Gompertz tracking of the growth trajectories of the human-liver-cancer xenograft-tumors in nude mice

A network pharmacology integrated serum pharmacochemistry strategy for uncovering efficacy of YXC on hepatocellular carcinoma

ETHNOPHARMACOLOGICAL RELEVANCE

The YangzhengXiaoji capsule (YXC) has a wide range of applications as effective traditional Chinese medicine (TCM) preparation for hepatocellular carcinoma (HCC) in China. However, the potential bioactive components and the mechanisms are yet unclear.

AIM OF THE STUDY

The treatment mechanism of YXC on HCC using a network pharmacology integrated serum pharmacochemistry strategy to investigate associated targets and pathways.

MATERIALS AND METHODS

We utilised HPLC-Q-TOF-MS/MS technology to identify components of the serum samples from both the model group and the YXC (H) group serum, which were collected from nude mice with orthotopic liver tumours. Following this, we conducted compound-target prediction and identified the overlap between the target genes in the YXC group and the oncogenes associated with HCC. The anticancer mechanisms of YXC were investigated by creating a compound-target-pathway network using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analysis. The anticancer efficacy was evaluated in vitro and in vivo. Also, potential predictive targets and pathways associated with YXC in HCC treatment were assessed by western blotting.

RESULTS

The YXC (H) serum had 47 bioactive compounds compared to other models, and identified 173 specific target genes. Using the compound-target-disease network, 141 possible target genes were identified. The KEGG pathway analysis revealed vital enrichment of pathways associated with HCC, including regulating Oncology related pathways of inflammation, immunity, apoptosis, and necrosis biological processes. YXC significantly inhibited HCC cell growth in vitro and in vivo. After YXC treatment, western blotting detected alterations in the p53/Bcl-2/Bax/Caspase-3 and PI3K/Akt pathways.

CONCLUSIONS

YXC can inhibit HCC development and advancement by a variety of components, targets and pathways, especially apoptosis-induction.

Extracellular matrix drives tumor organoids toward desmoplastic matrix deposition and mesenchymal transition

Patient-derived tumor organoids have been established as promising tools for in vitro modelling of multiple tumors, including cholangiocarcinoma (CCA). However, organoids are commonly cultured in basement membrane extract (BME) which does not recapitulate the intricacies of the extracellular matrix (ECM). We combined CCA organoids (CCAOs) with native tumor and liver scaffolds, obtained by decellularization, to effectuate a model to study the interaction between epithelial tumor cells and their surrounding ECM. Decellularization resulted in removal of cells while preserving ECM structure and retaining important characteristics of the tissue origin, including stiffness and presence of desmoplasia. The transcriptome of CCAOs in a tumor scaffold much more resembled that of patient-paired CCA tissue in vivo compared to CCAOs cultured in BME or liver scaffolds. This was accompanied by an increase in chemoresistance to clinically-relevant chemotherapeutics. CCAOs in decellularized scaffolds revealed environment-dependent proliferation dynamics, driven by the occurrence of epithelial-mesenchymal transition. Furthermore, CCAOs initiated an environment-specific desmoplastic reaction by increasing production of multiple collagen types. In conclusion, convergence of organoid-based models with native ECM scaffolds will lead to better understanding of the in vivo tumor environment. STATEMENT OF SIGNIFICANCE: The extracellular matrix (ECM) influences various facets of tumor behavior. Understanding the exact role of the ECM in controlling tumor cell fate is pertinent to understand tumor progression and develop novel therapeutics. This is particularly the case for cholangiocarcinoma (CCA), whereby the ECM displays a distinct tumor environment, characterized by desmoplasia. However, current models to study the interaction between epithelial tumor cells and the environment are lacking. We have developed a fully patient-derived model encompassing CCA organoids (CCAOs) and human decellularized tumor and tumor-free liver ECM. The tumor ECM induced recapitulation of various aspects of CCA, including migration dynamics, transcriptome and proteome profiles, and chemoresistance. Lastly, we uncover that epithelial tumor cells contribute to matrix deposition, and that this phenomenon is dependent on the level of desmoplasia already present.

Spatiotemporal imaging and analysis of mouse and human liver bud morphogenesis

BACKGROUND

The process of liver organogenesis has served as a paradigm for organ formation. However, there remains a lack of understanding regarding early mouse and human liver bud morphogenesis and early liver volumetric growth. Elucidating dynamic changes in liver volumes is critical for understanding organ development, implementing toxicological studies, and for modeling hPSC-derived liver organoid growth. New visualization, analysis, and experimental techniques are desperately needed.

RESULTS

Here, we combine observational data with digital resources, new 3D imaging approaches, retrospective analysis of liver volume data, mathematical modeling, and experiments with hPSC-derived liver organoids. Mouse and human liver organogenesis, characterized by exponential growth, demonstrate distinct spatial features and growth curves over time, which we mathematically modeled using Gompertz models. Visualization of liver-epithelial and septum transversum mesenchyme (STM) interactions suggests extended interactions, which together with new spatial features may be responsible for extensive exponential growth. These STM interactions are modeled with a novel in vitro human pluripotent stem cell (hPSC)-derived hepatic organoid system that exhibits cell migration.

CONCLUSIONS

Our methods enhance our understanding of liver organogenesis, with new 3D visualization, analysis, mathematical modeling, and in vitro models with hPSCs. Our approach highlights mouse and human differences and provides potential hypothesis for further investigation in vitro and in vivo.

Comparison of an artificial neural network and Gompertz model for predicting the dynamics of deaths from COVID-19 in México

The present work is focused on modeling and predicting the cumulative number of deaths from COVID-19 in México by comparing an artificial neural network (ANN) with a Gompertz model applying multiple optimization algorithms for the estimation of coefficients and parameters, respectively. For the modeling process, the data published by the daily technical report COVID-19 in Mexico from March 19th to September 30th were used. The data published in the month of October were included to carry out the prediction. The results show a satisfactory comparison between the real data and those obtained by both models with a R2 > 0.999. The Levenberg-Marquardt and BFGS quasi-Newton optimization algorithm were favorable for fitting the coefficients during learning in the ANN model due to their fast and precision, respectively. On the other hand, the Nelder-Mead simplex algorithm fitted the parameters of the Gompertz model faster by minimizing the sum of squares. Therefore, the ANN model better fits the real data using ten coefficients. However, the Gompertz model using three parameters converges in less computational time. In the prediction, the inverse ANN model was solved by a genetic algorithm obtaining the best precision with a maximum error of 2.22% per day, as opposed to the 5.48% of the Gompertz model with respect to the real data reported from November 1st to 15th. Finally, according to the coefficients and parameters obtained from both models with recent data, a total of 109,724 cumulative deaths for the inverse ANN model and 100,482 cumulative deaths for the Gompertz model were predicted for the end of 2020.

Inference on an heteroscedastic Gompertz tumor growth model

We consider a non homogeneous Gompertz diffusion process whose parameters are modified by generally time-dependent exogenous factors included in the infinitesimal moments. The proposed model is able to describe tumor dynamics under the effect of anti-proliferative and/or cell death-induced therapies. We assume that such therapies can modify also the infinitesimal variance of the diffusion process. An estimation procedure, based on a control group and two treated groups, is proposed to infer the model by estimating the constant parameters and the time-dependent terms. Moreover, several concatenated hypothesis tests are considered in order to confirm or reject the need to include time-dependent functions in the infinitesimal moments. Simulations are provided to evaluate the efficiency of the suggested procedures and to validate the testing hypothesis. Finally, an application to real data is considered.