The DOI matches its Crossref record; 495 of the 495 references it cites were found in a registry or an index.
Targeting the tumor stroma for cancer therapy
Open accessAbstract
Tumors are comprised of both cancer cells and surrounding stromal components. As an essential part of the tumor microenvironment, the tumor stroma is highly dynamic, heterogeneous and commonly tumor-type specific, and it mainly includes noncellular compositions such as the extracellular matrix and the unique cancer-associated vascular system as well as a wide variety of cellular components including activated cancer-associated fibroblasts, mesenchymal stromal cells, pericytes. All these elements operate with each other in a coordinated fashion and collectively promote cancer initiation, progression, metastasis and therapeutic resistance. Over the past few decades, numerous studies have been conducted to study the interaction and crosstalk between stromal components and neoplastic cells. Meanwhile, we have also witnessed an exponential increase in the investigation and recognition of the critical roles of tumor stroma in solid tumors. A series of clinical trials targeting the tumor stroma have been launched continually. In this review, we introduce and discuss current advances in the understanding of various stromal elements and their roles in cancers. We also elaborate on potential novel approaches for tumor-stroma-based therapeutic targeting, with the aim to promote the leap from bench to bedside.
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2 retracted
Discoidin Domain Receptors: Potential Actors and Targets in Cancer
Rammal, H., Saby, C., Magnien, K., Gulick, L. V., Garnotel, R., Buache, É., Btaouri, H. E., Jeannesson, P., & Morjani, H. (2016) · Frontiers in Pharmacology · doi:10.3389/fphar.2016.00055 · Cited by 133
RetractedHan, J., Qu, H., Han, M., Ding, Y., Xie, M., Hu, J., Chen, Y., & Dong, H. (2020) · Oncogene · doi:10.1038/s41388-020-01574-8 · Cited by 92
RetractedSung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21660 · Cited by 108,699
Hallmarks of Cancer: The Next Generation
Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590
Hanahan, D., & Weinberg, R. A. (2000) · Cell · doi:10.1016/s0092-8674(00)81683-9 · Cited by 29,219
Bevacizumab plus Irinotecan, Fluorouracil, and Leucovorin for Metastatic Colorectal Cancer
Hurwitz, H., Fehrenbacher, L., Novotny, W., Cartwright, T., Hainsworth, J., Heim, W., Berlin, J., Baron, A., Griffing, S., Holmgren, E., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa032691 · Cited by 10,968 · cited 1×
“Targeting cancer vasculature In 2004, the American FDA granted an unprecedented approval to a humanized anti-VEGFA monoclonal antibody, named as bevacizumab, to treat patients with metastatic colorectal cancer [350].”
BackgroundTumor angiogenesis: therapeutic implications
Sherwood, L. M., Parris, E. E., & Folkman, J. (1971) · New England Journal of Medicine · doi:10.1056/nejm197111182852108 · Cited by 10,187
Hallmarks of Cancer: New Dimensions
Hanahan, D. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1059 · Cited by 9,887
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
Myeloid-derived-suppressor cells as regulators of the immune system
Gabrilovich, D. I., & Nagaraj, S. (2009) · Nature Reviews Immunology · doi:10.1038/nri2506 · Cited by 6,753
Cited by
Unveiling the mechanisms and challenges of cancer drug resistance
Khan, S. U., Fatima, K., Aisha, S., & Malik, F. (2024) · Cell Communication and Signaling · doi:10.1186/s12964-023-01302-1 · Cited by 417
Metadata
- Authors
- Xu, M., Zhang, T., Xia, R., Wei, Y., & Wei, X.
- Venue
- Molecular Cancer
- Published
- 2022-11-02
- Volume
- 21
- Issue
- 1
- Pages
- 208-208
- ISSN
- 1476-4598 (ISSN-L)
- OpenAlex
- W4307870433
- Cited by
- 319
- References
- 495
- FWCI
- 25.74
- Type
- journal article
- Language
- en
- Open access
- gold
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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.