Making sense of metabolomic data: comprehensive analysis of altered metabolic pathways in diabetes and obesity

Podeu consultar el III Workshop anual INSA-UB complet a: http://hdl.handle.net/2445/118993Sessió 1. Pòster núm.

Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data

Background: Bioinformatic tools for the enrichment of 'omics' datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined. Results: An exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard's distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites. Conclusions: We have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome

Clathrin switches transforming growth factor-β role to pro-tumorigenic in liver cancer

Background & Aims: Upon ligand binding, tyrosine kinase receptors, such as epidermal growth factor receptor (EGFR), are recruited into clathrin-coated pits for internalization by endocytosis, which is relevant for signalling and/or receptor degradation. In liver cells, transforming growth factor-beta (TGF-beta) induces both pro- and anti-apoptotic signals; the latter are mediated by the EGFR pathway. Since EGFR mainly traffics via clathrin-coated vesicles, we aimed to analyse the potential role of clathrin in TGF-beta-induced signalling in liver cells and its relevance in liver cancer. Methods: Real-Time PCR and immunohistochemistry were used to analyse clathrin heavy-chain expression in human (CLTC) and mice (Cltc) liver tumours. Transient knockdown (siRNA) or overexpression of CLTC were used to analyse its role on TGF-beta and EGFR signalling in vitro. Bioinformatic analysis was used to determine the effect of CLTC and TGEB1 expression on prognosis and overall survival in patients with hepatocellular carcinoma (HCC). Results: Clathrin expression increased during liver tumorigenesis in humans and mice. CLTC knockdown cells responded to TGF-beta phosphorylating SMADs (canonical signalling) but showed impairment in the anti-apoptotic signals (EGFR transactivation). Experiments of loss or gain of function in HCC cells reveal an essential role for clathrin in inhibiting TGF-beta-induced apoptosis and upregulation of its pro-apoptotic target NOX4. Autocrine TGF-beta signalling in invasive HCC cells upregulates CLTC expression, switching its role to pro-tumorigenic. A positive correlation between TGEB1 and CLTC was found in HCC cells and patients. Patients expressing high levels of TGEB1 and CLTC had a worse prognosis and lower overall survival. Conclusions: This work describes a novel role for clathrin in liver tumorigenesis, favouring non-canonical pro-tumorigenic TGF-beta pathways. CLTC expression in human HCC samples could help select patients that would benefit from TGF-beta-targeted therapy. Lay summary: Clathrin heavy-chain expression increases during liver tumorigenesis in humans (CLTC) and mice (Mc), altering the cellular response to TGF-beta in favour of anti-apoptotic/pro-tumorigenic signals. A positive correlation between TGEB1 and CLTC was found in HCC cells and patients. Patients expressing high levels of TGEB1 and CLTC had a worse prognosis and lower overall survival. CLTC expression in HCC human samples could help select patients that would benefit from therapies targeting TGF-beta. (C) 2019 European Association for the Study of the Liver. Published by Elsevier B

Integrative transcriptome analysis of malignant pleural mesothelioma reveals a clinically relevant immune-based classification

Background: Malignant pleural mesothelioma (MPM) is a rare and aggressive neoplasia affecting the lung mesothelium. Immune checkpoint inhibitors (ICI) in MPM have not been extremely successful, likely due to poor identification of suitable candidate patients for the therapy. We aimed to identify cellular immune fractions associated with clinical outcome and classify patients with MPM based on their immune contexture. For each defined group, we sought for molecular specificities that could help further define our MPM classification at the genomic and transcriptomic level, as well as identify differential therapeutic strategies based on transcriptional signatures predictive of drug response. Methods: The abundance of 20 immune cell fractions in 516 MPM samples from 7 gene expression datasets was inferred using gene set variation analysis. Identification of clinically relevant fractions was performed with Cox proportional-hazards models adjusted for age, stage, sex, and tumor histology. Immune-based groups were defined based on the identified fractions. Results: T-helper 2 (TH2) and cytotoxic T (TC) cells were found to be consistently associated with overall survival. Three immune clusters (IG) were subsequently defined based on TH2 and TC immune infiltration levels: IG1 (54.5%) was characterized by high TH2 and low TC levels, IG2 (37%) had either low or high levels of both fractions, and IG3 (8.5%) was defined by low TH2 and high TC levels. IG1 and IG3 groups were associated with worse and better overall survival, respectively. While no differential genomic alterations were identified among immune groups, at the transcriptional level, IG1 samples showed upregulation of proliferation signatures, while IG3 samples presented upregulation of immune and inflammation-related pathways. Finally, the integration of gene expression with functional signatures of drug response showed that IG3 patients might be more likely to respond to ICI. Conclusions: This study identifies a novel immune-based signature with potential clinical relevance based on TH2 and TC levels, unveiling a fraction of patients with MPM with better prognosis and who might benefit from immune-based therapies. Molecular specificities of the different groups might be used to tailor specific potential therapies in the future

Molecular profiling and feasibility using a comprehensive hybrid capture panel on a consecutive series of non-small-cell lung cancer patients from a single centre

Background: Targeted next-generation sequencing (NGS) is recommended to screen actionable genomic alterations (GAs) in patients with non-small-cell lung cancer (NSCLC). We determined the feasibility to detect actionable GAs using TruSight™ Oncology 500 (TSO500) in 200 consecutive patients with NSCLC. Materials and methods: DNA and RNA were sequenced on an Illumina® NextSeq 550 instrument and processed using the TSO500 Docker pipeline. Clinical actionability was defined within the molecular tumour board following European Society for Medical Oncology (ESMO) guidelines for oncogene-addicted NSCLC. Overall survival (OS) was estimated as per the presence of druggable GAs and treatment with targeted therapy. Results: Most patients were males (69.5%) and former or current smokers (86.5%). Median age was 64 years. The most common histological type and tumour stage were lung adenocarcinoma (81%) and stage IV (64%), respectively. Sequencing was feasible in most patients (93.5%) and actionable GAs were found in 26.5% of patients. A high concordance was observed between single-gene testing and TSO500 NGS panel. Patients harbouring druggable GAs and receiving targeted therapy achieved longer OS compared to patients without druggable GAs. Conversely, patients with druggable GAs not receiving targeted therapy had a trend toward shorter OS compared with driver-negative patients. Conclusions: Hybrid capture sequencing using TSO500 panel is feasible to analyse clinical samples from patients with NSCLC and is an efficient tool for screening actionable GAs

Dissecting the role of the NADPH oxidase NOX4 in TGF-beta signaling in hepatocellular carcinoma

The NADPH oxidase NOX4 has been proposed as necessary for the apoptosis induced by the Transforming Growth Factor-beta (TGF-I3) in hepatocytes and hepatocellular carcinoma (HCC) cells. However, whether NOX4 is required for TGF-I3-induced canonical (SMADs) or non-canonical signals is not fully understood yet, neither its potential involvement in other parallel actions induced by TGF-I3. In this work we have used CRISPR Cas9 technology to stable attenuate NOX4 expression in HCC cells. Results have indicated that NOX4 is required for an efficient SMAD2/3 phosphorylation in response to TGF-I3, whereas non-canonical signals, such as the phos-phorylation of the Epidermal Growth Receptor or AKT, are higher in NOX4 silenced cells. TGF-I3-mediated in-hibition of cell proliferation and viability is attenuated in NOX4 silenced cells, correlating with decreased response in terms of apoptosis, and maintenance of high expression of MYC and CYCLIN D1. These results would indicate that NOX4 is required for all the tumor suppressor actions of TGF-I3 in HCC. However, analysis in human HCC tumors has revealed a worse prognosis for patients showing high expression of TGF-I31-related genes concomitant with high expression of NOX4. Deepening into other tumorigenic actions of TGF-I3 that may contribute to tumor progression, we found that NOX4 is also required for TGF-I3-induced migratory effects. The Epithelial-Mesenchymal transition (EMT) program does not appear to be affected by attenuation of NOX4 levels. However, TGF-I3-mediated regulation of cytoskeleton dynamics and focal adhesions require NOX4, which is necessary for TGF-I3-induced increase in the chaperone Hsp27 and correct subcellular localization of Hic-5 within focal adhesions, as well for upregulation of the metalloprotease MMP9. All these results together point to NOX4 as a key element in the whole TGF-I3 signaling in HCC cells, revealing an unknown role for NOX4 as tumor promoter in HCC patients presenting activation of the TGF-I3 pathway

Efficacy of CDK4/6 inhibitors in preclinical models of malignant pleural mesothelioma

Background There is no effective therapy for patients with malignant pleural mesothelioma (MPM) who progressed to platinum-based chemotherapy and immunotherapy. Methods We aimed to investigate the antitumor activity of CDK4/6 inhibitors using in vitro and in vivo preclinical models of MPM. Results Based on publicly available transcriptomic data of MPM, patients with CDK4 or CDK6 overexpression had shorter overall survival. Treatment with abemaciclib or palbociclib at 100 nM significantly decreased cell proliferation in all cell models evaluated. Both CDK4/6 inhibitors significantly induced G1 cell cycle arrest, thereby increasing cell senescence and increased the expression of interferon signalling pathway and tumour antigen presentation process in culture models of MPM. In vivo preclinical studies showed that palbociclib significantly reduced tumour growth and prolonged overall survival using distinct xenograft models of MPM implanted in athymic mice. Conclusions Treatment of MPM with CDK4/6 inhibitors decreased cell proliferation, mainly by promoting cell cycle arrest at G1 and by induction of cell senescence. Our preclinical studies provide evidence for evaluating CDK4/6 inhibitors in the clinic for the treatment of MPM

Network methods for integrative Omics and pathway analysis

Omics data analysis is more accessible nowadays and it will become even more accessible in the future. A broad range of analysis can be done individually on each type of Omics, leading to conclusions on the factor of interest. Yet, due to human s variability, these results are not always concordant. Understanding the biological implications after combining different types of Omics together is of great interest and may reveal new results, only visible with an integrated approach. Different approaches to integrative Omics data analysis exist. Multivariate techniques, which provide dimension reduction approaches and a great numerical flexibility to Omics data, but fail to ease interpretation of the results; machine learning techniques, well-known for biomarker discovery; and network analysis approaches, which are yet in development. The aim of this thesis is to review methodologies developed in the field of network analysis related to integrative Omics data analysis and for pathway analysis. A state-of-the art review has been done, describing approaches for network-based integrative data analysis and their limitations. One of these approaches has also been tested in two case studies. On the other hand, a comparison of tools for pathway analysis in metabolomics is also performed. Even though different statistical approaches can be used to analyse Omics data with an integrative approach, and network-based integrative analysis being in a very juvenile stage yet, it may be the most suitable approach to take the logical step beyond statistics, leading to a more comprehensive approach for biologists

Network methods for integrative Omics and pathway analysis

Omics data analysis is more accessible nowadays and it will become even more accessible in the future. A broad range of analysis can be done individually on each type of Omics, leading to conclusions on the factor of interest. Yet, due to human s variability, these results are not always concordant. Understanding the biological implications after combining different types of Omics together is of great interest and may reveal new results, only visible with an integrated approach. Different approaches to integrative Omics data analysis exist. Multivariate techniques, which provide dimension reduction approaches and a great numerical flexibility to Omics data, but fail to ease interpretation of the results; machine learning techniques, well-known for biomarker discovery; and network analysis approaches, which are yet in development. The aim of this thesis is to review methodologies developed in the field of network analysis related to integrative Omics data analysis and for pathway analysis. A state-of-the art review has been done, describing approaches for network-based integrative data analysis and their limitations. One of these approaches has also been tested in two case studies. On the other hand, a comparison of tools for pathway analysis in metabolomics is also performed. Even though different statistical approaches can be used to analyse Omics data with an integrative approach, and network-based integrative analysis being in a very juvenile stage yet, it may be the most suitable approach to take the logical step beyond statistics, leading to a more comprehensive approach for biologists

Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer

Recent technological advances and the application of high-throughput mutation and transcriptome analyses have improved our understanding of cancer diseases, including non-small cell lung cancer. For instance, genomic profiling has allowed the identification of mutational events which can be treated with specific agents. However, detection of DNA alterations does not fully recapitulate the complexity of the disease and it does not allow selection of patients that benefit from chemo- or immunotherapy. In this context, transcriptional profiling has emerged as a promising tool for patient stratification and treatment guidance. For instance, transcriptional profiling has proven to be especially useful in the context of acquired resistance to targeted therapies and patients lacking targetable genomic alterations. Moreover, the comprehensive characterization of the expression level of the different pathways and genes involved in tumor progression is likely to better predict clinical benefit from different treatments than single biomarkers such as PD-L1 or tumor mutational burden in the case of immunotherapy. However, intrinsic technical and analytical limitations have hindered the use of these expression signatures in the clinical setting. In this review, we will focus on the data reported on molecular classification of non-small cell lung cancer and discuss the potential of transcriptional profiling as a predictor of survival and as a patient stratification tool to further personalize treatments