TABLE 2.
The application of cancer organoid models
| Tumor organoid model | Date | Cell derived | Sample size | Research type | Achievement | Ref |
|---|---|---|---|---|---|---|
| Colorectal cancer | 2020.01 | Patient | 11 | Drug resistance | Clusterin, a drug resistance marker used to detect colorectal cancer progression | [49] |
| Colorectal cancer | 2020.11 | Patient | 15 | Tumor metabolic properties and phenotypes | Established a basis for the development of new treatments which target metabolic parameters in colorectal cancer | [50] |
| Colorectal cancer | 2020.09 | Patient | 2 | Tumor metastasis | Development of an experimental model for investigating colorectal cancer progression | [51] |
| Colorectal cancer | 2019.09 | Patient | 40 | Tumor metastasis | Developed an evaluation method to assess existing hyperthermic intraperitoneal chemotherapy regimens on an individual patient level | [52] |
| Colorectal cancer | 2020.07 | Patient | 28 | Personalized therapy | Displayed the use of organoids in guiding precision treatment for patients with CRC and peritoneal metastases | [53] |
| Colorectal cancer | 2020.08 | Patient | 22 | Drug screening and gene profiling | The use of ex vivo drug screening to identify novel treatment options for metastatic colorectal cancer | [54] |
| Colorectal cancer | 2020.08 | Patient | 50 | Tumor gene profile | Distinguished genetic profiles of rectal and colon tumors using organoids | [55] |
| Colorectal cancer | 2020.06 | Patient and cells | 22 | Tumor biomarker | DACH1 as a potential prognostic marker and therapeutic target for colorectal cancer | [56] |
| Colorectal cancer | 2016.11 | Patient | NA | Drug screening | Demonstrating the potential of colorectal cancer organoid libraries in drug screening | [58] |
| Colorectal cancer | 2019.11 | Patient | 5 | Neoantigen presentation | Identified novel approaches to increase neoantigen presentation | [59] |
| Colorectal cancer | 2019.06 | Patient | NA | CAR‐mediated cytotoxicity | Colorectal cancer organoids successfully evaluate CAR efficacy and tumor specificity in a personalized manner | [62] |
| Colorectal cancer | 2019.09 | Patient | 90 | Chemotherapy and/or radiotherapy sensitivity | Predict treatment sensitivity for patients with cancer undergoing chemotherapy and/or radiotherapy | [64] |
| Lung cancer | 2019.09 | Patient | 80 | Biobank of lung cancer organoids construction | Successfully construct biobank of lung cancer organoids | [67] |
| Lung cancer | 2020.03 | Patient | 30 | Tumor modeling | Successfully construct NSCLC organoid for drug testing | [31] |
| Lung cancer | 2020.08 | Patient | 12 | Drug screening | To identify new therapeutic targets and advanced personalized medicine | [66] |
| Lung cancer | 2020.08 | Patient | 12 | Genomic characteristics and drug screening | PDOs are highly credible models for personalized precision medicine | [71] |
| Lung cancer | 2020.03 | Patient | 4 | Drug screening | PDOs were relatively more sensitive to CF10 | [72] |
| Lung cancer | 2020.03 | Patient | 10 | Drug screening | To identify the anticancer activity of chelerythrine chloride, cantharidin, and harmine in PDOs | [73] |
| Lung cancer | 2019.04 | Pleural effusion aspirate from patient | 2 | Drug response | Serve as more accurate disease models for the study of tumor progression and drug development | [74] |
| Lung cancer | 2019.05 | PDOs | 3 | Evaluating molecular targeted drugs | PDOs are suitable for evaluation molecular targeted drugs | [75] |
| Lung cancer | 2019.06 | Patient | 11 | Immunotherapy | Combining PD‐L1 with MEK‐I in 3D‐culture model, useful to predict sensitivity of patients to immunotherapy | [76] |
| Pancreatic cancer | 2019.12 | Patient | 30 | Personalized drug screening | Development of a platform for identification of novel therapeutics for pancreatic cancer using PDOs | [79] |
| Pancreatic cancer | 2020.08 | Patient | 10 | Personalized therapy | Generation of PDOs from a limited sample can allow molecular profiling and drug testing | [80] |
| Pancreatic cancer | 2020.09 | Patient | 76 | Precision medicine | To guide postoperative adjuvant chemotherapeutic selection | [82] |
| Pancreatic cancer | 2019.09 | PDOX models | NA | Drug sensitivity and resistance | Development of PDOX‐derived organoid system for use in prediction of treatment response in advanced pancreatic cancer | [81] |
| Pancreatic cancer | 2019.01 | Patient | NA | Immunotherapy | Exploring the role of PD‐L1 in pancreatic cancer organoids | [83] |
| Pancreatic cancer | 2019.11 | Patient | NA | Tumor resistance | Pan‐ERBB kinase inhibitor resulted in suppression of cell viability and tumor regressions when combined with MEK inhibition | [84] |
| Pancreatic cancer | 2020.06 | Patient | 6 | Investigate the metabolism in PDOs | A therapeutic intervention could delay PDA recurrence and prolong the survival of affected patients | [87] |
| Pancreatic cancer | 2020.07 | Patient | 25 | Study the pattern of invasion in PDA | Invasion programs in SMAD4‐mutant and SMAD4 wild‐type tumors are different in both morphology and molecular mechanism | [88] |
| Pancreatic cancer | 2020.12 | Patient | 8 | Study human PDA induced cachexia | To further understand the mechanisms driving cancer cachexia | [89] |
| Breast cancer | 2020.05 | Patient | 12 | Using CRISPR/Cas9 to model tumor organoids | Modeling breast cancer using CRISPR/Cas9‐mediated engineering of human breast cancer organoids | [93] |
| Breast cancer | 2019.08 | Primary patient‐derived breast cancer cells | NA | Personalized chemotherapy | Development of a new platform for culturing primary cells for developing personalized chemotherapy regimens | [94] |
| Breast cancer | 2019.06 | Genetically engineered mouse model | NA | Cellular metabolic heterogeneity | Found that metabolic heterogeneity after upon treatment is attributed to heterogeneous metabolic shifts within tumor cells | [95] |
| Breast cancer | 2019.05 | Patient | 26 | Metastasis cancer related translational research | Demonstrated metastatic breast cancer organoids closely resemble the transcriptome of their parent lesion | [96] |
| Breast cancer | 2020.03 | Patient | 1 | Drug screening | Identified possible treatments in patients with breast papillary carcinoma | [97] |
| Liver cancer | 2019.03 | Primary mouse liver tumors | 129 | Drug development and personalized medicine | The antitumor drug can be successfully used in the organoids from primary mouse liver tumors | [100] |
| Liver cancer | 2019.08 | Reprogrammed human hepatocytes | NA | Modeling liver cancer | Showed human‐induced hepatocyte organoids can be genetically manipulated to model cancer initiation | [101] |
| Liver cancer | 2019.06 | Patient | NA | CRISPR/Cas9 engineer human liver organoids | Demonstrate combination of organoid technology with CRISPR/Cas9 can serve as an experimental platform for mechanistic studies of human cancer gene function | [102] |
| Liver cancer | 2020.01 | Patient | 4 | Tumor resistance | Combination of sorafenib and Hedgehog signaling inhibitors might be effective in HCC patients with high CD44 levels as a personalized‐medicine approach | [104] |
| Liver cancer | 2019.01 | Patient | 5 | Drug response heterogeneity | This study lay the foundation for functional personalized oncology approaches | [105] |
| Liver cancer | 2019.05 | Primary mouse liver tumors | NA | Tumor growth | Mycophenolic acid inhibits liver tumor organoids initiation and growth | [106] |
| Ovarian cancer | 2020.07 | Patient | 7 | Drug sensitivity and resistance testing | PDOs are suitable cancer models that can be used to screen effective personalized ovarian cancer drugs | [110] |
| Ovarian cancer | 2020.06 | Patient | 23 | Tumor heterogeneity | Increase our knowledge of genetic and drug response heterogeneity | [111] |
| Ovarian cancer | 2019.05 | Patient | 32 | Genetic manipulations and drug screening | Ovarian cancer organoids illustrating intra‐ and inter‐patient heterogeneity to use for drug‐screening assays | [112] |
| Bladder cancer | 2019.3 | Patient | 53 | Construct a bladder organoids biobank | Bladder organoids biobank for drug testing in the future | [115] |
| Bladder cancer | 2020.10 | Patient | 77 | Predict cancer patient drug responses | Used pharmacogenomic data derived from organoids and developed a novel machine learning framework to identify biomarkers and predict drug response in bladder cancer | [117] |
| Prostate cancer | 2014.09 | Patient | 32 | Predict cancer patient drug responses |
Enable the generation of a large repertoire of patient‐derived prostate cancer lines amenable to genetic and pharmacologic studies |
[119] |
| Prostate cancer | 2021.08 | Patient | 81 | Explores determinants of outcome |
Ensure the reliable establishment of organoids derived from specific prostate cancer molecular subtypes |
[122] |
| HNSCC | 2018.12 | Patient | 43 | Predict drug sensitivity | Show organoids can predict drug sensitivity and potential of organoids in the development of precision treatments for HNSCC | [124] |
| HNSCC | 2019.11 | Patient | 7 | For PDT | Demonstrated HNSCC organoid as a useful model for in‐vitro testing of targeted PDT | [125] |
| Gastric cancer | 2019.02 | Patient | 20 | Modeling gastric cancer | Modeled human gastric cancer using organoids | [127] |
| Gastric cancer | 2019.01 | Patient | 7 | Personalized treatment | To predict individual therapy response and patient outcome | [128] |
| Glioblastoma organoids | 2020.01 | Patient | 53 | Personalized treatment | Establishment of a glioblastoma organoid biobank for testing personalized therapies | [130] |
DACH1, Dachshund homolog 1; NA, Not available; CAR, Chimeric antigen receptor; PDOs, Patient‐derived organoids; CF10, fluoropyrimidine polymer F10; PD‐L1, Programmed cell death ligand 1; MEK‐I, MAP‐ERK kinase inhibitor; PDOX, Patient‐derived orthotropic xenograft; ERBB, Receptor tyrosine‐protein kinase; PDA, Pancreatic ductal adenocarcinoma; SMAD4, Mothers against decapentaplegic homolog 4; HCC, Hepatocellular carcinoma; HNSCC, Head and neck squamous cell carcinoma; PDT, Photodynamic therapy.