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. 2023 Nov 21;4(11):101294. doi: 10.1016/j.xcrm.2023.101294

Table 1.

Comparisons of BTC PDO collections

Collections BTC PDO models
Tumor types
Characterizations
Drug screening
Total Success rate (%) iCCA eCCA GB Others Histopathologic Genomic Transcriptomic Drugs Prediction model
Broutier et al.5 8 3 5 H&E, HepPar-1, EpCAM WES RNA-seq 29 drugs including cisplatin, 5-FU, doxorubicin, and gemcitabine
Lee et al.6 16 69.5 16 H&E, CK19, SOX9, PD-L1 WES RNA-seq gemcitabine and cisplatin
Saito et al.7 6 33.3 3 1 2 H&E, CK7, MUC1 WES microarray 339 compounds, including antimicrotubule agents, mitomycin C, and aclarubicin
Kinoshita et al.8 60 88.2 5 49 1 5 H&E, CK7, HepPar-1 TP53, KRAS
Ren et al.1 61 74.4 44 13 4 H&E, CK19, CK7 WES RNA-seq gemcitabine, 5-FU, cisplatin, SN-38, oxaliplatin, mitomycin C, paclitaxel yes, with the NB and SVM models

Abbreviations: BTC, biliary tract cancer; PDO, patient-derived organoid; iCCA, intrahepatic cholangiocarcinoma; eCCA, extrahepatic cholangiocarcinoma; GB, gallbladder cancer; H&E, hematoxylin and eosin staining; HepPar-1, hepatocyte paraffin-1; EpCAM, epithelial cell adhesion molecule; CK19, cytokeratin 19; SOX9, SRY-box transcription factor 9; PD-L1, programmed death-ligand 1; CK7, cytokeratin 7; MUC1, Mucin 1; TP53, tumor protein p53; KRAS, Kirsten rat sarcoma viral oncogene homolog; WES, whole-exome sequencing; 5-FU, 5-Fluorouracil; NB, naive Bayes; SVM, support vector machine.