The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Protein typing of circulating microvesicles allows real-time monitoring of glioblastoma therapy
Open accessAbstract
Glioblastomas shed large quantities of small, membrane-bound microvesicles into the circulation. Although these hold promise as potential biomarkers of therapeutic response, their identification and quantification remain challenging. Here, we describe a highly sensitive and rapid analytical technique for profiling circulating microvesicles directly from blood samples of patients with glioblastoma. Microvesicles, introduced onto a dedicated microfluidic chip, are labeled with target-specific magnetic nanoparticles and detected by a miniaturized nuclear magnetic resonance system. Compared with current methods, this integrated system has a much higher detection sensitivity and can differentiate glioblastoma multiforme (GBM) microvesicles from nontumor host cell–derived microvesicles. We also show that circulating GBM microvesicles can be used to analyze primary tumor mutations and as a predictive metric of treatment-induced changes. This platform could provide both an early indicator of drug efficacy and a potential molecular stratifier for human clinical trials.
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
Comprehensive genomic characterization defines human glioblastoma genes and core pathways
The Cancer Genome Atlas Research Network (2008) · Nature · doi:10.1038/nature07385 · Cited by 7,735
Verhaak, R. G. W., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2009.12.020 · Cited by 7,575
An Integrated Genomic Analysis of Human Glioblastoma Multiforme
Parsons, D. W., Jones, S., Zhang, X., Lin, J. C.-H., Leary, R. J., Angenendt, P., Mankoo, P., Carter, H., Siu, I.-M., Gallia, G. L., et al. (2008) · Science · doi:10.1126/science.1164382 · Cited by 5,857
Self-citationIsolation and Characterization of Exosomes from Cell Culture Supernatants and Biological Fluids
Théry, C., Amigorena, S., Raposo, G., & Clayton, A. (2006) · Current Protocols in Cell Biology · doi:10.1002/0471143030.cb0322s30 · Cited by 5,766
Exosomes: composition, biogenesis and function
Théry, C., Zitvogel, L., & Amigorena, S. (2002) · Nature Reviews Immunology · doi:10.1038/nri855 · Cited by 5,346
Skog, J., Würdinger, T., van Rijn, S., Meijer, D. H., Gainche, L., Curry, W. T., Carter, B. S., Krichevsky, A. M., & Breakefield, X. O. (2008) · Nature Cell Biology · doi:10.1038/ncb1800 · Cited by 5,001
Self-citationCancer-associated IDH1 mutations produce 2-hydroxyglutarate
Dang, L., White, D. W., Gross, S., Bennett, B. D., Bittinger, M. A., Driggers, E. M., Fantin, V. R., Jang, H. G., Jin, S., Keenan, M. C., et al. (2009) · Nature · doi:10.1038/nature08617 · Cited by 4,154
Membrane vesicles as conveyors of immune responses
Théry, C., Ostrowski, M., & Segura, E. (2009) · Nature Reviews Immunology · doi:10.1038/nri2567 · Cited by 3,882
Exosomes – vesicular carriers for intercellular communication
Simons, M., & Raposo, G. (2009) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2009.03.007 · Cited by 2,351
Intercellular transfer of the oncogenic receptor EGFRvIII by microvesicles derived from tumour cells
Al-Nedawi, K., Meehan, B., Micallef, J., Lhotak, V., May, L., Guha, A., & Rak, J. (2008) · Nature Cell Biology · doi:10.1038/ncb1725 · Cited by 2,034
Cited by
Pegtel, D. M., & Gould, S. J. (2019) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-013118-111902 · Cited by 2,682
Akers, J. C., Gonda, D., Kim, R., Carter, B. S., & Chen, C. C. (2013) · Journal of Neuro-Oncology · doi:10.1007/s11060-013-1084-8 · Cited by 1,423
“[121] report the fabrication of a microfluidic chip that quantifies the presence Nucleus Apoptosis Apoptotic bodies”
Self-citationEV-TRACK: transparent reporting and centralizing knowledge in extracellular vesicle research
EV-TRACK Consortium, Van Deun, J., Mestdagh, P., Agostinis, P., Akay, Ö., Anand, S., Anckaert, J., Martinez, Z. A., Baetens, T., Beghein, E., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4185 · Cited by 1,284
Extracellular Vesicles in Cancer: Exosomes, Microvesicles and the Emerging Role of Large Oncosomes
Minciacchi, V. R., Freeman, M. R., & Di Vizio, D. (2015) · Seminars in Cell & Developmental Biology · doi:10.1016/j.semcdb.2015.02.010 · Cited by 963
Zarovni, N., Corrado, A., Guazzi, P., Zocco, D., Lari, E., Radano, G., Muhhina, J., Fondelli, C., Gavrilova, J., & Chiesi, A. (2015) · Methods · doi:10.1016/j.ymeth.2015.05.028 · Cited by 532
Evidence-Based Clinical Use of Nanoscale Extracellular Vesicles in Nanomedicine
Fais, S., O’Driscoll, L., Borras, F. E., Buzas, E., Camussi, G., Cappello, F., Carvalho, J., Cordeiro da Silva, A., Del Portillo, H., El Andaloussi, S., et al. (2016) · ACS Nano · doi:10.1021/acsnano.5b08015 · Cited by 481
Extracellular vesicles: A bright star of nanomedicine
Wu, P., Zhang, B., Ocansey, D. K. W., Xu, W., & Qian, H. (2021) · Biomaterials · doi:10.1016/j.biomaterials.2020.120467 · Cited by 368
Emerging treatment strategies for glioblastoma multiforme
Carlsson, S. K., Brothers, S. P., & Wahlestedt, C. (2014) · EMBO Molecular Medicine · doi:10.15252/emmm.201302627 · Cited by 365
Imaging extracellular vesicles: current and emerging methods
Chuo, S. T.-Y., Chien, J. C.-Y., & Lai, C. P.-K. (2018) · Journal of Biomedical Science · doi:10.1186/s12929-018-0494-5 · Cited by 359
Plasma Proteome Database as a resource for proteomics research: 2014 update
Nanjappa, V., Thomas, J. K., Marimuthu, A., Muthusamy, B., Radhakrishnan, A., Sharma, R., Ahmad Khan, A., Balakrishnan, L., Sahasrabuddhe, N. A., Kumar, S., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1251 · Cited by 314
Combining Machine Learning and Nanofluidic Technology To Diagnose Pancreatic Cancer Using Exosomes
Ko, J., Bhagwat, N., Yee, S. S., Ortiz, N., Sahmoud, A., Black, T., Aiello, N. M., McKenzie, L., O’Hara, M., Redlinger, C., et al. (2017) · ACS Nano · doi:10.1021/acsnano.7b05503 · Cited by 249
Glioblastoma: exosome and microRNA as novel diagnosis biomarkers
Saadatpour, L., Fadaee, E., Fadaei, S., Nassiri Mansour, R., Mohammadi, M., Mousavi, S. M., Goodarzi, M., Verdi, J., & Mirzaei, H. (2016) · Cancer Gene Therapy · doi:10.1038/cgt.2016.48 · Cited by 244
Xu, M., Feng, T., Liu, B., Qiu, F., Xu, Y., Zhao, Y., & Zheng, Y. (2021) · Theranostics · doi:10.7150/thno.62330 · Cited by 229
Bronisz, A., Wang, Y., Nowicki, M. O., Peruzzi, P., Ansari, K. I., Ogawa, D., Balaj, L., De Rienzo, G., Mineo, M., Nakano, I., et al. (2014) · Cancer Research · doi:10.1158/0008-5472.can-13-2650 · Cited by 222
Self-citationSubtyping of circulating exosome-bound amyloid β reflects brain plaque deposition
Lim, C. Z. J., Zhang, Y., Chen, Y., Zhao, H., Stephenson, M. C., Ho, N. R. Y., Chen, Y., Chung, J., Reilhac, A., Loh, T. P., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09030-2 · Cited by 209
Self-citationNew windows into the brain: Central nervous system-derived extracellular vesicles in blood
Shi, M., Sheng, L., Stewart, T., Zabetian, C. P., & Zhang, J. (2019) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2019.01.005 · Cited by 181
Alzahrani, F. A., Khan, M. I., Kameli, N., Alsahafi, E., & Riza, Y. M. (2023) · Biomolecules · doi:10.3390/biom13050839 · Cited by 143
“This can be achieved using nanoparticle tracking analysis (NTA) [41] or other methods, including transmission electron microscopy (TEM) [42] and scanning electron microscopy (SEM) [43], as well as dynamic light scattering (DLS) [42], atomic force microscopy (AFM) [42], and tunable resistive pulse…”
MethodNanotechnology: emerging tools for biology and medicine
Wong, I. Y., Bhatia, S. N., & Toner, M. (2013) · Genes & Development · doi:10.1101/gad.226837.113 · Cited by 132
Is Alzheimer’s Disease a Liver Disease of the Brain?
Bassendine, M. F., Taylor-Robinson, S. D., Fertleman, M., Khan, M., & Neely, D. (2020) · Journal of Alzheimer's Disease · doi:10.3233/jad-190848 · Cited by 124
Extracellular Vesicles in Brain Tumors and Neurodegenerative Diseases
Ciregia, F., Urbani, A., & Palmisano, G. (2017) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2017.00276 · Cited by 110
Ko, J., Hemphill, M., Yang, Z., Sewell, E., Na, Y. J., Sandsmark, D. K., Haber, M., Fisher, S. A., Torre, E. A., Svane, K. C., et al. (2018) · Lab on a Chip · doi:10.1039/c8lc00672e · Cited by 89
Information transfer by exosomes: A new frontier in hematologic malignancies
Boyiadzis, M., & Whiteside, T. L. (2015) · Blood Reviews · doi:10.1016/j.blre.2015.01.004 · Cited by 79
Gómez-Molina, C., Sandoval, M., Henzi, R., Ramírez, J. P., Varas-Godoy, M., Luarte, A., Lafourcade, C. A., Lopez-Verrilli, A., Smalla, K.-H., Kaehne, T., et al. (2019) · International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyy098 · Cited by 76
Analysis of extracellular vesicles as emerging theranostic nanoplatforms
Xing, Y., Cheng, Z., Wang, R., Lv, C., James, T. D., & Yu, F. (2020) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2020.213506 · Cited by 67
Recent advances in microfluidic methods in cancer liquid biopsy
Iliescu, F. S., Poenar, D. P., Yu, F., Ni, M., Chan, K. H., Cima, I., Taylor, H. K., Cima, I., & Iliescu, C. (2019) · Biomicrofluidics · doi:10.1063/1.5087690 · Cited by 51
Cancer Extracellular Vesicles: Next-Generation Diagnostic and Drug Delivery Nanotools
Palazzolo, S., Memeo, L., Hadla, M., Duzagac, F., Steffan, A., Perin, T., Canzonieri, V., Tuccinardi, T., Caligiuri, I., & Rizzolio, F. (2020) · Cancers · doi:10.3390/cancers12113165 · Cited by 34
Tian, T., Qiao, S., & Tannous, B. A. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c07981 · Cited by 27
Liquid biopsy: creating opportunities in brain space
Trivedi, R., & Bhat, K. P. (2023) · British Journal of Cancer · doi:10.1038/s41416-023-02446-0 · Cited by 27
Ghosh, S., & Ghosh, S. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.878058 · Cited by 13
Metadata
- Authors
- Shao, H., Chung, J., Balaj, L., Charest, A., Bigner, D. D., Carter, B. S., Hochberg, F. H., Breakefield, X. O., Weissleder, R., & Lee, H.
- Venue
- Nature Medicine
- Published
- 2012-11-11
- Volume
- 18
- Issue
- 12
- Pages
- 1835-1840
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W1987604193
- Cited by
- 759
- References
- 36
- FWCI
- 16.50
- Type
- journal article
- Language
- en
- Open access
- green
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
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.