The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
B Cells and T Follicular Helper Cells Mediate Response to Checkpoint Inhibitors in High Mutation Burden Mouse Models of Breast Cancer
Open accessAbstract
Summary: This study identifies mechanisms mediating response to immune checkpoint inhibitors using mouse models of triple negative breast cancer. By creating new mammary tumor models, we find that tumor mutation burden and specific immune cells associate with response. Further, we developed a rich resource of single cell RNA-seq and bulk mRNA-seq data of immunotherapy treated and non-treated tumors from sensitive and resistant murine models. Using this, we uncover immune checkpoint therapy induces T follicular helper cell activation of B cells to facilitate the anti-tumor response in these models. We also show B cell activation of T cells and generation of antibody as key to response and propose a new biomarker for immune checkpoint therapy. In total, this work presents resources of new preclinical models of breast cancer with large mRNA-seq and single-cell seq datasets annotated for sensitivity to therapy and uncovers new components of response to immune checkpoint inhibitors.
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
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
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F.-A., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E.-M., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,845
Robust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 13,224
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
Nishimura, R., Osako, T., Nishiyama, Y., Tashima, R., Nakano, M., Fujisue, M., Toyozumi, Y., & Arima, N. (2013) · Oncology · doi:10.1159/000353099 · Cited by 13,166
Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700
Safety, activity, and immune correlates of anti–PD-1 antibody in cancer
Topalian, S. L., Hodi, F. S., Brahmer, J. R., Gettinger, S. N., Smith, D. C., McDermott, D. F., Powderly, J. D., Carvajal, R. D., Sosman, J. A., Atkins, M. B., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1200690 · Cited by 12,628
Comprehensive molecular portraits of human breast tumours
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11412 · Cited by 12,129
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F., Ben Miled, S., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,803
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
Cited by
Zhang, Y., Chen, H., Mo, H., Hu, X., Gao, R., Zhao, Y., Liu, B., Niu, L., Sun, X., Yu, X., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.09.010 · Cited by 783
Yang, F., Xiao, Y., Ding, J.-H., Jin, X., Ma, D., Li, D.-Q., Shi, J.-X., Huang, W., Wang, Y.-P., Jiang, Y.-Z., et al. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2022.09.021 · Cited by 624
Cytotoxic CD4+ T cells in cancer: Expanding the immune effector toolbox
Oh, D. Y., & Fong, L. (2021) · Immunity · doi:10.1016/j.immuni.2021.11.015 · Cited by 467
“, 2018), there is evidence to the contrary that Tfh cells can mediate response to checkpoint inhibition in genetically engineered mouse models (Hollern et al., 2019).”
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
Overacre-Delgoffe, A. E., Bumgarner, H. J., Cillo, A. R., Burr, A. H. P., Tometich, J. T., Bhattacharjee, A., Bruno, T. C., Vignali, D. A. A., & Hand, T. W. (2021) · Immunity · doi:10.1016/j.immuni.2021.11.003 · Cited by 318
Metadata
- Authors
- Hollern, D. P., Xu, N., Thennavan, A., Glodowski, C., Garcia-Recio, S., Mott, K. R., He, X., Garay, J. P., Carey-Ewend, K., Marron, D., Ford, J., Liu, S., Vick, S. C., Martin, M., Parker, J. S., Vincent, B. G., Serody, J. S., & Perou, C. M.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 179
- Issue
- 5
- Pages
- 1191-1206.e21
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W2982897711
- Cited by
- 478
- References
- 79
- FWCI
- 21.49
- Type
- journal article
- Language
- en
- Open access
- bronze
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 and T Follicular Helper Cells Mediate Response to Checkpoint Inhibitors in High Mutation Burden Mouse Models of Breast Cancer. https://app.cochat.ai/papers/10.1016/j.cell.2019.10.028
- The paper
- Hollern, D. P., Xu, N., Thennavan, A., Glodowski, C., Garcia-Recio, S., Mott, K. R., He, X., Garay, J. P., Carey-Ewend, K., Marron, D., Ford, J., Liu, S., Vick, S. C., Martin, M., Parker, J. S., Vincent, B. G., Serody, J. S., & Perou, C. M. (2019). B Cells and T Follicular Helper Cells Mediate Response to Checkpoint Inhibitors in High Mutation Burden Mouse Models of Breast Cancer. Cell. https://doi.org/10.1016/j.cell.2019.10.028
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.
