An organoid platform for ovarian cancer captures intra- and interpatient heterogeneity
Open accessAbstract
Ovarian cancer (OC) is a heterogeneous disease usually diagnosed at a late stage. Experimental in vitro models that faithfully capture the hallmarks and tumor heterogeneity of OC are limited and hard to establish. We present a protocol that enables efficient derivation and long-term expansion of OC organoids. Utilizing this protocol, we have established 56 organoid lines from 32 patients, representing all main subtypes of OC. OC organoids recapitulate histological and genomic features of the pertinent lesion from which they were derived, illustrating intra- and interpatient heterogeneity, and can be genetically modified. We show that OC organoids can be used for drug-screening assays and capture different tumor subtype responses to the gold standard platinum-based chemotherapy, including acquisition of chemoresistance in recurrent disease. Finally, OC organoids can be xenografted, enabling in vivo drug-sensitivity assays. Taken together, this demonstrates their potential application for research and personalized medicine.
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Tumor tissue from biopsies or surgical resections is typically processed similarly to normal tissue to isolate tumor cells for organoid growth.
“Tumor tissue, derived from either biopsies or surgical resections, is also typically processed akin to normal tissue to isolate tumour cells to grow as organoids16,49–51.”
From Organoids (2022) · cites this paper as [49]
Several patient-derived organoid models, including pancreas, colorectal, and liver cancer, exhibit heterogeneous responses to known chemotherapeutic and anticancer agents.
“In particular, for cancer treatment, several different patient-derived organoid models tested, including but not limited to: pancreas, colorectal and liver cancer all exhibit heterogeneous responses to known chemotherapeutic and anticancer agents49,132,212.”
From Organoids (2022) · cites this paper as [49]
The number of CTCs in blood samples correlates with early recurrence and poor prognosis.
“the number of CTCs in blood samples correlates with early recurrence and poor prognosis.”
From 3D Biomimetic Models to Reconstitute Tumor Microenvironment In Vitro: Spheroids, Organoids, and Tumor‐on‐a‐Chip (2023) · cites this paper as [110]
Patient-derived organoids or tumoroids retain cellular heterogeneity and immune cells, thus are able to more strongly recapitulate a three-dimensional tumor microenvironment ex vivo compared to homogeneous cell lines.
“Patient-derived organoids or tumoroids retain cellular heterogeneity and immune cells, thus are able to more strongly recapitulate a three-dimensional (3D) tumor microenvironment ex vivo compared to homogeneous cell lines (Hill et al., 2018; Kopper et al., 2019).”
From Three-dimensional modelling of ovarian cancer: from cell lines to organoids for discovery and personalized medicine (2022) · cites this paper as Kopper et al., 2019
Organoid cultures from high-risk women with germline mutations in BRCA1 or BRCA2 have been developed for research into early tumor development.
“Organoid cultures from high-risk women with germline mutations in BRCA1 or BRCA2 have been developed for research into early tumor development (Kopper et al., 2019).”
From Three-dimensional modelling of ovarian cancer: from cell lines to organoids for discovery and personalized medicine (2022) · cites this paper as Kopper et al., 2019
Organoids from normal ovarian tissue have been utilized in studies of tumor development.
“As the initial triggers for the development of ovarian cancer is still being debated, organoids from normal ovarian tissue have also been utilized in studies of tumor development.”
From Three-dimensional modelling of ovarian cancer: from cell lines to organoids for discovery and personalized medicine (2022) · cites this paper as Kopper et al., 2019
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Jacob, F., Salinas, R. D., Zhang, D. Y., Nguyen, P. T. T., Schnoll, J. G., Wong, S. Z. H., Thokala, R., Sheikh, S., Saxena, D., Prokop, S., et al. (2020) · Cell · doi:10.1016/j.cell.2019.11.036 · Cited by 1,149 · see citing works
Zhao, Z., Chen, X., Dowbaj, A. M., Sljukic, A., Bratlie, K., Lin, L., Fong, E. L. S., Balachander, G. M., Chen, Z., Soragni, A., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00174-y · Cited by 1,001 · see citing works
Cancer modeling meets human organoid technology
Tuveson, D., & Clevers, H. (2019) · Science · doi:10.1126/science.aaw6985 · Cited by 935 · see citing works
Self-citationXenograft and organoid model systems in cancer research
Bleijs, M., van de Wetering, M., Clevers, H., & Drost, J. (2019) · The EMBO Journal · doi:10.15252/embj.2019101654 · Cited by 400 · see citing works
Self-citationPatient-derived organoids as a predictive biomarker for treatment response in cancer patients
Wensink, G. E., Elias, S. G., Mullenders, J., Koopman, M., Boj, S. F., Kranenburg, O. W., & Roodhart, J. M. L. (2021) · npj Precision Oncology · doi:10.1038/s41698-021-00168-1 · Cited by 323 · see citing works
de Witte, C. J., Espejo Valle-Inclan, J., Hami, N., Lõhmussaar, K., Kopper, O., Vreuls, C. P. H., Jonges, G. N., van Diest, P., Nguyen, L., Clevers, H., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107762 · Cited by 303 · see citing works
Developing Organoids from Ovarian Cancer as Experimental and Preclinical Models
Maenhoudt, N., Defraye, C., Boretto, M., Jan, Z., Heremans, R., Boeckx, B., Hermans, F., Arijs, I., Cox, B., Van Nieuwenhuysen, E., et al. (2020) · Stem Cell Reports · doi:10.1016/j.stemcr.2020.03.004 · Cited by 186 · see citing works
An Automated Organoid Platform with Inter-organoid Homogeneity and Inter-patient Heterogeneity
Jiang, S., Zhao, H., Zhang, W., Wang, J., Liu, Y., Cao, Y., Zheng, H., Hu, Z., Wang, S., Zhu, Y., et al. (2020) · Cell Reports Medicine · doi:10.1016/j.xcrm.2020.100161 · Cited by 188 · see citing works
Applications of organoids for cancer biology and precision medicine
Lo, Y.-H., Karlsson, K., & Kuo, C. J. (2020) · Nature Cancer · doi:10.1038/s43018-020-0102-y · Cited by 182 · see citing works
Nanki, Y., Chiyoda, T., Hirasawa, A., Ookubo, A., Itoh, M., Ueno, M., Akahane, T., Kameyama, K., Yamagami, W., Kataoka, F., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69488-9 · Cited by 165 · see citing works
Next-Generation Surrogate Wnts Support Organoid Growth and Deconvolute Frizzled Pleiotropy In Vivo
Miao, Y., Ha, A., de Lau, W., Yuki, K., Santos, A. J. M., You, C., Geurts, M. H., Puschhof, J., Pleguezuelos-Manzano, C., Peng, W. C., et al. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2020.07.020 · Cited by 151 · see citing works
Self-citationCancer research using organoid technology
Kretzschmar, K. (2021) · Journal of Molecular Medicine · doi:10.1007/s00109-020-01990-z · Cited by 120 · see citing works
Allgayer, H., Mahapatra, S., Mishra, B., Swain, B., Saha, S., Khanra, S., Kumari, K., Panda, V. K., Malhotra, D., Patil, N. S., et al. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02338-2 · Cited by 127 · see citing works
Li, W., Zhou, Z., Zhou, X., Khoo, B. L., Gunawan, R., Chin, Y. R., Zhang, L., Yi, C., Guan, X., & Yang, M. (2023) · Advanced Healthcare Materials · doi:10.1002/adhm.202202609 · Cited by 109 · see citing works
Xiang, D., He, A., Zhou, R., Wang, Y., Xiao, X., Gong, T., Kang, W., Lin, X., Wang, X., Consortium11, P.-B. D., et al. (2024) · Theranostics · doi:10.7150/thno.96027 · Cited by 108 · see citing works
Dadgar, N., Gonzalez-Suarez, A. M., Fattahi, P., Hou, X., Weroha, J. S., Gaspar-Maia, A., Stybayeva, G., & Revzin, A. (2020) · Microsystems & Nanoengineering · doi:10.1038/s41378-020-00201-6 · Cited by 99 · see citing works
Organoids in image-based phenotypic chemical screens
Lukonin, I., Zinner, M., & Liberali, P. (2021) · Experimental & Molecular Medicine · doi:10.1038/s12276-021-00641-8 · Cited by 99 · see citing works
De Angelis, M. L., Francescangeli, F., Nicolazzo, C., Signore, M., Giuliani, A., Colace, L., Boe, A., Magri, V., Baiocchi, M., Ciardi, A., et al. (2022) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-022-02263-y · Cited by 75 · see citing works
Yee, C., Dickson, K.-A., Muntasir, M. N., Ma, Y., & Marsh, D. J. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2022.836984 · Cited by 66 · see citing works
Single cell-derived spheroids capture the self-renewing subpopulations of metastatic ovarian cancer
Velletri, T., Villa, C. E., Cilli, D., Barzaghi, B., Lo Riso, P., Lupia, M., Luongo, R., López-Tobón, A., De Simone, M., Bonnal, R. J. P., et al. (2022) · Cell Death & Differentiation · doi:10.1038/s41418-021-00878-w · Cited by 48 · see citing works
Roos, F. J. M., Wu, H., Willemse, J., Lieshout, R., Albarinos, L. A. M., Kan, Y., Poley, J., Bruno, M. J., de Jonge, J., Bártfai, R., et al. (2021) · Clinical and Translational Medicine · doi:10.1002/ctm2.566 · Cited by 40 · see citing works
Abreu, S., Silva, F., Mendes, R., Mendes, T. F., Teixeira, M., Santo, V. E., Boghaert, E. R., Félix, A., & Brito, C. (2020) · Scientific Reports · doi:10.1038/s41598-020-76291-z · Cited by 31 · see citing works
PDAC as an immune evasive disease: can 3D model systems aid to tackle this clinical problem?
Narayanan, S., Vicent, S., & Ponz-Sarvisé, M. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.787249 · Cited by 24 · see citing works
Sartini, S., & Soragni, A. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2021.07.007 · Cited by 4 · see citing works
- Authors
- Kopper, O., de Witte, C. J., Lõhmussaar, K., Valle-Inclan, J. E., Hami, N., Kester, L., Balgobind, A. V., Korving, J., Proost, N., Begthel, H., van Wijk, L. M., Revilla, S. A., Theeuwsen, R., van de Ven, M., van Roosmalen, M. J., Ponsioen, B., Ho, V. W. H., Neel, B. G., Bosse, T., Gaarenstroom, K. N., Vrieling, H., Vreeswijk, M. P. G., van Diest, P. J., Witteveen, P. O., Jonges, T., Bos, J. L., van Oudenaarden, A., Zweemer, R. P., Snippert, H. J. G., Kloosterman, W. P., & Clevers, H.
- Affiliations
- Royal Netherlands Academy of Arts and Sciences (NL)
- Utrecht University (NL)
- The Netherlands Cancer Institute (NL)
- University Health Network (CA)
- Leiden University Medical Center (NL)
- Corresponding author
- Kloosterman, Wigard P.
- Clevers, Hans
- ORCID
- Venue
- Nature Medicine
- Published
- 2019-04-22
- Volume
- 25
- Issue
- 5
- Pages
- 838-849
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W2939543377
- Cited by
- 834
- References
- 77
- Citations by year
- 2026: 95 · 2025: 115 · 2024: 143 · 2023: 110 · 2022: 120 · 2021: 129 · 2020: 91 · 2019: 22 · 2015: 1 · 2013: 1 · 2012: 2
- FWCI
- 87.50
- Type
- journal article
- Language
- en
- Open access
- green
- dspace.library.uu.nl
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- KNAW research portal (Royal Academy of Art and Sciences (KNAW)) (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Reproductive Medicine › Ovarian cancer diagnosis and treatment
- Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
- Physical Sciences › Engineering › Biomedical Engineering › 3D Printing in Biomedical Research
- Keywords
- ovarian cancer, organoids, tumor heterogeneity, intratumoral heterogeneity, intertumoral heterogeneity, recurrent ovarian cancer, drug screening, xenotransplantation, personalized medicine, ovarian cancer subtypes
- MeSH terms
- Adult, Aged, Animals, Drug Screening Assays, Antitumor, Female, Humans, Middle Aged, Mutation, Organoids, Ovarian Neoplasms, Mice, SCID, Genomics, Cell Line, Tumor, Precision Medicine, Heterografts
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.
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