The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.
Three subtypes of lung cancer fibroblasts define distinct therapeutic paradigms
Open accessAbstract
Cancer-associated fibroblasts (CAFs) are highly heterogeneous. With the lack of a comprehensive understanding of CAFs' functional distinctions, it remains unclear how cancer treatments could be personalized based on CAFs in a patient's tumor. We have established a living biobank of CAFs derived from biopsies of patients' non-small lung cancer (NSCLC) that encompasses a broad molecular spectrum of CAFs in clinical NSCLC. By functionally interrogating CAF heterogeneity using the same therapeutics received by patients, we identify three functional subtypes: (1) robustly protective of cancers and highly expressing HGF and FGF7; (2) moderately protective of cancers and highly expressing FGF7; and (3) those providing minimal protection. These functional differences among CAFs are governed by their intrinsic TGF-β signaling, which suppresses HGF and FGF7 expression. This CAF functional classification correlates with patients' clinical response to targeted therapies and also associates with the tumor immune microenvironment, therefore providing an avenue to guide personalized 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.
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
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
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
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
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
Inferring tumour purity and stromal and immune cell admixture from expression data
Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013) · Nature Communications · doi:10.1038/ncomms3612 · Cited by 9,778
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
Microenvironmental regulation of tumor progression and metastasis
Quail, D. F., & Joyce, J. A. (2013) · Nature Medicine · doi:10.1038/nm.3394 · Cited by 7,994
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101
ShinyGO: a graphical gene-set enrichment tool for animals and plants
Ge, S. X., Jung, D., & Yao, R. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz931 · Cited by 5,270
The senescence-associated secretory phenotype: the dark side of tumor suppression
Coppé, J.-P., Desprez, P.-Y., Krtolica, A., & Campisi, J. (2010) · Annual Review of Pathology: Mechanisms of Disease · doi:10.1146/annurev-pathol-121808-102144 · Cited by 5,206
Cited by
Cancer-associated fibroblasts: from basic science to anticancer therapy
Yang, D., Liu, J., Qian, H., & Zhuang, Q. (2023) · Experimental & Molecular Medicine · doi:10.1038/s12276-023-01013-0 · Cited by 572
Fibroblasts in cancer: Unity in heterogeneity
Chhabra, Y., & Weeraratna, A. T. (2023) · Cell · doi:10.1016/j.cell.2023.03.016 · Cited by 474
Zhu, G.-Q., Tang, Z., Huang, R., Qu, W.-F., Fang, Y., Yang, R., Tao, C.-Y., Gao, J., Wu, X.-L., Sun, H.-X., et al. (2023) · Cell Discovery · doi:10.1038/s41421-023-00529-z · Cited by 336
Self-citationCords, L., Engler, S., Haberecker, M., Rüschoff, J. H., Moch, H., de Souza, N., & Bodenmiller, B. (2024) · Cancer Cell · doi:10.1016/j.ccell.2023.12.021 · Cited by 304
Enfield, K. S. S., Colliver, E., Lee, C., Magness, A., Moore, D. A., Sivakumar, M., Grigoriadis, K., Pich, O., Karasaki, T., Hobson, P. S., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1380 · Cited by 151
Metadata
- Authors
- Hu, H., Piotrowska, Z., Hare, P. J., Chen, H., Mulvey, H. E., Mayfield, A., Noeen, S., Kattermann, K., Greenberg, M., Williams, A., Riley, A. K., Wilson, J. J., Mao, Y.-Q., Huang, R.-P., Banwait, M. K., Ho, J., Crowther, G. S., Hariri, L. P., Heist, R. S., Kodack, D. P., Pinello, L., Shaw, A. T., Mino-Kenudson, M., Hata, A. N., Sequist, L. V., Benes, C. H., Niederst, M. J., & Engelman, J. A.
- Venue
- Cancer Cell
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 39
- Issue
- 11
- Pages
- 1531-1547.e10
- ISSN
- 1535-6108 (ISSN-L)
- OpenAlex
- W3204600913
- Cited by
- 265
- References
- 94
- FWCI
- 11.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: Three subtypes of lung cancer fibroblasts define distinct therapeutic paradigms. https://app.cochat.ai/papers/10.1016/j.ccell.2021.09.003
- The paper
- Hu, H., Piotrowska, Z., Hare, P. J., Chen, H., Mulvey, H. E., Mayfield, A., Noeen, S., Kattermann, K., Greenberg, M., Williams, A., Riley, A. K., Wilson, J. J., Mao, Y.-Q., Huang, R.-P., Banwait, M. K., Ho, J., Crowther, G. S., Hariri, L. P., Heist, R. S., . . . Engelman, J. A. (2021). Three subtypes of lung cancer fibroblasts define distinct therapeutic paradigms. Cancer Cell. https://doi.org/10.1016/j.ccell.2021.09.003
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.
