Dataset Information

Prediction of chemical respiratory sensitizers using GARD, a novel in vitro assay based on a genomic biomarker signature.

ABSTRACT:

Background

Repeated exposure to certain low molecular weight (LMW) chemical compounds may result in development of allergic reactions in the skin or in the respiratory tract. In most cases, a certain LMW compound selectively sensitize the skin, giving rise to allergic contact dermatitis (ACD), or the respiratory tract, giving rise to occupational asthma (OA). To limit occurrence of allergic diseases, efforts are currently being made to develop predictive assays that accurately identify chemicals capable of inducing such reactions. However, while a few promising methods for prediction of skin sensitization have been described, to date no validated method, in vitro or in vivo, exists that is able to accurately classify chemicals as respiratory sensitizers.

Results

Recently, we presented the in vitro based Genomic Allergen Rapid Detection (GARD) assay as a novel testing strategy for classification of skin sensitizing chemicals based on measurement of a genomic biomarker signature. We have expanded the applicability domain of the GARD assay to classify also respiratory sensitizers by identifying a separate biomarker signature containing 389 differentially regulated genes for respiratory sensitizers in comparison to non-respiratory sensitizers. By using an independent data set in combination with supervised machine learning, we validated the assay, showing that the identified genomic biomarker is able to accurately classify respiratory sensitizers.

Conclusions

We have identified a genomic biomarker signature for classification of respiratory sensitizers. Combining this newly identified biomarker signature with our previously identified biomarker signature for classification of skin sensitizers, we have developed a novel in vitro testing strategy with a potent ability to predict both skin and respiratory sensitization in the same sample.

SUBMITTER: Forreryd A

PROVIDER: S-EPMC4356558 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Prediction of chemical respiratory sensitizers using GARD, a novel in vitro assay based on a genomic biomarker signature.

Forreryd Andy A Johansson Henrik H Albrekt Ann-Sofie AS Borrebaeck Carl A K CA Lindstedt Malin M

PloS one 20150311 3

<h4>Background</h4>Repeated exposure to certain low molecular weight (LMW) chemical compounds may result in development of allergic reactions in the skin or in the respiratory tract. In most cases, a certain LMW compound selectively sensitize the skin, giving rise to allergic contact dermatitis (ACD), or the respiratory tract, giving rise to occupational asthma (OA). To limit occurrence of allergic diseases, efforts are currently being made to develop predictive assays that accurately identify c ...[more]

PMID: 25760038

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Project description:The use of animal models in chemical safety testing will be significantly limited due to the recent introduction of the 3Rs principle of animal experimentation in research. Although several in vitro assays to predict the sensitizing potential of chemicals have been developed, these methods cannot distinguish chemical respiratory sensitizers and skin sensitizers. In the present study, we describe a novel in vitro assay that can discriminate respiratory sensitizers from chemical skin sensitizers by taking advantage of the fundamental difference between their modes of action, namely the development of the T helper 2 immune response, which is critically important for respiratory sensitization. First, we established a novel three-dimensional (3D) coculture system of human upper airway epithelium using a commercially available scaffold. It consists of human airway epithelial cell line BEAS-2B, immature dendritic cells (DCs) derived from human peripheral blood CD14+ monocytes, and human lung fibroblast cell line MRC-5. Respective cells were first cultured in individual scaffolds and subsequently assembled into a 3D multi-cell tissue model to more closely mimic the in vivo situation. Then, three typical chemicals that are known respiratory sensitizers (ortho-phthaldialdehyde, hexamethylene diisocyanate, and trimellitic anhydride) and skin sensitizers (oxazolone, formaldehyde, and dinitrochlorobenzene) were added individually to the 3D coculture system. Immunohistochemical analysis revealed that DCs do not migrate into other scaffolds under the experimental conditions. Therefore, the 3D structure was disassembled and real-time reverse transcriptase-PCR analysis was performed in individual scaffolds to analyze the expression levels of molecules critical for Th2 differentiation such as OX40 ligand (OX40L), interleukin (IL)-4, IL-10, IL-33, and thymic stromal lymphopoietin. Both sensitizers showed similarly augmented expression of DC maturation markers (e.g., CD86), but among these molecules, OX40L expression in DCs was most consistently and significantly enhanced by respiratory sensitizers as compared to that by skin sensitizers. Thus, we have established a 3D coculture system mimicking the airway upper epithelium that may be successfully applied to discriminate chemical respiratory sensitizers from skin sensitizers by measuring the critical molecule for Th2 differentiation, OX40L, in DCs.

Project description:BACKGROUND:Peptide receptor radionuclide therapy (PRRT) utilizes somatostatin receptor (SSR) overexpression on neuroendocrine tumors (NET) to deliver targeted radiotherapy. Intensity of uptake at imaging is considered related to efficacy but has low sensitivity. A pretreatment strategy to determine individual PRRT response remains a key unmet need. NET transcript expression in blood integrated with tumor grade provides a PRRT predictive quotient (PPQ) which stratifies PRRT "responders" from "non-responders". This study clinically validates the utility of the PPQ in NETs. METHODS:The development and validation of the PPQ was undertaken in three independent 177Lu-PRRT treated cohorts. Specificity was tested in two separate somatostatin analog-treated cohorts. Prognostic value of the marker was defined in a cohort of untreated patients. The developmental cohort included lung and gastroenteropancreatic [GEP] NETs (n?=?72) from IRST Meldola, Italy. The majority were GEP (71%) and low grade (86% G1-G2). Prospective validation cohorts were from Zentralklinik Bad Berka, Germany (n?=?44), and Erasmus Medical Center, Rotterdam, Netherlands (n?=?42). Each cohort included predominantly well differentiated, low grade (86-95%) lung and GEP-NETs. The non-PRRT comparator cohorts included SSA cohort I, n?=?28 (100% low grade, 100% GEP-NET); SSA cohort II, n?=?51 (98% low grade; 76% GEP-NET); and an untreated cohort, n?=?44 (64% low grade; 91% GEP-NET). Baseline evaluations included clinical information (disease status, grade, SSR) and biomarker (CgA). NET blood gene transcripts (n?=?8: growth factor signaling and metabolism) were measured pre-therapy and integrated with tumor Ki67 using a logistic regression model. This provided a binary output: "predicted responder" (PPQ+); "predicted non-responder" (PPQ-). Treatment response was evaluated using RECIST criteria [Responder (stable, partial and complete response) vs Non-Responder)]. Sample measurement and analyses were blinded to study outcome. Statistical evaluation included Kaplan-Meier survival and standard test evaluation analyses. RESULTS:In the developmental cohort, 56% responded to PRRT. The PPQ predicted 100% of responders and 84% of non-responders (accuracy: 93%). In the two validation cohorts (response: 64-79%), the PPQ was 95% accurate (Bad Berka: PPQ?+?=97%, PPQ-?=?93%; Rotterdam: PPQ?+?=94%, PPQ-?=?100%). Overall, the median PFS was not reached in PPQ+ vs PPQ- (10-14 months; HR: 18-77, p?<?0.0001). In the comparator cohorts, the predictor (PPQ) was 47-50% accurate for SSA-treatment and 50% as a prognostic. No differences in PFS were respectively noted (PPQ+: 10-12 months vs. PPQ-: 9-15 months). CONCLUSION:The PPQ derived from circulating NET specific genes and tumor grade prior to the initiation of therapy is a highly specific predictor of the efficacy of PRRT with an accuracy of 95%.

Dataset Information

Prediction of chemical respiratory sensitizers using GARD, a novel in vitro assay based on a genomic biomarker signature.

Background

Results

Conclusions

Publications

Prediction of chemical respiratory sensitizers using GARD, a novel in vitro assay based on a genomic biomarker signature.

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