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.
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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.
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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.
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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.
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T.S. is an inventor on several patents related to organoid culture.
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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.
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.
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.
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.
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. 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.
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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
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DOI: https://doi.org/10.1038/s41589-022-00984-x
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