Aplicação de simulated annealing para descobrir mutações drivers
Resumo
As células com mutações drivers podem proliferar mais rapidamente do que outras células, gerando mais células-filhas e ocasionando o surgimento do tumor. Nesse contexto, existem métodos computacionais para identificar possíveis mutações drivers em uma amostra de células. O DriverNet é um método para classificar as mutações de uma amostra, indicando as que possuem maior probabilidade de serem mutações drivers. Este trabalho apresenta uma nova proposta para identificar mutações drivers baseado no método DriverNet. A nova proposta, que necessita de um conjunto menor de dados de entrada que o método DriverNet, identificou genes relacionados ao câncer de conjuntos de dados do projeto TCGA.
Referências
Bashashati, A., Haffari, G., Ding, J., Ha, G., Lui, K., Rosner, J., Huntsman, D., Caldas, C., Aparicio, S., and Shah, S. (2012). Drivernet: Uncovering the impact of somatic driver mutations on transcriptional networks in cancer. Genome Biology, 13(12):1 - 14.
Cheng, F., Zhao, J., and Zhao, Z. (2016). Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes. Briefings in bioinformatics, 17 4:642-56.
Croft, D., Mundo, A. F., Haw, R., Milacic, M.,Weiser, J.,Wu, G., Caudy, M., Garapati, P., Gillespie, M., Kamdar, M. R., Jassal, B., Jupe, S., Matthews, L., May, B., Palatnik, S., Rothfels, K., Shamovsky, V., Song, H., Williams, M., Birney, E., Hermjakob, H., Stein, L., and D'Eustachio, P. (2014). The reactome pathway knowledgebase. Nucleic acids research, 42(D1):D472-D477.
Cutigi, J. F., Evangelista, A. F., and Simão, A. d. S. (2020). Approaches for the identification of driver mutations in cancer: a tutorial from a computational perspective. Journal of Bioinformatics and Computational Biology, 18(3):2050016:1-2050016:32.
Dimitrakopoulos, C. and Beerenwinkel, N. (2016). Computational approaches for the identification of cancer genes and pathways. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 9.
Greenman, C., Stephens, P., Smith, R., Dalgliesh, G., Hunter, C., Bignell, G., Davies, H., Teague, J., Butler, A., Stevens, C., Edkins, S., O'Meara, S., Vastrik, I., Schmidt, E., Avis, T., Barthorpe, S., Bhamra, G., Buck, G., Choudhury, B., and Stratton, M. (2007). Patterns of somatic mutation in human cancer genomes. Nature, 446:153-8.
Haber, D. and Settleman, J. (2007). Cancer: Drivers and passengers. Nature, 446:145-6.
Hou, J. and Ma, J. (2013). Identifying Driver Mutations in Cancer, volume 4, pages 33-56. Genome Medicine.
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598):671-680.
Lever, J., Zhao, E. Y., Grewal, J., Jones, M. R., and Jones, S. J. M. (2019). Cancermine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer. Nature Methods, 16(6):505-507.
Li, H. T., Zhang, Y. L., Zheng, C. H., and Wang, H. Q. (2014). Simulated annealing based algorithm for identifying mutated driver pathways in cancer. BioMed Research International, 2014.
Moreira, L. M. (2015). Ciências genômicas: fundamentos e aplicações. Sociedade Brasileira de Genética.
Pham, V., Liu, L., Bracken, C., Goodall, G., Li, J., and le, T. (2021). Computational methods for cancer driver discovery: A survey. Theranostics, 11:5553-5568.
Raphael, B. J., Dobson, J., Oesper, L., and Vandin, F. (2014). Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine. Genome Medicine, 6:5 - 5.
TCGA data portal (2018). The Cancer Genome Atlas Program.
Vandin, F., Upfal, E., and Raphael, B. J. (2012). De novo discovery of mutated driver pathways in cancer. Genome research, 22(2):375-385.
Yang, L., Chen, R., Goodison, S., and Sun, Y. (2021). An efficient and effective method to identify significantly perturbed subnetworks in cancer. Nature Computational Science, 1:79-88.
Cerny, V. (1985). Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45:41-51.
Cheng, F., Zhao, J., and Zhao, Z. (2016). Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes. Briefings in bioinformatics, 17 4:642-56.
Croft, D., Mundo, A. F., Haw, R., Milacic, M.,Weiser, J.,Wu, G., Caudy, M., Garapati, P., Gillespie, M., Kamdar, M. R., Jassal, B., Jupe, S., Matthews, L., May, B., Palatnik, S., Rothfels, K., Shamovsky, V., Song, H., Williams, M., Birney, E., Hermjakob, H., Stein, L., and D'Eustachio, P. (2014). The reactome pathway knowledgebase. Nucleic acids research, 42(D1):D472-D477.
Cutigi, J. F., Evangelista, A. F., and Simão, A. d. S. (2020). Approaches for the identification of driver mutations in cancer: a tutorial from a computational perspective. Journal of Bioinformatics and Computational Biology, 18(3):2050016:1-2050016:32.
Dimitrakopoulos, C. and Beerenwinkel, N. (2016). Computational approaches for the identification of cancer genes and pathways. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 9.
Greenman, C., Stephens, P., Smith, R., Dalgliesh, G., Hunter, C., Bignell, G., Davies, H., Teague, J., Butler, A., Stevens, C., Edkins, S., O'Meara, S., Vastrik, I., Schmidt, E., Avis, T., Barthorpe, S., Bhamra, G., Buck, G., Choudhury, B., and Stratton, M. (2007). Patterns of somatic mutation in human cancer genomes. Nature, 446:153-8.
Haber, D. and Settleman, J. (2007). Cancer: Drivers and passengers. Nature, 446:145-6.
Hou, J. and Ma, J. (2013). Identifying Driver Mutations in Cancer, volume 4, pages 33-56. Genome Medicine.
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598):671-680.
Lever, J., Zhao, E. Y., Grewal, J., Jones, M. R., and Jones, S. J. M. (2019). Cancermine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer. Nature Methods, 16(6):505-507.
Li, H. T., Zhang, Y. L., Zheng, C. H., and Wang, H. Q. (2014). Simulated annealing based algorithm for identifying mutated driver pathways in cancer. BioMed Research International, 2014.
Moreira, L. M. (2015). Ciências genômicas: fundamentos e aplicações. Sociedade Brasileira de Genética.
Pham, V., Liu, L., Bracken, C., Goodall, G., Li, J., and le, T. (2021). Computational methods for cancer driver discovery: A survey. Theranostics, 11:5553-5568.
Raphael, B. J., Dobson, J., Oesper, L., and Vandin, F. (2014). Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine. Genome Medicine, 6:5 - 5.
TCGA data portal (2018). The Cancer Genome Atlas Program.
Vandin, F., Upfal, E., and Raphael, B. J. (2012). De novo discovery of mutated driver pathways in cancer. Genome research, 22(2):375-385.
Yang, L., Chen, R., Goodison, S., and Sun, Y. (2021). An efficient and effective method to identify significantly perturbed subnetworks in cancer. Nature Computational Science, 1:79-88.
Cerny, V. (1985). Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45:41-51.
Publicado
07/06/2022
Como Citar
RIBEIRO, Paulo Henrique; CUTIGI, Jorge Francisco; EVANGELISTA, Adriane Feijó; SIMÃO, Adenilso da Silva.
Aplicação de simulated annealing para descobrir mutações drivers. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 22. , 2022, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 60-71.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2022.222451.
