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:

Organoid screening reveals epigenetic vulnerabilities in human colorectal cancer

Nature Chemical Biology volume 18, pages 605–614 (2022)Cite this article

Subjects

Abstract

Precision oncology presumes an accurate prediction of drug response on the basis of the molecular profile of tumors. However, the extent to which patient-derived tumor organoids recapitulate the response of in vivo tumors to a given drug remains obscure. To gain insights into the pharmacobiology of human colorectal cancer (CRC), we here created a robust drug screening platform for patient-derived colorectal organoids. Application of suspension culture increased organoid scalability, and a refinement of the culture condition enabled incorporation of normal and precursor organoids to high-throughput drug screening. Drug screening identified bromodomain and extra-terminal (BET) bromodomain protein inhibitor as a cancer-selective growth suppressor that targets genes aberrantly activated in CRC. A multi-omics analysis identified an association between checkpoint with forkhead and ring finger domaines (CHFR) silencing and paclitaxel sensitivity, which was further validated by gene engineering of organoids and in xenografts. Our findings highlight the utility of multiparametric validation in enhancing the biological and clinical fidelity of a drug screening system.

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: The modified culture condition improves the fidelity of organoid-based HTS.
Fig. 2: Overview of the drug responses in normal and CRC organoids.
Fig. 3: Genetically defined drug sensitivity in CRC organoids.
Fig. 4: Cancer-selective growth inhibition by JQ1.
Fig. 5: Paclitaxel is selectively effective against CIMP+ CRCs.
Fig. 6: Validation of the organoid drug response in vivo.

Similar content being viewed by others

Data availability

All data relevant to this study are available from the corresponding author upon reasonable request. Of the gene expression data used in the study, the data on 37 and 2 organoid lines can be accessed via our previous datasets GSE74843 (ref. 21) and GSE137336 (ref. 19). The expression microarray data of the remaining three lines are available in Gene Expression Omnibus under the accession ID GSE184732. The RNA sequencing and ChIP sequencing data are available in the National Bioscience Database Centre (NBDC) under the accession ID JGAS000378. The ethical protocol approved for this study requires the raw sequencing data to be deposited to the NBDC (https://biosciencedbc.jp/en/) and be available under controlled access for protection of patients’ privacy. Data users need to fulfill the NBDC Guidelines for Human Data Sharing (https://humandbs.biosciencedbc.jp/en/guidelines/data-sharing-guidelines) and the NBDC Security Guidelines for Human Data (for Data Users) (https://humandbs.biosciencedbc.jp/en/guidelines/security-guidelines-for-users). In detail, those who wish to use the data need to conform to the following requirements. First, indicate that the head of the institution to which the data users belong has given permission to implement the research plan that includes the dataset the data users plan to use. Second, provide evidence that the data users have engaged in research related to the dataset the data users plan to use. Third, indicate that the data users have implemented security measures appropriate to the access level of the dataset the data user plans to use. Fourth, obtain approval from the NBDC Human Data Review Board (details on how to apply can be found at https://humandbs.biosciencedbc.jp/en/data-use). Source data are provided with this paper.

References

  1. Stratton, M. R., Campbell, P. J. & Futreal, P. A. The cancer genome. Nature 458, 719–724 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Holohan, C., Schaeybroeck, S. V., Longley, D. B. & Johnston, P. G. Cancer drug resistance: an evolving paradigm. Nat. Rev. Cancer 13, 714–726 (2013).

    Article  CAS  PubMed  Google Scholar 

  3. Druker, B. J. et al. Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia. N. Engl. J. Med. 355, 2408–2417 (2006).

    Article  CAS  PubMed  Google Scholar 

  4. Slamon, D. J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med. 344, 783–792 (2001).

    Article  CAS  PubMed  Google Scholar 

  5. Bang, Y.-J. et al. Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): a phase 3, open-label, randomised controlled trial. Lancet 376, 687–697 (2010).

    Article  CAS  PubMed  Google Scholar 

  6. Chapman, P. B. et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N. Engl. J. Med. 364, 2507–2516 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ramzy, G. M. et al. Patient-derived in vitro models for drug discovery in colorectal carcinoma. Cancers 12, 1423 (2020).

    Article  CAS  PubMed Central  Google Scholar 

  8. Wilson, T. R. et al. Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors. Nature 487, 505–509 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Basu, A. et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154, 1151–1161 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kodack, D. P. et al. Primary patient-derived cancer cells and their potential for personalized cancer patient care. Cell Rep. 21, 3298–3309 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Dangles-Marie, V. et al. Establishment of human colon cancer cell lines from fresh tumors versus xenografts: comparison of success rate and cell line features. Cancer Res. 67, 398–407 (2007).

    Article  CAS  PubMed  Google Scholar 

  16. Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318–1325 (2015).

    Article  CAS  PubMed  Google Scholar 

  17. Bruna, A. et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167, 260–274.e222 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sato, T. et al. Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett’s epithelium. Gastroenterology 141, 1762–1772 (2011).

    Article  CAS  PubMed  Google Scholar 

  19. Kawasaki, K. et al. Chromosome engineering of human colon-derived organoids to develop a model of traditional serrated adenoma. Gastroenterology 158, 638–651 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Fujii, M. et al. A colorectal tumor organoid library demonstrates progressive loss of niche factor requirements during tumorigenesis. Cell Stem Cell 18, 827–838 (2016).

    Article  CAS  PubMed  Google Scholar 

  22. Drost, J. & Clevers, H. Organoids in cancer research. Nat. Rev. Cancer 18, 407–418 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Vlachogiannis, G. et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359, 920–926 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ooft, S. N. et al. Patient-derived organoids can predict response to chemotherapy in metastatic colorectal cancer patients. Sci. Transl. Med. 11, eaay2574 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Yao, Y. et al. Patient-derived organoids predict chemoradiation responses of locally advanced rectal cancer. Cell Stem Cell 26, 17–26 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Verissimo, C. S. et al. Targeting mutant RAS in patient-derived colorectal cancer organoids by combinatorial drug screening. eLife 5, e18489 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Boehnke, K. et al. Assay establishment and validation of a high-throughput screening platform for three-dimensional patient-derived colon cancer organoid cultures. J. Biomol. Screen. 21, 931–941 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Schütte, M. et al. Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors. Nat. Commun. 8, 14262 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Fujii, M. et al. Human intestinal organoids maintain self-renewal capacity and cellular diversity in niche-inspired culture condition. Cell Stem Cell 23, 787–793.e786 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Liu, Y. et al. Comparative molecular analysis of gastrointestinal adenocarcinomas. Cancer Cell 33, 721–735 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Vassilev, L. T. et al. In vivo activation of the p53 pathway by small-molecule antagonists of MDM2. Science 303, 844–848 (2004).

    Article  CAS  PubMed  Google Scholar 

  32. Pahuja, K. B. et al. Actionable activating oncogenic ERBB2/HER2 transmembrane and juxtamembrane domain mutations. Cancer Cell 34, 792–806.e795 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Poulin, E. J. et al. Tissue-specific oncogenic activity of KRASA146T. Cancer Discov. 9, 738–755 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kopetz, S. et al. Phase II pilot study of vemurafenib in patients with metastatic BRAF-mutated colorectal cancer. J. Clin. Oncol. 33, 4032–4038 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hyman, D. M. et al. Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations. N. Engl. J. Med. 373, 726–736 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Prahallad, A. et al. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 483, 100–103 (2012).

    Article  CAS  PubMed  Google Scholar 

  37. Kopetz, S. et al. Encorafenib, binimetinib, and cetuximab in BRAF V600E-mutated colorectal cancer. N. Engl. J. Med. 381, 1632–1643 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. Matano, M. et al. Modeling colorectal cancer using CRISPR–Cas9-mediated engineering of human intestinal organoids. Nat. Med. 21, 256–262 (2015).

    Article  CAS  PubMed  Google Scholar 

  39. Shi, J. & Vakoc, C. R. The mechanisms behind the therapeutic activity of BET bromodomain inhibition. Mol. Cell 54, 728–736 (2014).

    Article  CAS  PubMed  Google Scholar 

  40. Donati, B., Lorenzini, E. & Ciarrocchi, A. BRD4 and cancer: going beyond transcriptional regulation. Mol. Cancer 17, 164 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Guinney, J. et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 21, 1350–1356 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bradner, J. E., Hnisz, D. & Young, R. A. Transcriptional addiction in cancer. Cell 168, 629–643 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bolden, J. E. et al. Inducible in vivo silencing of Brd4 identifies potential toxicities of sustained BET protein inhibition. Cell Rep. 8, 1919–1929 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Roerink, S. F. et al. Intra-tumour diversification in colorectal cancer at the single-cell level. Nature 556, 457–462 (2018).

    Article  CAS  PubMed  Google Scholar 

  45. Overman, M. et al. Phase II study of nab-paclitaxel in refractory small bowel adenocarcinoma and CpG island methylator phenotype (CIMP)-high colorectal cancer. Ann. Oncol. 29, 139–144 (2018).

    Article  CAS  PubMed  Google Scholar 

  46. Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Sugimoto, S. et al. Reconstruction of the human colon epithelium in vivo. Cell Stem Cell 22, 171–176 (2018).

    Article  CAS  PubMed  Google Scholar 

  48. Mihara, E. et al. Active and water-soluble form of lipidated Wnt protein is maintained by a serum glycoprotein afamin/α-albumin. eLife 5, e11621 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Ootani, A. et al. Sustained in vitro intestinal epithelial culture within a Wnt-dependent stem cell niche. Nat. Med. 15, 701–706 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Fujii, M., Matano, M., Nanki, K. & Sato, T. Efficient genetic engineering of human intestinal organoids using electroporation. Nat. Protoc. 10, 1474–1485 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Schmidl, C., Rendeiro, A. F., Sheffield, N. C. & Bock, C. ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors. Nat. Methods 12, 963–965 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, https://doi.org/10.14806/ej.17.1.200 (2011).

  53. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Guo, Q. et al. ‘Cold shock’ increases the frequency of homology directed repair gene editing in induced pluripotent stem cells. Sci. Rep. 8, 2080 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Randolph, L. N., Bao, X., Zhou, C. & Lian, X. An all-in-one, Tet-On 3G inducible PiggyBac system for human pluripotent stem cells and derivatives. Sci. Rep. 7, 1549 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the Project for Cancer Research and Therapeutic Evolution (P-CREATE) from the Japan Agency for Medical Research and Development (AMED) (Grant Number 20cm0106206), JSPS KAKENHI (Grant Numbers JP18J21346, JP17K09395 and JP17H06176) and JST Moonshot R&D (Grant Number JPMJMS2022). K.Toshimitsu was supported by the Japan Society for the Promotion of Science Research Fellowships for Young Scientists. We thank the Screening Committee of Anticancer Drugs supported by Grant-in-Aid for Scientific Research on Innovative Areas, Scientific Support Programs for Cancer Research, from The Ministry of Education, Culture, Sports, Science and Technology, Japan, for providing the SCADS Inhibitor Kit. We also thank the Collaborative Research Resources at the School of Medicine, Keio University for providing technical assistance.

Author information

Author notes

  1. These authors contributed equally: K. Toshimitsu, A. Takano.

Authors and Affiliations

  1. Department of Organoid Medicine, Sakaguchi Laboratory, Keio University School of Medicine, Tokyo, Japan

    Kohta Toshimitsu, Ai Takano, Masayuki Fujii, Mami Matano, Sirirat Takahashi & Toshiro Sato

  2. Department of Gastroenterology, Keio University School of Medicine, Tokyo, Japan

    Kohta Toshimitsu, Kazuhiro Togasaki & Takanori Kanai

  3. Department of Surgical Oncology, The University of Tokyo, Tokyo, Japan

    Masayuki Fujii

Authors
  1. Kohta Toshimitsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ai Takano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Masayuki Fujii
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kazuhiro Togasaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mami Matano
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sirirat Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Takanori Kanai
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Toshiro Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K. Toshimitsu, A.T. and T.S. conceived the project and designed experiments. A.T., M.M. and M.F. performed organoid experiments. A.T. performed animal experiments. S.T. performed immunostaining experiments. K. Toshimitsu and K. Togasaki performed data analysis. M.F. and T.K. provided resources. K. Toshimitsu, M.F., A.T. and T.S. wrote the manuscript.

Corresponding author

Correspondence to Toshiro Sato.

Ethics declarations

Competing interests

T.S. is an inventor on several patents related to organoid culture.

Peer review

Peer review information

Nature Chemical Biology thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Metrics of the modified organoid HTS system.

a, The growth of organoids in Matrigel-embedded culture, static floating culture and floating culture in a rotating vessel. The number of cells in each culture condition was counted on day 0, 2, 5, 7 and 9. n = 3 technical replicates for each condition. b, Doubling time of organoids in the indicated culture conditions. The doubling time was calculated by linear regression of the day of harvesting and logarithmic average cell numbers from day 0 to day 7. c, Comparison of organoid growth metrics measured using confocal fluorescence imaging, ATP abundance and bright-field imaging. Each dot indicates one well. Organoid viability is shown as the relative to the mean of vehicle-treated control. Note that the viability of organoids treated with mTOR inhibitors appear lower in the fluorescence measurement than in the ATP-based assay. d, Comparison of the Z’-factors in conventional and refined culture conditions.

Source data

Extended Data Fig. 2 Drug sensitivity of organoids based on drug targets.

a, Biological (left) and pathway (right) targets of the 56 drugs used for screening. b, log2IC50 values and their standard deviation (SD) in replicate experiments using a normal colonic organoids line (NLCRC15). Each dot represents one assay. c, The response of 6 normal and 20 cancer organoids to the drugs that share the same targets. The Z-scores of logIC50 values are shown. Samples are sorted by the average Z-score. NA; not available.

Source data

Extended Data Fig. 3 Genetic alterations of CRC organoids.

Genetic mutations in CRC organoids used for HTS (top). Mutations in recurrently mutated genes in CRC (MutSigCV FDR < 0.2 in Pan-Cancer Atlas30) are shown. Chromosomal copy number variations in CRC organoids (bottom). Chromosome states are shown as the log2 ratio of the copy number between the sample and euploid genome.

Source data

Extended Data Fig. 4 Gene-drug correlation in organoids.

a, A volcano plot representation of MANOVA incorporating drug sensitivity and genetic mutations. Each dot indicates a genetic mutation-drug pair. Statistically significant pairs (TP53 mutation/nutlin-3 and TP53 mutation/paclitaxel pairs, FDR < 0.25) are highlighted in blue. b, The response of CRC18a (TP53-mutant) and CRC18b (TP53-wild type) to nutlin-3. The organoids are subclones that were derived from the same tumor. Data are shown as mean ± SD. n = 3 c, Capillary-based immunodetection of p53 in CRC18a and CRC18b with or without nutlin-3 treatment. d, IC50 values of EGFR inhibitors and RTK/MAPK pathway mutations in a refined screening condition. The organoids were treated with the indicated drugs from day 1 after plating and cultured without IGF-1 and FGF-2. e, IC50 values of vemurafenib. Each dot represents one organoid line. Samples were sorted by the IC50 value.

Source data

Extended Data Fig. 5 Sensitivity of patient-derived organoids to JQ1.

a, Response of normal and cancer organoids to JQ1. Representative images from three technical replicates with similar results. Scale bar, 500 μm. b, Response of organoids derived from tubular adenoma and serrated lesion to JQ1. Representative images from three technical replicates with similar results. Scale bar, 500 μm. c, Response of genetically engineered normal organoids with single (APC) and quadruple (APC, KRAS, TP53 and SMAD4) mutations to JQ1. Representative images from three technical replicates with similar results. Scale bar, 500 μm.

Extended Data Fig. 6 Relationship between drug sensitivity and CIMP.

a, Hierarchical clustering of CRC organoids based using variably methylated probes (probes in CpG island and with an SD of M-value > 3). The CRC organoids were assigned to either CIMP+ or CIMP– based on the cluster membership. b, Association between methylation clusters and consensus molecular subtypes (CMS). The integers indicate the number of samples assigned to each subtype. c, Principal component analysis of the organoid transcriptomes. Each dot represents one organoid line. d, The relationship between the expression levels of the 50 CIMP genes and the responses to the 56 drugs. The expression of the CIMP genes were ranked based on the Spearman’s correlation between the gene expression value and the IC50 value of the drugs, and were summarized as the normalized enrichment score by gene set enrichment analysis. The drugs of which the resistance or sensitivity was significantly associated with the expression of the CIMP genes are highlighted in red (positive NES, FDR < 0.01) or blue (negative NES, FDR < 0.01), respectively. NES, normalized enrichment score. e, Response of normal, CIMP– CRC and CIMP+ CRC organoids treated to paclitaxel. Representative images from three technical replicates with similar results. Scale bar, 500 μm. f, Response of tubular adenoma (Ad3) and serrated lesion (SL3) organoids treated to paclitaxel. Representative images from three technical replicates with similar results. Scale bar, 500 μm.

Extended Data Fig. 7 Paclitaxel sensitivity of patient-derived organoids.

a, Response of CHFR KO organoids to with paclitaxel. Representative images from those analyzed in Fig. 6i. Scale bars, 100 μm. b, Response of CHFR-uninduced and -induced organoids treated to paclitaxel. Representative images from those analyzed in Fig. 6k. Scale bars, 100 μm.

Extended Data Fig. 8 Response of CRC organoid xenografts to nab-paclitaxel or cetuximab treatment.

a, The growth curves of cetuximab- and vehicle-treated xenografts. Each dot and line indicates one mouse. b, Schedule for treating CRC organoid xenografts with cetuximab (top). Sensitivity of CRC organoid xenografts to cetuximab (bottom). Each dot and line indicates one organoid line. c, The relationship between the sensitivity to cetuximab in vitro and in vivo. Each dot shows one organoid line, and the organoids were colored according to the possession of KRAS or BRAF mutation. d, The growth curves of nab-paclitaxel- and vehicle-treated CRC organoid xenografts. Each dot and line indicates one mouse.

Source data

Supplementary information

Source data

Source Data Fig. 1

Statistical source data for Fig. 1.

Source Data Fig. 2

Statistical Source Data for Fig. 2.

Source Data Fig. 3

Uncropped pseudo-gel images for Fig. 3.

Source Data Fig. 3

Statistical source data for Fig. 3.

Source Data Fig. 4

Statistical source data for Fig. 4.

Source Data Fig. 5

Statistical source data for Fig. 5.

Source Data Fig. 5

Uncropped pseudo-gel images for Fig. 5.

Source Data Fig. 6

Statistical source data for Fig. 6.

Source Data Extended Data Fig. 1

Statistical Source Data for Extended Data Fig. 1.

Source Data Extended Data Fig. 2

Statistical source data for Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Statistical source data for Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Statistical source data for Extended Data Fig. 4.

Source Data Extended Data Fig. 4

Uncropped pseudo-gel images for Extended Data Fig. 4c.

Source Data Extended Data Fig. 8

Statistical source data for Extended Data Fig. 8.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Toshimitsu, K., Takano, A., Fujii, M. et al. Organoid screening reveals epigenetic vulnerabilities in human colorectal cancer. Nat Chem Biol 18, 605–614 (2022). https://doi.org/10.1038/s41589-022-00984-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41589-022-00984-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