Proteomic identification of putative biomarkers of radiotherapy resistance

BackgroundCurrently, tumour response to radiotherapy cannot be predicted meaning that those patients with tumours resistant to the therapy endure the harmful side effects associated with ionising radiation in the absence of therapeutic gain. The aim of this project was to identify protein biomarkers predictive of radiotherapy response using comparative proteomic platforms to study radioresistant cell line models. The identification of such biomarkers will enable radiotherapy to be tailored on an individual patient basis and hence increase treatment efficacy.MethodsSeven radioresistant (RR) cell line models derived from breast, head and neck (oral), and rectal cancers were investigated to identify differentially expressed proteins (DEPs) associated with radiotherapy resistance. This included the establishment of 2 RR rectal cancer cell line models and the proteomic analysis of 2 RR oral cancer cell lines and 2 RR rectal cancer cell lines. Proteomic analysis included 3 different platforms, namely antibody microarray, 2D MS and iTRAQ. Data mining of all biomarker discovery data, from all 7 novel RR cell lines was carried out using Ingenuity Pathway Analysis (IPA) which identified canonical pathways associated with the data. Protein candidates from selected canonical pathways were confirmed by western blotting and assessed clinically using immunohistochemistry.ResultsFollowing the combination of all biomarker discovery data for all 7 RR cell lines, 373 unique DEPs were successfully mapped onto the Ingenuity Knowledge Base, generating 339 canonical pathways. Of these, 13 of the most relevant pathways were selected for further interpretation. Several proteasomal subunits were identified during the biomarker discovery phase and were mapped onto the protein ubiquitination pathway by IPA. DR4, was identified in 4/7 RR cell lines and was mapped onto the death receptor signalling pathway by IPA. Radiotherapy is typically thought to induce cellular apoptosis via the intrinsic (mitochondrial) pathway, therefore the repeated identification of the DR4 protein involved in the extrinsic apoptotic pathway has potentially lead to the discovery of a novel relationship between radiotherapy and the extrinsic death receptor pathway. The differential expression of both the 26S Proteasome and DR4 were confirmed by western blotting. Clinical assessment using immunohistochemistry revealed a significant association between expression of the 26S Proteasome and radioresistance in breast cancer.DiscussionA large number of DEPs which may be associated with radiotherapy resistance in breast, oral and rectal cancers have been identified using comparative proteomic platforms. The protein ubiquitination pathway and the death receptor signalling pathway may play a significant role in radioresistance and proteins within these pathways may be putative biomarkers of radiotherapy response

Phenocopy – A Strategy to Qualify Chemical Compounds during Hit-to-Lead and/or Lead Optimization

A phenocopy is defined as an environmentally induced phenotype of one individual which is identical to the genotype-determined phenotype of another individual. The phenocopy phenomenon has been translated to the drug discovery process as phenotypes produced by the treatment of biological systems with new chemical entities (NCE) may resemble environmentally induced phenotypic modifications. Various new chemical entities exerting inhibition of the kinase activity of Transforming Growth Factor β Receptor I (TGF-βR1) were qualified by high-throughput RNA expression profiling. This chemical genomics approach resulted in a precise time-dependent insight to the TGF-β biology and allowed furthermore a comprehensive analysis of each NCE's off-target effects. The evaluation of off-target effects by the phenocopy approach allows a more accurate and integrated view on optimized compounds, supplementing classical biological evaluation parameters such as potency and selectivity. It has therefore the potential to become a novel method for ranking compounds during various drug discovery phases

Meeting Report: Validation of Toxicogenomics-Based Test Systems: ECVAM–ICCVAM/NICEATM Considerations for Regulatory Use

This is the report of the first workshop “Validation of Toxicogenomics-Based Test Systems” held 11–12 December 2003 in Ispra, Italy. The workshop was hosted by the European Centre for the Validation of Alternative Methods (ECVAM) and organized jointly by ECVAM, the U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM), and the National Toxicology Program (NTP) Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM). The primary aim of the workshop was for participants to discuss and define principles applicable to the validation of toxicogenomics platforms as well as validation of specific toxicologic test methods that incorporate toxicogenomics technologies. The workshop was viewed as an opportunity for initiating a dialogue between technologic experts, regulators, and the principal validation bodies and for identifying those factors to which the validation process would be applicable. It was felt that to do so now, as the technology is evolving and associated challenges are identified, would be a basis for the future validation of the technology when it reaches the appropriate stage. Because of the complexity of the issue, different aspects of the validation of toxicogenomics-based test methods were covered. The three focus areas include a) biologic validation of toxicogenomics-based test methods for regulatory decision making, b) technical and bioinformatics aspects related to validation, and c) validation issues as they relate to regulatory acceptance and use of toxicogenomics-based test methods. In this report we summarize the discussions and describe in detail the recommendations for future direction and priorities

A Three Stage Integrative Pathway Search (TIPS©) framework to identify toxicity relevant genes and pathways

<p>Abstract</p> <p>Background</p> <p>The ability to obtain profiles of gene expressions, proteins and metabolites with the advent of high throughput technologies has advanced the study of pathway and network reconstruction. Genome-wide network reconstruction requires either interaction measurements or large amount of perturbation data, often not available for mammalian cell systems. To overcome these shortcomings, we developed a Three Stage Integrative Pathway Search (<it>TIPS</it>^©) approach to reconstruct context-specific active pathways involved in conferring a specific phenotype, from limited amount of perturbation data. The approach was tested on human liver cells to identify pathways that confer cytotoxicity.</p> <p>Results</p> <p>This paper presents a systems approach that integrates gene expression and cytotoxicity profiles to identify a network of pathways involved in free fatty acid (FFA) and tumor necrosis factor-α (TNF-α) induced cytotoxicity in human hepatoblastoma cells (HepG2/C3A). Cytotoxicity relevant genes were first identified and then used to reconstruct a network using Bayesian network (BN) analysis. BN inference was used subsequently to predict the effects of perturbing a gene on the other genes in the network and on the cytotoxicity. These predictions were subsequently confirmed through the published literature and further experiments.</p> <p>Conclusion</p> <p>The <it>TIPS</it>^©approach is able to reconstruct active pathways that confer a particular phenotype by integrating gene expression and phenotypic profiles. A web-based version of <it>TIPS</it>^©that performs the analysis described herein can be accessed at <url>http://www.egr.msu.edu/tips</url>.</p

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics

Developing genomic models for cancer prevention and treatment stratification

Malignant tumors remain one of the leading causes of mortality with over 8.2 million deaths worldwide in 2012. Over the last two decades, high-throughput profiling of the human transcriptome has become an essential tool to investigate molecular processes involved in carcinogenesis. In this thesis I explore how gene expression profiling (GEP) can be used in multiple aspects of cancer research, including prevention, patient stratification and subtype discovery. The first part details how GEP could be used to supplement or even replace the current gold standard assay for testing the carcinogenic potential of chemicals. This toxicogenomic approach coupled with a Random Forest algorithm allowed me to build models capable of predicting carcinogenicity with an area under the curve of up to 86.8% and provided valuable insights into the underlying mechanisms that may contribute to cancer development. The second part describes how GEP could be used to stratify heterogeneous populations of lymphoma patients into therapeutically relevant disease sub-classes, with a particular focus on diffuse large B-cell lymphoma (DLBCL). Here, I successfully translated established biomarkers from the Affymetrix platform to the clinically relevant Nanostring nCounter© assay. This translation allowed us to profile custom sets of transcripts from formalin-fixed samples, transforming these biomarkers into clinically relevant diagnostic tools. Finally, I describe my effort to discover tumor samples dependent on altered metabolism driven by oxidative phosphorylation (OxPhos) across multiple tissue types. This work was motivated by previous studies that identified a therapeutically relevant OxPhos sub-type in DLBCL, and by the hypothesis that this stratification might be applicable to other solid tumor types. To that end, I carried out a transcriptomics-based pan-cancer analysis, derived a generalized PanOxPhos gene signature, and identified mTOR as a potential regulator in primary tumor samples. High throughput GEP coupled with statistical machine learning methods represent an important toolbox in modern cancer research. It provides a cost effective and promising new approach for predicting cancer risk associated to chemical exposure, it can reduce the cost of the ever increasing drug development process by identifying therapeutically actionable disease subtypes, and it can increase patients’ survival by matching them with the most effective drugs.2016-12-01T00:00:00