A web platform for the network analysis of high-throughput data in melanoma and its use to investigate mechanisms of resistance to anti-PD1 immunotherapy

Florian S. Dreyer, Martina Cantone, Martin Eberhardt, Tanushree Jaitly, Lisa Walter, Jürgen Wittmann, Shailendra K. Gupta, Faiz M. Khan, Olaf Wolkenhauer, Brigitte M. Pützer, Hans Martin Jäck, Lucie Heinzerling, Julio Vera*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    21 Scopus citations

    Abstract

    Cellular phenotypes are established and controlled by complex and precisely orchestrated molecular networks. In cancer, mutations and dysregulations of multiple molecular factors perturb the regulation of these networks and lead to malignant transformation. High-throughput technologies are a valuable source of information to establish the complex molecular relationships behind the emergence of malignancy, but full exploitation of this massive amount of data requires bioinformatics tools that rely on network-based analyses. In this report we present the Virtual Melanoma Cell, an online tool developed to facilitate the mining and interpretation of high-throughput data on melanoma by biomedical researches. The platform is based on a comprehensive, manually generated and expert-validated regulatory map composed of signaling pathways important in malignant melanoma. The Virtual Melanoma Cell is a tool designed to accept, visualize and analyze user-generated datasets. It is available at: https://www.vcells.net/melanoma. To illustrate the utilization of the web platform and the regulatory map, we have analyzed a large publicly available dataset accounting for anti-PD1 immunotherapy treatment of malignant melanoma patients.

    Original languageEnglish
    Pages (from-to)2315-2328
    Number of pages14
    JournalBiochimica et Biophysica Acta - Molecular Basis of Disease
    Volume1864
    Issue number6
    DOIs
    StatePublished - Jun 2018

    Keywords

    • Databases
    • Immunotherapy
    • Melanoma
    • Network genomics
    • Systems biology
    • Therapy resistance

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