The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Targeting Tumor-Associated Fibroblasts for Therapeutic Delivery in Desmoplastic Tumors
Open accessPublished version of a preprint
Abstract
The off-target distribution of anticancer nanoparticles to fibroblasts creates a barrier to the effective treatment of desmoplastic tumors. However, we hypothesized that this nanoparticle detriment might be exploited to target the expression of secreted cytotoxic proteins from tumor-associated fibroblasts (TAF) as an anticancer strategy. In addressing this hypothesis, plasmids encoding the secretable TNF-related factor sTRAIL were loaded into lipid-coated protamine DNA complexes and administered by infusion in a murine xenograft model of human desmoplastic bladder carcinoma. Three doses were sufficient to generate approximately 70% of TAFs as sTRAIL-producing cells. sTRAIL triggered apoptosis in tumor cell nests adjacent to TAFs. Furthermore, it reverted residual fibroblasts to a quiescent state due to insufficient activation, further compromising tumor growth and remodeling the microenvironment to favor second-wave nanotherapy. We confirmed the efficacy of this strategy in an orthotopic xenograft model of human pancreatic cancer, where the desmoplastic stroma is well known to be a major barrier to the delivery of therapeutic nanoparticles. Collectively, our results offer a proof of concept for the use of nanoparticles to modify TAFs as an effective strategy to treat desmoplastic cancers. Cancer Res; 77(3); 719–31. ©2016 AACR.
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 cited source says what the claim attributes to it.
In the citing paper
“Thus the use of NP to modify CAFs can be an effective strategy to treat desmoplastic cancers (Fig. 2b) [83].”
From Tumor microenvironment complexity and therapeutic implications at a glance (2020) · cites this paper as [83]
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
Normalization of Tumor Vasculature: An Emerging Concept in Antiangiogenic Therapy
Jain, R. K. (2005) · Science · doi:10.1126/science.1104819 · Cited by 5,527 · cited 3×
“Secondly, the reduction of the fibrotic content, which decompresses the intratumoral vasculature, creates a window for the second-wave nanotherapy (9,34).”
The extracellular matrix: A dynamic niche in cancer progression
Lu, P., Weaver, V. M., & Werb, Z. (2012) · Journal of Cell Biology · doi:10.1083/jcb.201102147 · Cited by 3,024
Accumulation of sub-100 nm polymeric micelles in poorly permeable tumours depends on size
Cabral, H., Matsumoto, Y., Mizuno, K., Chen, Q., Murakami, M., Kimura, M., Terada, Y., Kano, M. R., Miyazono, K., Uesaka, M., et al. (2011) · Nature Nanotechnology · doi:10.1038/nnano.2011.166 · Cited by 2,429
Moffitt, R. A., Marayati, R., Flate, E. L., Volmar, K. E., Loeza, S. G. H., Hoadley, K. A., Rashid, N. U., Williams, L. A., Eaton, S. C., Chung, A. H., et al. (2015) · Nature Genetics · doi:10.1038/ng.3398 · Cited by 2,194
Tumour micro-environment elicits innate resistance to RAF inhibitors through HGF secretion
Straussman, R., Morikawa, T., Shee, K., Barzily-Rokni, M., Qian, Z. R., Du, J., Davis, A., Mongare, M. M., Gould, J., Frederick, D. T., et al. (2012) · Nature · doi:10.1038/nature11183 · Cited by 1,771
Principles and mechanisms of vessel normalization for cancer and other angiogenic diseases
Carmeliet, P., & Jain, R. K. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3455 · Cited by 1,740
Bissell, M. J., & Hines, W. C. (2011) · Nature Medicine · doi:10.1038/nm.2328 · Cited by 1,584
Sherman, M. H., Yu, R. T., Engle, D. D., Ding, N., Atkins, A. R., Tiriac, H., Collisson, E. A., Connor, F., Van Dyke, T., Kozlov, S., et al. (2014) · Cell · doi:10.1016/j.cell.2014.08.007 · Cited by 1,158
Chauhan, V. P., Stylianopoulos, T., Martin, J. D., Popović, Z., Chen, O., Kamoun, W. S., Bawendi, M. G., Fukumura, D., & Jain, R. K. (2012) · Nature Nanotechnology · doi:10.1038/nnano.2012.45 · Cited by 1,101
Macrophage Regulation of Tumor Responses to Anticancer Therapies
De Palma, M., & Lewis, C. E. (2013) · Cancer Cell · doi:10.1016/j.ccr.2013.02.013 · Cited by 1,079
Cited by
Sorted by citations
Turning foes to friends: targeting cancer-associated fibroblasts
Chen, X., & Song, E. (2018) · Nature Reviews Drug Discovery · doi:10.1038/s41573-018-0004-1 · Cited by 1,929
Tumor microenvironment complexity and therapeutic implications at a glance
Baghban, R., Roshangar, L., Jahanban-Esfahlan, R., Seidi, K., Ebrahimi-Kalan, A., Jaymand, M., Kolahian, S., Javaheri, T., & Zare, P. (2020) · Cell Communication and Signaling · doi:10.1186/s12964-020-0530-4 · Cited by 1,784
“2b) [83] .”
Chehelgerdi, M., Chehelgerdi, M., Allela, O. Q. B., Pecho, R. D. C., Narayanan, J., Rao, D. P., Thamaraikani, T., Vasanthan, M., Viktor, P., Lakshmaiya, N., et al. (2023) · Molecular Cancer · doi:10.1186/s12943-023-01865-0 · Cited by 1,132
Signaling pathways in cancer-associated fibroblasts and targeted therapy for cancer
Wu, F., Yang, J., Liu, J., Wang, Y., Mu, J., Zeng, Q., Deng, S., & Zhou, H. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00641-0 · Cited by 773
Self-citationFibrosis and cancer: A strained relationship
Piersma, B., Hayward, M.-K., & Weaver, V. M. (2020) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2020.188356 · Cited by 715
Metadata
- Authors
- Miao, L., Liu, Q., Lin, C. M., Luo, C., Wang, Y., Liu, L., Yin, W., Hu, S., Kim, W. Y., & Huang, L.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Shenyang Pharmaceutical University (CN)
- China Pharmaceutical University (CN)
- Corresponding author
- Huang, Leaf
- ORCID
- Venue
- Cancer Research
- Published
- 2017-01-31
- Volume
- 77
- Issue
- 3
- Pages
- 719-731
- ISSN
- 0008-5472 (ISSN-L)
- 1538-7445
- PMC
- PMC5290135
- OpenAlex
- W2555883270
- Cited by
- 215
- References
- 50
- Citations by year
- 2026: 13 · 2025: 16 · 2024: 16 · 2023: 16 · 2022: 26 · 2021: 29 · 2020: 31 · 2019: 31 · 2018: 21 · 2017: 16
- FWCI
- 9.26
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Cancer Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- NIH (CA149363, CA151652, and CA149387)
- North Carolina Biotech Center Institutional (2005-IDG-1016)
- Topics
- Physical Sciences › Materials Science › Biomaterials › Nanoparticle-Based Drug Delivery
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Interference and Gene Delivery
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biotechnology › Bacterial Cancer Therapy
- Keywords
- cancer-associated fibroblasts, desmoplastic tumors, tumor microenvironment, lipid-coated nanoparticles, pancreatic cancer, nanoparticle delivery
- MeSH terms
- Cancer-Associated Fibroblasts, Animals, Urinary Bladder Neoplasms, Carcinoma, Transitional Cell, Flow Cytometry, Fluorescent Antibody Technique, Humans, Immunohistochemistry, Neoplasms, Experimental, Pancreatic Neoplasms, Blotting, Western, Polymerase Chain Reaction, Apoptosis, Xenograft Model Antitumor Assays, NIH 3T3 Cells, Cell Line, Tumor, Mice, TNF-Related Apoptosis-Inducing Ligand, Nanoconjugates, Tumor Microenvironment
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