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An organoid platform for ovarian cancer captures intra- and interpatient heterogeneity

Nature Medicine volume 25, pages 838–849 (2019)Cite this article

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Abstract

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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Fig. 1: Subtype diversity and histological characterization of OC organoids.
Fig. 2: Organoids maintain genomic landscape of corresponding tumors.
Fig. 3: Somatic mutations and amplifications/deletions in OC organoids.
Fig. 4: OC organoids capture tumor heterogeneity.
Fig. 5: Gene expression analysis of OC organoids.
Fig. 6: In vitro and in vivo drug sensitivity assays.

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Data availability

BAM files for DNA and RNA sequencing data are made available through controlled access at the European Genome-phenome Archive (EGA) which is hosted at the EBI and the CRG (https://ega-archive.org), under accession number EGA: EGAS00001003073. Data access requests will be evaluated by the UMCU Department of Genetics Data Access Board (EGAC00001000432) and transferred on completion of a material transfer agreement and authorization by the medical ethical committee UMCU at request of the HUB to ensure compliance with the Dutch ‘medical research involving human subjects’ act.

Code availability

Illumina data processing pipeline v2.2.1 is available at https://github.com/UMCUGenetics/IAP/releases/tag/v2.2.1 and RNA analysis pipeline v2.3.0 is available at https://github.com/UMCUGenetics/RNASeq. All other custom code used for this study is available at https://github.com/UMCUGenetics/OvCaBiobank

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Acknowledgements

We thank T. Bayram for supporting of ethical regulatory affairs. We acknowledge A. Brousali, P. van der Groep, A. Constantinides, A. Snelting and O. Kranenburg of the Utrecht Platform for Organoid Technology (U-PORT; UMC Utrecht) for patient inclusion and tissue acquisition. We thank the Integraal Kankercentrum Nederland (IKNL) and M. van der Aa for supplying clinical data, and I. Renkens for help with DNA isolations. We acknowledge E. Stelloo for her help with culturing organoids. We thank the people from the Preclinical Intervention Unit of the Mouse Clinic for Cancer and Ageing (MCCA) at the NKI for performing the intervention studies. We thank B. Artegiani and T. Dayton for critically reading the manuscript. O.K. was supported by Marie Skłodowska-Curie IF grant 658933 – HGSOC. This work was funded by the gravitation program CancerGenomiCs.nl from the Netherlands Organisation for Scientific Research (NWO), MKMD grant (114021012) from Netherlands Organization for Scientific Research (NWO-ZonMw), Stand Up to Cancer International Translational Cancer Research Grant, a program of the Entertainment Industry Foundation administered by the AACR, Dutch Cancer Society (KWF) grant UU2015-7743, Dutch Cancer Society (Alpe d’HuZes) grant EMCR 2014-7048, and a grant from the Gieskes Strijbis Foundation (1816199). The Oncode Institute is supported by the Dutch Cancer Society.

Author information

Author notes

  1. These authors contributed equally: Chris J. De Witte, Kadi Lõhmussaar, Jose Espejo Valle-Inclan

Authors and Affiliations

  1. Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences and UMC Utrecht, Utrecht, the Netherlands

    Oded Kopper, Kadi Lõhmussaar, Lennart Kester, Anjali Vanita Balgobind, Jeroen Korving, Harry Begthel, Sonia Aristín Revilla, Alexander van Oudenaarden & Hans Clevers

  2. Oncode Institute, Utrecht, the Netherlands

    Oded Kopper, Kadi Lõhmussaar, Nizar Hami, Lennart Kester, Anjali Vanita Balgobind, Jeroen Korving, Harry Begthel, Sonia Aristín Revilla, Bas Ponsioen, Johannes L. Bos, Alexander van Oudenaarden, Hugo J. G. Snippert & Hans Clevers

  3. Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands

    Chris J. de Witte, Jose Espejo Valle-Inclan, Markus J. van Roosmalen & Wigard P. Kloosterman

  4. Molecular Cancer Research, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands

    Nizar Hami, Bas Ponsioen, Johannes L. Bos & Hugo J. G. Snippert

  5. Preclinical Intervention Unit of the Mouse Clinic for Cancer and Ageing (MCCA) at the NKI, Amsterdam, the Netherlands

    Natalie Proost, Rebecca Theeuwsen & Marieke van de Ven

  6. Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands

    Lise M. van Wijk, Harry Vrieling & Maaike P. G. Vreeswijk

  7. Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada

    Victor W. H. Ho & Benjamin G. Neel

  8. Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA

    Benjamin G. Neel

  9. Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands

    Tjalling Bosse

  10. Department of Gynecology, Leiden University Medical Center, Leiden, the Netherlands

    Katja N. Gaarenstroom

  11. Department of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands

    Paul J. van Diest & Trudy Jonges

  12. Department of Medical Oncology, Cancer Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands

    Petronella O. Witteveen

  13. Department of Gynaecological Oncology, Cancer Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands

    Ronald P. Zweemer

  14. The Netherlands Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands

    Hans Clevers

Authors
  1. Oded Kopper
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  2. Chris J. de Witte
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Contributions

Conceptualization: O.K. and H.C. Methodology: O.K. and H.C. Software: J.E.V.-I., M.J.v.R., L.K. and W.P.K. Formal analysis: O.K., K.L., N.H., J.E.V.-I., M.J.v.R., T.J., P.J.V.D., S.A.R., L.K. and W.P.K. Investigation: O.K., K.L., C.J.d.W., N.H., A.V.B., H.B., J.K., S.A.R., L.K., N.P., R.T., L.M.v.W. and B.P. Resources: C.J.d.W., L.M.v.W., H.V., M.P.G.V., V.W.H.H., B.G.N., P.O.W., M.V.D.V., T.B, K.N.G. and R.P.Z. Data curation: O.K., C.J.d.W. and J.E.V.-I. Writing—original draft: O.K., C.J.d.W., J.E.V.-I., W.P.K. and H.C. Visualization: O.K., C.J.d.W., K.L., J.E.V.-I., W.P.K., L.K. and N.H. Supervision: M.V.D.V., J.L.B., R.P.Z., H.J.G.S., W.P.K., A.v.O. and H.C. Project administration: O.K., C.J.d.W., W.P.K. and H.C. Funding acquisition: J.L.B., R.P.Z., P.O.W., W.P.K. and H.C.

Corresponding authors

Correspondence to Wigard P. Kloosterman or Hans Clevers.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Derivation and morphological differences of OC organoids.

a, Schematic of OC organoid derivation. b, Bright-field images of MBT, SBT, MC, LGS, END and CCC organoids (left to right), depicting different organoid morphologies. Morphological description of 50 independent organoid lines is provided in Supplementary Table 6. Scale bar, 100 μm. c, Bright-field (top) and SEM (bottom) images demonstrating main morphologies among different HGS organoid lines. Starting with cystic and well-organized cellular polarity, where microvilli are directed toward the organoid lumen (most left) to dense organoids that gradually (from left to right) show reduced circularity and cellular cohesiveness up to a grape-like shape morphology (most right). Scale bar, 100 μm. d, High-magnification H&E staining images displaying representative examples of HGS organoid morphologies as well as nuclear and cellular atypia, typically displayed by HGS tumors. Histological description of 50 independent organoid lines is provided in Supplementary Table 6. Scale bar, 100 μm.

Extended Data Fig. 2 Organoid passage number overview and normal cell contamination in tumors and organoids.

a, Column bar graph depicting organoid maximum passage number up until the moment of submission. Organoids that stopped/slowed down their growth are indicated in orange. b, Representative images of Ki67 staining of six independent organoid lines show a high percentage of ki67-positive proliferating cells. c, Histological and immunohistochemical images of tumor tissue (derived from two independent patients) showing tumor cell purity within different samples, based on H&E and p53 staining. Scale bar, 0.5 mm. d, Tukey box-and-whisker plot (1.5× interquartile range) presenting bioinformatic estimation of tumor cell purity percentage of both tissue (n = 35) and organoid (n = 36) based on WGS data using PURPLE. Horizontal bars represent median of all dots. Mean and standard deviation across all samples are as follows: 45 ± 9.2% (tissue) and 88.1 ± 23% (organoids). e, Stacked bar chart showing the percentage of organoid lines that are positive for p53, PAX8 and periodic acid–Schiff (PAS) staining (orange) and negative (blue) grouped per original tumor staining status (see also Supplementary Table 6). Total number (n) of tissues stained per group are indicated.

Extended Data Fig. 3 FT and OSE organoids.

a, An overview image of normal FT organoids embedded in 40 μl BME drops, displaying a cystic morphology. All FT organoid lines that were established (n = 22) displayed cystic morphology. b, Representative SEM image showing ciliated cells facing FT organoid lumen. Scale bar, 50 μm. SEM was performed on one FT organoid line. c, Histological analysis of FT organoids demonstrating H&E, Ki67, PAX8 and Ac-α-tubb staining. Histological analysis was performed on three independent FT organoid lines with similar results. Scale bar, 100 μm. d, An overview image of normal OSE organoids embedded in 40 μl BME drops displaying cystic morphology (top left image). Seven out of eight OSE organoid lines that were established displayed cystic morphology. OSE organoid images of H&E, Ki67 and cytokeratin 8 (CK8) staining, demonstrating a cystic morphology of proliferative epithelial cells. Histological analysis was performed on two independent OSE organoid lines with similar results. Scale bar, 100 μm. e, First row: bright-field images of LGS-2.2 (left) and OSE(P)7 (right) organoid lines. Unlike normal FT and OSE that display cystic morphology both lines show dense phenotype. OSE(P)7 is the only OSE organoid line that display dense phenotype. Scale bar, 200 μm. Second to last rows: histological and immunohistochemical images demonstrate that organoids are positively stained for PAX8 and WT1, markers of OC serous subtypes. Organoids display reduced cellular organization in comparison to normal FT and OSE organoids. Scale bar, 100 μm. f, Scatter plot presenting metaphase spread analysis and mean for each line. Both lines present aneuploidy.

Extended Data Fig. 4 Genome-wide tumor and organoid pair comparison.

a, Genome-wide CNVs in tumor/organoid pairs (black, tumors; pink, organoids early passage; blue, organoids late passage) depicting gains (red) and losses (blue). b, Number of shared (yellow) and unique (blue) SNVs (on the left) and SVs (on the right) between tumor/organoid pairs. Shared variants are those that can be found in the corresponding paired sample. Passage number at which organoid lines were sequenced is given in Supplementary Table 7.

Extended Data Fig. 5 Molecular characterization, drug screening and xenografts of OC organoids.

a, Tukey box and whisker plot (1.5× interquartile range) summarizing the percentage of shared variants across all tumor (red) and organoid (green) samples. Right and left panels display SNVs and SVs, respectively. Horizontal bars represent median of all dots. Mean and standard deviation across all samples are as follows: SNVs, 82.95 ± 8.18% (tissue, n = 31) and 75.62 ± 23.13% (organoids, n = 31); SVs, 78.14 ± 22.11% (tissue, n = 31) and 60.47 ± 29.13% (organoids, n = 31). Samples with a low percentage of shared variants are indicated. b, Heat map of five independent organoid lines from both early and late passages based on 11,720 methylation probes. The heat map colors represent Pearson correlation values, as calculated from the methylation beta-values. Clustering of the correlation values was performed using hierarchical clustering based on complete linkage. c, Scatter plot of AUC values across all drug screening data, displaying high correlation between technical replicates (Pearson correlation = 0.94, R2 = 0.88, n = 105). d, Scatter plot of AUC values of biological replicates, displaying high correlation (Pearson correlation = 0.87, R2 = 0.74, n = 45). Colored dots represent biological replicates in which passage differences between experimental repetition is as follows: 1–2 passages, n = 29 (black); 3–5 passages, n = 10 (blue) and 13–22 passages, n = 6, (red), demonstrating stable drug sensitivity even after prolonged passaging. e, Box-and-whisker plot (10th–90th percentile) showing Z-factor distribution and mean across all drug screening plates. Mean = 0.61, ranging between 0.2 and 0.91, n = 55. f, Bioluminescence imaging of mice, orthotopically transplanted with luciferase expressing organoid lines depicting tumor growth. A summary of organoid-derived xenograft experiments is presented in Supplementary Table 8. g, p53 staining of organoid-derived xenograft (HGS-3.1) on orthotopic transplantation into the mouse bursa shows p53 overexpression in tumor cells. h, Histological analysis of an organoid-derived xenograft (MC-2.1) on subcutaneous transplantation. H&E staining shows haphazardly arranged neoplastic glands lined by columnar cells with variable numbers of goblet cells (arrows), which are specific features of MC. A summary of organoid-derived xenograft experiments is presented in Supplementary Table 8. Left image scale bar, 1 mm. Right image scale bar, 200 μm.

Extended Data Fig. 6 CRISPR–Cas9 mediated genetic manipulation in FT organoids.

a, Schematic of normal FT organoid electroporation. FT organoids were dissociated into small cell clumps and electroporated with either an empty vector or a vector containing a gRNA directed against TP53. Cells were plated and after 2 d of recovery nutlin3a was added. b, Overview images of organoids 2 weeks after electroporation. Organoids that were electroporated with an empty vector and not treated with nutlin3a showed nice recovery following electroporation (top), whereas the growth of organoids electroporated in a similar manner was dramatically inhibited when nutlin3a was added (middle). Surviving clones that are not inhibited by nutlin3a treatment are visible only when organoids were electroporated with a vector containing TP53 gRNA (bottom). Four independent electroporation experiments followed by nutlin3A treatment were conducted giving rise to multiple Nutlin3A resistant clones. c, A representative flow cytometry analysis of organoids 48 h following electroporation demonstrating 25% of the cell express GFP. Summary of six independent repetitions of this experiment are presented in d. d, Box-and-whisker plot (minimum to maximum) showing the percentage of GFP positive cells following electroporation. Horizontal bars and dashed horizonal bars represent median and mean of all dots, respectively. Mean ± s.d. = 23.8 ± 5.5%, median = 25.5%. Six independent experiments that were conducted with three different FT organoid lines are presented, demonstrating high and robust electroporation efficiency. e, An example of CRISPR–Cas9 mediated editing of TP53 gene in FT organoids. Targeted locus is presented and gRNA (solid line), PAM sequence (red highlight) and cut point (arrow head) are indicated. Sequencing results revealed out-of-frame deletions induced by CRISPR–Cas9 editing. f, Table presenting six FT genetically engineered clones derived from two independent donors (FT(P)1 and FT(P)2). For each clone, targeted gene description (in both TP53 and RB1 genes) including HGVS nomenclature is presented. (HET, heterozygous; HOM, homozygous). g, BF images (top) and H&E staining (bottom) of four independent clones show deviation from cystic and well-organized normal FT organoid morphology. Passage number is indicated. This analysis was conducted on three independent TP clones (loss-of-function mutations in the TP53 gene) and three independent TPR clones (loss-of-function mutations in the TP53 and RB1 genes) with similar results. h, Heat map of Spearman correlation values of three independent normal FT organoid lines (derived from different donors) and genetically engineered clones (n = 3 independent TP clones (loss-of-function mutations in the TP53 gene) and 3 independent TPR clones (loss-of-function mutations in the TP53 and RB1 genes)), using RNA-seq expression data. Read counts were normalized for sequencing depth and the 1,000 most-variable genes were used. Clones were assigned into different groups according to their mutational profile.

Supplementary information

Supplementary Tables

Supplementary Tables 2, 4 and 8

Reporting Summary

Supplementary Tables

Supplementary Tables 1, 3, 5–7 and 9–11

Supplementary Video 1

High speed time-lapse imaging of normal FT line with beating cilia. High magnification of FT organoid epithelial cell layer and lumen. Beating ciliated cells are directed into the organoid lumen. High speed time-lapse imaging was conducted on one FT organoid line.

Supplementary Video 2

Live cell imaging of chromosomal segregation in OC organoids (HGS-3.2). Four independent organoid lines were subjected to confocal imaging for 11 h with 4 min intervals. All demonstrated normal and abnormal chromosomal segregations. Shown is a representative time-lapse imaging of HGS-3.2 line. Upper right panel shows H2B-Neon fluorescence after maximum-projection of three-dimensional z-stacks; upper left panel displays the same data, color coded for depth (z), facilitating tracking of individual events; lower panels consist of transmitted light images with and without merged H2B-Neon (green). Scale bar, 20 μm

Supplementary Video 3

Live cell imaging of chromosomal segregation in OC organoids (HGS-3.2). Example of normal chromosomal segregation. Color coded for depth (z).

Supplementary Video 4

Live cell imaging of chromosomal segregation in OC organoids (HGS-3.2). Example of chromosomal segregation into three poles and lagging chromosomes. Color coded for depth (z).

Supplementary Video 5

Live cell imaging of chromosomal segregation in OC organoids (HGS-3.2). Example of chromatin bridge during chromosomal segregation. Color coded for depth (z).

Supplementary Video 6

Live cell imaging of chromosomal segregation in OC organoids (HGS-3.2). Example of mitotic catastrophe that follows chromosomal segregation into three poles. Color coded for depth (z).

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Kopper, O., de Witte, C.J., Lõhmussaar, K. et al. An organoid platform for ovarian cancer captures intra- and interpatient heterogeneity. Nat Med 25, 838–849 (2019). https://doi.org/10.1038/s41591-019-0422-6

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