Development and characterization of a microfluidic model of the tumour microenvironment
Open accessAbstract
The physical microenvironment of tumours is characterized by heterotypic cell interactions and physiological gradients of nutrients, waste products and oxygen. This tumour microenvironment has a major impact on the biology of cancer cells and their response to chemotherapeutic agents. Despite this, most in vitro cancer research still relies primarily on cells grown in 2D and in isolation in nutrient- and oxygen-rich conditions. Here, a microfluidic device is presented that is easy to use and enables modelling and study of the tumour microenvironment in real-time. The versatility of this microfluidic platform allows for different aspects of the microenvironment to be monitored and dissected. This is exemplified here by real-time profiling of oxygen and glucose concentrations inside the device as well as effects on cell proliferation and growth, ROS generation and apoptosis. Heterotypic cell interactions were also studied. The device provides a live ‘window’ into the microenvironment and could be used to study cancer cells for which it is difficult to generate tumour spheroids. Another major application of the device is the study of effects of the microenvironment on cellular drug responses. Some data is presented for this indicating the device’s potential to enable more physiological in vitro drug screening.
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
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?
Perfusion of DOX at 30 μM through the lateral microchannels significantly reduced the number of viable HCT-116 cancer cells in the hypoxic core.
“For instance, perfusion of DOX at a dose of 30 µM through the lateral microchannels significantly reduced the number of viable HCT-116 cancer cells in the hypoxic core.”
From The Tumor-on-Chip: Recent Advances in the Development of Microfluidic Systems to Recapitulate the Physiology of Solid Tumors (2019) · cites this paper as [36]
The Methods (Drug toxicity assays; page unavailable) confirm that 30 μM DOX was delivered through both lateral microchannels to HCT-116 cells. The Results (Drug penetration and evaluation of the impact of the microenvironment on drug responses; page unavailable) report significant cell killing near the channels, but much less killing farther away; they do not establish a significant reduction in viable cells specifically in the hypoxic core. (LLM, conf=0.90)
Experimental — accuracy not yet measuredThere is a correlation between differences in the cytotoxic response of tumor cells and differences in the proliferative rate of cells within different regions of the tumor-on-chip.
“These experiments suggested a correlation between differences in the cytotoxic response of tumor cells and differences in the proliferative rate of cells within different regions of the tumor-on-chip.”
From The Tumor-on-Chip: Recent Advances in the Development of Microfluidic Systems to Recapitulate the Physiology of Solid Tumors (2019) · cites this paper as [36]
The device developed by Ayuso et al. enabled the establishment of clear oxygen gradients and hypoxic conditions and the study of anticancer drugs in cell populations under controlled oxygen gradients without spheroids.
“Due to its configuration, this device enabled the establishment of clear oxygen gradients and hypoxic conditions and the study of anticancer drugs in cell populations under controlled oxygen gradients, without resorting to the use of spheroids.”
From The Tumor-on-Chip: Recent Advances in the Development of Microfluidic Systems to Recapitulate the Physiology of Solid Tumors (2019) · cites this paper as [36]
Steep oxygen gradients were established within the middle chamber.
“Experiments using a fluorescent dye responsive to low oxygen concentrations further demonstrated the establishment of steep oxygen gradients within the middle chamber”
From The Tumor-on-Chip: Recent Advances in the Development of Microfluidic Systems to Recapitulate the Physiology of Solid Tumors (2019) · cites this paper as [36]
The configuration of the central chamber in Ayuso et al.'s device allowed the spontaneous generation of normoxic, hypoxic, and necrotic regions.
“Indeed, the configuration of the central chamber allowed the spontaneous generation of normoxic, hypoxic, and necrotic regions within the device.”
From The Tumor-on-Chip: Recent Advances in the Development of Microfluidic Systems to Recapitulate the Physiology of Solid Tumors (2019) · cites this paper as [36]
A necrotic core existed in the middle portion of the central chamber, suggesting the development of gradients of nutrients and oxygen.
“They demonstrated the existence of a necrotic core in the middle portion of the central chamber, suggesting the development of gradients of nutrients and oxygen”
From The Tumor-on-Chip: Recent Advances in the Development of Microfluidic Systems to Recapitulate the Physiology of Solid Tumors (2019) · cites this paper as [36]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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 · see citing works
The present and future role of microfluidics in biomedical research
Sackmann, E. K., Fulton, A. L., & Beebe, D. J. (2014) · Nature · doi:10.1038/nature13118 · Cited by 2,995 · see citing works
Drug penetration in solid tumours
Minchinton, A. I., & Tannock, I. F. (2006) · Nature reviews. Cancer · doi:10.1038/nrc1893 · Cited by 2,805 · see citing works
Influence of tumour micro-environment heterogeneity on therapeutic response
Junttila, M. R., & de Sauvage, F. J. (2013) · Nature · doi:10.1038/nature12626 · Cited by 2,694 · see citing works
The tumor microenvironment and its role in promoting tumor growth
Whiteside, T. L. (2008) · Oncogene · doi:10.1038/onc.2008.271 · Cited by 2,376 · see citing works
The tumor microenvironment at a glance
Balkwill, F. R., Capasso, M., & Hagemann, T. (2012) · Journal of Cell Science · doi:10.1242/jcs.116392 · Cited by 1,857 · see citing works
Mitochondrial ROS in cancer: initiators, amplifiers or an Achilles' heel?
Sabharwal, S. S., & Schumacker, P. T. (2014) · Nature reviews. Cancer · doi:10.1038/nrc3803 · Cited by 1,643 · see citing works
Organs-on-chips at the frontiers of drug discovery
Esch, E. W., Bahinski, A., & Huh, D. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4539 · Cited by 1,185 · see citing works
Cancer Cell Metabolism: One Hallmark, Many Faces
Cantor, J. R., & Sabatini, D. M. (2012) · Cancer Discovery · doi:10.1158/2159-8290.cd-12-0345 · Cited by 945 · see citing works
Three-dimensional culture systems in cancer research: Focus on tumor spheroid model
Nath, S., & Devi, G. R. (2016) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2016.03.013 · Cited by 919 · see citing works
Three-dimensional microfluidic model for tumor cell intravasation and endothelial barrier function
Zervantonakis, I. K., Hughes-Alford, S. K., Charest, J. L., Condeelis, J. S., Gertler, F. B., & Kamm, R. D. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1210182109 · Cited by 872 · see citing works
New horizons in tumor microenvironment biology: challenges and opportunities
Chen, F., Zhuang, X., Lin, L., Yu, P., Wang, Y., Shi, Y., Hu, G., & Sun, Y. (2015) · BMC Medicine · doi:10.1186/s12916-015-0278-7 · Cited by 704 · see citing works
Advances in the formation, use and understanding of multi-cellular spheroids
Achilli, T.-M., Meyer, J., & Morgan, J. R. (2012) · Expert Opinion on Biological Therapy · doi:10.1517/14712598.2012.707181 · Cited by 574 · see citing works
Cell migration into scaffolds under co-culture conditions in a microfluidic platform
Chung, S. H., Sudo, R., Mack, P. J., Wan, C.-R., Vickerman, V., & Kamm, R. D. (2008) · Lab on a Chip · doi:10.1039/b807585a · Cited by 517 · see citing works
Tannock, I. F., Lee, C. M., Tunggal, J. K., Cowan, D., & Egorin, M. J. (2002) · PubMed · Cited by 505 · see citing works
Three-dimensional in vitro tumor models for cancer research and drug evaluation
Xu, X., Farach‐Carson, M. C., & Jia, X. (2014) · Biotechnology Advances · doi:10.1016/j.biotechadv.2014.07.009 · Cited by 462 · see citing works
Improving chemotherapeutic drug penetration in tumors by vascular targeting and barrier alteration
Curnis, F., Sacchi, A., & Corti, A. (2002) · Journal of Clinical Investigation · doi:10.1172/jci15223 · Cited by 238 · see citing works
Mitochondrial ROS and cancer drug resistance: Implications for therapy
Okon, I. S., & Zou, M.-H. (2015) · Pharmacological Research · doi:10.1016/j.phrs.2015.06.013 · Cited by 207 · see citing works
Funamoto, K., Zervantonakis, I. K., Liu, Y., Ochs, C. J., Kim, C., & Kamm, R. D. (2012) · Lab on a Chip · doi:10.1039/c2lc40306d · Cited by 151 · see citing works
Morfoisse, F., Kuchnio, A., Frainay, C., Gomez‐Brouchet, A., Delisle, M., Marzi, S., Helfer, A., Hantelys, F., Pujol, F., Guillermet‐Guibert, J., et al. (2014) · Cell Reports · doi:10.1016/j.celrep.2013.12.011 · Cited by 145 · see citing works
How can attrition rates be reduced in cancer drug discovery?
Moreno, L., & Pearson, A. D. J. (2013) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2013.768984 · Cited by 124 · see citing works
Malignant Glioma Physiology: Cellular Response to Hypoxia and Its Role in Tumor Progression
Brat, D. J., & Mapstone, T. B. (2003) · Annals of Internal Medicine · doi:10.7326/0003-4819-138-8-200304150-00014 · Cited by 110 · see citing works
Lorenzo, C., Frongia, C., Jorand, R., Fehrenbach, J., Weiss, P. O., Maandhui, A., Gay, G., Ducommun, B., & Lobjois, V. (2011) · Cell Division · doi:10.1186/1747-1028-6-22 · Cited by 104 · see citing works
Microfluidic Culture Models of Tumor Angiogenesis
Stroock, A. D., & Fischbach, C. (2010) · Tissue Engineering Part A · doi:10.1089/ten.tea.2009.0689 · Cited by 81 · see citing works
Characterization of Changes in the Proteome in Different Regions of 3D Multicell Tumor Spheroids
McMahon, K. M., Volpato, M., Chi, H. Y., Musiwaro, P., Poterlowicz, K., Peng, Y., Scally, A., Patterson, L. H., Phillips, R. M., & Sutton, C. W. (2012) · Journal of Proteome Research · doi:10.1021/pr2012472 · Cited by 66 · see citing works
Self-citationThe emerging role of hypoxia-inducible factor-2 involved in chemo/radioresistance in solid tumors
Zhao, J., Du, F., Luo, Y., Shen, G., Zheng, F., & Xu, B. (2015) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2015.05.004 · Cited by 59 · see citing works
A microfluidic co-culture system to monitor tumor-stromal interactions on a chip
Menon, N. V., Chuah, Y. J., Cao, B., Lim, M., & Kang, Y. (2014) · Biomicrofluidics · doi:10.1063/1.4903762 · Cited by 53 · see citing works
An overview of the drug development process.
Tonkens, R. M. (2005) · PubMed · Cited by 50 · see citing works
Sánchez‐Martínez, D., Azaceta, G., Muntasell, A., Aguiló, N., Núñez-Jurado, D., Gálvez, E. M., Naval, J., Anel, A., Palomera, L., Vilches, C., et al. (2015) · OncoImmunology · doi:10.4161/2162402x.2014.991613 · Cited by 41 · see citing works
Self-citation3D functional and perfusable microvascular networks for organotypic microfluidic models
Bersini, S., & Moretti, M. (2015) · Journal of Materials Science Materials in Medicine · doi:10.1007/s10856-015-5520-5 · Cited by 38 · see citing works
Study of the Chemotactic Response of Multicellular Spheroids in a Microfluidic Device
Ayuso, J. M., Basheer, H. A., Monge, R., Sánchez-Álvarez, P., Doblaré, M., Shnyder, S. D., Vinader, V. M., Afarinkia, K., Fernández, L. J., & Ochoa, I. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139515 · Cited by 37 · see citing works
Self-citationRoux, L. L., Volgin, A., Maxwell, D. S., Ishihara, K., Gelovani, J. G., & Schellingerhout, D. (2008) · Molecular Imaging · doi:10.2310/7290.2008.00023 · Cited by 37 · see citing works
Live Spheroid Formation Recorded with Light Sheet-Based Fluorescence Microscopy
Pampaloni, F., Richa, R., Ansari, N., & Stelzer, E. H. K. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-2080-8_3 · Cited by 23 · see citing works
SU-8 Based Microdevices to Study Self-Induced Chemotaxis in 3D Microenvironments
Ayuso, J. M., Monge, R., Llamazares, G. A., Moreno, M., Agirregabiria, M., Berganzo, J., Doblaré, M., Ochoa, I., & Fernández, L. J. (2015) · Frontiers in Materials · doi:10.3389/fmats.2015.00037 · Cited by 20 · see citing works
Self-citationSimple microfluidic device for studying chemotaxis in response to dual gradients
Moussavi-Harami, S. F., Pezzi, H. M., Anna Huttenlocher, A., & Beebe, D. J. (2015) · Biomedical Microdevices · doi:10.1007/s10544-015-9955-8 · Cited by 13 · see citing works
Breaking into clinical research.
Tonkens, R. M. (2005) · PubMed · Cited by 12 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Tumour-on-a-chip: microfluidic models of tumour morphology, growth and microenvironment
Tsai, H.-F., Trubelja, A., Shen, A. Q., & Bao, G. (2017) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0137 · Cited by 214 · see citing works
Microfluidic organ-on-chip system for multi-analyte monitoring of metabolites in 3D cell cultures
Dornhof, J., Kieninger, J., Muralidharan, H., Maurer, J., Urban, G. A., & Weltin, A. (2021) · Lab on a Chip · doi:10.1039/d1lc00689d · Cited by 198 · see citing works
Trujillo-de Santiago, G., Flores-Garza, B. G., Tavares-Negrete, J. A., Lara-Mayorga, I. M., González-Gamboa, I., Zhang, Y. S., Rojas-Martínez, A., Ortiz-López, R., & Álvarez, M. M. (2019) · Materials · doi:10.3390/ma12182945 · Cited by 184 · see citing works
“developed a polystyrene-based microfluidic device to study a tumor microenvironment in real time (Figure 5d) [36].”
BackgroundMethodInfluentialAdvances in Engineering Human Tissue Models
Moysidou, C., Barberio, C., & Owens, R. M. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.620962 · Cited by 182 · see citing works
Truong, D., Fiorelli, R., Barrientos, E. S., Melendez, E. L., Sanai, N., Mehta, S., & Nikkhah, M. (2019) · Biomaterials · doi:10.1016/j.biomaterials.2018.07.048 · Cited by 150 · see citing works
Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis
Liu, L., Bi, M., Wang, Y., Liu, J., Jiang, X., Xu, Z., & Zhang, X. (2021) · Nanoscale · doi:10.1039/d1nr06195j · Cited by 129 · see citing works
Tumor-on-chip modeling of organ-specific cancer and metastasis
Piccolo, N. D., Shirure, V. S., Bi, Y., Goedegebuure, S. P., Gholami, S., Hughes, C. C. W., Fields, R. C., & George, S. C. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.05.008 · Cited by 130 · see citing works
Metastasis in context: modeling the tumor microenvironment with cancer-on-a-chip approaches
Sleeboom, J. J. F., Eslami Amirabadi, H., Nair, P., Sahlgren, C. M., & den Toonder, J. M. J. (2018) · Disease Models & Mechanisms · doi:10.1242/dmm.033100 · Cited by 122 · see citing works
A novel 3D breast-cancer-on-chip platform for therapeutic evaluation of drug delivery systems
Chen, Y., Gao, D., Wang, Y., Lin, S., & Jiang, Y. (2018) · Analytica Chimica Acta · doi:10.1016/j.aca.2018.06.038 · Cited by 121 · see citing works
Tumor-on-a-chip for integrating a 3D tumor microenvironment: chemical and mechanical factors
Wan, L., Neumann, C. A., & LeDuc, P. R. (2020) · Lab on a Chip · doi:10.1039/c9lc00550a · Cited by 120 · see citing works
A multiplexed microfluidic system for evaluation of dynamics of immune–tumor interactions
Moore, N., Doty, D. T., Zielstorff, M., Kariv, I., Moy, L. Y., Gimbel, A. A., Chevillet, J. R., Lowry, N., Santos, J. A. L., Mott, V. L., et al. (2018) · Lab on a Chip · doi:10.1039/c8lc00256h · Cited by 106 · see citing works
Tumor-on-a-chip: a microfluidic model to study cell response to environmental gradients
Ayuso, J. M., Virumbrales-Muñoz, M., McMinn, P. H., Rehman, S., Gomez, I., Karim, M. R., Trusttchel, R., Wisinski, K. B., Beebe, D. J., & Skala, M. C. (2019) · Lab on a Chip · doi:10.1039/c9lc00270g · Cited by 101 · see citing works
Self-citationTowards the development of human immune-system-on-a-chip platforms
Polini, A., del Mercato, L. L., Adriano Barra, A., Zhang, Y. S., Calabi, F., & Gigli, G. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.10.003 · Cited by 98 · see citing works
A Microvascularized Tumor-mimetic Platform for Assessing Anti-cancer Drug Efficacy
Pradhan, S., Smith, A. M., Garson, C. J., Hassani, I., Seeto, W. J., Pant, K., Arnold, R. D., Prabhakarpandian, B., & Lipke, E. A. (2018) · Scientific Reports · doi:10.1038/s41598-018-21075-9 · Cited by 97 · see citing works
Intestinal organ chips for disease modelling and personalized medicine
Özkan, A., LoGrande, N., Feitor, J. F., Goyal, G., & Ingber, D. E. (2024) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-024-00968-3 · Cited by 89 · see citing works
Rodenhizer, D., Dean, T., D'Arcangelo, E., & McGuigan, A. P. (2018) · Advanced Healthcare Materials · doi:10.1002/adhm.201701174 · Cited by 80 · see citing works
Vat photopolymerization 3D printed microfluidic devices for organ-on-a-chip applications
Milton, L. A., Viglione, M., Ong, L. J. Y., Nordin, G. P., & Toh, Y. (2023) · Lab on a Chip · doi:10.1039/d3lc00094j · Cited by 80 · see citing works
Mimicking Human Pathophysiology in Organ‐on‐Chip Devices
Yeşil-Çeliktaş, Ö., Hassan, S., Miri, A. K., Maharjan, S., Al‐kharboosh, R., Quiñones‐Hinojosa, A., & Zhang, Y. S. (2018) · Advanced Biosystems · doi:10.1002/adbi.201800109 · Cited by 72 · see citing works
Organ-on-a-Chip Platforms for Drug Screening and Delivery in Tumor Cells: A Systematic Review
Gonçalves, I. M., Carvalho, V. M., Rodrigues, R. O., Pinho, D., Teixeira, S., Moita, A., Hori, T., Kaji, H., Lima, R. A., & Minas, G. M. H. (2022) · Cancers · doi:10.3390/cancers14040935 · Cited by 73 · see citing works
Tumor Microenvironment on a Chip: The Progress and Future Perspective
Ahn, J., Sei, Y. J., Jeon, N. L., & Kim, Y. (2017) · Bioengineering · doi:10.3390/bioengineering4030064 · Cited by 69 · see citing works
Advanced technological tools to study multidrug resistance in cancer
Andrei, L., Kasas, S., Ochoa, I., Stanković, T., Korsnes, M. S., Václavíková, R., Assaraf, Y. G., & Pešić, M. (2019) · Drug Resistance Updates · doi:10.1016/j.drup.2019.100658 · Cited by 67 · see citing works
Self-citationMarrella, A., Fedi, A., Varani, G., Vaccari, I., Fato, M., Firpo, G., Guida, P., Aceto, N., & Scaglione, S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0245536 · Cited by 64 · see citing works
Campillo, N., Falcones, B., Otero, J., Colina, R., Gozal, D., Navajas, D., Farré, R., & Almendros, I. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00043 · Cited by 61 · see citing works
In vitro biomimetic models for glioblastoma-a promising tool for drug response studies
Stanković, T., Ranđelović, T., Dragoj, M., Burić, S. S., Fernández, L. J., Ochoa, I., Pérez‐García, V. M., & Pešić, M. (2021) · Drug Resistance Updates · doi:10.1016/j.drup.2021.100753 · Cited by 59 · see citing works
Self-citationAsadian, E., Bahramian, F., Siavashy, S., Movahedi, S., Keçili, R., Hussain, C. M., & Ghorbani‐Bidkorbeh, F. (2024) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2024.118004 · Cited by 59 · see citing works
Engineering in vitro vascular microsystems
Liu, Q., Ying, G., Hu, C., Du, L., Zhang, H., Wang, Z., Yue, H., Yetisen, A. K., Wang, G., Shen, Y., et al. (2025) · Microsystems & Nanoengineering · doi:10.1038/s41378-025-00956-w · Cited by 61 · see citing works
Bridging the gap between tumor-on-chip and clinics: a systematic review of 15 years of studies
Bouquerel, C., Dubrova, A., Hofer, I., Phan, D. T. T., Bernheim, M., Ladaigue, S., Cavaniol, C., Maddalo, D., Cabel, L., Mechta-Grigoriou, F., et al. (2023) · Lab on a Chip · doi:10.1039/d3lc00531c · Cited by 58 · see citing works
Lanuza, P. M., Vigueras, A., Oliván, S., Prats, A., Costas, S., Llamazares, G. A., Sánchez‐Martínez, D., Ayuso, J. M., Fernández, L. J., Ochoa, I., et al. (2018) · OncoImmunology · doi:10.1080/2162402x.2017.1395123 · Cited by 58 · see citing works
Self-citationCellular Analysis Using Microfluidics
Sibbitts, J., Sellens, K. A., Jia, S., Klasner, S. A., & Culbertson, C. T. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.7b04519 · Cited by 57 · see citing works
3D Cancer Models: Depicting Cellular Crosstalk within the Tumour Microenvironment
Franchi-Mendes, T., Eduardo, R., Domenici, G., & Brito, C. (2021) · Cancers · doi:10.3390/cancers13184610 · Cited by 57 · see citing works
Recent advances in microfluidic technology and applications for anti-cancer drug screening
Shi, Y., Cai, Y., Cao, Y., Hong, Z., & Chai, Y. (2020) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2020.116118 · Cited by 57 · see citing works
Monolithic, 3D-Printed Microfluidic Platform for Recapitulation of Dynamic Tumor Microenvironments
Beckwith, A. L., Borenstein, J. T., & Velásquez–García, L. F. (2018) · Journal of Microelectromechanical Systems · doi:10.1109/jmems.2018.2869327 · Cited by 56 · see citing works
Oxygen control: the often overlooked but essential piece to create better in vitro systems
Palacio-Castañeda, V., Velthuijs, N., Gac, S. L., & Verdurmen, W. P. R. (2022) · Lab on a Chip · doi:10.1039/d1lc00603g · Cited by 54 · see citing works
Metabolic Switching of Tumor Cells under Hypoxic Conditions in a Tumor-on-a-chip Model
Palacio-Castañeda, V., Kooijman, L. J., Venzac, B., Verdurmen, W. P. R., & Gac, S. L. (2020) · Micromachines · doi:10.3390/mi11040382 · Cited by 52 · see citing works
Imiquimod-gemcitabine nanoparticles harness immune cells to suppress breast cancer
Singh, B., Maharjan, S., Pan, D. C., Zhao, Z., Gao, Y., Zhang, Y. S., & Mitragotri, S. (2022) · Biomaterials · doi:10.1016/j.biomaterials.2021.121302 · Cited by 49 · see citing works
Gerigk, M., Bulstrode, H. J. C. J., Shi, H., Tönisen, F., Cerutti, C., Morrison, G. M., Rowitch, D. H., & Huang, Y. Y. S. (2021) · Lab on a Chip · doi:10.1039/d1lc00271f · Cited by 48 · see citing works
A Microdevice Platform Recapitulating Hypoxic Tumor Microenvironments
Ando, Y., Ta, H. P., Yen, D. P., Lee, S., Raola, S., & Shen, K. (2017) · Scientific Reports · doi:10.1038/s41598-017-15583-3 · Cited by 47 · see citing works
Jiménez-Torres, J. A., Virumbrales-Muñoz, M., Sung, K. E., Lee, M. H., Abel, E. J., & Beebe, D. J. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.03.026 · Cited by 46 · see citing works
Self-citationShahpouri, M., Adili-Aghdam, M. A., Mahmudi, H., Jaymand, M., Amoozgar, Z., Akbari, M., Hamblin, M. R., & Jahanban‐Esfahlan, R. (2022) · Journal of Controlled Release · doi:10.1016/j.jconrel.2022.12.009 · Cited by 43 · see citing works
Virumbrales-Muñoz, M., Ayuso, J. M., Lacueva-Aparicio, A., Ranđelović, T., Livingston, M. K., Beebe, D. J., Oliván, S., Pereboom, D., Doblaré, M., Fernández, L. J., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-42529-8 · Cited by 40 · see citing works
Self-citationAyensa-Jiménez, J., Pérez-Aliacar, M., Ranđelović, T., Oliván, S., Fernández, L. J., Sanz‐Herrera, J. A., Ochoa, I., Doweidar, M. H., & Doblaré, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-78215-3 · Cited by 39 · see citing works
Self-citationMicrofluidic Model for Evaluation of Immune Checkpoint Inhibitors in Human Tumors
Beckwith, A. L., Velásquez–García, L. F., & Borenstein, J. T. (2019) · Advanced Healthcare Materials · doi:10.1002/adhm.201900289 · Cited by 40 · see citing works
Virumbrales-Muñoz, M., Ayuso, J. M., Olave, M., Monge, R., de Miguel, D., Martínez‐Lostao, L., Gac, S. L., Doblaré, M., Ochoa, I., & Fernández, L. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-12049-4 · Cited by 39 · see citing works
Self-citationCarvalho, V. M., Bañobre‐López, M., Minas, G. M. H., Teixeira, S., Lima, R. A., & Rodrigues, R. O. (2022) · Bioprinting · doi:10.1016/j.bprint.2022.e00224 · Cited by 39 · see citing works
Ortiz-Cárdenas, J. E., Zatorski, J. M., Arneja, A., Montalbine, A. N., Munson, J. M., Luckey, C. J., & Pompano, R. R. (2022) · Organs-on-a-Chip · doi:10.1016/j.ooc.2022.100018 · Cited by 37 · see citing works
Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach
Pérez-Aliacar, M., Doweidar, M. H., Doblaré, M., & Ayensa-Jiménez, J. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104547 · Cited by 35 · see citing works
Organ-on-a-Chip and Microfluidic Platforms for Oncology in the UK
Nolan, J., Pearce, O. M. T., Screen, H. R. C., Knight, M. M., & Verbruggen, S. W. (2023) · Cancers · doi:10.3390/cancers15030635 · Cited by 35 · see citing works
Rodenhizer, D., Dean, T., Xu, B., Cojocari, D., & McGuigan, A. P. (2018) · Nature Protocols · doi:10.1038/s41596-018-0022-9 · Cited by 33 · see citing works
Organ-on-Chip platforms to study tumor evolution and chemosensitivity
Dsouza, V. L., Kuthethur, R., Kabekkodu, S. P., & Chakrabarty, S. (2022) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2022.188717 · Cited by 33 · see citing works
Treissman, J., Yuan, V., Baltayeva, J., Le, H., Castellana, B., Robinson, W. P., & Beristain, A. G. (2019) · Development · doi:10.1242/dev.181263 · Cited by 32 · see citing works
Optimizing an Injectable Composite Oxygen-Generating System for Relieving Tissue Hypoxia
Hsieh, T.-E., Lin, S.-J., Chen, L.-C., Chen, C.-C., Lai, P.-L., & Huang, C.-C. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00511 · Cited by 31 · see citing works
Key aspects for conception and construction of co-culture models of tumor-stroma interactions
Mason, J. E., & Öhlund, D. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1150764 · Cited by 29 · see citing works
Choudhary, S., Dubey, A., Singh, A., Zamboni, P., Gupta, N., Singh, R., Tisato, V., Aggarwal, L. M., & Gemmati, D. (2025) · Exploration of BioMat-X · doi:10.37349/ebmx.2025.101351 · Cited by 31 · see citing works
A review of organ-on-chip fabrication methods: From early developments to overcoming inert barriers
Olaizola‐Rodrigo, C., Bayona, C., Oliván, S., & Monge, R. (2025) · iScience · doi:10.1016/j.isci.2025.113992 · Cited by 31 · see citing works
Biomaterial-based in vitro 3D modeling of glioblastoma multiforme
Ahmed, T. (2023) · Cancer Pathogenesis and Therapy · doi:10.1016/j.cpt.2023.01.002 · Cited by 28 · see citing works
High Quality Multicellular Tumor Spheroid Induction Platform Based on Anisotropic Magnetic Hydrogel
Tang, S., Hu, K., Sun, J., Li, Y., Guo, Z., Liu, M., Liu, Q., Zhang, F., & Gu, N. (2017) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.6b15918 · Cited by 28 · see citing works
Carrasco-Mantis, A., Ranđelović, T., Castro-Abril, H. A., Ochoa, I., Doblaré, M., & Sanz‐Herrera, J. A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106897 · Cited by 26 · see citing works
Self-citationBurić, S. S., Podolski-Renić, A., Dinić, J., Stanković, T., Jovanović, M., Hadžić, S., Ayuso, J. M., Virumbrales-Muñoz, M., Fernández, L. J., Ochoa, I., et al. (2019) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2019/3061607 · Cited by 25 · see citing works
Self-citationTechnological Advances in Tumor-On-Chip Technology: From Bench to Bedside
Bērziņa, S., Harrison, A., Taly, V., & Xiao, W. (2021) · Cancers · doi:10.3390/cancers13164192 · Cited by 24 · see citing works
Krysko, D. V., Demuynck, R., Efimova, I., Naessens, F., Krysko, O., & Catanzaro, E. (2022) · Cells · doi:10.3390/cells11223705 · Cited by 25 · see citing works
Cancer-on-chip: a breakthrough organ-on-a-chip technology in cancer cell modeling
Nejati, B., Shahhosseini, R., Hajiabbasi, M., Ardabili, N. S., Baktash, K. B., Alivirdiloo, V., Moradi, S., Rad, M. F., Rahimi, F., Farani, M. R., et al. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03199-5 · Cited by 23 · see citing works
Dragoj, M., Stojkovska, J., Stanković, T., Dinić, J., Podolski-Renić, A., Obradović, B., & Pešić, M. (2021) · Brain Sciences · doi:10.3390/brainsci11081025 · Cited by 21 · see citing works
Development of a Prodrug of Camptothecin for Enhanced Treatment of Glioblastoma Multiforme
Checa-Chavarria, E., Rivero-Buceta, E. M., Martos, M. A. S., Martı́nez, G., Soto‐Sánchez, C., Botella, P., & Fernández, E. (2021) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.0c00968 · Cited by 22 · see citing works
Self-citationMimicking and surpassing the xenograft model with cancer-on-chip technology
Komen, J., van Neerven, S. M., van den Berg, A., Vermeulen, L., & van der Meer, A. D. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103303 · Cited by 21 · see citing works
Ahmed, M. A. M., & Nagelkerke, A. (2021) · Organs-on-a-Chip · doi:10.1016/j.ooc.2021.100012 · Cited by 21 · see citing works
Gaebler, D., Hachey, S. J., & Hughes, C. C. W. (2024) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2024.1462293 · Cited by 19 · see citing works
Phenocopying Glioblastoma: A Review
Oraiopoulou, M., Tzamali, E., Papamatheakis, J. D., & Sakkalis, V. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3111744 · Cited by 18 · see citing works
Precise and fast control of the dissolved oxygen level for tumor-on-chip
Bouquerel, C., César, W., Barthod, L., Arrak, S., Battistella, A., Gropplero, G., Mechta‐Grigoriou, F., Zalcman, G., Parrini, M. C., Verhulsel, M., et al. (2022) · Lab on a Chip · doi:10.1039/d2lc00696k · Cited by 18 · see citing works
In vitro models to study natural killer cell dynamics in the tumor microenvironment
Carannante, V., Wiklund, M., & Önfelt, B. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1135148 · Cited by 17 · see citing works
Plesselova, S., Calar, K., Axemaker, H., Sahly, E., Bhagia, A., Faragher, J. L., Fink, D. M., & de la Puente, P. (2024) · Cellular and Molecular Bioengineering · doi:10.1007/s12195-024-00817-y · Cited by 17 · see citing works
Ngo, H. T., Amartumur, S., Tran, V. T. A., Tran, M., Diep, Y. N., Cho, H., & Lee, L. P. (2023) · Biosensors · doi:10.3390/bios13020231 · Cited by 15 · see citing works
Fibrosis‐on‐Chip: A Guide to Recapitulate the Essential Features of Fibrotic Disease
Streutker, E. M., Devamoglu, U., Vonk, M. C., Verdurmen, W. P. R., & Gac, S. L. (2024) · Advanced Healthcare Materials · doi:10.1002/adhm.202303991 · Cited by 15 · see citing works
Kim, H., Kim, H., Nam, M., Park, J. K., Lee, M., Chung, S. H., Lee, K., & Kuh, H. (2023) · Cancer Cell International · doi:10.1186/s12935-023-03064-9 · Cited by 15 · see citing works
González‐Lana, S., Ranđelović, T., Ciriza, J., López‐Valdeolivas, M., Monge, R., Sánchez‐Somolinos, C., & Ochoa, I. (2023) · Lab on a Chip · doi:10.1039/d3lc00075c · Cited by 14 · see citing works
Self-citationBayona, C., Alza, L., Ranđelović, T., Sallan, M. C., Visa, A., Cantı́, C., Ochoa, I., Oliván, S., & Herreros, J. (2024) · Cell Death and Disease · doi:10.1038/s41419-024-06492-1 · Cited by 14 · see citing works
Self-citationÖzkayar, G., Lötters, J. C., Tichem, M., & Ghatkesar, M. K. (2022) · Biomicrofluidics · doi:10.1063/5.0074156 · Cited by 14 · see citing works
Microphysiological systems to study tumor-stroma interactions in brain cancer
Neves, E. R., Harley, B. A. C., & Pedrón, S. (2021) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2021.06.012 · Cited by 12 · see citing works
Unraveling Cancer Metastatic Cascade Using Microfluidics-based Technologies
Hakim, M., Kermanshah, L., Abouali, H., Hashemi, H. M., Yari, A., Khorasheh, F., Alemzadeh, I., & Vossoughi, M. (2022) · Biophysical Reviews · doi:10.1007/s12551-022-00944-8 · Cited by 13 · see citing works
Understanding glioblastoma invasion using physically-guided neural networks with internal variables
Ayensa-Jiménez, J., Doweidar, M. H., Sanz‐Herrera, J. A., & Doblaré, M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010019 · Cited by 11 · see citing works
Doty, D. T., Schüler, J. B., Mott, V. L., Bryan, C. M., Moore, N. F., Ho, J. C., & Borenstein, J. T. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21186478 · Cited by 10 · see citing works
T Cells Chemotaxis Migration Studies with a Multi-Channel Microfluidic Device
Liu, Y., Ren, X., Wu, J., Wilkins, J. A., & Lin, F. (2022) · Micromachines · doi:10.3390/mi13101567 · Cited by 10 · see citing works
Bayona, C., Wrona, M., Ranđelović, T., Nerı́n, C., Salafranca, J., & Ochoa, I. (2024) · Biofabrication · doi:10.1088/1758-5090/ad5764 · Cited by 9 · see citing works
Self-citationCieślak, A., Krakos, A., Kulbacka, J., & Detyna, J. D. (2024) · Microchimica Acta · doi:10.1007/s00604-024-06683-9 · Cited by 9 · see citing works
Zhang-Zhou, J., Movilla, N., Salafranca, C. O., Gómez‐Benito, M. J., Guerrero, P. E., Jimeno, J. P., & García‐Aznar, J. M. (2024) · Life Sciences · doi:10.1016/j.lfs.2024.123335 · Cited by 9 · see citing works
Lim, J., Choi, H., Ahn, J., & Jeon, N. L. (2021) · Advanced NanoBiomed Research · doi:10.1002/anbr.202100078 · Cited by 9 · see citing works
Biomaterials meet organ-on-chips – a perspective on tumor modeling
Monteiro, C. F., Deus, I. A., Custódio, C. A., & Mano, J. F. (2024) · International Materials Reviews · doi:10.1177/09506608241303436 · Cited by 8 · see citing works
Modelling the effect of vascular status on tumour evolution and outcome after thermal therapy
Bosque, J. J., Calvo, G. F., & Navarro, M. C. (2022) · Applied Mathematical Modelling · doi:10.1016/j.apm.2022.05.029 · Cited by 8 · see citing works
Szafran, R. G., Gąsiorowski, K., & Wiatrak, B. (2021) · Molecules · doi:10.3390/molecules26113215 · Cited by 8 · see citing works
Tumor-on-a-Chip: Microfluidic Models of Hypoxic Tumor Microenvironment
Parihar, A., Choudhary, N. K., Parihar, D. S., & Khan, R. (2023) · Springer eBooks · doi:10.1007/978-981-99-0313-9_14 · Cited by 8 · see citing works
Sharallah, O. A., Poddar, N. K., & Alwadan, O. A. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102394 · Cited by 7 · see citing works
Liu, P., Fu, L., Li, B., Man, M., Ji, Y., Kang, Q., Sun, X., Shen, D., & Chen, L. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-23688-0 · Cited by 7 · see citing works
Pérez-Aliacar, M., Ayensa-Jiménez, J., & Doblaré, M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107291 · Cited by 7 · see citing works
Bonifácio, E., Torres, L. A. G., Meireles, A. B., Guimarães, M. V., & ARAÚJO, C. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105039 · Cited by 7 · see citing works
3D Hydrogel Cell Cultures and Their Biomedical Applications
Thai, T. L., Manchanda, A., Higginbottom, S. L., Rodriguez, E. S., Wilson, R., Tomaskovic‐Crook, E., Crook, J. M., & Paull, B. (2025) · Advanced NanoBiomed Research · doi:10.1002/anbr.202500133 · Cited by 6 · see citing works
Insights into 2D and 3D cell culture models for nanoparticle-based drug delivery to glioblastoma
Dabkevičiūtė, G., & Petrikaitė, V. (2025) · Biochemical Pharmacology · doi:10.1016/j.bcp.2025.116931 · Cited by 6 · see citing works
Komen, J., van Neerven, S. M., Bossink, E. G. B. M., de Groot, N. E., Nijman, L. E., van den Berg, A., Vermeulen, L., & van der Meer, A. D. (2022) · Micromachines · doi:10.3390/mi13050739 · Cited by 6 · see citing works
Microfluidic technologies in tumour metabolism
Doherty, M., Wang, T., Lamprou, D. A., & Coulter, J. A. (2022) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2022.122370 · Cited by 6 · see citing works
A comprehensive overview of advanced dynamic in vitro intestinal and hepatic cell culture models
Leal, F., Zeiringer, S., Jeitler, R., Costa, P. F., & Roblegg, E. (2023) · Tissue Barriers · doi:10.1080/21688370.2022.2163820 · Cited by 7 · see citing works
Bayona, C., Ranđelović, T., Olaizola‐Rodrigo, C., & Ochoa, I. (2025) · Bioengineering & Translational Medicine · doi:10.1002/btm2.70032 · Cited by 5 · see citing works
Self-citationde Nigris, F., Meo, C., & Palinski, W. (2023) · Cells · doi:10.3390/cells12172204 · Cited by 5 · see citing works
- Authors
- Ayuso, J. M., Virumbrales-Muñoz, M., Lacueva, A., Lanuza, P. M., Checa-Chavarria, E., Botella, P., Fernández, E., Doblare, M., Allison, S. J., Phillips, R. M., Pardo, J., Fernandez, L. J., & Ochoa, I.
- Affiliations
- Universidad de Zaragoza (ES)
- Universitat de Miguel Hernández d'Elx (ES)
- Consejo Superior de Investigaciones Científicas (ES)
- University of Huddersfield (GB)
- Corresponding author
- Ochoa, Ignacio
- ORCID
- Venue
- Scientific Reports
- Published
- 2016-10-31
- Volume
- 6
- Issue
- 1
- Pages
- 36086-36086
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5086897
- OpenAlex
- W2541191495
- Cited by
- 134
- References
- 36
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 16 · 2023: 19 · 2022: 18 · 2021: 23 · 2020: 12 · 2019: 11 · 2018: 12 · 2017: 7 · 2015: 1
- FWCI
- 4.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Huddersfield Repository (University of Huddersfield) (acceptedVersion)
- Huddersfield Research Portal (University of Huddersfield) (submittedVersion, cc-by)
- Zaguan (Universidad de Zaragoza) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › 3D Printing in Biomedical Research
- Physical Sciences › Engineering › Biomedical Engineering › Microfluidic and Capillary Electrophoresis Applications
- Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
- Keywords
- tumor microenvironment, microfluidics, heterotypic cell interactions, oxygen gradients, glucose gradient, real-time monitoring, reactive oxygen species, apoptosis, cell proliferation, drug response, in vitro drug testing, cancer cell models
- MeSH terms
- Antineoplastic Agents, Cell Survival, Doxorubicin, Glucose, Humans, Killer Cells, Natural, Microscopy, Fluorescence, Oxygen, Apoptosis, Reactive Oxygen Species, Spheroids, Cellular, Cell Culture Techniques, Hydrogels, Microfluidics, HCT116 Cells, Cell Line, Tumor, Cell Proliferation, Time-Lapse Imaging, 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