The DOI matches its Crossref record; 155 of the 155 references it cites were found in a registry or an index.
Tumor microenvironment targeted nanotherapeutics for cancer therapy and diagnosis: A review
Abstract
Recent findings suggest that the cellular and extracellular materials surrounding the cancerous cells from an atypical tumor microenvironment (TM) play a pivotal role in the process of tumor initiation and progression. TM comprises an intricate system involving diverse cell types including endothelial cells, pericytes, smooth muscle cells, fibroblasts, various inflammatory cells, dendritic cells, and cancer stem cells (CSCs). The TM-forming cells dynamically interact with the cancerous cells through various signaling mechanisms and pathways. The existence of this dynamic cellular communication is responsible for creating an environment suitable for sustaining a reasonably high cellular proliferation. Presently, researchers are showing interest to use these TM conditions to mediate effective targeting measures for cancer therapy. The use of nanotherapeutics-based combination therapy; stimuli-responsive nanotherapeutics targeting acidic pH, hypoxic environment; and nanoparticle-induced hyperthermia are some of the approaches that are under intense investigation for cancer therapy. This review discusses TM and its role in cancer progression and crosstalk understanding, opportunities, and epigenetic modifications involved therein to materialize the capability of nanotherapeutics to target cancer by availing TM. STATEMENT OF SIGNIFICANCE: This article presents various recent reports, proof-of-concept studies, patents, and clinical trials on the concept of tumor microenvironment for mediating the cancer-specific delivery of nanotechnology-based systems bearing anticancer drug and diagnostics. We highlight the potential of tumor microenvironment; its role in disease progression, opportunities, challenges, and allied treatment strategies for effective cancer therapy by conceptual understanding of tumor microenvironment and epigenetic modifications involved. Specifically, nanoparticle-based approaches to target various processes related to tumor microenvironment (pH responsive, hypoxic environment responsive, targeting of specific cells involved in tumor microenvironment, etc.) are dealt in detail.
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Cites
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
Nanocarriers as an emerging platform for cancer therapy
Peer, D., Karp, J. M., Hong, S., Farokhzad, O. C., Margalit, R., & Langer, R. (2007) · Nature Nanotechnology · doi:10.1038/nnano.2007.387 · Cited by 8,710
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
Safety and activity of anti-PD-L1 antibody in patients with advanced cancer
Brahmer, J. R., Tykodi, S. S., Chow, L. Q. M., Hwu, W.-J., Topalian, S. L., Hwu, P., Drake, C. G., Camacho, L. H., Kauh, J., Odunsi, K., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1200694 · Cited by 7,945
The emerging hallmarks of cancer metabolism
Pavlova, N. N., & Thompson, C. B. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2015.12.006 · Cited by 5,926
D'Amico, A. V. (1998) · JAMA · doi:10.1001/jama.280.11.969 · Cited by 4,523
The Biology of Cancer: Metabolic Reprogramming Fuels Cell Growth and Proliferation
DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G., & Thompson, C. B. (2008) · Cell Metabolism · doi:10.1016/j.cmet.2007.10.002 · Cited by 4,088
Metabolic Reprogramming: A Cancer Hallmark Even Warburg Did Not Anticipate
Ward, P. S., & Thompson, C. B. (2012) · Cancer Cell · doi:10.1016/j.ccr.2012.02.014 · Cited by 3,233
Targeting hypoxia in cancer therapy
Wilson, W. R., & Hay, M. P. (2011) · Nature Reviews Cancer · doi:10.1038/nrc3064 · Cited by 3,172
Nanoparticles in cancer therapy and diagnosis
Brigger, I., Dubernet, C., & Couvreur, P. (2002) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(02)00044-3 · Cited by 3,039
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Fu, S., Li, G., Zang, W., Zhou, X., Shi, K., & Zhai, Y. (2022) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2021.08.012 · Cited by 266
Deep learning in digital pathology for personalized treatment plans of cancer patients
Wen, Z., Wang, S., Yang, D. M., Xie, Y., Chen, M., Bishop, J., & Xiao, G. (2023) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2023.02.003 · Cited by 34
Metadata
- Authors
- Thakkar, S., Sharma, D., Kalia, K., & Tekade, R. K.
- Venue
- Acta Biomaterialia
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 101
- Pages
- 43-68
- ISSN
- 1742-7061 (ISSN-L)
- OpenAlex
- W2972802464
- Cited by
- 316
- References
- 155
- FWCI
- 10.62
- Type
- journal article
- Language
- en
- Open access
- closed
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- Thakkar, S., Sharma, D., Kalia, K., & Tekade, R. K. (2020). Tumor microenvironment targeted nanotherapeutics for cancer therapy and diagnosis: A review. Acta Biomaterialia. https://doi.org/10.1016/j.actbio.2019.09.009
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