The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index.
B cells sustain inflammation and predict response to immune checkpoint blockade in human melanoma
Open accessAbstract
Tumor associated inflammation predicts response to immune checkpoint blockade in human melanoma. Current theories on regulation of inflammation center on anti-tumor T cell responses. Here we show that tumor associated B cells are vital to melanoma associated inflammation. Human B cells express pro- and anti-inflammatory factors and differentiate into plasmablast-like cells when exposed to autologous melanoma secretomes in vitro. This plasmablast-like phenotype can be reconciled in human melanomas where plasmablast-like cells also express T cell-recruiting chemokines CCL3, CCL4, CCL5. Depletion of B cells in melanoma patients by anti-CD20 immunotherapy decreases tumor associated inflammation and CD8 + T cell numbers. Plasmablast-like cells also increase PD-1 + T cell activation through anti-PD-1 blockade in vitro and their frequency in pretherapy melanomas predicts response and survival to immune checkpoint blockade. Tumor associated B cells therefore orchestrate and sustain melanoma inflammation and may represent a predictor for survival and response to immune checkpoint blockade therapy.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
Liu, J., Wang, H., Yu, Q., Zheng, S., Jiang, Y., Liu, Y., Yuan, G., & Qiu, L. (2015) · Human Immunology · doi:10.1016/j.humimm.2015.10.015 · Cited by 48
RetractedCytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
Cerami, E., Gao, J., Dogrusoz, U., Gross, B. E., Sumer, S. O., Aksoy, B. A., Jacobsen, A., Byrne, C. J., Heuer, M. L., Larsson, E., et al. (2012) · Cancer Discovery · doi:10.1158/2159-8290.cd-12-0095 · Cited by 17,455
GSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440
Tissue-based map of the human proteome
Uhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., Sivertsson, Å., Kampf, C., Sjöstedt, E., Asplund, A., et al. (2015) · Science · doi:10.1126/science.1260419 · Cited by 15,286
The Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Universal sample preparation method for proteome analysis
Wiśniewski, J. R., Zougman, A., Nagaraj, N., & Mann, M. (2009) · Nature Methods · doi:10.1038/nmeth.1322 · Cited by 8,352
Cited by
B cells and tertiary lymphoid structures promote immunotherapy response
Helmink, B. A., Reddy, S. M., Gao, J., Zhang, S., Basar, R., Thakur, R., Yizhak, K., Sade-Feldman, M., Blando, J., Han, G., et al. (2020) · Nature · doi:10.1038/s41586-019-1922-8 · Cited by 2,731
Top 10 Challenges in Cancer Immunotherapy
Hegde, P. S., & Chen, D. S. (2020) · Immunity · doi:10.1016/j.immuni.2019.12.011 · Cited by 2,238
Neoadjuvant checkpoint blockade for cancer immunotherapy
Topalian, S. L., Taube, J. M., & Pardoll, D. M. (2020) · Science · doi:10.1126/science.aax0182 · Cited by 1,001
van Dijk, N., Gil-Jimenez, A., Silina, K., Hendricksen, K., Smit, L. A., de Feijter, J. M., van Montfoort, M. L., van Rooijen, C., Peters, D., Broeks, A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1085-z · Cited by 404
Moving towards personalized treatments of immune-related adverse events
Esfahani, K., Elkrief, A., Calabrese, C., Lapointe, R., Hudson, M., Routy, B., Miller, W. H., & Calabrese, L. (2020) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-020-0352-8 · Cited by 351
Metadata
- Authors
- Griss, J., Bauer, W., Wagner, C., Simon, M., Chen, M., Grabmeier-Pfistershammer, K., Maurer-Granofszky, M., Roka, F., Penz, T., Bock, C., Zhang, G., Herlyn, M., Glatz, K., Läubli, H., Mertz, K. D., Petzelbauer, P., Wiesner, T., Hartl, M., Pickl, W. F., Somasundaram, R., Steinberger, P., & Wagner, S. N.
- Venue
- Nature Communications
- Published
- 2019-09-13
- Volume
- 10
- Issue
- 1
- Pages
- 4186-4186
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2973051889
- Cited by
- 369
- References
- 93
- FWCI
- 15.14
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: B cells sustain inflammation and predict response to immune checkpoint blockade in human melanoma. https://app.cochat.ai/papers/10.1038/s41467-019-12160-2
- The paper
- Griss, J., Bauer, W., Wagner, C., Simon, M., Chen, M., Grabmeier-Pfistershammer, K., Maurer-Granofszky, M., Roka, F., Penz, T., Bock, C., Zhang, G., Herlyn, M., Glatz, K., Läubli, H., Mertz, K. D., Petzelbauer, P., Wiesner, T., Hartl, M., Pickl, W. F., . . . Wagner, S. N. (2019). B cells sustain inflammation and predict response to immune checkpoint blockade in human melanoma. Nature Communications. https://doi.org/10.1038/s41467-019-12160-2
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.