Advertisement

Endocrine

pp 1–8 | Cite as

BRAFV600E-mutant cancers display a variety of networks by SWIM analysis: prediction of vemurafenib clinical response

  • Rosa Falcone
  • Federica Conte
  • Giulia Fiscon
  • Valeria Pecce
  • Marialuisa Sponziello
  • Cosimo Durante
  • Lorenzo Farina
  • Sebastiano Filetti
  • Paola PaciEmail author
  • Antonella Verrienti
Original Article
  • 68 Downloads

Abstract

Purpose

Several studies have shown that different tumour types sharing a driver gene mutation do not respond uniformly to the same targeted agent. Our aim was to use an unbiased network-based approach to investigate this fundamental issue using BRAFV600E mutant tumours and the BRAF inhibitor vemurafenib.

Methods

We applied SWIM, a software able to identify putative regulatory (switch) genes involved in drastic changes to the cell phenotype, to gene expression profiles of different BRAFV600E mutant cancers and their normal counterparts in order to identify the switch genes that could potentially explain the heterogeneity of these tumours’ responses to vemurafenib.

Results

We identified lung adenocarcinoma as the tumour with the highest number of switch genes (298) compared to its normal counterpart. By looking for switch genes encoding for kinases with homology sequences similar to known vemurafenib targets, we found that thyroid cancer and lung adenocarcinoma have a similar number of putative targetable switch gene kinases (5 and 6, respectively) whereas colorectal cancer has just one.

Conclusions

We are persuaded that our network analysis may aid in the comprehension of molecular mechanisms underlying the different responses to vemurafenib in BRAFV600E mutant tumours.

Keywords

BRAFV600E Vemurafenib Network medicine Prediction of response 

Notes

Author contributions

S.F., A.V., and L.F. conceived and designed the project. P.P. developed the software. F.C. and G.F. performed computational data analysis. A.V., R.F., V.P., and M.S. performed biological analysis. R.F. wrote the manuscript. C.D. prepared figures and tables. All authors have read and approved the final article.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the author.

Supplementary material

12020_2019_1890_MOESM1_ESM.docx (17 kb)
Supplementary DataS2.
12020_2019_1890_MOESM2_ESM.docx (16 kb)
Supplementary DataS3.
12020_2019_1890_MOESM3_ESM.xlsx (115 kb)
Supplementary DataS4.
12020_2019_1890_MOESM4_ESM.xlsx (21 kb)
Supplementary DataS5.
12020_2019_1890_MOESM5_ESM.xlsx (10 kb)
Supplementary DataS1.

References

  1. 1.
    M. Greaves, C. Maley, Clonal evolution in cancer. Nature 481(7381), 306–313 (2012)CrossRefGoogle Scholar
  2. 2.
    H. Davies, G.R. Bignell, C. Cox, P. Stephens, S. Edkins, S. Clegg, J. Teague, H. Woffendin, M.J. Garnett, W. Bottomley, N. Davis, E. Dicks, R. Ewing, Y. Floyd, K. Gray, S. Hall, R. Hawes, J. Hughes, V. Kosmidou, A. Menzies, S. Hooper, R. Wilson, H. Jayatilake, B.A. Gusterson, M.R. Stratton, P.A. Futreal, Mutations of the BRAF gene in human cancer. Nature 417(6892), 949–954 (2002)CrossRefGoogle Scholar
  3. 3.
    B. Falini, M.P. Martelli, E. Tiacci, Review article BRAF V600E mutation in hairy cell leukemia: from bench to bedside. Blood 128(15), 1918–1928 (2019)CrossRefGoogle Scholar
  4. 4.
    P.A. Ascierto, J.M. Kirkwood, J. Grob, E. Simeone, A.M. Grimaldi, M. Maio, G. Palmieri, A. Testori, F.M. Marincola, N. Mozzillo, The role of BRAF V600 mutation in melanoma. J Transl Med. 10, 85 (2012)Google Scholar
  5. 5.
    S. Kopetz, J. Desai, E. Chan, J.R. Hecht, P.J. O’Dwyer, D. Maru, V. Morris, F. Janku, A. Dasari, W. Chung, J.P.J. Issa, P. Gibbs, B. James, G. Powis, K.B. Nolop, S. Bhattacharya, L. Saltz, Phase II pilot study of vemurafenib in patients with metastatic BRAF-mutated colorectal cancer. J. Clin. Oncol. 33(34), 4032–4038 (2015)CrossRefGoogle Scholar
  6. 6.
    G. Safaee Ardekani, S.M. Jafarnejad, L. Tan, A. Saeedi, G. Li, The prognostic value of BRAF mutation in colorectal cancer and melanoma: a systematic review and meta-analysis. PLoS ONE 7(10), e47054 (2012)CrossRefGoogle Scholar
  7. 7.
    G. Tallini, D. De Biase, C. Durante, G. Acquaviva, M. Bisceglia, R. Bruno, M.L. Bacchi Reggiani, G.P. Casadei, G. Costante, N. Cremonini, L. Lamartina, D. Meringolo, F. Nardi, A. Pession, K.J. Rhoden, G. Ronga, M. Torlontano, A. Verrienti, M. Visani, S. Filetti, BRAF V600E and risk stratification of thyroid microcarcinoma: a multicenter pathological and clinical study. Mod. Pathol. 28(10), 1343–1359 (2015)CrossRefGoogle Scholar
  8. 8.
    E. Puxeddu, S. Filetti, BRAF mutation assessment in papillary thyroid cancer: are we ready to use it in clinical practice? Endocrine 45(3), 341–343 (2014)CrossRefGoogle Scholar
  9. 9.
    M. Xing, A.S. Alzahrani, K.A. Carson, Y.K. Shong, T.Y. Kim, D. Viola, R. Elisei, B. Bendlová, L. Yip, C. Mian, F. Vianello, R.M. Tuttle, E. Robenshtok, J.A. Fagin, E. Puxeddu, L. Fugazzola, A. Czarniecka, B. Jarzab, C.J. O’Neill, M.S. Sywak, A.K. Lam, G. Riesco-Eizaguirre, P. Santisteban, H. Nakayama, R. Clifton-Bligh, G. Tallini, E.H. Holt, V. Sýkorová, Association between BRAF V600E mutation and recurrence of papillary thyroid cancer. J. Clin. Oncol. 33(1), 42–50 (2015)CrossRefGoogle Scholar
  10. 10.
    E. Puxeddu, C. Durante, N. Avenia, S. Filetti, D. Russo, Clinical implications of BRAF mutation in thyroid carcinoma. Trends Endocrinol. Metab. 19(4), 138–145 (2008)CrossRefGoogle Scholar
  11. 11.
    C. Durante, E. Puxeddu, E. Ferretti, R. Morisi, S. Moretti, R. Bruno, F. Barbi, N. Avenia, A. Scipioni, A. Verrienti, E. Tosi, A. Cavaliere, A. Gulino, S. Filetti, D. Russo, Brief report: BRAF mutations in papillary thyroid carcinomas inhibit genes involved in iodine metabolism. J. Clin. Endocrinol. Metab. 92(7), 2840–2843 (2007)CrossRefGoogle Scholar
  12. 12.
    T. Zhang, G. Zhu, M. Xing, Regulation of mutant TERT by BRAF V600E/MAP kinase pathway through FOS/GABP in human cancer. Nat. Commun. 9, 579 (2018)CrossRefGoogle Scholar
  13. 13.
    S.W. Cho, J.H. Moon, Effects of coexistent BRAF V600E and TERT promoter mutations on poor clinical outcomes in papillary thyroid cancer. Thyroid 27(5), 651–660 (2017)CrossRefGoogle Scholar
  14. 14.
    V. Maggisano, M. Celano, G.E. Lombardo, S.M. Lepore, M. Sponziello, F. Rosignolo, A. Verrienti, F. Baldan, E. Puxeddu, C. Durante, S. Filetti, G. Damante, D. Russo, S. Bulotta, Silencing of hTERT blocks growth and migration of anaplastic thyroid cancer cells. Mol. Cell. Endocrinol. 448, 34–40 (2017)CrossRefGoogle Scholar
  15. 15.
    P.B. Chapman, A. Hauschild, C. Robert, J.B. Haanen, P. Ascierto, J. Larkin, R. Dummer, C. Garbe, A. Testori, M. Maio, D. Hogg, P. Lorigan, C. Lebbe, T. Jouary, D. Schadendorf, A. Ribas, S.J. O’Day, J.A. Sosman, J.M. Kirkwood, A.M.M. Eggermont, B. Dreno, K. Nolop, J. Li, B. Nelson, J. Hou, R.J. Lee, K.T. Flaherty, G.A. McArthur, Improved Survival with Vemurafenib in Melanoma with BRAF V600E Mutation. N. Engl. J. Med. 364(26), 2507–2516 (2011)CrossRefGoogle Scholar
  16. 16.
    E. Tiacci, J.H. Park, L. De Carolis, S.S. Chung, A. Broccoli, S. Scott, F. Zaja, S. Devlin, A. Pulsoni, Y.R. Chung, M. Cimminiello, E. Kim, D. Rossi, R.M. Stone, G. Motta, A. Saven, M. Varettoni, J.K. Altman, A. Anastasia, M.R. Grever, A. Ambrosetti, K.R. Rai, V. Fraticelli, M.E. Lacouture, A.M. Carella, R.L. Levine, P. Leoni, A. Rambaldi, F. Falzetti, S. Ascani, M. Capponi, M.P. Martelli, C.Y. Park, S.A. Pileri, N. Rosen, R. Foà, M.F. Berger, P.L. Zinzani, O. Abdel-Wahab, B. Falini, M.S. Tallman, Targeting mutant BRAF in relapsed or refractory hairy-cell leukemia. N. Engl. J. Med. 373(18), 1733–1747 (2015)CrossRefGoogle Scholar
  17. 17.
    D. Planchard, T.M. Kim, J. Mazieres, E. Quoix, G. Riely, F. Barlesi, P.-J. Souquet, E.F. Smit, H.J.M. Groen, R.J. Kelly, B.C. Cho, M.A. Socinski, L. Pandite, C. Nase, B. Ma, A. D’Amelio, B. Mookerjee, C.M. Curtis, B.E. Johnson, Dabrafenib in patients with BRAFV600E-positive advanced non-small-cell lung cancer: a single-arm, multicentre, open-label, phase 2 trial. Lancet Oncol. 17(5), 642–650 (2016)CrossRefGoogle Scholar
  18. 18.
    M.S. Brose, M.E. Cabanillas, E.E.W. Cohen, L.J. Wirth, T. Riehl, H. Yue, S.I. Sherman, E.J. Sherman, Vemurafenib in patients with BRAFV600E-positive metastatic or unresectable papillary thyroid cancer refractory to radioactive iodine: a non-randomised, multicentre, open-label, phase 2 trial. Lancet Oncol. 17(9), 1272–1282 (2016)CrossRefGoogle Scholar
  19. 19.
    P. Paci, T. Colombo, G. Fiscon, A. Gurtner, G. Pavesi, L. Farina, SWIM: a computational tool to unveiling crucial nodes in complex biological networks. Sci. Rep. 7, 44797 (2017)CrossRefGoogle Scholar
  20. 20.
    R.C. Edgar, MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32(5), 1792–1797 (2004)CrossRefGoogle Scholar
  21. 21.
    S. Bulotta, M. Celano, G. Costante, D. Russo, Emerging strategies for managing differentiated thyroid cancers refractory to radioiodine. Endocrine 52(2), 214–221 (2015)CrossRefGoogle Scholar
  22. 22.
    S.P. Weitzman, S.I. Sherman, Novel drug treatments of progressive radioiodine-refractory differentiated thyroid cancer. Endocrinol Metab Clin North Am. 48(1), 253–268 (2018)CrossRefGoogle Scholar
  23. 23.
    J.C.R. Fernandes, S.M. Acuña, J.I. Aoki, L.M. Floeter-winter, S.M. Muxel, Long non-coding RNAs in the regulation of gene expression: physiology and disease. Noncoding RNA. 5(1) (2019).  https://doi.org/10.3390/ncrna5010017
  24. 24.
    L. Poliseno, L. Salmena, J. Zhang, B. Carver, J. William, P.P. Pandolfi, A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature 465(7301), 1033–1038 (2011)CrossRefGoogle Scholar
  25. 25.
    L. Xiao-jie, G. Ai-mei, J. Li-juan, X. Jiang, Pseudogene in cancer: real functions and promising signature. J Med Genet. 52(1), 17–24 (2015)CrossRefGoogle Scholar
  26. 26.
    J.R. Kho, HHS public access. Pac Symp Biocomput. 23, 536–547 (2018)Google Scholar
  27. 27.
    C.W. Chien, P.C. Hou, H.C. Wu, Y.L. Chang, S.C. Lin, S.C. Lin, B.W. Lin, J.C. Lee, Y.J. Chang, H.S. Sun, S.J. Tsai, Targeting TYRO3 inhibits epithelial-mesenchymal transition and increases drug sensitivity in colon cancer. Oncogene 35(45), 5872–5881 (2016)CrossRefGoogle Scholar
  28. 28.
    S. Hu, D. Liu, R.P. Tufano, K.A. Carson, E. Rosenbaum, Y. Cohen, E.H. Holt, K. Kiseljak-Vassiliades, K.J. Rhoden, S. Tolaney, S. Condouris, G. Tallini, W.H. Westra, C.B. Umbricht, M.A. Zeiger, J.A. Califano, V. Vasko, M. Xing, Association of aberrant methylation of tumor suppressor genes with tumor aggressiveness and BRAF mutation in papillary thyroid cancer. Int. J. Cancer 119(10), 2322–2329 (2006)CrossRefGoogle Scholar
  29. 29.
    J.J. Li, Z.J. Sun, Y.M. Yuan, F.F. Yin, Y.G. Bian, L.Y. Long, X.L. Zhang, D. Xie, EphB3 stimulates cell migration and metastasis in a kinase-dependent manner through Vav2-Rho GTPase axis in papillary thyroid cancer. J. Biol. Chem. 292(3), 1112–1121 (2017)CrossRefGoogle Scholar
  30. 30.
    S. Lee, Y. Jin, Y.A. Jeong, J. Lim, S. Cheul, Upregulation of EphB3 in gastric cancer with acquired resistance to a FGFR inhibitor. Int. J. Biochem. Cell. Biol. 102, 128–137 (2018)CrossRefGoogle Scholar
  31. 31.
    Z. Xuan, J. Huang, L. Gao, Y. Wang, J. Wang, Receptor tyrosine kinase EphB3: a prognostic indicator in colorectal carcinoma. Pathol Oncol Res. (2018).  https://doi.org/10.1007/s12253-018-0562-x
  32. 32.
    M.C. Palumbo, S. Zenoni, M. Fasoli, M. Massonnet, L. Farina, F. Castiglione, M. Pezzotti, P. Paci, Integrated network analysis identifies fight-club nodes as a class of hubs encompassing key putative switch genes that induce major transcriptome reprogramming during grapevine development. Plant Cell Online 26(12), 4617–4635 (2014)CrossRefGoogle Scholar
  33. 33.
    G. Fiscon, F. Conte, V. Licursi, S. Nasi, P. Paci, Computational identification of specific genes for glioblastomastem-like cells identity. Sci. Rep. 8(1), 41598 (2018)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Translational and Precision MedicineSapienza University of RomeRomeItaly
  2. 2.Institute for Systems Analysis and Computer Science “Antonio Ruberti”National Research CouncilRomeItaly
  3. 3.ACT Operations Research, Research & DevelopmentRomaItaly
  4. 4.Department of Computer, Control and Management EngineeringSapienza University of RomeRomeItaly

Personalised recommendations