Molecules and Cells

Korean Society for Molecular and Cellular Biology (한국분자세포생물학회)
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Overexpression and Selective Anticancer Efficacy of ENO3 in STK11 Mutant Lung Cancers

  • Park, Choa (Department of Biological Sciences, Sookmyung Women's University) ;
  • Lee, Yejin (Department of Biological Sciences, Sookmyung Women's University) ;
  • Je, Soyeon (Department of Biological Sciences, Sookmyung Women's University) ;
  • Chang, Shengzhi (Department of Biological Sciences, Sookmyung Women's University) ;
  • Kim, Nayoung (Department of Biological Sciences, Sookmyung Women's University) ;
  • Jeong, Euna (Research Institute of Women's Health, Sookmyung Women's University) ;
  • Yoon, Sukjoon (Department of Biological Sciences, Sookmyung Women's University)
  • Received : 2019.05.20
  • Accepted : 2019.10.13
  • Published : 2019.11.30
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Abstract

Oncogenic gain-of-function mutations are clinical biomarkers for most targeted therapies, as well as represent direct targets for drug treatment. Although loss-of-function mutations involving the tumor suppressor gene, STK11 (LKB1) are important in lung cancer progression, STK11 is not the direct target for anticancer agents. We attempted to identify cancer transcriptome signatures associated with STK11 loss-of-function mutations. Several new sensitive and specific gene expression markers (ENO3, TTC39C, LGALS3, and MAML2) were identified using two orthogonal measures, i.e., fold change and odds ratio analyses of transcriptome data from cell lines and tissue samples. Among the markers identified, the ENO3 gene over-expression was found to be the direct consequence of STK11 loss-of-function. Furthermore, the knockdown of ENO3 expression exhibited selective anticancer effect in STK11 mutant cells compared with STK11 wild type (or recovered) cells. These findings suggest that ENO3-based targeted therapy might be promising for patients with lung cancer harboring STK11 mutations.

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References

  1. Ali, H., Du, Z., Li, X., Yang, Q., Zhang, Y.C., Wu, M., Li, Y., and Zhang, G. (2015). Identification of suitable reference genes for gene expression studies using quantitative polymerase chain reaction in lung cancer in vitro. Mol. Med. Rep. 11, 3767-3773. https://doi.org/10.3892/mmr.2015.3159
  2. Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A.A., Kim, S., Wilson, C.J., Lehar, J., Kryukov, G.V., Sonkin, D., et al. (2012). The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603-607. https://doi.org/10.1038/nature11003
  3. Cancer Genome Atlas Research Network, Weinstein, J.N., Collisson, E.A., Mills, G.B., Shaw, K.R., Ozenberger, B.A., Ellrott, K., Shmulevich, I., Sander, C., and Stuart, J.M. (2013). The Cancer Genome Atlas Pan-cancer analysis project. Nat. Genet. 45, 1113-1120. https://doi.org/10.1038/ng.2764
  4. Carretero, J., Medina, P.P., Pio, R., Montuenga, L.M., and Sanchez-Cespedes, M. (2004). Novel and natural knockout lung cancer cell lines for the LKB1/STK11 tumor suppressor gene. Oncogene 23, 4037-4040. https://doi.org/10.1038/sj.onc.1207502
  5. Chen, L., Engel, B.E., Welsh, E.A., Yoder, S.J., Brantley, S.G., Chen, D.T., Beg, A.A., Cao, C., Kaye, F.J., Haura, E.B., et al. (2016). A sensitive NanoStringbased assay to score STK11 (LKB1) pathway disruption in lung adenocarcinoma. J. Thorac. Oncol. 11, 838-849. https://doi.org/10.1016/j.jtho.2016.02.009
  6. Clough, E. and Barrett, T. (2016). The gene expression omnibus database. In Statistical Genomics, E. Mathe and S. Davis, eds. (New York: Humana Press), pp. 93-110.
  7. Facchinetti, F., Bluthgen, M.V., Tergemina-Clain, G., Faivre, L., Pignon, J.P., Planchard, D., Remon, J., Soria, J.C., Lacroix, L., and Besse, B. (2017). LKB1/STK11 mutations in non-small cell lung cancer patients: descriptive analysis and prognostic value. Lung Cancer 112, 62-68. https://doi.org/10.1016/j.lungcan.2017.08.002
  8. Gao, Y., Xiao, Q., Ma, H., Li, L., Liu, J., Feng, Y., Fang, Z., Wu, J., Han, X., Zhang, J., et al. (2010). LKB1 inhibits lung cancer progression through lysyl oxidase and extracellular matrix remodeling. Proc. Natl. Acad. Sci. U. S. A. 107, 18892-18897. https://doi.org/10.1073/pnas.1004952107
  9. Gill, R.K., Yang, S.H., Meerzaman, D., Mechanic, L.E., Bowman, E.D., Jeon, H.S., Roy Chowdhuri, S., Shakoori, A., Dracheva, T., Hong, K.M., et al. (2011). Frequent homozygous deletion of the LKB1/STK11 gene in non-small cell lung cancer. Oncogene 30, 3784-3791. https://doi.org/10.1038/onc.2011.98
  10. He, N., Kim, N., Song, M., Park, C., Kim, S., Park, E.Y., Yim, H.Y., Kim, K., Park, J.H., and Kim, K.I. (2014). Integrated analysis of transcriptomes of cancer cell lines and patient samples reveals STK11/LKB1-driven regulation of cAMP phosphodiesterase-4D. Mol. Cancer Ther. 13, 2463-2473. https://doi.org/10.1158/1535-7163.MCT-14-0297
  11. Ho, J.A., Chang, H.C., Shih, N.Y., Wu, L.C., Chang, Y.F., Chen, C.C., and Chou, C. (2010). Diagnostic detection of human lung cancer-associated antigen using a gold nanoparticle-based electrochemical immunosensor. Anal. Chem. 82, 5944-5950. https://doi.org/10.1021/ac1001959
  12. Isgro, M.A., Bottoni, P., and Scatena, R. (2015). Neuron-specific enolase as a biomarker: biochemical and clinical aspects. In Advances in Cancer Biomarkers, R. Scatena, ed. (Dordrecht, The Netherlands: Springer), pp. 125-143.
  13. Kim, N., Yim, H.Y., He, N., Lee, C.J., Kim, J.H., Choi, J.S., Lee, H.S., Kim, S., Jeong, E., and Song, M. (2016). Cardiac glycosides display selective efficacy for STK11 mutant lung cancer. Sci. Rep. 6, 29721. https://doi.org/10.1038/srep29721
  14. Liu, D.W., Chen, S.T., and Liu, H.P. (2005). Choice of endogenous control for gene expression in nonsmall cell lung cancer. Eur. Respir. J. 26, 1002-1008. https://doi.org/10.1183/09031936.05.00050205
  15. Liu, K.J. and Shih, N.Y. (2007). The role of enolase in tissue invasion and metastasis of pathogens and tumor cells. J. Cancer Mol. 3, 45-48.
  16. Lung, J., Chen, K.L., Hung, C.H., Chen, C.C., Hung, M.S., Lin, Y.C., Wu, C.Y., Lee, K.D., Shih, N.Y., and Tsai, Y.H. (2017). In silico-based identification of human ${\alpha}$-enolase inhibitors to block cancer cell growth metabolically. Drug Des. Devel. Ther. 11, 3281-3290. https://doi.org/10.2147/DDDT.S149214
  17. Muller, F.L., Colla, S., Aquilanti, E., Manzo, V.E., Genovese, G., Lee, J., Eisenson, D., Narurkar, R., Deng, P., and Nezi, L. (2012). Passenger deletions generate therapeutic vulnerabilities in cancer. Nature 488, 337-342. https://doi.org/10.1038/nature11331
  18. Novello, S., Barlesi, F., Califano, R., Cufer, T., Ekman, S., Levra, M.G., Kerr, K., Popat, S., Reck, M., Senan, S., et al. (2016). Metastatic non-small-cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 27(Suppl 5), v1-v27. https://doi.org/10.1093/annonc/mdw326
  19. Peshavaria, M. and Day, I.N. (1991). Molecular structure of the human muscle-specific enolase gene (ENO3). Biochem. J. 275, 427-433. https://doi.org/10.1042/bj2750427
  20. Planchard, D., Popat, S., Kerr, K., Novello, S., Smit, E.F., Faivre-Finn, C., Mok, T.S., Reck, M., Van Schil, P.E., Hellmann, M.D., et al. (2018). Metastatic nonsmall cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 29(Suppl 4), iv192-iv237. https://doi.org/10.1093/annonc/mdy275
  21. Song, M., Lee, H., Nam, M.H., Jeong, E., Kim, S., Hong, Y., Kim, N., Yim, H.Y., Yoo, Y.J., Kim, J.S., et al. (2017). Loss-of-function screens of druggable targetome against cancer stem-like cells. FASEB J. 31, 625-635. https://doi.org/10.1096/fj.201600953

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