Multimodel preclinical platform predicts clinical response of melanoma to immunotherapy View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-04-13

AUTHORS

Eva Pérez-Guijarro, Howard H. Yang, Romina E. Araya, Rajaa El Meskini, Helen T. Michael, Suman Kumar Vodnala, Kerrie L. Marie, Cari Smith, Sung Chin, Khiem C. Lam, Andres Thorkelsson, Anthony J. Iacovelli, Alan Kulaga, Anyen Fon, Aleksandra M. Michalowski, Willy Hugo, Roger S. Lo, Nicholas P. Restifo, Shyam K. Sharan, Terry Van Dyke, Romina S. Goldszmid, Zoe Weaver Ohler, Maxwell P. Lee, Chi-Ping Day, Glenn Merlino

ABSTRACT

Although immunotherapy has revolutionized cancer treatment, only a subset of patients demonstrate durable clinical benefit. Definitive predictive biomarkers and targets to overcome resistance remain unidentified, underscoring the urgency to develop reliable immunocompetent models for mechanistic assessment. Here we characterize a panel of syngeneic mouse models, representing a variety of molecular and phenotypic subtypes of human melanomas and exhibiting their diverse range of responses to immune checkpoint blockade (ICB). Comparative analysis of genomic, transcriptomic and tumor-infiltrating immune cell profiles demonstrated alignment with clinical observations and validated the correlation of T cell dysfunction and exclusion programs with resistance. Notably, genome-wide expression analysis uncovered a melanocytic plasticity signature predictive of patient outcome in response to ICB, suggesting that the multipotency and differentiation status of melanoma can determine ICB benefit. Our comparative preclinical platform recapitulates melanoma clinical behavior and can be employed to identify mechanisms and treatment strategies to improve patient care. More... »

PAGES

781-791

References to SciGraph publications

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  • Journal

    TITLE

    Nature Medicine

    ISSUE

    5

    VOLUME

    26

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41591-020-0818-3

    DOI

    http://dx.doi.org/10.1038/s41591-020-0818-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1126634024

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/32284588


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