Xiaoqi Jiang

CYP3A5 mediates basal and acquired therapy resistance in different subtypes of pancreatic ductal adenocarcinoma

Although subtypes of pancreatic ductal adenocarcinoma (PDAC) have been described, this malignancy is clinically still treated as a single disease. Here we present patient-derived models representing the full spectrum of previously identified quasi-mesenchymal (QM-PDA), classical and exocrine-like PDAC subtypes, and identify two markers—HNF1A and KRT81—that enable stratification of tumors into different subtypes by using immunohistochemistry. Individuals with tumors of these subtypes showed substantial differences in overall survival, and their tumors differed in drug sensitivity, with the exocrine-like subtype being resistant to tyrosine kinase inhibitors and paclitaxel. Cytochrome P450 3A5 (CYP3A5) metabolizes these compounds in tumors of the exocrine-like subtype, and pharmacological or short hairpin RNA (shRNA)-mediated CYP3A5 inhibition sensitizes tumor cells to these drugs. Whereas hepatocyte nuclear factor 4, alpha (HNF4A) controls basal expression of CYP3A5, drug-induced CYP3A5 upregulation is mediated by the nuclear receptor NR1I2. CYP3A5 also contributes to acquired drug resistance in QM-PDA and classical PDAC, and it is highly expressed in several additional malignancies. These findings designate CYP3A5 as a predictor of therapy response and as a tumor cell–autonomous detoxification mechanism that must be overcome to prevent drug resistance.

Screening drug effects in patient-derived cancer cells links organoid responses to genome alterations

Cancer drug screening in patient‐derived cells holds great promise for personalized oncology and drug discovery but lacks standardization. Whether cells are cultured as conventional monolayer or advanced, matrix‐dependent organoid cultures influences drug effects and thereby drug selection and clinical success. To precisely compare drug profiles in differently cultured primary cells, we developed DeathPro, an automated microscopy‐based assay to resolve drug‐induced cell death and proliferation inhibition. Using DeathPro, we screened cells from ovarian cancer patients in monolayer or organoid culture with clinically relevant drugs. Drug‐induced growth arrest and efficacy of cytostatic drugs differed between the two culture systems. Interestingly, drug effects in organoids were more diverse and had lower therapeutic potential. Genomic analysis revealed novel links between drug sensitivity and DNA repair deficiency in organoids that were undetectable in monolayers. Thus, our results highlight the dependency of cytostatic drugs and pharmacogenomic associations on culture systems, and guide culture selection for drug tests.

The impact of data transformations on concentration–response modeling

Concentration-response studies are performed to investigate the potency of the substance under investigation. Data are typically evaluated using non-linear regression. A common model is the log-logistic model which includes parameters for lower and upper boundary of mean response, EC50 and Hill slope. Often, response and/or concentration data are transformed before proceeding with the analysis of their relationship. This is motivated by practical reasons, including comparability of results across different assays. We prove mathematically that a linear transformation of data will not change the EC50 and Hill slope estimates and only results in an identical transformation of the estimated parameters for lower and upper boundary of mean response. However, fixing some of the parameters may lead to erroneous estimates. This is of practical relevance when data are corrected for background signal and normalized by background corrected solvent control and a reduced model is used in which the response range is fixed between 100% and 0%. Computer simulations and a real data example are used to illustrate the impact of data transformations on parameter estimation. We further shed light on some common problems arising in the analysis of concentration-response data. Recommendations for practical implementation in concentration-response analysis are provided.

Transcriptomic analysis of untreated and drug-treated differentiated HepaRG cells over a 2-week period

Previous works have shown that differentiated human HepaRG cells can exhibit drug metabolism activities close to those of primary human hepatocytes for several weeks at confluence. The present study was designed to evaluate their long-term functional stability and their response to repeated daily drug treatments over a 14-day period, using a transcriptomic approach. Our data show that less than 1% of the expressed genes were markedly deregulated over this two weeks period and mainly included down-regulation of genes related to the cell cycle and from 3 days, overexpression of genes involved in xenobiotic and lipid metabolism. After daily treatment with the three PPAR agonists, fenofibrate, troglitazone and rosiglitazone qualitative and/or quantitative changes in gene profiling were observed depending on the compound and duration of treatment. The highest increase in the number of deregulated genes as a function of drug treatment was seen with rosiglitazone. The most up-regulated genes common across the three compounds were mainly related to lipid and xenobiotic metabolisms. All the data support the conclusion that human HepaRG cells have an exceptional functional stability at confluence and that they are suitable for investigations on chronic effects of drugs and other chemicals.

Summarizing EC50 estimates from multiple dose-response experiments: A comparison of a meta-analysis strategy to a mixed-effects model approach

Dose-response studies are performed to investigate the potency of a compound. EC50 is the concentration of the compound that gives half-maximal response. Dose-response data are typically evaluated by using a log-logistic model that includes EC50 as one of the model parameters. Often, more than one experiment is carried out to determine the EC50 value for a compound, requiring summarization of EC50 estimates from a series of experiments. In this context, mixed-effects models are designed to estimate the average behavior of EC50 values over all experiments by considering the variabilities within and among experiments simultaneously. However, fitting nonlinear mixed-effects models is more complicated than in a linear situation, and convergence problems are often encountered. An alternative strategy is the application of a meta-analysis approach, which combines EC50 estimates obtained from separate log-logistic model fitting. These two proposed strategies to summarize EC50 estimates from multiple experiments are compared in a simulation study and real data example. We conclude that the meta-analysis strategy is a simple and robust method to summarize EC50 estimates from multiple experiments, especially suited in the case of a small number of experiments.

Omics-based responses induced by bosentan in human hepatoma HepaRG cell cultures

Bosentan is well known to induce cholestatic liver toxicity in humans. The present study was set up to characterize the hepatotoxic effects of this drug at the transcriptomic, proteomic, and metabolomic levels. For this purpose, human hepatoma-derived HepaRG cells were exposed to a number of concentrations of bosentan during different periods of time. Bosentan was found to functionally and transcriptionally suppress the bile salt export pump as well as to alter bile acid levels. Pathway analysis of both transcriptomics and proteomics data identified cholestasis as a major toxicological event. Transcriptomics results further showed several gene changes related to the activation of the nuclear farnesoid X receptor. Induction of oxidative stress and inflammation were also observed. Metabolomics analysis indicated changes in the abundance of specific endogenous metabolites related to mitochondrial impairment. The outcome of this study may assist in the further optimization of adverse outcome pathway constructs that mechanistically describe the processes involved in cholestatic liver injury.

Functional analysis of high-content high-throughput imaging data

ABSTRACT High-content automated imaging platforms allow the multiplexing of several targets simultaneously to generate multi-parametric single-cell data sets over extended periods of time. Typically, standard simple measures such as mean value of all cells at every time point are calculated to summarize the temporal process, resulting in loss of time dynamics of the single cells. Multiple experiments are performed but observation time points are not necessarily identical, leading to difficulties when integrating summary measures from different experiments. We used functional data analysis to analyze continuous curve data, where the temporal process of a response variable for each single cell can be described using a smooth curve. This allows analyses to be performed on continuous functions, rather than on original discrete data points. Functional regression models were applied to determine common temporal characteristics of a set of single cell curves and random effects were employed in the models to explain variation between experiments. The aim of the multiplexing approach is to simultaneously analyze the effect of a large number of compounds in comparison to control to discriminate between their mode of action. Functional principal component analysis based on T-statistic curves for pairwise comparison to control was used to study time-dependent compound effects.

Statistical strategies for averaging EC50 from multiple dose–response experiments

Abstract In most dose–response studies, repeated experiments are conducted to determine the EC50 value for a chemical, requiring averaging EC50 estimates from a series of experiments. Two statistical strategies, the mixed-effect modeling and the meta-analysis approach, can be applied to estimate average behavior of EC50 values over all experiments by considering the variabilities within and among experiments. We investigated these two strategies in two common cases of multiple dose–response experiments in (a) complete and explicit dose–response relationships are observed in all experiments and in (b) only in a subset of experiments. In case (a), the meta-analysis strategy is a simple and robust method to average EC50 estimates. In case (b), all experimental data sets can be first screened using the dose–response screening plot, which allows visualization and comparison of multiple dose–response experimental results. As long as more than three experiments provide information about complete dose–response relationships, the experiments that cover incomplete relationships can be excluded from the meta-analysis strategy of averaging EC50 estimates. If there are only two experiments containing complete dose–response information, the mixed-effects model approach is suggested. We subsequently provided a web application for non-statisticians to implement the proposed meta-analysis strategy of averaging EC50 estimates from multiple dose–response experiments.