Melanoblast transcriptome analysis reveals pathways promoting melanoma metastasis View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-01-16

AUTHORS

Kerrie L. Marie, Antonella Sassano, Howard H. Yang, Aleksandra M. Michalowski, Helen T. Michael, Theresa Guo, Yien Che Tsai, Allan M. Weissman, Maxwell P. Lee, Lisa M. Jenkins, M. Raza Zaidi, Eva Pérez-Guijarro, Chi-Ping Day, Kris Ylaya, Stephen M. Hewitt, Nimit L. Patel, Heinz Arnheiter, Sean Davis, Paul S. Meltzer, Glenn Merlino, Pravin J. Mishra

ABSTRACT

Cutaneous malignant melanoma is an aggressive cancer of melanocytes with a strong propensity to metastasize. We posit that melanoma cells acquire metastatic capability by adopting an embryonic-like phenotype, and that a lineage approach would uncover metastatic melanoma biology. Using a genetically engineered mouse model to generate a rich melanoblast transcriptome dataset, we identify melanoblast-specific genes whose expression contribute to metastatic competence and derive a 43-gene signature that predicts patient survival. We identify a melanoblast gene, KDELR3, whose loss impairs experimental metastasis. In contrast, KDELR1 deficiency enhances metastasis, providing the first example of different disease etiologies within the KDELR-family of retrograde transporters. We show that KDELR3 regulates the metastasis suppressor, KAI1, and report an interaction with the E3 ubiquitin-protein ligase gp78, a regulator of KAI1 degradation. Our work demonstrates that the melanoblast transcriptome can be mined to uncover targetable pathways for melanoma therapy. More... »

PAGES

333

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  • Journal

    TITLE

    Nature Communications

    ISSUE

    1

    VOLUME

    11

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41467-019-14085-2

    DOI

    http://dx.doi.org/10.1038/s41467-019-14085-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1124121200

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/31949145


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