hoadley, K. A., Akbani, R., et al. (2017). Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell, 171(3):540-556.

Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2014). String v10: protein-protein interaction networks, integrated over the tree of life. Nucleic acids research, 43(D1):D447-D452.

Tamborero, D., Gonzalez-Perez, A., Perez-Llamas, C., Deu-Pons, J., Kandoth, C., Reimand, J., Lawrence, M. S., Getz, G., Bader, G. D., Ding, L., et al. (2013). Comprehensive identification of mutational cancer driver genes across 12 tumor types. Scientific reports, 3(1):1-10.

Tranchevent, L.-C., Ardeshirdavani, A., ElShal, S., Alcaide, D., Aerts, J., Auboeuf, D., and Moreau, Y. (2016). Candidate gene prioritization with endeavour. Nucleic acids research, 44(W1):W117-W121.

Yang, H., Robinson, P. N., and Wang, K. (2015). Phenolyzer: phenotype-based prioritization of candidate genes for human diseases. Nature methods, 12(9):841-843.

Zolotareva, O. and Kleine, M. (2019). A survey of gene prioritization tools for mendelian and complex human diseases. Journal of integrative bioinformatics, 16(4).