Isolated cancer stem cells from human liver cancer: morphological and functional characteristics in primary culture

Diesel exhaust promoted diethylnitrosamine-induced hepatocarcinogenesis in mice

Exposure to diesel exhaust (DE) has been linked to an increased risk of various cancers, including liver cancer. However, the underlying mechanisms driving this association remain insufficiently understood. In this study, we employed a diethylnitrosamine (DEN)-induced mouse liver tumor model and conducted a 19-week combined exposure (750 μg/m3) using a DE exposure system. Our results demonstrated that long-term DE exposure activates cancer-related genes and enhances the formation of DEN-induced liver tumors. Compared to the DEN group, mice in the DEN + diesel exhaust exposure (DEE) group exhibited lower body weight, higher tumor formation rates and more severe DNA damage. The tumor-promoting effect of DE may be associated with the upregulation of SEMA4D and the activation of the PI3K/AKT signaling pathway. Additionally, liver cells in the DEE group exhibited nuclear atypia, a characteristic feature of cancerous transformation. In vitro studies have revealed that exposure to diesel exhaust particles (DEP) promotes the proliferation of HepG2 cells and HUH7 cells by upregulating SEMA4D and activating the PI3K/AKT signaling pathway. This effect was attenuated by inhibiting either SEMA4D or PI3K. This study was the first to identify that DE exposure promotes the development of DEN-induced liver tumors in mice, with the mechanism potentially involving the SEMA4D/PI3K/AKT pathway. These findings provide novel insights into the hepatotoxic effects of DE and highlight the need for further investigation into its carcinogenic potential.

Fibre tracing in biomedical images: An objective comparison between seven algorithms

Obtaining the traces and the characteristics of elongated structures is an important task in computer vision pipelines. In biomedical applications, the analysis of traces of vasculature, nerves or fibres of the extracellular matrix can help characterise processes like angiogenesis or the effect of a certain treatment. This paper presents an objective comparison of six existing methodologies (Edge detection, CT Fire, Scale Space, Twombli, U-Net and Graph Based) and one novel approach called Trace Ridges to trace biomedical images with fibre-like structures. Trace Ridges is a fully automatic and fast algorithm that combines a series of image-processing algorithms including filtering, watershed transform and edge detection to obtain an accurate delineation of the fibre-like structures in a rapid time. To compare the algorithms, four biomedical data sets with vastly distinctive characteristics were selected. Ground truth was obtained by manual delineation of the fibre-like structures. Three pre-processing filtering options were used as a first step: no filtering, Gaussian low-pass and DnCnn, a deep-learning filtering. Three distance error metrics (total, average and maximum distance from the obtained traces to the ground truth) and processing time were calculated. It was observed that no single algorithm outperformed the others in all metrics. For the total distance error, which was considered the most significative, Trace Ridges ranked first, followed by Graph Based, U-Net, Twombli, Scale Space, CT Fire and Edge Detection. In terms of speed, Trace Ridges ranked second, only slightly slower than Edge Detection. Code is freely available at github.com/youssefarafat/Trace_Ridges.

Heterogeneity, inherent and acquired drug resistance in patient-derived organoid models of primary liver cancer

PURPOSE

We aimed to elucidate the applicability of tumor organoids for inherent drug resistance of primary liver cancer (PLC) and mechanisms of acquired drug resistance.

METHODS

PLC tissues were used to establish organoids, organoid-derived xenograft (ODX) and patient-derived xenograft (PDX) models. Acquired drug resistance was induced in hepatocellular carcinoma (HCC) organoids. Gene expression profiling was performed by RNA-sequencing.

RESULTS

Fifty-two organoids were established from 153 PLC patients. Compared with establishing PDX models, establishing organoids of HCC showed a trend toward a higher success rate (29.0% vs. 23.7%) and took less time (13.0 ± 4.7 vs. 25.1 ± 5.4 days, p = 2.28 × 10^-13). Larger tumors, vascular invasion, higher serum AFP levels, advanced stages and upregulation of stemness- and proliferation-related genes were significantly associated with the successful establishment of HCC organoids and PDX. Organoids and ODX recapitulated PLC histopathological features, but were enriched in more aggressive cell types. PLC organoids were mostly resistant to lenvatinib in vitro but sensitive to lenvatinib in ODX models. Stemness- and epithelial-mesenchymal transition (EMT)-related gene sets were found to be upregulated, whereas liver development- and liver specific molecule-related gene sets were downregulated in acquired sorafenib-resistant organoids. Targeting the mTOR signaling pathway was effective in treating acquired sorafenib-resistant HCC organoids, possibly via inducing phosphorylated S6 kinase. Genes upregulated in acquired sorafenib-resistant HCC organoids were associated with an unfavorable prognosis.

CONCLUSIONS

HCC organoids perform better than PDX for drug screening. Acquired sorafenib resistance in organoids promotes HCC aggressiveness via facilitating stemness, retro-differentiation and EMT. Phosphorylated S6 kinase may be predictive for drug resistance in HCC.