Molecular Alterations in Asbestos-Related Lung Cancer

Background: Asbestos is a well known cancer-causing mineral fibre, which has a synergistic effect on lung cancer risk in combination with tobacco smoking. Several in vitro and in vivo experiments have demonstrated that asbestos can evoke chromosomal damage and cause alterations as well as gene expression changes. Lung tumours, in general, have very complex karyotypes with several recurrently gained and lost chromosomal regions and this has made it difficult to identify specific molecular changes related primarily to asbestos exposure. The main aim of these studies has been to characterize asbestos-related lung cancer at a molecular level. Methods: Samples from asbestos-exposed and non-exposed lung cancer patients were studied using array comparative genomic hybridization (aCGH) and fluorescent in situ hybridization (FISH) to detect copy number alterations (CNA) as well as microsatellite analysis to detect allelic imbalance (AI). In addition, asbestos-exposed cell lines were studied using gene expression microarrays. Results: Eighteen chromosomal regions showing differential copy number in the lung tumours of asbestos-exposed patients compared to those of non-exposed patients were identified. The most significant differences were detected at 2p21-p16.3, 5q35.3, 9q33.3-q34.11, 9q34.13-q34.3, 11p15.5, 14q11.2 and 19p13.1-p13.3 (p<0.005). The alterations at 2p and 9q were validated and characterized in detail using AI and FISH analysis in a larger study population. Furthermore, in vitro studies were performed to examine the early gene expression changes induced by asbestos in three different lung cell lines. The results revealed specific asbestos-associated gene expression profiles and biological processes as well as chromosomal regions enriched with genes believed to contribute to the common asbestos-related responses in the cell lines. Interestingly, the most significant region enriched with asbestos-response genes was identified at 2p22, close to the previously identified region showing asbestos-related CNA in lung tumours. Additionally, in this thesis, the dysregulated biological processes (Gene Ontology terms) detected in the cell line experiment were compared to dysregulated processes identified in patient samples in a later study (Ruosaari et al., 2008a). Commonly affected processes such as those related to protein ubiquitination, ion transport and surprisingly sensory perception of smell were identified. Conclusions: The identification of specific CNA and dysregulated biological processes shed some light on the underlying genes acting as mediators in asbestos-related lung carcinogenesis. It is postulated that the combination of several asbestos-specific molecular alterations could be used to develop a diagnostic method for the identification of asbestos-related lung cancer.Bakgrund: Asbest är en välkänd cancerframkallande mineralfiber, som i kombination med tobaksrökning ökar risken för lungcancer flerfalt. Flera in vitro och in vivo studier har visat att asbest kan framkalla kromosomskador och förändringar i genexpression. Lungtumörer har i allmänhet mycket komplicerade karyotyper med flera återkommande duplicerade och deleterade kromosomregioner, vilket har gjort det svårt att identifiera specifika molekylära avvikelser som kan associeras med asbestexponering. Det främsta syftet med dessa studier har varit att karaktärisera asbestrelaterad lungcancer på molekylär nivå. Metoder: Prover från asbestexponerade och icke exponerade patienter med lungcancer undersöktes med hjälp av jämförande genomisk hybridisering på mikroarray (aCGH) och fluorescerande in situ hybridisering (FISH) för att identifiera kromosomavvikelser, samt med mikrosatellitanalys för att identifiera allel-obalans (AI). Dessutom utfördes in vitro studier på asbestexponerade cellinjer med hjälp av mikroarrays som mäter enskilda geners expression. Resultat: Aderton kromosomregioner med asbestrelaterade avvikelser identifierades. De mest markanta skillnaderna upptäcktes i 2p21-p16.3, 5q35.3, 9q33.3-q34.11, 9q34.13-q34.3, 11p15.5, 14q11.2 och 19p13.1-p13.3 (p<0,005). Avvikelserna på kromosomarmarna 2p och 9q karakteriserades i detalj och verifierades med hjälp av AI- och FISH-analys på prover från ett ökat antal patienter. In vitro studier genomfördes för att undersöka tidiga förändringar i genexpression som orsakats av asbestexponering i tre olika lungcellinjer. Resultaten avslöjade särskilda asbestrelaterade genexpressionsprofiler och biologiska processer, samt kromosomregioner berikade med gener som antas bidra till de gemensamma asbestrelaterade responserna i cellinjerna. Den mest signifikanta regionen, överrepresenterad med asbestresponsgener, var 2p22 som ligger nära det tidigare identifierade området på 2p med asbestrelaterade kromosomavvikelser i lungtumörer. I denna avhandling jämfördes också de förändrade biologiska processerna (genontologiska termerna) som upptäcktes i cellinjeexperimentet med förändrade processer som identifierats i asbestexponerade och icke exponerade patienters prover, i en senare studie (Ruosaari et al., 2008a). Gemensamt förändrade processer var bl.a. kopplade till protein ubiquitinering, jontransport och överraskande nog, luktförnimmelse. Slutsatser: Kartläggningen av specifika kromosomavvikelser och förändrade biologiska processer kastar ljus över de bakomliggande generna som fungerar som medlare i asbestrelaterad lungkarcinogenes. Det kan antas att en kombination av flera asbestspecifika molekylära förändringar kunde användas för att utveckla en diagnostisk metod, som följaktligen kunde särskilja mellan asbestrelaterad och icke asbestrelaterad lungcancer.TAUSTA: Asbesti on tunnettu syöpää aiheuttava mineraalikuitu, jolla on tupakoinnin yhteydessä synergistinen vaikutus keuhkosyövän riskiin. Useat in vitro- ja in vivo-tutkimukset ovat osoittaneet, että asbesti voi aiheuttaa kromosomivaurioita ja muutoksia geenien ilmentymisessä. Keuhkosyövän karyotyyppi on yleensä hyvin monimutkainen ja toistuvat kromosomialueiden monistumat sekä häviämät ovat yleisiä. Tästä syystä on ollut vaikeaa tunnistaa spesifisiä molekyylitason muutoksia, jotka liittyvät pääasiassa asbestialtistumiseen. Päätavoite näissä tutkimuksissa on ollut asbestiin liittyvän keuhkosyövän tunnistaminen molekyylitasolla. MENETELMÄT: Asbestialtistuneiden ja altistumattomien keuhkosyöpäpotilaiden näytteet tutkittiin käyttäen vertailevaa genomista hybridisaatiota mikrosiruilla (aCGH) ja fluoresenssi in situ hybridisaatiota (FISH), joilla havaitaan kromosomialueiden kopiolukumuutokset, sekä mikrosatelliittianalyysia, jolla havaitaan alleeliepätasapaino (AI). Lisäksi asbestialtistuneita solulinjoja tutkittiin käyttäen geeniekspressiomikrosiruja. TULOKSET: Kahdeksallatoista kromosomialueella osoitettiin kopiolukueroja asbestialtistuneiden ja altistumattomien potilaiden näytteiden välillä. Merkittävimmät erot havaittiin kromosomialueilla 2p21-p16.3, 5q35.3, 9q33.3-q34.11, 9q34.13-q34.3, 11p15.5, 14q11.2 ja 19p13.1-p13.3 (p <0,005). Muutokset 2p ja 9q alueilla karakterisoitiin tarkemmin ja varmennettiin käyttäen AI- ja FISH-analyysejä laajemmassa tutkimusaineistossa. Lisäksi mikrosiruilla tutkittiin muutokset geenien ilmentymisessä asbestialtistuksen jälkeen kolmessa eri keuhkosolulinjassa. Tutkimuksessa tunnistettiin asbestialtistukseen liittyviä geenien ilmentymisprofiileja sekä muuttuneita biologisia prosesseja. Lisäksi havaittiin solulinjoille yhteisten asbestiin liittyvien vastegeenien rikastuttamia kromosomaalisia alueita. Merkittävin asbestivastegeenejä sisältävä alue oli 2p22, joka sijaitsee lähellä aiemmin keuhkosyövissä tunnistettua asbestiin liittyviä kopiolukumuutoksia sisältävää aluetta 2p:ssa. Tässä väitöskirjassa vertailtiin myös asbestialtistuneiden solulinjojen muuttuneita biologisia prosesseja (geeniontologiatermejä) niihin muuttuneisiin prosesseihin, joita myöhemmin havaittiin asbestialtistuneiden potilaiden näytteissä (RUOSAARI et al., 2008a). Yhteiset muuttuneet prosessit liittyivät proteiinien ubikitinaatioon, ionikuljetukseen ja yllättävästi hajuaistimukseen. JOHTOPÄÄTÖKSET: Spesifisten kopiolukumuutosten ja muuttuneiden biologisten prosessien tunnistaminen asbestiin liittyvässä keuhkosyövässä valottaa taustalla olevia geenejä, jotka toimivat välittäjinä asbestin aiheuttamassa keuhkokarsinogeneesissä. Useita asbestiin liittyviä molekyylitason muutoksia voitaisiin käyttää asbestiin liittyvän ja liittymättömän keuhkosyövän erottavien diagnostisten menetelmien kehittämisessä

Gene expression profiles in asbestos-exposed epithelial and mesothelial lung cell lines

BACKGROUND: Asbestos has been shown to cause chromosomal damage and DNA aberrations. Exposure to asbestos causes many lung diseases e.g. asbestosis, malignant mesothelioma, and lung cancer, but the disease-related processes are still largely unknown. We exposed the human cell lines A549, Beas-2B and Met5A to crocidolite asbestos and determined time-dependent gene expression profiles by using Affymetrix arrays. The hybridization data was analyzed by using an algorithm specifically designed for clustering of short time series expression data. A canonical correlation analysis was applied to identify correlations between the cell lines, and a Gene Ontology analysis method for the identification of enriched, differentially expressed biological processes. RESULTS: We recognized a large number of previously known as well as new potential asbestos-associated genes and biological processes, and identified chromosomal regions enriched with genes potentially contributing to common responses to asbestos in these cell lines. These include genes such as the thioredoxin domain containing gene (TXNDC) and the potential tumor suppressor, BCL2/adenovirus E1B 19kD-interacting protein gene (BNIP3L), GO-terms such as "positive regulation of I-kappaB kinase/NF-kappaB cascade" and "positive regulation of transcription, DNA-dependent", and chromosomal regions such as 2p22, 9p13, and 14q21. We present the complete data sets as Additional files. CONCLUSION: This study identifies several interesting targets for further investigation in relation to asbestos-associated diseases

Introducing WikiPathways as a Data-Source to Support Adverse Outcome Pathways for Regulatory Risk Assessment of Chemicals and Nanomaterials

A paradigm shift is taking place in risk assessment to replace animal models, reduce the number of economic resources, and refine the methodologies to test the growing number of chemicals and nanomaterials. Therefore, approaches such as transcriptomics, proteomics, and metabolomics have become valuable tools in toxicological research, and are finding their way into regulatory toxicity. One promising framework to bridge the gap between the molecular-level measurements and risk assessment is the concept of adverse outcome pathways (AOPs). These pathways comprise mechanistic knowledge and connect biological events from a molecular level toward an adverse effect outcome after exposure to a chemical. However, the implementation of omics-based approaches in the AOPs and their acceptance by the risk assessment community is still a challenge. Because the existing modules in the main repository for AOPs, the AOP Knowledge Base (AOP-KB), do not currently allow the integration of omics technologies, additional tools are required for omics-based data analysis and visualization. Here we show how WikiPathways can serve as a supportive tool to make omics data interoperable with the AOP-Wiki, part of the AOP-KB. Manual matching of key events (KEs) indicated that 67% could be linked with molecular pathways. Automatic connection through linkage of identifiers between the databases showed that only 30% of AOP-Wiki chemicals were found on WikiPathways. More loose linkage through gene names in KE and Key Event Relationships descriptions gave an overlap of 70 and 71%, respectively. This shows many opportunities to create more direct connections, for example with extended ontology annotations, improving its interoperability. This interoperability allows the needed integration of omics data linked to the molecular pathways with AOPs. A new AOP Portal on WikiPathways is presented to allow the community of AOP developers to collaborate and populate the molecular pathways that underlie the KEs of AOP-Wiki. We conclude that the integration of WikiPathways and AOP-Wiki will improve risk assessment because omics data will be linked directly to KEs and therefore allow the comprehensive understanding and description of AOPs. To make this assessment reproducible and valid, major changes are needed in both WikiPathways and AOP-Wiki

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.Peer reviewe

Transcriptomics in Toxicogenomics, Part II : Preprocessing and Differential Expression Analysis for High Quality Data

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.Peer reviewe

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

Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects

The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms’ responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series

Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects

The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms’ responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series