Measuring cancer driving force of chromosomal aberrations through multi-layer Boolean implication networks.

Multi-layer Complex networks are commonly used for modeling and analysing biological entities. This paper presents the advantage of using COMBO (Combining Multi Bio Omics) to suggest a new role of the chromosomal aberration as a cancer driver factor. Exploiting the heterogeneous multi-layer networks, COMBO integrates gene expression and DNA-methylation data in order to identify complex bilateral relationships between transcriptome and epigenome. We evaluated the multi-layer networks generated by COMBO on different TCGA cancer datasets (COAD, BLCA, BRCA, CESC, STAD) focusing on the effect of a specific chromosomal numerical aberration, broad gain in chromosome 20, on different cancer histotypes. In addition, the effect of chromosome 8q amplification was tested in the same TCGA cancer dataset. The results demonstrate the ability of COMBO to identify the chromosome 20 amplification cancer driver force in the different TCGA Pan Cancer project datasets.

TMBcalc: a computational pipeline for identifying pan-cancer Tumor Mutational Burden gene signatures.

Background: In the precision medicine era, identifying predictive factors to select patients most likely to benefit from treatment with immunological agents is a crucial and open challenge in oncology. Methods: This paper presents a pan-cancer analysis of Tumor Mutational Burden (TMB). We developed a novel computational pipeline, TMBcalc, to calculate the TMB. Our methodology can identify small and reliable gene signatures to estimate TMB from custom targeted-sequencing panels. For this purpose, our pipeline has been trained on top of 17 cancer types data obtained from TCGA. Results: Our results show that TMB, computed through the identified signature, strongly correlates with TMB obtained from whole-exome sequencing (WES). Conclusion: We have rigorously analyzed the effectiveness of our methodology on top of several independent datasets. In particular we conducted a comprehensive testing on: (i) 126 samples sourced from the TCGA database; few independent whole-exome sequencing (WES) datasets linked to colon, breast, and liver cancers, all acquired from the EGA and the ICGC Data Portal. This rigorous evaluation clearly highlights the robustness and practicality of our approach, positioning it as a promising avenue for driving substantial progress within the realm of clinical practice.

tRFUniverse: A comprehensive resource for the interactive analyses of tRNA-derived ncRNAs in human cancer.

tRNA-derived ncRNAs are a heterogeneous class of non-coding RNAs recently proposed to be active regulators of gene expression and be involved in many diseases, including cancer. Consequently, several online resources on tRNA-derived ncRNAs have been released. Although interesting, such resources present only basic features and do not adequately exploit the wealth of knowledge available about tRNA-derived ncRNAs. Therefore, we introduce tRFUniverse, a novel online resource for the analysis of tRNA-derived ncRNAs in human cancer. tRFUniverse presents an extensive collection of classes of tRNA-derived ncRNAs analyzed across all the TCGA and TARGET tumor cohorts, NCI-60 cell lines, and biological fluids. Moreover, public AGO CLASH/CLIP-Seq data were analyzed to identify the molecular interactions between tRNA-derived ncRNAs and other transcripts. Importantly, tRFUniverse combines in a single resource a comprehensive set of features that we believe may be helpful to investigate the involvement of tRNA-derived ncRNAs in cancer biology.

Expression signature of human endogenous retroviruses in chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) is one of the most diagnosed forms of leukemia worldwide and it is usually classified into two forms: indolent and aggressive. These two forms are characterized by distinct molecular features that drive different responses to treatment and clinical outcomes. In this context, a better understanding of the molecular landscape of the CLL forms may potentially lead to the development of new drugs or the identification of novel biomarkers. Human endogenous retroviruses (HERVs) are a class of transposable elements that have been associated with the development of different human cancers, including different forms of leukemias. However, no studies about HERVs in CLL have ever been reported so far. Here, we present the first locus-specific profiling of HERV expression in both the aggressive and indolent forms of CLL. Our analyses revealed several dysregulations in HERV expression occurring in CLL and some of them were specific for either the aggressive or indolent form of CLL. Such results were also validated by analyzing an external cohort of CLL patients and by RT-qPCR. Moreover, in silico analyses have shown relevant signaling pathways associated with them suggesting a potential involvement of the dysregulated HERVs in these pathways and consequently in CLL development.

The Ratio of Key Metabolic Transcripts Is a Predictive Biomarker of Breast Cancer Metastasis to the Lung.

Understanding the rewired metabolism underlying organ-specific metastasis in breast cancer could help identify strategies to improve the treatment and prevention of metastatic disease. Here, we used a systems biology approach to compare metabolic fluxes used by parental breast cancer cells and their brain- and lung-homing derivatives. Divergent lineages had distinct, heritable metabolic fluxes. Lung-homing cells maintained high glycolytic flux despite low levels of glycolytic intermediates, constitutively activating a pathway sink into lactate. This strong Warburg effect was associated with a high ratio of lactate dehydrogenase (LDH) to pyruvate dehydrogenase (PDH) expression, which correlated with lung metastasis in patients with breast cancer. Although feature classification models trained on clinical characteristics alone were unable to predict tropism, the LDH/PDH ratio was a significant predictor of metastasis to the lung but not to other organs, independent of other transcriptomic signatures. High lactate efflux was also a trait in lung-homing metastatic pancreatic cancer cells, suggesting that lactate production may be a convergent phenotype in lung metastasis. Together, these analyses highlight the essential role that metabolism plays in organ-specific cancer metastasis and identify a putative biomarker for predicting lung metastasis in patients with breast cancer. SIGNIFICANCE: Lung-homing metastatic breast cancer cells express an elevated ratio of lactate dehydrogenase to pyruvate dehydrogenase, indicating that ratios of specific metabolic gene transcripts have potential as metabolic biomarkers for predicting organ-specific metastasis.

Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing.

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis.

Transcriptome Analysis of Human Endogenous Retroviruses at Locus-Specific Resolution in Non-Small Cell Lung Cancer.

Lung cancer is the second most commonly diagnosed cancer and the leading cause of cancer deaths worldwide. Among its subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the most common, accounting for more than 85% of lung cancer diagnoses. Despite the incredible efforts and recent advances in lung cancer treatments, patients affected by this condition still have a poor prognosis. Therefore, novel diagnostic biomarkers are needed. Recently, a class of transposable elements called human endogenous retroviruses (HERVs) has been found to be implicated in cancer development and later employed as novel biomarkers for several tumor types. In this study, we first ever characterized the expression of HERVs at genomic locus-specific resolution in both LUAD and LUSC cohorts available in The Cancer Genome Atlas (TCGA). Precisely, (i) we profiled the expression of HERVs in TCGA-LUAD and TCGA-LUSC cohorts; (ii) we identified the dysregulated HERVs in both lung cancer subtypes; (iii) we evaluated the impact of the dysregulated HERVs on signaling pathways using neural network-based predictions; and (iv) we assessed their association with overall survival (OS) and relapse-free survival (RFS). In conclusion, we believe this study may help elucidate another layer of dysregulation that occurs in lung cancer involving HERVs, paving the way for identifying novel lung cancer biomarkers.

Network analysis of synovial RNA sequencing identifies gene-gene interactions predictive of response in rheumatoid arthritis.

BACKGROUND: To determine whether gene-gene interaction network analysis of RNA sequencing (RNA-Seq) of synovial biopsies in early rheumatoid arthritis (RA) can inform our understanding of RA pathogenesis and yield improved treatment response prediction models. METHODS: We utilized four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We extracted specific gene-gene interaction networks in synovial RNA-Seq to characterize histologically defined pathotypes in early RA and leverage these synovial specific gene-gene networks to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). Differential interactions identified within each network were statistically evaluated through robust linear regression models. Ability to predict response to DMARD treatment was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: Analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. We identified a key role for angiogenesis, observing significant statistical interactions between NOS3 (eNOS) and both CAMK1 and eNOS activator AKT3 when comparing responders and non-responders. The ratio of CAMKD2/NOS3 enhanced a prediction model of response improving ROC AUC from 0.63 to 0.73. CONCLUSIONS: We demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

Pathway Analysis for Cancer Research and Precision Oncology Applications.

With the advent of OMICs technologies, several bioinformatics methods have been developed to infer biological knowledge from such data. Pathway analysis methodologies help integrate multi-OMICs data and find altered function in known metabolic and signaling pathways. As widely known, such alterations promote the cancer cells' progression and the maintenance of the malignant state. In this chapter, we provide (i) a comprehensive description of the primary data sources for omics data, cancer "omics" projects, and precision oncology knowledge bases; (ii) a survey of the main biological pathway databases; (iii) and a global view of the principal pathway analysis tools and methodologies, describing their main characteristics and shortcomings highlighting their potential applications in cancer research and precision oncology.

Computational Resources for the Interpretation of Variations in Cancer.

A broad ecosystem of resources, databases, and systems to analyze cancer variations is present in the literature. These are a strategic element in the interpretation of NGS experiments. However, the intrinsic wealth of data from RNA-seq, ChipSeq, and DNA-seq can be fully exploited only with the proper skill and knowledge. In this chapter, we survey relevant literature concerning databases, annotators, and variant prioritization tools.

Novel Mechanisms of Tumor Promotion by the Insulin Receptor Isoform A in Triple-Negative Breast Cancer Cells.

The insulin receptor isoform A (IR-A) plays an increasingly recognized role in fetal growth and tumor biology in response to circulating insulin and/or locally produced IGF2. This role seems not to be shared by the IR isoform B (IR-B). We aimed to dissect the specific impact of IR isoforms in modulating insulin signaling in triple negative breast cancer (TNBC) cells. We generated murine 4T1 TNBC cells deleted from the endogenous insulin receptor (INSR) gene and expressing comparable levels of either human IR-A or IR-B. We then measured IR isoform-specific in vitro and in vivo biological effects and transcriptome in response to insulin. Overall, the IR-A was more potent than the IR-B in mediating cell migration, invasion, and in vivo tumor growth. Transcriptome analysis showed that approximately 89% of insulin-stimulated transcripts depended solely on the expression of the specific isoform. Notably, in cells overexpressing IR-A, insulin strongly induced genes involved in tumor progression and immune evasion including chemokines and genes related to innate immunity. Conversely, in IR-B overexpressing cells, insulin predominantly induced the expression of genes primarily involved in the regulation of metabolic pathways and, to a lesser extent, tumor growth and angiogenesis.

Psychopathological outcomes and defence mechanisms in clinically healed adults with a paediatric cancer history: an exploratory study.

BACKGROUND: The incidence of paediatric cancers has increased in recent years; however, with advances in the treatment of paediatric cancer, almost 80% of children and adolescents who receive a diagnosis of cancer become long-term survivors. Given the high stress levels associated with cancer, it becomes important to ascertain the risk and likelihood of psychiatric disorders in adult paediatric cancer survivors. AIMS: This study aims to investigate the relationship between defence styles and predisposition to psychiatric diseases in adults with a history of paediatric cancer. METHODS: We performed an explorative study on a sample of 66 clinically healed adults with a history of paediatric cancer (survivors) during follow-up visits at the University Hospital 'Policlinico G Rodolico' of Catania (Italy) and 98 healthy controls among medicine students. We administered the Defence Mechanism Inventory (DMI) to assess defence styles. The Symptom Checklist-90-Revised (SCL-90-R) and the Davidson Trauma Scale (DTS) were administered to assess psychopathological indices. We conducted comprehensive statistical analysis based on correlation analysis and mediation analysis to investigate the relationship between defence styles and psychopathological outcomes in survivors compared with controls. RESULTS: The survivors obtained statistically significant lower values in TAO, PRO and TAS defence styles and a higher value in REV. Both groups showed non-pathlogical mean scores in DTS and SCL-90-R (with an exception of the obsessive-compulsive subscale), with lower mean values among survivors. The results of mediation analysis showed that TAS had mediation effects on interpersonal sensitivity, anxiety, PSDI, GSI and avoidance, while TAO had mediation effects on DTS total score and intrusivity. Thus, for these psychopathological indices, the effect of the oncological pathology was indirect and mediated by TAO or TAS. Our analysis exlcluded mediation effects between the remaining variables and defence styles. CONCLUSION: Integrating data from mediation and correlation analysis, we found how the decreasing of TAS utilization in survivors as the consequence of cancer history, has decreased interpersonal sensitivity, anxiety and GSI score in these subjects compared with controls. Similary, the decrease of TAO utilization played a role in lower values of DTS total score and intrusivity subscale. Unexpectedly, our analysis excluded relationships between cancer history, other defence styles and psycopathological scores as we initially assumed.

Bioinformatics Approach to Mitigate Mislabeling in EU Seafood Market and Protect Consumer Health.

Fisheries products are some of the most traded commodities world-wide and the potential for fraud is a serious concern. Fish fraud represents a threat to human health and poses serious concerns due to the consumption of toxins, highly allergenic species, contaminates or zoonotic parasites, which may be present in substituted fish. The substitution of more expensive fish by cheaper species, with similar morphological characteristics but different origins, reflects the need for greater transparency and traceability upon which which the security of the entire seafood value-chain depends. Even though EU regulations have made significant progress in consumer information by stringent labelling requirements, fraud is still widespread. Many molecular techniques such as DNA barcoding provide valuable support to enhance the Common Fisheries Policy (CFP) in the protection of consumer interests by unequivocally detecting any kind of fraud. This paper aims to highlight both the engagement of EU fishery policy and the opportunity offered by new biotechnology instruments to mitigate the growing fraud in the globalized fish market and to enforce the food security system to protect consumers' health. In this paper, after a presentation of EU rules on fish labeling and a general overview on the current state of the global fish market, we discuss the public health implications and the opportunities offered by several techniques based on genetics, reporting a case study to show the efficacy of the DNA barcoding methodology in assessing fish traceability and identification, comparing different species of the Epinephelus genus, Mottled Grouper (Mycteroperca rubra) and Wreckfish (Polyprion americanus), often improperly sold with the commercial name of "grouper".

AKT3-mediated IWS1 phosphorylation promotes the proliferation of EGFR-mutant lung adenocarcinomas through cell cycle-regulated U2AF2 RNA splicing.

AKT-phosphorylated IWS1 regulates alternative RNA splicing via a pathway that is active in lung cancer. RNA-seq studies in lung adenocarcinoma cells lacking phosphorylated IWS1, identified a exon 2-deficient U2AF2 splice variant. Here, we show that exon 2 inclusion in the U2AF2 mRNA is a cell cycle-dependent process that is regulated by LEDGF/SRSF1 splicing complexes, whose assembly is controlled by the IWS1 phosphorylation-dependent deposition of histone H3K36me3 marks in the body of target genes. The exon 2-deficient U2AF2 mRNA encodes a Serine-Arginine-Rich (RS) domain-deficient U2AF65, which is defective in CDCA5 pre-mRNA processing. This results in downregulation of the CDCA5-encoded protein Sororin, a phosphorylation target and regulator of ERK, G2/M arrest and impaired cell proliferation and tumor growth. Analysis of human lung adenocarcinomas, confirmed activation of the pathway in EGFR-mutant tumors and showed that pathway activity correlates with tumor stage, histologic grade, metastasis, relapse after treatment, and poor prognosis.

VECTOR: An Integrated Correlation Network Database for the Identification of CeRNA Axes in Uveal Melanoma.

Uveal melanoma (UM) is the most common primary intraocular malignant tumor in adults and, although its genetic background has been extensively studied, little is known about the contribution of non-coding RNAs (ncRNAs) to its pathogenesis. Indeed, its competitive endogenous RNA (ceRNA) regulatory network comprising microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and mRNAs has been insufficiently explored. Thanks to UM findings from The Cancer Genome Atlas (TCGA), it is now possible to statistically elaborate these data to identify the expression relationships among RNAs and correlative interaction data. In the present work, we propose the VECTOR (uVeal mElanoma Correlation NeTwORk) database, an interactive tool that identifies and visualizes the relationships among RNA molecules, based on the ceRNA model. The VECTOR database contains: i) the TCGA-derived expression correlation values of miRNA-mRNA, miRNA-lncRNA and lncRNA-mRNA pairs combined with predicted or validated RNA-RNA interactions; ii) data of sense-antisense sequence overlapping; iii) correlation values of Transcription Factor (TF)-miRNA, TF-lncRNA, and TF-mRNA pairs associated with ChiPseq data; iv) expression data of miRNAs, lncRNAs and mRNAs both in UM and physiological tissues. The VECTOR web interface can be queried, by inputting the gene name, to retrieve all the information about RNA signaling and visualize this as a graph. Finally, VECTOR provides a very detailed picture of ceRNA networks in UM and could be a very useful tool for researchers studying RNA signaling in UM. The web version of Vector is freely available at the URL reported at the end of the Introduction.

PHENSIM: Phenotype Simulator.

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues' physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. Here we propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. Our tool's applications include predicting the outcome of drug administration, knockdown experiments, gene transduction, and exposure to exosomal cargo. Importantly, PHENSIM enables the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. To assess our approach's reliability, we built a benchmark from transcriptomics data gathered from NCBI GEO and performed four case studies on known biological experiments. Our results show high prediction accuracy, thus highlighting the capabilities of this methodology. PHENSIM standalone Java application is available at https://github.com/alaimos/phensim, along with all data and source codes for benchmarking. A web-based user interface is accessible at https://phensim.tech/.

"Diagnosis on the Dock" project: A proactive screening program for diagnosing pulmonary tuberculosis in disembarking refugees and new SEI model.

OBJECTIVE: From 2011 to 2017, the total number of refugees arriving in Europe, particularly in Italy, climbed dramatically. Our aim was to diagnose pulmonary TB in migrants coming from the African coast using a clinical-based port of arrival (PoA) screening program. METHODS: From 2016 to 2018, migrants coming via the Mediterranean Route were screened for body temperature and the presence of cough directly on the dock: if they were feverish with productive cough, their sputum was examined with NAAT; with a dry cough, they underwent Chest-X-ray (CXR). Those migrants with positive NAAT or CXR suggestive for TB were admitted to our ward. In addition, we plotted an SEI simulation of our project to evaluate the epidemiological impact of our screening. RESULTS: Out of 33.676 disembarking migrants, 314 (0.9%) had fever and cough: 80 (25.47%) with productive cough underwent NAAT in sputum, and 16 were positive for TB; 234 (74.52%) with dry cough had a CXR examination, and 39 were suggestive of TB, later confirmed by mycobacterial culture. The SEI-new model analysis demonstrated that our screening program significantly reduced TB spreading all over the country. CONCLUSIONS: For possible future high migrant flows, PoA screening for TB has to be considered feasible and effective in decreasing TB spreading.

TACITuS: transcriptomic data collector, integrator, and selector on big data platform.

BACKGROUND: Several large public repositories of microarray datasets and RNA-seq data are available. Two prominent examples include ArrayExpress and NCBI GEO. Unfortunately, there is no easy way to import and manipulate data from such resources, because the data is stored in large files, requiring large bandwidth to download and special purpose data manipulation tools to extract subsets relevant for the specific analysis. RESULTS: TACITuS is a web-based system that supports rapid query access to high-throughput microarray and NGS repositories. The system is equipped with modules capable of managing large files, storing them in a cloud environment and extracting subsets of data in an easy and efficient way. The system also supports the ability to import data into Galaxy for further analysis. CONCLUSIONS: TACITuS automates most of the pre-processing needed to analyze high-throughput microarray and NGS data from large publicly-available repositories. The system implements several modules to manage large files in an easy and efficient way. Furthermore, it is capable deal with Galaxy environment allowing users to analyze data through a user-friendly interface.

Identification of tRNA-derived ncRNAs in TCGA and NCI-60 panel cell lines and development of the public database tRFexplorer.

Next-generation sequencing is increasing our understanding and knowledge of non-coding RNAs (ncRNAs), elucidating their roles in molecular mechanisms and processes such as cell growth and development. Within such a class, tRNA-derived ncRNAs have been recently associated with gene expression regulation in cancer progression. In this paper, we characterize, for the first time, tRNA-derived ncRNAs in NCI-60. Furthermore, we assess their expression profile in The Cancer Genome Atlas (TCGA). Our comprehensive analysis allowed us to report 322 distinct tRNA-derived ncRNAs in NCI-60, categorized in tRNA-derived fragments (11 tRF-5s, 55 tRF-3s), tRNA-derived small RNAs (107 tsRNAs) and tRNA 5' leader RNAs (149 sequences identified). In TCGA, we were able to identify 232 distinct tRNA-derived ncRNAs categorized in 53 tRF-5s, 58 tRF-3s, 63 tsRNAs and 58 5' leader RNAs. This latter group represents an additional evidence of tRNA-derived ncRNAs originating from the 5' leader region of precursor tRNA. We developed a public database, tRFexplorer, which provides users with the expression profile of each tRNA-derived ncRNAs in every cell line in NCI-60 as well as for each TCGA tumor type. Moreover, the system allows us to perform differential expression analyses of such fragments in TCGA, as well as correlation analyses of tRNA-derived ncRNAs expression in TCGA and NCI-60 with gene and miRNA expression in TCGA samples, in association with all omics and compound activities data available on CellMiner. Hence, the tool provides an important opportunity to investigate their potential biological roles in absence of any direct experimental evidence. Database URL: https://trfexplorer.cloud/.