The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
Single-cell analyses reveal key immune cell subsets associated with response to PD-L1 blockade in triple-negative breast cancer
Open accessAbstract
In triple-negative breast cancer (TNBC), the benefit of combining chemotherapy with checkpoint inhibitors is still not very clear. We utilize single-cell RNA- and ATAC-sequencing to examine the immune cell dynamics in 22 patients with advanced TNBC treated with paclitaxel or its combination with the anti-PD-L1 atezolizumab. We demonstrate that high levels of baseline CXCL13 + T cells are linked to the proinflammatory features of macrophages and can predict effective responses to the combination therapy. In responsive patients, lymphoid tissue inducer (LTi) cells, follicular B (Bfoc) cells, CXCL13 + T cells, and conventional type 1 dendritic cells (cDC1) concertedly increase following the combination therapy, but instead decrease after paclitaxel monotherapy. Our data highlight the importance of CXCL13 + T cells in effective responses to anti-PD-L1 therapies and suggest that their reduction by paclitaxel regimen may compromise the clinical outcomes of accompanying atezolizumab for TNBC treatment.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These cell subsets show different responses to anti-PD(L)-1 therapies,”
From Contrasting cytotoxic and regulatory T cell responses underlying distinct clinical outcomes to anti-PD-1 plus lenvatinib therapy in cancer (2025) · cites this paper as [19]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Applying ICB and combination therapies as neoadjuvant treatments provides an opportunity to observe immune cell dynamics in tumor microenvironment (TME) with temporal samples.”
From Contrasting cytotoxic and regulatory T cell responses underlying distinct clinical outcomes to anti-PD-1 plus lenvatinib therapy in cancer (2025) · cites this paper as [19]
Experimental — accuracy not yet measured
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
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
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
New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)
Eisenhauer, E. A., Therasse, P., Bogaerts, J., Schwartz, L. H., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., et al. (2009) · European Journal of Cancer · doi:10.1016/j.ejca.2008.10.026 · Cited by 29,534
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
Wang, K., Li, M., & Hakonarson, H. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq603 · Cited by 14,724
The blockade of immune checkpoints in cancer immunotherapy
Pardoll, D. M. (2012) · Nature Reviews Cancer · doi:10.1038/nrc3239 · Cited by 13,838
Comprehensive molecular portraits of human breast tumours
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11412 · Cited by 12,129
A framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R. E., Garimella, K. V., Maguire, J., Hartl, C., Philippakis, A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,508
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Cited by
Sorted by citations
Deciphering breast cancer: from biology to the clinic
Nolan, E., Lindeman, G. J., & Visvader, J. E. (2023) · Cell · doi:10.1016/j.cell.2023.01.040 · Cited by 792
Clinical relevance of tumour-associated macrophages
Pittet, M. J., Michielin, O. A., & Migliorini, D. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-022-00620-6 · Cited by 793
Long, Y., Ang, K. S., Li, M., Chong, K. L. K., Sethi, R., Zhong, C., Xu, H., Ong, Z., Sachaphibulkij, K., Chen, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36796-3 · Cited by 700
Fridman, W. H., Meylan, M., Petitprez, F., Sun, C., Italiano, A., & Sautès‐Fridman, C. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-022-00619-z · Cited by 710
Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance
Chu, Y., Dai, E., Li, Y., Han, G., Pei, G., Ingram, D. R., Thakkar, K., Qin, J., Dang, M., Le, X., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02371-y · Cited by 518
Metadata
- Authors
- Zhang, Y., Chen, H., Mo, H., Hu, X., Gao, R., Zhao, Y., Liu, B., Niu, L., Sun, X., Yu, X., Wang, Y., Chang, Q., Gong, T., Guan, X., Hu, T., Qian, T., Xu, B., Ma, F., Zhang, Z., & Liu, Z.
- Affiliations
- Peking University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Cancer Hospital of Huanxing Chaoyang District Beijing (CN)
- Corresponding author
- Xu, Binghe
- Ma, Fei
- Zhang, Zemin
- Liu, Zhihua
- ORCID
- Venue
- Cancer Cell
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 39
- Issue
- 12
- Pages
- 1578-1593.e8
- ISSN
- 1535-6108 (ISSN-L)
- OpenAlex
- W3205385275
- Cited by
- 783
- References
- 77
- Citations by year
- 2026: 135 · 2025: 206 · 2024: 183 · 2023: 158 · 2022: 93 · 2021: 8
- FWCI
- 41.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cancer Cell (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (31991171, 81988101, 82030089, 91942307)
- Chinese Academy of Meteorological Sciences (2016-I2M-1-001, 2017-I2M-3-004, 2019-I2M-1-003)
- Peking University
- Beijing Municipal Science and Technology Commission (Z201100005320014)
- National Key Research and Development Program of China (2018YFC1312101, 2020YFA0803300)
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Immunology and Microbiology › Immunology › Immune cells in cancer
- Keywords
- triple-negative breast cancer, PD-L1 blockade, atezolizumab, paclitaxel, immune cell dynamics, lymphoid tissue inducer cells, follicular B cells, conventional type 1 dendritic cells, single-cell RNA sequencing, single-cell ATAC-seq
- MeSH terms
- Immune Checkpoint Inhibitors, Female, Humans, Single-Cell Analysis, Triple Negative Breast Neoplasms
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
