Background Many drugs have the potential to induce the expression of drug-metabolizing enzymes, particularly cytochrome P450 3A4 (CYP3A4), in hepatocytes. Hepatocytes can be accurately evaluated for drug-mediated CYP3A4 induction; this is the gold standard for in vitro hepatic toxicology testing. However, the variation from lot to lot is an issue that needs to be addressed. Only a limited number of immortalized hepatocyte cell lines have been reported. In this study, immortalized cells expressing CYP3A4 were generated from a patient with drug-induced liver injury (DILI). Methods To generate DILI-derived cells with high expression of CYP3A4, a three-step approach was employed: (1) Differentiation of DILI-induced pluripotent stem cells (DILI-iPSCs); (2) Immortalization of the differentiated cells; (3) Selection of the cells by puromycin. It was hypothesized that cells with high cytochrome P450 gene expression would be able to survive exposure to cytotoxic antibiotics because of their increased drug-metabolizing activity. Puromycin, a cytotoxic antibiotic, was used in this study because of its rapid cytocidal effect at low concentrations. Results The hepatocyte-like cells differentiated from DILI-iPSCs were purified by exposure to puromycin. The puromycin-selected cells (HepaSM or SI cells) constitutively expressed the CYP3A4 gene at extremely high levels and exhibited hepatocytic features over time. However, unlike primary hepatocytes, the established cells did not produce bile or accumulate glycogen. Conclusions iPSC-derived hepatocyte-like cells with intrinsic drug-metabolizing enzymes can be purified from non-hepatocytes and undifferentiated iPSCs using the cytocidal antibiotic puromycin. The puromycin-selected hepatocyte-like cells exhibited characteristics of hepatocytes after immortalization and may serve as another useful source for in vitro hepatotoxicity testing of low molecular weight drugs.
Ammonia has a cytotoxic effect and can therefore be used as a selection agent for enrichment of zone I hepatocytes. However, it has not yet been determined whether ammonia-treated hepatocyte-like cells are able to proliferate in vitro. In this study, we employed an ammonia selection strategy to purify hepatocyte-like cells that were differentiated from human embryonic stem cells (ESCs) and from induced pluripotent stem cells (iPSCs). The resistance to cytotoxicity or cell death by ammonia is likely attributable to the metabolism of ammonia in the cells. In addition to ammonia metabolism-related genes, ammonia-selected hepatocytes showed increased expression of the cytochrome P450 genes. Additionally, the ammonia-selected cells achieved immortality or at least an equivalent life span to human pluripotent stem cells without affecting expression of the liver-associated genes. Ammonia treatment in combination with in vitro propagation is useful for obtaining large quantities of hepatocytes.
Ovarian clear cell carcinoma (OCCC), one of the histopathological types of ovarian cancer, has a poor prognosis when it recurs; however, it is difficult to precisely predict the risk of recurrence. Here, we analyzed pathological images of OCCC to elucidate the relationship between pathological findings and recurrence, and using machine learning, we established a classifier to predict the recurrence and several other prognosis indicators of this disease. In total, 110 patients with OCCC treated with primary surgery at a single institution were enrolled in this study. We used the deep-learning neural networks to process the whole slide images of OCCC obtained by digitally scanning the original hematoxylin and eosin-stained glass slides. The images were preprocessed and used as input to the machine learning pipeline. We fine-tuned its parameters to predict the recurrence, progression-free survival, and the overall survival days of all patients. We predicted the recurrence of OCCC with an overall accuracy of 93%, area under the receiver operating characteristic curve of 0.98, and sensitivity/specificity above 0.92 using Resnet 34. Furthermore, we predicted progression-free survival/overall survival of the patients with ~90% accuracy. In conclusion, our study demonstrates the feasibility of using a machine learning system to predict different features of OCCC samples using histopathological images as input. This novel application provides accurate prognosis information and aids in the development of personalized treatment strategies.
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