Spatial omics techniques and data analysis for cancer immunotherapy applications.

In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.

Global Epidemiology and Genetics of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the leading cancers worldwide. Classically, HCC develops in genetically susceptible individuals who are exposed to risk factors, especially in the presence of liver cirrhosis. Significant temporal and geographic variations exist for HCC and its etiologies. Over time, the burden of HCC has shifted from the low-moderate to the high sociodemographic index regions, reflecting the transition from viral to nonviral causes. Geographically, the hepatitis viruses predominate as the causes of HCC in Asia and Africa. Although there are genetic conditions that confer increased risk for HCC, these diagnoses are rarely recognized outside North America and Europe. In this review, we will evaluate the epidemiologic trends and risk factors of HCC, and discuss the genetics of HCC, including monogenic diseases, single-nucleotide polymorphisms, gut microbiome, and somatic mutations.

Choice of PD-L1 immunohistochemistry assay influences clinical eligibility for gastric cancer immunotherapy.

BACKGROUND: Immune checkpoint inhibitors (ICI) are now standard-of-care treatment for patients with metastatic gastric cancer (GC). To guide patient selection for ICI therapy, programmed death ligand-1 (PD-L1) biomarker expression is routinely assessed via immunohistochemistry (IHC). However, with an increasing number of approved ICIs, each paired with a different PD-L1 antibody IHC assay used in their respective landmark trials, there is an unmet clinical and logistical need for harmonization. We investigated the interchangeability between the Dako 22C3, Dako 28-8 and Ventana SP-142 assays in GC PD-L1 IHC. METHODS: In this cross-sectional study, we scored 362 GC samples for PD-L1 combined positive score (CPS), tumor proportion score (TPS) and immune cells (IC) using a multiplex immunohistochemistry/immunofluorescence technique. Samples were obtained via biopsy or resection of gastric cancer. RESULTS: The percentage of PD-L1-positive samples at clinically relevant CPS ≥ 1, ≥ 5 and ≥ 10 cut-offs for the 28-8 assay were approximately two-fold higher than that of the 22C3 (CPS ≥ 1: 70.3 vs 49.4%, p < 0.001; CPS ≥ 5: 29.1 vs 13.4%, p < 0.001; CPS ≥ 10: 13.7 vs 7.0%, p = 0.004). The mean CPS score on 28-8 assay was nearly double that of the 22C3 (6.39 ± 14.5 vs 3.46 ± 8.98, p < 0.001). At the clinically important CPS ≥ 5 cut-off, there was only moderate concordance between the 22C3 and 28-8 assays. CONCLUSION: Our findings suggest that scoring PD-L1 CPS with the 28-8 assay may result in higher PD-L1 scores and higher proportion of PD-L1 positivity compared to 22C3 and other assays. Until stronger evidence of inter-assay concordance is found, we urge caution in treating the assays as equivalent.

Group VIII Metal Carbonyl Cluster-Boronic Acid Conjugates: Cytotoxicity and Mode of Action Studies.

A set of metal carbonyl cluster-boronic acid conjugates of the group VIII metals (Fe, Ru, and Os) were synthesized and their antiproliferative effects measured against two breast cancer cell lines (MCF-7 and MDA-MB-231) and a noncancerous breast epithelial (MCF-10A) cell line. The cytotoxicity followed the order Ru > Os > Fe for the MDA-MB-231 cells, although the latter two exhibited similar cytotoxicity against MCF-7 and MCF-10A cells. The osmium species {Os3(CO)10(µ-H)[µ-SC6H4-p-B(OH)2]} (2) could be chemically oxidized to its hydroxy analogue [Os3(CO)10(µ-H)(µ-SC6H4 -p-OH)] (2-OH), which showed comparable cytotoxicity. Mode of action studies pointed to an apoptotic pathway for cell death.

Leveraging advances in immunopathology and artificial intelligence to analyze in vitro tumor models in composition and space.

Cancer is the leading cause of death worldwide. Unfortunately, efforts to understand this disease are confounded by the complex, heterogenous tumor microenvironment (TME). Better understanding of the TME could lead to novel diagnostic, prognostic, and therapeutic discoveries. One way to achieve this involves in vitro tumor models that recapitulate the in vivo TME composition and spatial arrangement. Here, we review the potential of harnessing in vitro tumor models and artificial intelligence to delineate the TME. This includes (i) identification of novel features, (ii) investigation of higher-order relationships, and (iii) analysis and interpretation of multiomics data in a (iv) holistic, objective, reproducible, and efficient manner, which surpasses previous methods of TME analysis. We also discuss limitations of this approach, namely inadequate datasets, indeterminate biological correlations, ethical concerns, and logistical constraints; finally, we speculate on future avenues of research that could overcome these limitations, ultimately translating to improved clinical outcomes.

Structure-based virtual screening of CYP1A1 inhibitors: towards rapid tier-one assessment of potential developmental toxicants.

Cytochrome P450 1A1 (CYP1A1) metabolizes estrogens, melatonin, and other key endogenous signaling molecules critical for embryonic/fetal development. The enzyme has increasing expression during pregnancy, and its inhibition or knockout increases embryonic/fetal lethality and/or developmental problems. Here, we present a virtual screening model for CYP1A1 inhibitors based on the orthosteric and predicted allosteric sites of the enzyme. Using 1001 reference compounds with CYP1A1 activity data, we optimized the decision thresholds of our model and classified the training compounds with 68.3% balanced accuracy (91.0% sensitivity and 45.7% specificity). We applied our final model to 11 known CYP1A1 orthosteric binders and related compounds, and found that our ranking of the known orthosteric binders generally agrees with the relative activity of CYP1A1 in metabolizing these compounds. We also applied the model to 22 new test compounds with unknown/unclear CYP1A1 inhibitory activity, and predicted 16 of them are CYP1A1 inhibitors. The CYP1A1 potency and modes of inhibition of these 22 compounds were experimentally determined. We confirmed that most predicted inhibitors, including drugs contraindicated during pregnancy (amiodarone, bicalutamide, cyproterone acetate, ketoconazole, and tamoxifen) and environmental agents suspected to be endocrine disruptors (bisphenol A, diethyl and dibutyl phthalates, and zearalenone), are indeed potent inhibitors of CYP1A1. Our results suggest that virtual screening may be used as a rapid tier-one method to screen for potential CYP1A1 inhibitors, and flag them out for further experimental evaluations.

Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids.

Nuclear receptors (NRs) are key regulators of energy homeostasis, body development, and sexual reproduction. Xenobiotics binding to NRs may disrupt natural hormonal systems and induce undesired adverse effects in the body. However, many chemicals of concerns have limited or no experimental data on their potential or lack-of-potential endocrine-disrupting effects. Here, we propose a virtual screening method based on molecular docking for predicting potential endocrine-disrupting chemicals (EDCs) that bind to NRs. For 12 NRs, we systematically analyzed how multiple crystal structures can be used to distinguish actives and inactives found in previous high-throughput experiments. Our method is based on (i) consensus docking scores from multiple structures at a single functional state (agonist-bound or antagonist-bound), (ii) multiple functional states (agonist-bound and antagonist-bound), and (iii) multiple pockets (orthosteric site and alternative sites) of these NRs. We found that the consensus enrichment from multiple structures is better than or comparable to the best enrichment from a single structure. The discriminating power of this consensus strategy was further enhanced by a chemical similarity-weighted scoring scheme, yielding better or comparable enrichment for all studied NRs. Applying this optimized method, we screened 252 fatty acids against peroxisome proliferator-activated receptor gamma (PPARγ) and successfully identified 3 previously unknown fatty acids with Kd = 100-250 µM including two furan fatty acids: furannonanoic acid (FNA) and furanundecanoic acid (FUA), and one cyclopropane fatty acid: phytomonic acid (PTA). These results suggested that the proposed method can be used to rapidly screen and prioritize potential EDCs for further experimental evaluations.

Optimum concentration-response curve metrics for supervised selection of discriminative cellular phenotypic endpoints for chemical hazard assessment.

High-content imaging (HCI) provides quantitative and information-rich measurements of chemical effects on human in vitro cell models. Identification of discriminative phenotypic endpoints from cellular features obtained from HCI is required for accurate assessments of potential chemical hazards. However, the use of suboptimal metrics to quantify the concentration-response curves (CRC) of chemicals based on these features may obscure discriminative features, and lead to non-predictive endpoints and poor chemical classifications or hazard assessments. Here, we present a systematic and data-driven study on the performances of different CRC metrics in identifying image-based phenotypic features that can accurately classify the effects of reference chemicals with known in vivo toxicities. We studied four previous HCI in vitro nephro- or pulmono-toxicity datasets, which contain phenotypic feature measurements from different cell and feature types. Within a feature type, we found that efficacy metrics at higher chemical concentrations tend to give higher classification accuracy, whereas potency metrics do not have obvious trends across different response levels. Across different cell and feature types, efficacy metrics generally gave higher classification accuracy than potency metrics and area under the curve (AUC). Our results suggest that efficacy metrics, especially at higher concentrations, are more likely to help us to identify discriminative phenotypic endpoints. Therefore, HCI experiments for toxicological applications should include measurements at sufficiently high chemical concentrations, and efficacy metrics should always be analyzed. The identified features may be used as specific toxicity endpoints for further chemical hazard assessment.

Predicting direct hepatocyte toxicity in humans by combining high-throughput imaging of HepaRG cells and machine learning-based phenotypic profiling.

Accurate prediction of drug- and chemical-induced hepatotoxicity remains to be a problem for pharmaceutical companies as well as other industries and regulators. The goal of the current study was to develop an in vitro/in silico method for the rapid and accurate prediction of drug- and chemical-induced hepatocyte injury in humans. HepaRG cells were employed for high-throughput imaging in combination with phenotypic profiling. A reference set of 69 drugs and chemicals was screened at a range of 7 concentrations, and the cellular response values were used for training a supervised classifier and for determining assay performance by using tenfold cross-validation. The results showed that the best performing phenotypic features were related to nuclear translocation of RELA (RELA proto-oncogene, NF-kB subunit; also known as NF-kappa B p65), DNA organization, and the F-actin cytoskeleton. Using a subset of 30 phenotypic features, direct hepatocyte toxicity in humans could be predicted with a test sensitivity, specificity and balanced accuracy of 73%, 92%, and 83%, respectively. The method was applied to another set of 26 drugs and chemicals with unclear annotation and their hepatocyte toxicity in humans was predicted. The results also revealed that the identified discriminative phenotypic changes were related to cell death and cellular senescence. Whereas cell death-related endpoints are widely applied in in vitro toxicology, cellular senescence-related endpoints are not, although cellular senescence can be induced by various drugs and other small molecule compounds and plays an important role in liver injury and disease. These findings show how phenotypic profiling can reveal unexpected chemical-induced mechanisms in toxicology.

A Case Study with Triazole Fungicides to Explore Practical Application of Next-Generation Hazard Assessment Methods for Human Health.

The ongoing developments in chemical risk assessment have led to new concepts building on integration of sophisticated nonanimal models for hazard characterization. Here we explore a pragmatic approach for implementing such concepts, using a case study of three triazole fungicides, namely, flusilazole, propiconazole, and cyproconazole. The strategy applied starts with evaluating the overall level of concern by comparing exposure estimates to toxicological potential, followed by a combination of in silico tools and literature-derived high-throughput screening assays and computational elaborations to obtain insight into potential toxicological mechanisms and targets in the organism. Additionally, some targeted in vitro tests were evaluated for their utility to confirm suspected mechanisms of toxicity and to generate points of departure. Toxicological mechanisms instead of the current "end point-by-end point" approach should guide the selection of methods and assays that constitute a toolbox for next-generation risk assessment. Comparison of the obtained in silico and in vitro results with data from traditional in vivo testing revealed that, overall, nonanimal methods for hazard identification can produce adequate qualitative hazard information for risk assessment. Follow-up studies are needed to further refine the proposed approach, including the composition of the toolbox, toxicokinetics models, and models for exposure assessment.

Utility of In Vitro Bioactivity as a Lower Bound Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization.

Use of high-throughput, in vitro bioactivity data in setting a point-of-departure (POD) has the potential to accelerate the pace of human health safety evaluation by informing screening-level assessments. The primary objective of this work was to compare PODs based on high-throughput predictions of bioactivity, exposure predictions, and traditional hazard information for 448 chemicals. PODs derived from new approach methodologies (NAMs) were obtained for this comparison using the 50th (PODNAM, 50) and the 95th (PODNAM, 95) percentile credible interval estimates for the steady-state plasma concentration used in in vitro to in vivo extrapolation of administered equivalent doses. Of the 448 substances, 89% had a PODNAM, 95 that was less than the traditional POD (PODtraditional) value. For the 48 substances for which PODtraditional < PODNAM, 95, the PODNAM and PODtraditional were typically within a factor of 10 of each other, and there was an enrichment of chemical structural features associated with organophosphate and carbamate insecticides. When PODtraditional < PODNAM, 95, it did not appear to result from an enrichment of PODtraditional based on a particular study type (eg, developmental, reproductive, and chronic studies). Bioactivity:exposure ratios, useful for identification of substances with potential priority, demonstrated that high-throughput exposure predictions were greater than the PODNAM, 95 for 11 substances. When compared with threshold of toxicological concern (TTC) values, the PODNAM, 95 was greater than the corresponding TTC value 90% of the time. This work demonstrates the feasibility, and continuing challenges, of using in vitro bioactivity as a protective estimate of POD in screening-level assessments via a case study.

PI3K Catalytic Subunits α and ß Modulate Cell Death and IL-6 Secretion Induced by Talc Particles in Human Lung Carcinoma Cells.

Hydrated magnesium silicate (or "talc" particles) is a sclerosis agent commonly used in the management of malignant pleural effusions, a common symptom of metastatic diseases, including lung cancers. However, the direct effects of talc particles to lung carcinoma cells, which can be found in the malignant pleural effusion fluids from patients with lung cancer, are not fully understood. Here, we report a study of the signaling pathways that can modulate the cell death and IL-6 secretion induced by talc particles in human lung carcinoma cells. We found that talc-sensitive cells have higher mRNA and protein expression of PI3K catalytic subunits α and ß. Further experiments confirmed that modulation (inhibition or activation) of the PI3K pathway reduces or enhances cellular sensitivity to talc particles, respectively, independent of the inflammasome. By knocking down specific PI3K isoforms, we also confirmed that both PI3Kα and -ß mediate the observed talc effects. Our results suggest a novel role of the PI3K pathway in talc-induced cell death and IL-6 secretion in lung carcinoma cells. These cellular events are known to drive fibrosis, and thus further studies of the PI3K pathway may provide a better understanding of the mechanisms of talc sclerosis in the malignant pleural space.

Emerging technologies for food and drug safety.

Emerging technologies are playing a major role in the generation of new approaches to assess the safety of both foods and drugs. However, the integration of emerging technologies in the regulatory decision-making process requires rigorous assessment and consensus amongst international partners and research communities. To that end, the Global Coalition for Regulatory Science Research (GCRSR) in partnership with the Brazilian Health Surveillance Agency (ANVISA) hosted the seventh Global Summit on Regulatory Science (GSRS17) in Brasilia, Brazil on September 18-20, 2017 to discuss the role of new approaches in regulatory science with a specific emphasis on applications in food and medical product safety. The global regulatory landscape concerning the application of new technologies was assessed in several countries worldwide. Challenges and issues were discussed in the context of developing an international consensus for objective criteria in the development, application and review of emerging technologies. The need for advanced approaches to allow for faster, less expensive and more predictive methodologies was elaborated. In addition, the strengths and weaknesses of each new approach was discussed. And finally, the need for standards and reproducible approaches was reviewed to enhance the application of the emerging technologies to improve food and drug safety. The overarching goal of GSRS17 was to provide a venue where regulators and researchers meet to develop collaborations addressing the most pressing scientific challenges and facilitate the adoption of novel technical innovations to advance the field of regulatory science.

Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence.

Human lungs are susceptible to the toxicity induced by soluble xenobiotics. However, the direct cellular effects of many pulmonotoxic chemicals are not always clear, and thus, a general in vitro assay for testing pulmonotoxicity applicable to a wide variety of chemicals is not currently available. Here, we report a study that uses high-throughput imaging and artificial intelligence to build an in vitro pulmonotoxicity assay by automatically comparing and selecting human lung-cell lines and their associated quantitative phenotypic features most predictive of in vivo pulmonotoxicity. This approach is called "High-throughput In vitro Phenotypic Profiling for Toxicity Prediction" (HIPPTox). We found that the resulting assay based on two phenotypic features of a human bronchial epithelial cell line, BEAS-2B, can accurately classify 33 reference chemicals with human pulmonotoxicity information (88.8% balance accuracy, 84.6% sensitivity, and 93.0% specificity). In comparison, the predictivity of a standard cell-viability assay on the same set of chemicals is much lower (77.1% balanced accuracy, 84.6% sensitivity, and 69.5% specificity). We also used the assay to evaluate 17 additional test chemicals with unknown/unclear human pulmonotoxicity, and experimentally confirmed that many of the pulmonotoxic reference and predicted-positive test chemicals induce DNA strand breaks and/or activation of the DNA-damage response (DDR) pathway. Therefore, HIPPTox helps us to uncover these common modes-of-action of pulmonotoxic chemicals. HIPPTox may also be applied to other cell types or models, and accelerate the development of predictive in vitro assays for other cell-type- or organ-specific toxicities.

High-throughput prediction of nephrotoxicity in humans.

The Lush Science Prize 2016 was awarded to Daniele Zink and Lit-Hsin Loo for the interdisciplinary and collaborative work between their research groups in developing alternative methods for the prediction of nephrotoxicity in humans. The collaboration has led to the establishment of a series of pioneering alternative methods for nephrotoxicity prediction, which includes: predictive gene expression markers based on pro-inflammatory responses; predictive in vitro cellular models based on pluripotent stem cell-derived proximal tubular-like cells; and predictive cellular phenotypic markers based on chromatin and cytoskeletal changes. A high-throughput method was established for chemical testing, which is currently being used to predict the potential human nephrotoxicity of ToxCast compounds in collaboration with the US Environmental Protection Agency. Similar high-throughput imaging-based methodologies are currently being developed and adapted by the Zink and Loo groups, to include other human organs and cell types. The ultimate goal is to develop a portfolio of methods accepted for the accurate prediction of human organ-specific toxicity and the consequent replacement of animal experiments.

Early spatiotemporal-specific changes in intermediate signals are predictive of cytotoxic sensitivity to TNFα and co-treatments.

Signaling pathways can generate different cellular responses to the same cytotoxic agents. Current quantitative models for predicting these differential responses are usually based on large numbers of intracellular gene products or signals at different levels of signaling cascades. Here, we report a study to predict cellular sensitivity to tumor necrosis factor alpha (TNFα) using high-throughput cellular imaging and machine-learning methods. We measured and compared 1170 protein phosphorylation events in a panel of human lung cancer cell lines based on different signals, subcellular regions, and time points within one hour of TNFα treatment. We found that two spatiotemporal-specific changes in an intermediate signaling protein, p90 ribosomal S6 kinase (RSK), are sufficient to predict the TNFα sensitivity of these cell lines. Our models could also predict the combined effects of TNFα and other kinase inhibitors, many of which are not known to target RSK directly. Therefore, early spatiotemporal-specific changes in intermediate signals are sufficient to represent the complex cellular responses to these perturbations. Our study provides a general framework for the development of rapid, signaling-based cytotoxicity screens that may be used to predict cellular sensitivity to a cytotoxic agent, or identify co-treatments that may sensitize or desensitize cells to the agent.

Large-scale image-based screening and profiling of cellular phenotypes.

Cellular phenotypes are observable characteristics of cells resulting from the interactions of intrinsic and extrinsic chemical or biochemical factors. Image-based phenotypic screens under large numbers of basal or perturbed conditions can be used to study the influences of these factors on cellular phenotypes. Hundreds to thousands of phenotypic descriptors can also be quantified from the images of cells under each of these experimental conditions. Therefore, huge amounts of data can be generated, and the analysis of these data has become a major bottleneck in large-scale phenotypic screens. Here, we review current experimental and computational methods for large-scale image-based phenotypic screens. Our focus is on phenotypic profiling, a computational procedure for constructing quantitative and compact representations of cellular phenotypes based on the images collected in these screens. © 2016 International Society for Advancement of Cytometry.

High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures.

The kidney is a major target for xenobiotics, which include drugs, industrial chemicals, environmental toxicants and other compounds. Accurate methods for screening large numbers of potentially nephrotoxic xenobiotics with diverse chemical structures are currently not available. Here, we describe an approach for nephrotoxicity prediction that combines high-throughput imaging of cultured human renal proximal tubular cells (PTCs), quantitative phenotypic profiling, and machine learning methods. We automatically quantified 129 image-based phenotypic features, and identified chromatin and cytoskeletal features that can predict the human in vivo PTC toxicity of 44 reference compounds with ~82 % (primary PTCs) or 89 % (immortalized PTCs) test balanced accuracies. Surprisingly, our results also revealed that a DNA damage response is commonly induced by different PTC toxicants that have diverse chemical structures and injury mechanisms. Together, our results show that human nephrotoxicity can be predicted with high efficiency and accuracy by combining cell-based and computational methods that are suitable for automation.

Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods.

The renal proximal tubule is a main target for drug-induced toxicity. The prediction of proximal tubular toxicity during drug development remains difficult. Any in vitro methods based on induced pluripotent stem cell-derived renal cells had not been developed, so far. Here, we developed a rapid 1-step protocol for the differentiation of human induced pluripotent stem cells (hiPSC) into proximal tubular-like cells. These proximal tubular-like cells had a purity of >90% after 8 days of differentiation and could be directly applied for compound screening. The nephrotoxicity prediction performance of the cells was determined by evaluating their responses to 30 compounds. The results were automatically determined using a machine learning algorithm called random forest. In this way, proximal tubular toxicity in humans could be predicted with 99.8% training accuracy and 87.0% test accuracy. Further, we studied the underlying mechanisms of injury and drug-induced cellular pathways in these hiPSC-derived renal cells, and the results were in agreement with human and animal data. Our methods will enable the development of personalized or disease-specific hiPSC-based renal in vitro models for compound screening and nephrotoxicity prediction.

Supervised prediction of drug-induced nephrotoxicity based on interleukin-6 and -8 expression levels.

BACKGROUND: Drug-induced nephrotoxicity causes acute kidney injury and chronic kidney diseases, and is a major reason for late-stage failures in the clinical trials of new drugs. Therefore, early, pre-clinical prediction of nephrotoxicity could help to prioritize drug candidates for further evaluations, and increase the success rates of clinical trials. Recently, an in vitro model for predicting renal-proximal-tubular-cell (PTC) toxicity based on the expression levels of two inflammatory markers, interleukin (IL)-6 and -8, has been described. However, this and other existing models usually use linear and manually determined thresholds to predict nephrotoxicity. Automated machine learning algorithms may improve these models, and produce more accurate and unbiased predictions. RESULTS: Here, we report a systematic comparison of the performances of four supervised classifiers, namely random forest, support vector machine, k-nearest-neighbor and naive Bayes classifiers, in predicting PTC toxicity based on IL-6 and -8 expression levels. Using a dataset of human primary PTCs treated with 41 well-characterized compounds that are toxic or not toxic to PTC, we found that random forest classifiers have the highest cross-validated classification performance (mean balanced accuracy = 87.8%, sensitivity = 89.4%, and specificity = 85.9%). Furthermore, we also found that IL-8 is more predictive than IL-6, but a combination of both markers gives higher classification accuracy. Finally, we also show that random forest classifiers trained automatically on the whole dataset have higher mean balanced accuracy than a previous threshold-based classifier constructed for the same dataset (99.3% vs. 80.7%). CONCLUSIONS: Our results suggest that a random forest classifier can be used to automatically predict drug-induced PTC toxicity based on the expression levels of IL-6 and -8.