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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



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.


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.


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.


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.


BRAFV600E Vemurafenib Network medicine Prediction of response 


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

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Supplementary DataS2.
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Supplementary DataS3.
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Supplementary DataS4.
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Supplementary DataS5.
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Supplementary DataS1.


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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

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