The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Immune Cell–Poor Melanomas Benefit from PD-1 Blockade after Targeted Type I IFN Activation
Abstract
Infiltration of human melanomas with cytotoxic immune cells correlates with spontaneous type I IFN activation and a favorable prognosis. Therapeutic blockade of immune-inhibitory receptors in patients with preexisting lymphocytic infiltrates prolongs survival, but new complementary strategies are needed to activate cellular antitumor immunity in immune cell–poor melanomas. Here, we show that primary melanomas in Hgf-Cdk4R24C mice, which imitate human immune cell–poor melanomas with a poor outcome, escape IFN-induced immune surveillance and editing. Peritumoral injections of immunostimulatory RNA initiated a cytotoxic inflammatory response in the tumor microenvironment and significantly impaired tumor growth. This critically required the coordinated induction of type I IFN responses by dendritic, myeloid, natural killer, and T cells. Importantly, antibody-mediated blockade of the IFN-induced immune-inhibitory interaction between PD-L1 and PD-1 receptors further prolonged the survival. These results highlight important interconnections between type I IFNs and immune-inhibitory receptors in melanoma pathogenesis, which serve as targets for combination immunotherapies. Significance: Using a genetically engineered mouse melanoma model, we demonstrate that targeted activation of the type I IFN system with immunostimulatory RNA in combination with blockade of immune-inhibitory receptors is a rational strategy to expose immune cell–poor tumors to cellular immune surveillance. Cancer Discov; 4(6); 674–87. ©2014 AACR. This article is highlighted in the In This Issue feature, p. 621
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The blockade of immune checkpoints in cancer immunotherapy
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Safety and Tumor Responses with Lambrolizumab (Anti–PD-1) in Melanoma
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Interferons, immunity and cancer immunoediting
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PD-1 blockade induces responses by inhibiting adaptive immune resistance
Tumeh, P. C., Harview, C. L., Yearley, J. H., Shintaku, I. P., Taylor, E. J. M., Robert, L., Chmielowski, B., Spasic, M., Henry, G., Ciobanu, V., et al. (2014) · Nature · doi:10.1038/nature13954 · Cited by 6,609
Pembrolizumab for the Treatment of Non–Small-Cell Lung Cancer
Garon, E. B., Rizvi, N. A., Hui, R., Leighl, N., Balmanoukian, A. S., Eder, J. P., Patnaik, A., Aggarwal, C., Gubens, M., Horn, L., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1501824 · Cited by 6,198
IFN-γ–related mRNA profile predicts clinical response to PD-1 blockade
Ayers, M., Lunceford, J., Nebozhyn, M., Murphy, E., Loboda, A., Kaufman, D. R., Albright, A., Cheng, J. D., Kang, S. P., Shankaran, V., et al. (2017) · Journal of Clinical Investigation · doi:10.1172/jci91190 · Cited by 3,788
The immune contexture in cancer prognosis and treatment
Fridman, W. H., Zitvogel, L., Sautès–Fridman, C., & Kroemer, G. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.101 · Cited by 2,263
Cancer immunoediting and resistance to T cell-based immunotherapy
O’Donnell, J. S., Teng, M. W. L., & Smyth, M. J. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-018-0142-8 · Cited by 1,759
Metadata
- Authors
- Bald, T., Landsberg, J., Lopez-Ramos, D., Renn, M., Glodde, N., Jansen, P., Gaffal, E., Steitz, J., Tolba, R., Kalinke, U., Limmer, A., Jönsson, G., Hölzel, M., & Tüting, T.
- Venue
- Cancer Discovery
- Published
- 2014-06-01
- Volume
- 4
- Issue
- 6
- Pages
- 674-687
- ISSN
- 2159-8274 (ISSN-L)
- 2159-8290
- OpenAlex
- W2147598914
- Cited by
- 256
- References
- 48
- FWCI
- 12.21
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bald, T., Landsberg, J., Lopez-Ramos, D., Renn, M., Glodde, N., Jansen, P., Gaffal, E., Steitz, J., Tolba, R., Kalinke, U., Limmer, A., Jönsson, G., Hölzel, M., & Tüting, T. (2014). Immune Cell–Poor Melanomas Benefit from PD-1 Blockade after Targeted Type I IFN Activation. Cancer Discovery. https://doi.org/10.1158/2159-8290.cd-13-0458
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.