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A B cell-derived gene expression signature associates with an immunologically active tumor microenvironment and response to immune checkpoint blockade therapy

Immune checkpoint inhibitors have shown great potential in treating solid tumors, inducing durable remission and prolonged survival time in responders. Despite their promise, a large fraction of patients remains unresponsive to these treatments highlighting the need for biomarkers that can predict p...

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Detalles Bibliográficos
Autores principales: Varn, Frederick S., Wang, Yue, Cheng, Chao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287894/
https://www.ncbi.nlm.nih.gov/pubmed/30546953
http://dx.doi.org/10.1080/2162402X.2018.1513440
Descripción
Sumario:Immune checkpoint inhibitors have shown great potential in treating solid tumors, inducing durable remission and prolonged survival time in responders. Despite their promise, a large fraction of patients remains unresponsive to these treatments highlighting the need for biomarkers that can predict patient sensitivity. Pre-treatment gene expression profiles for patients receiving immune checkpoint inhibitors have recently become available, establishing a new medium by which to discover biomarkers that predict therapy response. In this study, we mined for transcriptomic correlates of response by applying immune cell-derived gene expression signatures to publicly available datasets containing matched gene expression and response efficacy information. These datasets were comprised of urothelial carcinoma patients receiving anti-PD-L1 (n = 25), melanoma patients receiving anti-PD-1 (n = 28), and melanoma patients receiving anti-CTLA-4 (n = 42). We identified one signature, derived from a subpopulation of B cells, with scores that were significantly and reproducibly elevated in patients experiencing clinical benefit following therapy targeting the PD-1/PD-L1 axis and were additionally elevated in patients responsive to anti-CTLA-4 therapy. Multivariate models revealed that this signature was associated with response independent of other response-predictive biomarkers, including tumor mutation burden. Functional annotation of the signature revealed it to be associated with features indicative of an immunologically active microenvironment, including B and T cell activation as well as antigen presentation activity. The preliminary findings presented detail a transcriptomic signature associated with response to multiple checkpoint inhibitors and suggest novel biological associations that warrant further investigation.