Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

An organoid-based screen for epigenetic inhibitors that stimulate antigen presentation and potentiate T-cell-mediated cytotoxicity

Nature Biomedical Engineering volume 5, pages 1320–1335 (2021)Cite this article

Subjects

An Author Correction to this article was published on 30 August 2023

This article has been updated

Abstract

In breast cancer, genetic heterogeneity, the lack of actionable targets and immune evasion all contribute to the limited clinical response rates to immune checkpoint blockade therapy. Here, we report a high-throughput screen based on the functional interaction of mouse- or patient-derived breast tumour organoids and tumour-specific cytotoxic T cells for the identification of epigenetic inhibitors that promote antigen presentation and potentiate T-cell-mediated cytotoxicity. We show that the epigenetic inhibitors GSK-LSD1, CUDC-101 and BML-210, identified by the screen, display antitumour activities in orthotopic mammary tumours in mice, that they upregulate antigen presentation mediated by the major histocompatibility complex class I on breast tumour cells and that treatment with BML-210 substantially sensitized breast tumours to the inhibitor of the checkpoint programmed death-1. Standardized measurements of tumour-cell killing activity facilitated by tumour-organoid–T-cell screens may help with the identification of candidate immunotherapeutics for a range of cancers.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: A tumour-organoid-based approach for screening immunotherapy drugs.
Fig. 2: Characterization and optimization of mouse breast tumour organoids.
Fig. 3: Screening of epigenetic inhibitors that enhance T-cell-mediated tumour-cell killing.
Fig. 4: Validation of antitumour activity of the three drug candidates in mouse and human tumour organoids.
Fig. 5: Antitumour activity of drug candidates in mouse breast tumour models.
Fig. 6: BML-210 treatment upregulates tumour-antigen processing and presentation.
Fig. 7: BML-210 treatment enhances antitumour responses in combination with PD-1 blockade.
Fig. 8: Treatment of BML-210, CUDC-101 or GSK-LSD1 promotes the cytotoxicity of autologous CD8+ T cells in PDOs.

Similar content being viewed by others

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are too large to be publicly shared, yet they are available for research purposes from the corresponding authors on reasonable request. The RNA sequence data are available from the GEO database under accession number GSE182954. Source data are provided with this paper.

Change history

References

  1. Gatti-Mays, M. E. et al. If we build it they will come: targeting the immune response to breast cancer. NPJ Breast Cancer 5, 37 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Dijkstra, K. K. et al. Generation of tumor-reactive T cells by co-culture of peripheral blood lymphocytes and tumor organoids. Cell 174, 1586–1598 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Sachs, N. et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 172, 373–386 (2018).

    Article  CAS  PubMed  Google Scholar 

  4. Stevanovic, S. et al. Landscape of immunogenic tumor antigens in successful immunotherapy of virally induced epithelial cancer. Science 356, 200–205 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Schmid, P. et al. Atezolizumab and nab-paclitaxel in advanced triple-negative breast cancer. N. Engl. J. Med. 379, 2108–2121 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. Zacharakis, N. et al. Immune recognition of somatic mutations leading to complete durable regression in metastatic breast cancer. Nat. Med. 24, 724–730 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kim, I. S. et al. Immuno-subtyping of breast cancer reveals distinct myeloid cell profiles and immunotherapy resistance mechanisms. Nat. Cell Biol. 21, 1113–1126 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Villanueva, L., Alvarez-Errico, D. & Esteller, M. The contribution of epigenetics to cancer immunotherapy. Trends Immunol. 41, 676–691 (2020).

    Article  CAS  PubMed  Google Scholar 

  9. Romero, D. HDAC inhibitors tested in phase III trial. Nat. Rev. Clin. Oncol. 16, 465 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. Sulaiman, A. et al. Co-inhibition of mTORC1, HDAC and ESR1α retards the growth of triple-negative breast cancer and suppresses cancer stem cells. Cell Death Dis. 9, 815 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Dravis, C. et al. Epigenetic and transcriptomic profiling of mammary gland development and tumor models disclose regulators of cell state plasticity. Cancer Cell 34, 466–482 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. D’Abreo, N. & Adams, S. Immune-checkpoint inhibition for metastatic triple-negative breast cancer: safety first? Nat. Rev. Clin. Oncol. 16, 399–400 (2019).

    Article  PubMed  Google Scholar 

  13. Wein, L., Luen, S. J., Savas, P., Salgado, R. & Loi, S. Checkpoint blockade in the treatment of breast cancer: current status and future directions. Br. J. Cancer 119, 4–11 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Feder-Mengus, C., Ghosh, S., Reschner, A., Martin, I. & Spagnoli, G. C. New dimensions in tumor immunology: what does 3D culture reveal? Trends Mol. Med. 14, 333–340 (2008).

    Article  CAS  PubMed  Google Scholar 

  15. Friedrich, J., Seidel, C., Ebner, R. & Kunz-Schughart, L. A. Spheroid-based drug screen: considerations and practical approach. Nat. Protoc. 4, 309–324 (2009).

    Article  CAS  PubMed  Google Scholar 

  16. Junttila, M. R. & de Sauvage, F. J. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature 501, 346–354 (2013).

    Article  CAS  PubMed  Google Scholar 

  17. Palucka, A. K. & Coussens, L. M. The basis of oncoimmunology. Cell 164, 1233–1247 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Neal, J. T. et al. Organoid modeling of the tumor immune microenvironment. Cell 175, 1972–1988 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Grassi, L. et al. Organoids as a new model for improving regenerative medicine and cancer personalized therapy in renal diseases. Cell Death Dis. 10, 201 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Crespo, M. et al. Colonic organoids derived from human induced pluripotent stem cells for modeling colorectal cancer and drug testing. Nat. Med. 23, 878–884 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Drost, J. et al. Organoid culture systems for prostate epithelial and cancer tissue. Nat. Protoc. 11, 347–358 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lee, S. H. et al. Tumor evolution and drug response in patient-derived organoid models of bladder cancer. Cell 173, 515–528 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kim, M. et al. Patient-derived lung cancer organoids as in vitro cancer models for therapeutic screening. Nat. Commun. 10, 3991 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Broutier, L. et al. Human primary liver cancer-derived organoid cultures for disease modeling and drug screening. Nat. Med. 23, 1424–1435 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Minton, K. Studying tumour-specific T cell responses in 3D. Nat. Rev. Immunol. 18, 602–603 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. de Souza, N. A model for tumor-immune interaction. Nat. Methods 15, 762 (2018).

    Article  PubMed  Google Scholar 

  27. Cattaneo, C. M. et al. Tumor organoid-T-cell coculture systems. Nat. Protoc. 15, 15–39 (2020).

    Article  CAS  PubMed  Google Scholar 

  28. Cafri, G. et al. Memory T cells targeting oncogenic mutations detected in peripheral blood of epithelial cancer patients. Nat. Commun. 10, 449 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Clarke, S. R. et al. Characterization of the ovalbumin-specific TCR transgenic line OT-I: MHC elements for positive and negative selection. Immunol. Cell Biol. 78, 110–117 (2000).

    Article  CAS  PubMed  Google Scholar 

  30. Rapoport, A. P. et al. NY-ESO-1-specific TCR-engineered T cells mediate sustained antigen-specific antitumor effects in myeloma. Nat. Med. 21, 914–921 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Wu, R. et al. Adoptive T-cell therapy using autologous tumor-infiltrating lymphocytes for metastatic melanoma current status and future outlook. Cancer J. 18, 160–175 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Banh, R. S. et al. PTP1B controls non-mitochondrial oxygen consumption by regulating RNF213 to promote tumour survival during hypoxia. Nat. Cell Biol. 18, 803–813 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Gogna, R., Madan, E., Kuppusamy, P. & Pati, U. Re-oxygenation causes hypoxic tumor regression through restoration of p53 wild-type conformation and post-translational modifications. Cell Death Dis. 3, e286 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Quail, D. F. & Joyce, J. A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 19, 1423–1437 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Joyce, J. A. & Pollard, J. W. Microenvironmental regulation of metastasis. Nat. Rev. Cancer 9, 239–252 (2009).

    Article  CAS  PubMed  Google Scholar 

  36. Ivashkiv, L. B. IFNγ: signalling, epigenetics and roles in immunity, metabolism, disease and cancer immunotherapy. Nat. Rev. Immunol. 18, 545–558 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Motyka, B. et al. Mannose 6-phosphate/insulin-like growth factor II receptor is a death receptor for granzyme B during cytotoxic T cell-induced apoptosis. Cell 103, 491–500 (2000).

    Article  CAS  PubMed  Google Scholar 

  38. Croft, M. The role of TNF superfamily members in T-cell function and diseases. Nat. Rev. Immunol. 9, 271–285 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Sheng, W. et al. LSD1 ablation stimulates anti-tumor immunity and enables checkpoint blockade. Cell 174, 549–563 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Blum, J. S., Wearsch, P. A. & Cresswell, P. Pathways of antigen processing. Annu Rev. Immunol. 31, 443–473 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Leone, P. et al. MHC class I antigen processing and presenting machinery: organization, function, and defects in tumor cells. J. Natl Cancer Inst. 105, 1172–1187 (2013).

    Article  CAS  PubMed  Google Scholar 

  42. Ostrand-Rosenberg, S. & Fenselau, C. Myeloid-derived suppressor cells: immune-suppressive cells that impair antitumor immunity and are sculpted by their environment. J. Immunol. 200, 422–431 (2018).

    Article  CAS  PubMed  Google Scholar 

  43. Sacchi, A. et al. Myeloid-derived suppressor cells specifically suppress IFN-γ production and antitumor cytotoxic activity of Vδ2 T cells. Front. Immunol. 9, 1271 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Gray, M. J. et al. Phosphatidylserine-targeting antibodies augment the anti-tumorigenic activity of anti-PD-1 therapy by enhancing immune activation and downregulating pro-oncogenic factors induced by T-cell checkpoint inhibition in murine triple-negative breast cancers. Breast Cancer Res. 18, 50 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Gray, M., Gong, J., Nguyen, V., Hutchins, J. & Freimark, B. Targeting of phosphatidylserine by monoclonal antibodies augments the activity of immune checkpoint inhibitor PD-1/PD-L1 therapy in murine breast tumors. Cancer Res. 76, abstr. P4-04-03 (2016).

  46. Ceccacci, E. & Minucci, S. Inhibition of histone deacetylases in cancer therapy: lessons from leukaemia. Br. J. Cancer 114, 605–611 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Munster, P. N. et al. A phase II study of the histone deacetylase inhibitor vorinostat combined with tamoxifen for the treatment of patients with hormone therapy-resistant breast cancer. Br. J. Cancer 104, 1828–1835 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Terranova-Barberio, M. et al. HDAC inhibition potentiates immunotherapy in triple negative breast cancer. Oncotarget 8, 114156–114172 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Phan, N. et al. A simple high-throughput approach identifies actionable drug sensitivities in patient-derived tumor organoids. Commun. Biol. 2, 78 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Bradley, C. A. Gastrointestinal cancer: organoids predict clinical responses. Nat. Rev. Gastroenterol. Hepatol. 15, 189 (2018).

    Article  PubMed  Google Scholar 

  51. Clevers, H. Modeling development and disease with organoids. Cell 165, 1586–1597 (2016).

    Article  CAS  PubMed  Google Scholar 

  52. Kondo, J. & Inoue, M. Application of cancer organoid model for drug screening and personalized therapy. Cells 8, 470 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Sato, T. et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009).

    Article  CAS  PubMed  Google Scholar 

  54. Fatehullah, A., Tan, S. H. & Barker, N. Organoids as an in vitro model of human development and disease. Nat. Cell Biol. 18, 246–254 (2016).

    Article  PubMed  Google Scholar 

  55. Appay, V., Douek, D. C. & Price, D. A. CD8+ T cell efficacy in vaccination and disease. Nat. Med. 14, 623–628 (2008).

    Article  CAS  PubMed  Google Scholar 

  56. Qiu, G. Z. et al. Reprogramming of the tumor in the hypoxic niche: the emerging concept and associated therapeutic strategies. Trends Pharmacol. Sci. 38, 669–686 (2017).

    Article  CAS  PubMed  Google Scholar 

  57. Lai, C. J. et al. CUDC-101, a multitargeted inhibitor of histone deacetylase, epidermal growth factor receptor, and human epidermal growth factor receptor 2, exerts potent anticancer activity. Cancer Res. 70, 3647–3656 (2010).

    Article  CAS  PubMed  Google Scholar 

  58. Jayathilaka, N. et al. Inhibition of the function of class IIa HDACs by blocking their interaction with MEF2. Nucleic Acids Res. 40, 5378–5388 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Banik, D., Moufarrij, S. & Villagra, A. Immunoepigenetics combination therapies: an overview of the role of HDACs in cancer immunotherapy. Int. J. Mol. Sci. 20, 2241 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Xu, H. et al. Organoid technology and applications in cancer research. J. Hematol. Oncol. 11, 116 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Yang, W. et al. Potentiating the antitumour response of CD8+ T cells by modulating cholesterol metabolism. Nature 531, 651–655 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Li, Y. et al. Heterozygous deletion of chromosome 17p renders prostate cancer vulnerable to inhibition of RNA polymerase II. Nat. Commun. 9, 4394 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Liu, Y. H. et al. TP53 loss creates therapeutic vulnerability in colorectal cancer. Nature 520, 697–U286 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Van der Jeught, K. et al. ST2 as checkpoint target for colorectal cancer immunotherapy. JCI Insight 5, e136073 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  66. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  67. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  68. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Dennis, G.Jr. et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 4, R60 (2003).

    Article  PubMed Central  Google Scholar 

  70. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the staff at the Laboratory Animal Resource Center of Indiana University for their technical support in animal studies; the staff at the Indiana Center for Biological Microscopy, the Flow Cytometry Resource Facility (FCRF) and the Center for Medical Genomics of Indiana University for use of instruments and technical assistance; and the staff at the Indiana University Simon Comprehensive Cancer Center (IUSCCC) for providing human breast tissue samples. This work was supported in part by US National Institutes of Health grants R01CA203737 (to X.L.), R01CA206366 (to X.L. and X.H.), R01CA243023 (to X.L. and X.H.) and R01CA222251 (to X.L.), and by the Indiana University Strategic Research Initiative fund (to X.L.) and the Vera Bradley Foundation for Breast Cancer Research (to X.L. and X.Z.).

Author information

Authors and Affiliations

  1. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA

    Zhuolong Zhou, Kevin Van der Jeught, Yuanzhang Fang, Tao Yu, Yujing Li, Lu Zhang, Yang Yang, Haniyeh Eyvani, Xiyu Wang, Jun Wan, Xinna Zhang & Xiongbin Lu

  2. Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA

    Zheng Ao & Feng Guo

  3. Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA

    Sheng Liu, Jun Wan & Xiongbin Lu

  4. Institute of Digestive Diseases, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China

    Lu Zhang & Guang Ji

  5. Experiment Center for Science and Technology, Shanghai University of Traditional Chinese Medicine, Shanghai, China

    Yang Yang

  6. Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, USA

    Mary L. Cox, Bryan P. Schneider, Jun Wan, Xinna Zhang & Xiongbin Lu

  7. Fischell Department of Bioengineering, University of Maryland, College Park, MD, USA

    Xiaoming He

  8. Division of Hematology/Oncology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA

    Bryan P. Schneider

Authors
  1. Zhuolong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kevin Van der Jeught
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuanzhang Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tao Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yujing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zheng Ao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Haniyeh Eyvani
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Mary L. Cox
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Xiyu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Xiaoming He
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Guang Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Bryan P. Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Feng Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Jun Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Xinna Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Xiongbin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Z.Z., X.Z. and X.L. designed experiments in the study. K.V.d.J., Y.F., T.Y., Y.L. and L.Z. provided technical support and conducted animal studies and immunological analyses. Y.Y. and H.E. provided technical assistance in molecular studies. Z.A., X.W. and F.G. provided technical support for tumour organoid studies. M.L.C. coordinated breast cancer tissue procurement. G.J., B.P.S., X.H. and F.G. discussed results and provided valuable advice for the project. S.L. and J.W. conducted bioinformatics and statistical analyses. Z.Z., X.Z. and X.L. wrote and revised the manuscript.

Corresponding authors

Correspondence to Xinna Zhang or Xiongbin Lu.

Ethics declarations

Competing interests

X.L. and X.Z. are the inventors on the US Provisional Patent Application (Serial No. 63/129,762) submitted by The Trustees of Indiana University for the organoid-based screen method and identified drugs in this study.

Additional information

Peer review information Nature Biomedical Engineering thanks Paola Scaffidi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Functional evaluation of the compounds screened from the 2D and tumour-organoid systems.

a, Pie chart of the positive compounds screened from the 2D and tumour organoid systems. b, Effect of the above compounds (a) on T cell infiltration in tumour organoids. The CD8+ T cells (from OT-I mouse) were co-cultured with the compound-treated GFP+Luc+OVA+ EO771 tumour organoids for 48 h. The tumour organoids with T cells infiltrated or attached were dissociated to single cells and stained with APC/Cy7-conjugated anti-mouse CD8 and SYTOX Blue reagent for 15 min. The T cell proportions from the tumour organoids were analyzed by flow cytometry. Data from 3 biologically parallel experiments were analyzed using One-way ANOVA and presented as mean ± SD. ***, p < 0.001; ****, p < 0.0001. c, Representative flow cytometry data showing the CD8+ T cell proportion in the GFP+Luc+OVA+ EO771 tumour organoids. A total of 20,000 events per cell sample were collected for data analysis. d, Representative optical images of CD8+ T cells co-cultured with the GFP+Luc+OVA+ EO771 tumour organoids. e-g, Gross dissected mammary tumour images (e), tumour growth (f) and weight (g) of the EO771 tumours from the tumour-bearing C57BL/6 mice treated with vehicle control, PFI-1 (20 mg kg-1), or Bromosporine (20 mg kg-1). Tumours were harvested at day 28 post injection. For statistical analysis of data, one-way ANOVA test was used in (f,g). Data are presented as mean ± SD. ns, no significance.

Source data

Extended Data Fig. 2 Validation of antitumour activity of the three drug candidates in human tumour organoids.

a, Schematic illustration of the drug-validation test using NY-ESO-1+ MDA-MB-468 organoids co-cultured with NY-ESO-1-specific CD8+ T cells. Human breast cancer-associated fibroblasts (CAFs) were used with MDA-MB-468 cells to generate tumour organoids. b, Optical images showing the co-culture of the CD8+ T cells and MDA-MB-468 tumour organoids treated with control or drug candidates.

Extended Data Fig. 3 Antitumour activity of drug candidates in mouse breast tumour models.

a, Drug-treatment scheme of mouse breast tumour models. b, Gross dissected mammary tumour images of the EO771 tumours from the tumour-bearing C57BL/6 mice treated with vehicle control, BML-210 (20 mg kg-1), or CUDC-101 (20 mg kg-1). c, IHC staining images of Ki67+ and cleaved Caspase 3+ cells in the tumour tissues. d, Gross dissected mammary tumour images of the EO771 tumours from the tumour-bearing nude mice treated with vehicle control, BML-210 (20 mg kg-1), or CUDC-101 (20 mg kg-1). Tumours from nude mice were harvested at day 18 post injection. e, Gross mammary tumour images of the EO771 tumours from the tumour-bearing C57BL/6 mice treated with isotype control, CUDC-101 (20 mg kg-1), BML-210 (20 mg kg-1), anti-CD8 (10 mg kg-1) + CUDC-101 (20 mg kg-1), anti-CD8 (10 mg kg-1) + BML-210 (20 mg kg-1), anti-CD4 (10 mg kg-1) + CUDC-101 (20 mg kg-1) or anti-CD4 (10 mg kg-1) + BML-210 (20 mg kg-1). Tumours from tumour-bearing C57BL/6 mice were harvested at day 28 post injection.

Extended Data Fig. 4 Antitumour activity of BML-210 and CUDC-101 in mouse breast tumour models with CD4+ or CD8+ T-cell depletion.

a, Drug-treatment scheme of mouse breast tumour models. b, Flow cytometry gating strategy for analysis of CD4+ and CD8+ T cells from EO771 tumours in C57BL/6 mice. c, Typical graphs showing the proportion of CD4+ and CD8+ T cells in total T cells (CD3+) from EO771 tumours treated with vehicle control or indicated compound, and with CD4 or CD8 depletion. d,e, Proportions of CD8+ (d) and CD4+ (e) T cells in total T cells from EO771 tumours with CD4 or CD8 depletion. f,g, Weight of the EO771 tumours from the tumour-bearing C57BL/6 mice treated with control, CUDC-101 (20 mg kg-1), BML-210 (20 mg kg-1), anti-CD8 (10 mg kg-1) + CUDC-101 (20 mg kg-1), anti-CD8 (10 mg kg-1) + BML-210 (20 mg kg-1), anti-CD4 (10 mg kg-1) + CUDC-101 (20 mg kg-1) or anti-CD4 (10 mg kg-1) + BML-210 (20 mg kg-1). Tumours from tumour-bearing C57BL/6 mice were harvested at day 28 post injection. For statistical analysis of data, two-way ANOVA test was used in (d,e). One-way ANOVA test was used in (f,g). Data are presented as mean ± SD. ****, p < 0.0001; ns, no significance.

Extended Data Fig. 5 Antitumour activity of GSK-LSD1 in mouse breast tumour models.

a, Drug-treatment scheme of mouse breast tumour models. b-d, Gross dissected mammary tumour images (b), tumour growth (c) and weight (d) of the EO771 tumours from the tumour-bearing C57BL/6 mice treated with vehicle control, GSK-LSD1 (20 mg kg-1). e, Proportions of total T, CD4+ T, and CD8+ T cells in total immune (CD45+) cells in the EO771 tumours treated with control, GSK-LSD1. f, Percentage of active cells in total CD8+ T cells, indicated by GZMB+, IFNγ+, TNFα+ in flow cytometry analysis. g, IHC staining images of Ki67+ and cleaved Caspase 3+ cells in the tumour tissues. h,i, Quantitative results for (g). For statistical analysis of data, Two-sided Student’s t-test was used in (c,d,h,i) and two-way ANOVA test was used in (e,f). Data are presented as mean ± SD. ***, p < 0.001; ****, p < 0.0001; ns, no significance.

Source data

Extended Data Fig. 6 Drug candidates promotes the expression of genes in the antigen presentation of breast tumour cells.

a, Gene-set enrichment plot of the antigen processing and presentation pathway in EO771 cells treated with BML-210 in comparison with the cells treated with vehicle control. b, Validation of up-regulated genes from Fig. 6c in human MDA-MB-468 cells treated with vehicle control or BML-210 (1.0 μM) by quantitative RT-PCR. c, Levels of HLA-A,B,C on the NY-ESO-1+ MDA-MB-468 cells treated with control or BML-201, determined by MFI in flow cytometry analysis. d, H-2k expression levels in the OVA+ EO771 cells treated with CUDC-101 or GSK-LSD1 were determined by qRT-PCR. e, HLA gene expression levels in NY-ESO-1+ MDA-MB-468 cells treated with CUDC-101 or GSK-LSD1 were determined by qRT-PCR. f, B2M expression levels in the OVA+ EO771 cells treated with CUDC-101 or GSK-LSD1 were determined by qRT-PCR. g,h, The effect of drug treatment on the H-2Kb and HLA-A2 antigen presentation on OVA+ EO771 cells and NY-ESO-1+ MDA-MB-468 cells, respectively. Data (b,d-f) from 3 biologically parallel experiments were analyzed using Two-way ANOVA. Data (c,g,h) from 3 biologically parallel experiments were analyzed using One-way ANOVA. ***, p < 0.001; ****, p < 0.0001; ns, no significance.

Extended Data Fig. 7 BML-210 promotes the expression of genes in the antigen presentation of breast tumour cells.

a,b, Confocal images showing H-2Kb and HLA-A2 on OVA+ EO771 cells (a) and NY-ESO-1+ MDA-MB-468 cells (b), respectively. The immunofluorescence images were analyzed by ImageJ. c, Quantitative analysis of confocal images in (a) for assessing H-2Kb antigen presentation. d, Quantitative analysis of confocal images in (b) for assessing HLA-A2 antigen presentation. e, Western blot showing B2M protein expression levels in OVA+ EO771 cells treated with BML-210 drug with 0, 0.1, 1 μM for 48 h. f, Western blot showing B2M knockdown in OVA+ EO771 cells. One-way ANOVA test was conducted for statistical analysis in (c,d). Data are presented as mean ± SD. ****, p < 0.0001.

Source data

Supplementary information

Source data

Source Data Fig. 2

Source data for tumour burden (panel e).

Source Data Fig. 5

Source data for tumour burden (panels a, g, h and i).

Source Data Fig. 7

Source data for tumour burden (panel e).

Source Data Extended Data Fig. 1

Source data for tumour burden (panel f).

Source Data Extended Data Fig. 5

Source data for tumour burden (panel c).

Source Data Extended Data Fig. 7

Uncropped western blots (panels e and f).

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Z., Van der Jeught, K., Fang, Y. et al. An organoid-based screen for epigenetic inhibitors that stimulate antigen presentation and potentiate T-cell-mediated cytotoxicity. Nat Biomed Eng 5, 1320–1335 (2021). https://doi.org/10.1038/s41551-021-00805-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-021-00805-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer