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This work is partially funded by FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento (Grant CV20-36723), Consejeria de Salud (Grant PI-0173-2017) and by EU/EFPIA Innovative Medicines Initiative Joint Undertaking PRECISESADS (115565). JMM is partially funded by Ministerio de Economia, Industria y Competitividad. None of the funding bodies played any role in the design of the study and collection, analysis, and interpretation of data nor in writing the manuscript., Background: Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. Results: Here, we present Autoimmune Diseases Explorer (https:// adex. genyo. es), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis. Conclusions: This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies., FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento CV20-36723, Consejeria de Salud PI-0173-2017, EU/EFPIA Innovative Medicines Initiative Joint Undertaking PRECISESADS 115565, Ministerio de Economia, Industria y Competitividad

Genome-scale mechanistic models of pathways are gaining importance for genomic data interpretation because they provide a natural link between genotype measurements (transcriptomics or genomics data) and the phenotype of the cell (its functional behavior). Moreover, mechanistic models can be used to predict the potential effect of interventions, including drug inhibitions. Here, we present the implementation of a mechanistic model of cell signaling for the interpretation of transcriptomic data as an R/Bioconductor package, a Cytoscape plugin and a web tool with enhanced functionality which includes building interpretable predictors, estimation of the effect of perturbations and assessment of the effect of mutations in complex scenarios., This work is supported by grants SAF2017-88908-R from the Spanish Ministry of Economy and Competitiveness and PT17/0009/0006 and PI20/01305 from the ISCIII, both co-funded with European Regional Development Funds (ERDF) as well as H2020 Programme of the European Union grants Marie Curie Innovative Training Network “Machine Learning Frontiers in Precision Medicine” (MLFPM) (GA 813533) and “ELIXIR-EXCELERATE fast-track ELIXIR implementation and drive early user exploitation across the life sciences” (GA 676559) to JD. Funding for open access charge: from the Spanish Ministry of Economy and Competitiveness / SAF2017-88908-R.

Immunotherapy has been one of the great advances in the recent years for the treatment of advanced tumors, with nonsmall-cell lung cancer (NSCLC) being one of the cancers that has benefited most from this approach. Currently, the only validated companion diagnostic test for first-line immunotherapy in metastatic NSCLC patients is testing for programmed death ligand 1 (PD-L1) expression in tumor tissues. However, not all patients experience an effective response with the established selection criteria and immune checkpoint inhibitors (ICIs). Liquid biopsy offers a noninvasive opportunity to monitor disease in patients with cancer and identify those who would benefit the most from immunotherapy. This review focuses on the use of liquid biopsy in immunotherapy treatment of NSCLC patients. Circulating tumor cells (CTCs), cell-free DNA (cfDNA) and exosomes are promising tools for developing new biomarkers. We discuss the current application and future implementation of these parameters to improve therapeutic decision-making and identify the patients who will benefit most from immunotherapy.

This article belongs to the Special Issue Melanoma: Prevention and Molecular Epidemiology., [Simple Summary] The divergent pathway model established at least two approaches for melanoma development. One was related to a propensity to melanocytic proliferation (nevogenic), and the other was associated with an accumulation of solar damage (CSD). We conducted a retrospective study to examine whether this model had a molecular support using sequencing and bioinformatic tools on a set of cutaneous melanomas corresponding to these two groups. We found that the nevogenic melanomas were associated with mutations in BRAF, while the CSD melanomas were associated with mutations in NF1, ROS1, GNA11, and RAC1. We concluded that nevogenic and CSD melanomas constitute two different biological entities., [Abstract] According to the divergent pathway model, cutaneous melanoma comprises a nevogenic group with a propensity to melanocyte proliferation and another one associated with cumulative solar damage (CSD). While characterized clinically and epidemiologically, the differences in the molecular profiles between the groups have remained primarily uninvestigated. This study has used a custom gene panel and bioinformatics tools to investigate the potential molecular differences in a thoroughly characterized cohort of 119 melanoma patients belonging to nevogenic and CSD groups. We found that the nevogenic melanomas had a restricted set of mutations, with the prominently mutated gene being BRAF. The CSD melanomas, in contrast, showed mutations in a diverse group of genes that included NF1, ROS1, GNA11, and RAC1. We thus provide evidence that nevogenic and CSD melanomas constitute different biological entities and highlight the need to explore new targeted therapies., This study was supported by the Ministerio de Ciencia e Innovación-Instituto de Salud Carlos III (PI15/01860; PI19/00667), the Asociación Española Contra el Cáncer-Valencia through “Ayudas predoctorales en Oncología” grant, and the European Academy of Dermatology and Venereology (PPRC-2018-36).

Gut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being used to obtain non-intrusive predictive disease biomarkers. Here we performed a meta-analysis of 1042 fecal metagenomic samples from seven publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allow extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes undergone by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Functional profiles have demonstrated superior accuracy in predicting CRC and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions., This work is supported by Grants PID2020-117979RB-I00 from the Spanish Ministry of Science and Innovation and PI20/01305 and IMP/0019 from the ISCIII, both co-funded with European Regional Development Funds (ERDF) as well as H2020 Programme of the European Union Grants Marie Curie Innovative Training Network "Machine Learning Frontiers in Precision Medicine" (MLFPM) (GA 813533) and “ELIXIR-EXCELERATE fast-track ELIXIR implementation and drive early user exploitation across the life sciences” (GA 676559). The postdoctoral contract for CL PAIDI2020- DOC_00350 is funded by Junta de Andalucía and co-funded by the European Social Fund (FSE) 2014-2020.

BackgroundCOVID-19 is a major worldwide health problem because of acute respiratory distress syndrome, and mortality. Several lines of evidence have suggested a relationship between the vitamin D endocrine system and severity of COVID-19.MethodsWe present a retrospective survival study that includes all Andalusian patients hospitalized between January and November 2020 because of COVID-19 infection. Based on a central registry of electronic health records (the Andalusian Population Health Database, BPS), prescription of vitamin D or its metabolites within 15-30 days before hospitalization were recorded. The effect of treatment with vitamin D metabolites for other indication previous to the hospitalization was studied with respect to patient survival by means of Kaplan-Meyer survival curves and Log Hazard Ratios, using a propensity score to compensate the disbalance of compared classes and the confounding factors. The availability of detailed patient data in the BPS allowed to obtain Real-World Evidence (RWE) of the effects of prior use of vitamin D or its metabolites on the mortality due to COVID-19 infection.FindingsA retrospective cohort of 16.401patients was extracted from the BPS, which includes all the patients hospitalized with COVID-19 diagnosis between January and November 2020 in Andalusia, one of the largest regions in Europe with the size of an average median country. A total of 358 patients were found with cholecalciferol, and 193 with calcifediol, prescriptions 15 days before hospitalization. For a period extended to 30 days before hospitalization, the numbers increase to 416 and 210 and, respectively. Kaplan-Meyer survival curves and hazard ratios support an association between consumption of these metabolites and patient survival. Such association was stronger in calcifediol (Log Hazard Ratio, LHR = -1.27±0.32) than in cholecalciferol (LHR= -0.56±0.15), when prescribed 15 days before hospitalization This effect decreases when a larger 30 days period is considered (calcifediol LHR= -1.01±0.27 and cholecalciferol LHR= -0.27±0.12), suggesting that the closer was the treatment to the hospitalization the stronger the association.ConclusionsA significant reduction in mortality in patients hospitalized with COVID-19 is associated with the prescription of vitamin D, especially calcifediol, within 15-30 days prior to hospitalization.

We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.; We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

This is a PLOS Computational Biology Software paper., [Abstract] MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available., [Author summary] Currently, RNA massive sequencing RNA-seq is the most extensively used technique for gene expression profiling in a single assay. The output of RNA-seq experiments contains millions of sequences, generated from cDNA libraries produced by the retro-transcription of RNA samples, that need to be processed by computational methods to be transformed into meaningful biological information. Thus, a number of bioinformatic workflows and pipelines have been proposed to produce different types of gene expression measurements, including in some cases, functional annotations to facilitate biological interpretation. While most pipelines focus exclusively on transcriptional data, the ultimate activity of the resulting gene product also depends critically on its integrity. Although traditional hybridization-based transcriptomics methodologies (microarrays) miss this information, RNA-seq data also contains information on variants present in the transcripts that can affect the function of the gene product, which is systematically ignored by current RNA-seq pipelines. MIGNON is the first workflow able to perform an integrative analysis of transcriptomic and genomic data in the proper functional context, provided by a mechanistic model of signaling pathway activity, making thus the most of the information contained in RNA-Seq data. MIGNON is easy to use and to deploy and may become a valuable asset in fields such as personalized medicine., JD has received these grants: SAF2017-88908-R from the Ministerio de Economía y Competitividad and PT17/0009/0006 from the Instituto de Salud Carlos III, as well as an FP7 People Marie-Curie Actions 813533 and and Horizon 2020 Framework Programme 676559.

In the last decade, different omic technologies have experiences an exponential technological advancement. However, metabolomics has not followed a similarly vertiginous improving improvement and are far from genomics or transcriptomics in terms of throughput, cost and even accuracy. Therefore, genome-scale in-silico methodologies to estimate metabolic activities from genomic data constitute an active field. The solutions available fall into two extremes: those with few assumptions about the relationships among proteins and metabolites, which are easy-to-use but less accurate and those that account for the complex relationships among molecules and proteins defined in the metabolic pathways, which are more accurate but require mathematical skills. Here, we introduce Metabolica, an algorithm that considers the complex functional relationships among all the molecules and proteins involved in the metabolic pathway analyzed but keeping an easy use that do not require of advanced mathematical skills. Metabolica has been implemented in a freely available software R package. The software inputs transcriptomic data and infers the activities of the reactions that produce the different metabolites in the pathway analyzed. An example shows how detected dysregulated metabolites in several cancers are related to patient survival.

Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map. In this framework, the detailed activity of the human signaling circuits related to the viral infection, covering from the entry and replication mechanisms to the downstream consequences as inflammation and antigenic response, can be inferred from gene expression experiments. Moreover, the effect of potential interventions, such as knock-downs, or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied. This freely available tool not only provides an unprecedentedly detailed view of the mechanisms of viral invasion and the consequences in the cell but has also the potential of becoming an invaluable asset in the search for efficient antiviral treatments., This work is supported by grants SAF2017–88908-R from the Spanish Ministry of Economy and Competitiveness, PT17/0009/0006, ACCI2018/29 from CIBER-ISCIII and COV20/00788 from the ISCIII, co-funded with European Regional Development Funds (ERDF), the grant “Large-scale drug repurposing in rare diseases by genomic Big Data analysis with machine learning methods” from the Fundación BBVA (G999088Q), as well as H2020 Programme of the European Union grants Marie Curie Innovative Training Network “Machine Learning Frontiers in Precision Medicine” (MLFPM) (GA 813533).

Drug repurposing is a convenient alternative when the need for new drugs in an unexpected medical scenario is urgent, as is the case of emerging pathogens. In recent years, approaches based on network biology have demonstrated to be superior to genecentric ones.1 Here, we use an innovative methodology that combines mechanistic modeling of the signal transduction circuits related to SARS-CoV-2 infection (the COVID-19 disease map) with a machine-learning algorithm that learns potential causal interactions between proteins, already targets of drugs, and specific signaling circuits in the COVID-19 disease map, to suggest potentially repurposable drugs., This work is supported by grants SAF2017-88908-R from the Spanish Ministry of Economy and Competitiveness, PT17/0009/0006, ACCI2018/29 from CIBER-ISCIII and COV20/00788 from the ISCIII, co-funded with European Regional Development Funds (ERDF), the grant “Large-scale drug repurposing in rare diseases by genomic Big Data analysis with machine learning methods” from the Fundación BBVA (G999088Q), as well as H2020 Programme of the European Union grants Marie Curie Innovative Training Network “Machine Learning Frontiers in Precision Medicine” (MLFPM) (GA 813533).

Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. Here, we present ADEx (https://adex.genyo.es), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis.

Single-cell RNA sequencing is revealing an unexpectedly large degree of heterogeneity in gene expression levels across cell populations. However, little is known on the functional consequences of this heterogeneity and the contribution of individual cell fate decisions to the collective behavior of the tissues these cells are part of. Here, we use mechanistic modeling of signaling circuits, which reveals a complex functional landscape at single-cell level. Different clusters of neoplastic glioblastoma cells have been defined according to their differences in signaling circuit activity profiles triggering specific cancer hallmarks, which suggest different functional strategies with distinct degrees of aggressiveness. Moreover, mechanistic modeling of effects of targeted drug inhibitions at single-cell level revealed, how in some cells, the substitution of VEGFA, the target of bevacizumab, by other expressed proteins, like PDGFD, KITLG and FGF2, keeps the VEGF pathway active, insensitive to the VEGFA inhibition by the drug. Here, we describe for the first time mechanisms that individual cells use to avoid the effect of a targeted therapy, providing an explanation for the innate resistance to the treatment displayed by some cells. Our results suggest that mechanistic modeling could become an important asset for the definition of personalized therapeutic interventions.

Background Gut microbiome is gaining interest because its links with several diseases, including colorectal cancer (CRC). Results Here we performed a meta-analysis of 851 fecal metagenomic samples from five publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allows extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes underwent by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Conclusion Functional profiles provide superior accuracy in predicting CCR and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions.

Background: Breast cancer in very young women (BCVY) defined as &lt, 35 years old, presents with different molecular biology than in older patients. High HDAC5 expression has been associated with poor prognosis in breast cancer (BC) tissue. We aimed to analyze HDAC5 expression in BCVY and older patients and their correlation with clinical features, also studying the potential of HDAC5 inhibition in BC cell lines. Methods: HDAC5 expression in 60 BCVY and 47 older cases were analyzed by qRT-PCR and correlated with clinical data. The effect of the HDAC5 inhibitor, LMK-235, was analyzed in BC cell lines from older and young patients. We performed time and dose dependence viability, migration, proliferation, and apoptosis assays. Results: Our results correlate higher HDAC5 expression with worse prognosis in BCVY. However, we observed no differences between HDAC5 expression and pathological features. Our results showed greatly reduced progression in BCVY cell lines and also in all triple negative subtypes when cell lines were treated with LMK-235. Conclusions: In BCVY, we found higher expression of HDAC5. Overexpression of HDAC5 in BCVY correlates with lower survival rates. LMK-235 could be a potential treatment in BCVY.

This article belongs to the Special Issue Targeting Signal Transduction Pathways and Non-coding RNAs as Potential Therapy in Cancer and Aging., Despite the existence of differences in gene expression across numerous genes between males and females having been known for a long time, these have been mostly ignored in many studies, including drug development and its therapeutic use. In fact, the consequences of such differences over the disease mechanisms or the drug action mechanisms are completely unknown. Here we applied mechanistic mathematical models of signaling activity to reveal the ultimate functional consequences that gender-specific gene expression activities have over cell functionality and fate. Moreover, we also used the mechanistic modeling framework to simulate the drug interventions and unravel how drug action mechanisms are affected by gender-specific differential gene expression. Interestingly, some cancers have many biological processes significantly affected by these gender-specific differences (e.g., bladder or head and neck carcinomas), while others (e.g., glioblastoma or rectum cancer) are almost insensitive to them. We found that many of these gender-specific differences affect cancer-specific pathways or in physiological signaling pathways, also involved in cancer origin and development. Finally, mechanistic models have the potential to be used for finding alternative therapeutic interventions on the pathways targeted by the drug, which lead to similar results compensating the downstream consequences of gender-specific differences in gene expression., This work is supported by grants SAF2017-88908-R from the Spanish Ministry of Economy and Competitiveness and “Plataforma de Recursos Biomoleculares y Bioinformáticos” PT17/0009/0006 from the ISCIII, both co-funded with European Regional Development Funds (ERDF) as well as H2020 Programme of the European Union grants Marie Curie Innovative Training Network "Machine Learning Frontiers in Precision Medicine" (MLFPM) (GA 813533) and “ELIXIR-EXCELERATE fast-track ELIXIR implementation and drive early user exploitation across the life sciences” (GA 676559).

[Background] Solitary fibrous tumour is an ultra-rare sarcoma, which encompasses different clinicopathological subgroups. The dedifferentiated subgroup shows an aggressive course with resistance to pazopanib, whereas in the malignant subgroup, pazopanib shows higher activity than in previous studies with chemotherapy. We designed a trial to test pazopanib activity in two different cohorts of solitary fibrous tumour: the malignant-dedifferentiated cohort, which was previously published, and the typical cohort, which is presented here., [Methods] In this single-arm, phase 2 trial, adult patients (aged ≥18 years) diagnosed with confirmed metastatic or unresectable typical solitary fibrous tumour of any location, who had progressed in the previous 6 months (by Choi criteria or Response Evaluation Criteria in Solid Tumors [RECIST]) and an Eastern Cooperative Oncology Group (ECOG) performance status of 0–2 were enrolled at 11 tertiary hospitals in Italy, France, and Spain. Patients received pazopanib 800 mg once daily, taken orally, until progression, unacceptable toxicity, withdrawal of consent, non-compliance, or a delay in pazopanib administration of longer than 3 weeks. The primary endpoint was proportion of patients achieving an overall response measured by Choi criteria in patients who received at least 1 month of treatment with at least one radiological assessment. All patients who received at least one dose of the study drug were included in the safety analyses. This study is registered in ClinicalTrials.gov, NCT02066285, and with the European Clinical Trials Database, EudraCT 2013-005456-15., [Findings] From June 26, 2014, to Dec 13, 2018, of 40 patients who were assessed, 34 patients were enrolled and 31 patients were included in the response analysis. Median follow-up was 18 months (IQR 14–34), and 18 (58%) of 31 patients had a partial response, 12 (39%) had stable disease, and one (3%) showed progressive disease according to Choi criteria and central review. The proportion of overall response based on Choi criteria was 58% (95% CI 34–69). There were no deaths caused by toxicity, and the most frequent adverse events were diarrhoea (18 [53%] of 34 patients), fatigue (17 [50%]), and hypertension (17 [50%])., [Interpretation] To our knowledge, this is the first prospective trial of pazopanib for advanced typical solitary fibrous tumour. The manageable toxicity and activity shown by pazopanib in this cohort suggest that this drug could be considered as first-line treatment for advanced typical solitary fibrous tumour., Spanish Group for Research on Sarcomas (GEIS), Italian Sarcoma Group (ISG), French Sarcoma Group (FSG), GlaxoSmithKline, and Novartis.

[Background] Sarcomas exhibit low expression of factors related to immune response, which could explain the modest activity of PD-1 inhibitors. A potential strategy to convert a cold into an inflamed microenvironment lies on a combination therapy. As tumor angiogenesis promotes immunosuppression, we designed a phase Ib/II trial to test the double inhibition of angiogenesis (sunitinib) and PD-1/PD-L1 axis (nivolumab)., [Methods] This single-arm, phase Ib/II trial enrolled adult patients with selected subtypes of sarcoma. Phase Ib established two dose levels: level 0 with sunitinib 37.5 mg daily from day 1, plus nivolumab 3 mg/kg intravenously on day 15, and then every 2 weeks; and level −1 with sunitinib 37.5 mg on the first 14 days (induction) and then 25 mg per day plus nivolumab on the same schedule. The primary endpoint was to determine the recommended dose for phase II (phase I) and the 6-month progression-free survival rate, according to Response Evaluation Criteria in Solid Tumors 1.1 (phase II)., [Results] From May 2017 to April 2019, 68 patients were enrolled: 16 in phase Ib and 52 in phase II. The recommended dose of sunitinib for phase II was 37.5 mg as induction and then 25 mg in combination with nivolumab. After a median follow-up of 17 months (4–26), the 6-month progression-free survival rate was 48% (95% CI 41% to 55%). The most common grade 3–4 adverse events included transaminitis (17.3%) and neutropenia (11.5%)., [Conclusions] Sunitinib plus nivolumab is an active scheme with manageable toxicity in the treatment of selected patients with advanced soft tissue sarcoma, with almost half of patients free of progression at 6 months., [Trial registration number] NCT03277924., This work was supported by GEIS and ISG. BMS and Pfizer provided drug supply and partial funding for shipping. Translational studies were partially funded by Beca Buesa from the GEIS group.

The sustained generation of genomic data in the last decade has increased the knowledge on the causal mutations of a large number of diseases, especially for highly penetrant Mendelian diseases, typically caused by a unique or a few genes. However, the discovery of causal genes in complex diseases has been far less successful. Many complex diseases are actually a consequence of the failure of complex biological modules, composed by interrelated proteins, which can happen in many different ways, which conferring a multigenic nature to the condition that can hardly be attributed to one or a few genes. We present a mechanistic model, Hipathia, implemented in a web server that allows estimating the effect that mutations, or changes in the expression of genes, have over the whole system of human signaling and the corresponding functional consequences. We show several use cases where we demonstrate how different the ultimate impact of mutations with similar loss-of-function potential can be and how the potential pathological role of a damaged gene can be inferred within the context of a signaling network. The use of systems biology-based approaches, such as mechanistic models, allows estimating the potential impact of loss-of-function mutations occurring in proteins that are part of complex biological interaction networks, such as signaling pathways. This holistic approach provides an elegant alternative to gene-centric approaches that can open new avenues in the interpretation of the genomic variability in complex diseases.

Breast cancer in very young women (≤35 years; BCVY) presents more aggressive and complex biological features than their older counterparts (BCO). Our aim was to evaluate methylation differences between BCVY and BCO and their DNA epigenetic age. EPIC and 450k Illumina methylation arrays were used in 67 breast cancer tumours, including 32 from BCVY, for methylation study and additionally we analysed their epigenetic age. We identified 2 219 CpG sites differently-methylated in BCVY vs. BCO (FDR HOXD9, and PCDH10 genes in BCVY. Regions implicated in the hypermethylation profile were involved in Notch signalling pathways, the immune system or DNA repair. We further validated HDAC5 expression in BCVY. We have identified a DNA methylation signature that is specific to BCVY and have shown that epigenetic age-acceleration is increased in BCVY.

BackgroundIn spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.ResultsThe application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.ConclusionsThe use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

// Marta Jessica Llorca-Cardenosa 1, * , Tania Fleitas 1, * , Maider Ibarrola-Villava 1 , Maria Pena-Chilet 1 , Cristina Mongort 2 , Carolina Martinez-Ciarpaglini 2 , Lara Navarro 2 , Valentina Gambardella 1 , Josefa Castillo 1 , Susana Rosello 1 , Samuel Navarro 2 , Gloria Ribas 1 , Andres Cervantes 1 1 Medical Oncology, Biomedical Research Institute INCLIVA, University of Valencia, Valencia, Spain 2 Department of Pathology, Biomedical Research Institute INCLIVA, University of Valencia, Valencia, Spain * These authors contributed equally to this work Correspondence to: Gloria Ribas, email: gribas@incliva.es Andres Cervantes, email: andres.cervantes@uv.es Keywords: RUNX3, ARID1A, gastric cancer, gene methylation, immune microenvironment Received: June 16, 2016 Accepted: August 15, 2016 Published: August 23, 2016 ABSTRACT Gastric cancer (GC) pathogenesis involves genetic, epigenetic and environmental factors. Epigenetic alterations, such as DNA methylation are considered pivotal in the inactivation of tumor-related genes. We assessed a methylation panel of 5 genes to study their association to GC progression and microsatellite instability (MSI), and studied the role of RUNX3 in GC pathogenesis and the tumor immune microenvironment. The methylation status of 47 promoter-CpG islands was studied through MALDI-TOF mass spectrometry analysis in 35 Microsatellite stable (MSS) GC, 26 MSI, and 18 cancer-free samples (CFS), and 6 MSS GC and 4 MSI GC cell lines. We also studied RUNX3 expression by immunohistochemistry (IHC) in 40 samples, and validated differences in methylation levels between tumor, normal, and immune tissue in 14 additional samples. Unsupervised hierarchical clustering of methylation levels revealed no distinct subgroups between MSI and MSS samples or cell lines. CFSs clustered together showing higher levels of RUNX3 methylation compared to GC samples. RUNX3 showed protein silencing in cancer and normal mucosa, compared to inflammatory peritumoural infiltrate in almost all cases, showing a non-lymphocytic predominant pattern and being correlated with epigenetic silencing. Our results show aberrant promoter’s methylation in APC, CDH1, CDKN2A, MLH1 and RUNX3 associated with GC, as well as a non-lymphocytic predominant infiltrate with high expression of RUNX3 . Deep study of RUNX3 inflammation signaling could help in understanding inflammation and immune activation in the tumor microenvironment.

Purpose: Breast cancer (BC) in very young women (BCVY) is more aggressive than in older women. The purpose of this study was to evaluate the relevance of a range of clinico-pathological factors in the prognosis of BCVY patients. Methods: We retrospectively analyzed 258 patients diagnosed with BCVY at our hospital from 1998 to 2014; the control group comprised 101 older patients with BC. We correlated clinicopathological factors, treatments, relapse and exitus with age and with previously published miRNA expression data. Results: We identified some significant differences in risk factors between BCVY and older patients. The age at menarche, number of pregnancies, and age at first pregnancy were lower in the BCVY group and had a greater probability of recurrence and death in all cases. Lymph node-positive patients in the BCVY group are associated with a worse prognosis ( P = .02), an immunohistochemical HER2+ subtype, and disease relapse ( P = .03). Moreover, there was a shorter time between diagnosis and first relapse in BCVY patients compared with controls, and they were more likely to die from the disease ( P = .002). Finally, from our panel of miRNAs deregulated in BC, reduced miR-30c expression was associated with more aggressive BC in very young patients, lower overall survival, and with axillary lymph node metastases. Conclusions: Patient age and axillary lymph node status post-surgery are independent and significant predictors of distant disease-free survival, local recurrence-free survival, and overall survival. The HER2+ subtype and lower miR-30c expression are related to poor prognosis in lymph node-positive young BC patients.

[Background] A solitary fibrous tumour is a rare soft-tissue tumour with three clinicopathological variants: typical, malignant, and dedifferentiated. Preclinical experiments and retrospective studies have shown different sensitivities of solitary fibrous tumour to chemotherapy and antiangiogenics. We therefore designed a trial to assess the activity of pazopanib in a cohort of patients with malignant or dedifferentiated solitary fibrous tumour. The clinical and translational results are presented here., [Methods] In this single-arm, phase 2 trial, adult patients (aged ≥ 18 years) with histologically confirmed metastatic or unresectable malignant or dedifferentiated solitary fibrous tumour at any location, who had progressed (by RECIST and Choi criteria) in the previous 6 months and had an ECOG performance status of 0–2, were enrolled at 16 third-level hospitals with expertise in sarcoma care in Spain, Italy, and France. Patients received pazopanib 800 mg once daily, taken orally without food, at least 1 h before or 2 h after a meal, until progression or intolerance. The primary endpoint of the study was overall response measured by Choi criteria in the subset of the intention-to-treat population (patients who received at least 1 month of treatment with at least one radiological assessment). All patients who received at least one dose of the study drug were included in the safety analyses. This study is registered with ClinicalTrials.gov, number NCT02066285, and with the European Clinical Trials Database, EudraCT number 2013-005456-15., [Findings] From June 26, 2014, to Nov 24, 2016, of 40 patients assessed, 36 were enrolled (34 with malignant solitary fibrous tumour and two with dedifferentiated solitary fibrous tumour). Median follow-up was 27 months (IQR 16–31). Based on central radiology review, 18 (51%) of 35 evaluable patients had partial responses, nine (26%) had stable disease, and eight (23%) had progressive disease according to Choi criteria. Further enrolment of patients with dedifferentiated solitary fibrous tumour was stopped after detection of early and fast progressions in a planned interim analysis. 51% (95% CI 34–69) of 35 patients achieved an overall response according to Choi criteria. Ten (29%) of 35 patients died. There were no deaths related to adverse events and the most frequent grade 3 or higher adverse events were hypertension (11 [31%] of 36 patients), neutropenia (four [11%]), increased concentrations of alanine aminotransferase (four [11%]), and increased concentrations of bilirubin (three [8%])., [Interpretation] To our knowledge, this is the first trial of pazopanib for treatment of malignant solitary fibrous tumour showing activity in this patient group. The manageable toxicity profile and the activity shown by pazopanib suggests that this drug could be an option for systemic treatment of advanced malignant solitary fibrous tumour, and provides a benchmark for future trials., Spanish Group for Research on Sarcomas (GEIS), Italian Sarcoma Group (ISG), French Sarcoma Group (FSG), GlaxoSmithKline, and Novartis.

MiRNAs are part of the epigenetic machinery, and are also epigenetically modified by DNA methylation. MiRNAs regulate expression of different genes, so any alteration in their methylation status may affect their expression. We aimed to identify methylation differences in miRNA encoding genes in breast cancer affecting women under 35 years old (BCVY), in order to identify potential biomarkers in these patients. In Illumina Infinium MethylationEPIC BeadChip samples (metEPICVal), we analysed the methylation of 9,961 CpG site regulators of miRNA-encoding genes present in the array. We identified 193 differentially methylated CpG sites in BCVY (p-value p-value

Telomere length has been associated with the development of cancer. Studies have shown that shorter telomere length may be related to a decreased risk of cutaneous melanoma. Furthermore, deregulation of the telomere-maintaining gene complexes , has been related to this oncogenic process. Some variants in these genes seem to be correlated with a change in telomerase expression. We examined the effect of 10 single nucleotide polymorphisms (SNPs) in the TERT gene (encoding telomerase), one SNP in the related TERT-CLPTM1L locus and one SNP in the TRF1 gene with telomere length, and its influence on melanoma risk in 970 Spanish cases and 733 Spanish controls. Genotypes were determined using KASP technology, and telomere length was measured by quantitative polymerase chain reaction (PCR) on DNA extracted from peripheral blood leucocytes. Our results demonstrate that shorter telomere length is associated with a decreased risk of melanoma in our population (global p -value, 2.69 × 10 −11 ), which may be caused by a diminution of proliferative potential of nevi (melanoma precursor cells). We also obtained significant results when we tested the association between rs401681 variant ( TERT-CLPTM1L locus ) with melanoma risk (Odds ratio, OR; 95% confidence interval, CI = 1.24 (1.08–1.43); p -value, 3 × 10 −3 ). This is the largest telomere-related study undertaken in a Spanish population to date. Furthermore, this study represents a comprehensive analysis of some of the most relevant telomere pathway genes in relation to cutaneous melanoma susceptibility.

Poster presentado al 18th Meeting of the European Society for Pigment Cell Research Lisbon, Portugal | 9–12 September 2013. Abstract publicado en: Pigment Cell & Melanoma Research 26(5): E1-E31

Sunlight exposure induces signalling pathways leading to the activation of melanin synthesis and tanning response. MicroRNAs (miRNAs) can regulate the expression of genes involved in pigmentation pathways by binding to the complementary sequence in their 3'untranslated regions (3'UTRs). Therefore, 3'UTR SNPs are predicted to modify the ability of miRNAs to target genes, resulting in differential gene expression. In this study, we investigated the role in pigmentation and sun-sensitivity traits, as well as in melanoma susceptibility, of 38 different 3'UTR SNPs from 38 pigmentation-related genes. A total of 869 individuals of Spanish origin (526 melanoma cases and 343 controls) were analysed. The association of genotypic data with pigmentation traits was analysed via logistic regression. Web-based tools for predicting the effect of genetic variants in microRNA-binding sites in 3'UTR gene regions were also used. Seven 3'UTR SNPs showed a potential implication in melanoma risk phenotypes. This association is especially noticeable for two of them, rs2325813 in the MLPH gene and rs752107 in the WNT3A gene. These two SNPs were predicted to disrupt a miRNA-binding site and to impact on miRNA-mRNA interaction. To our knowledge, this is the first time that these two 3'UTR SNPs have been associated with sun-sensitivity traits. We state the potential implication of these SNPs in human pigmentation and sensitivity to sunlight, possibly as a result of changes in the level of gene expression through the disruption of putative miRNA-binding sites.

Effective data management of clinical information is well-known to be a very complex task. The breast cancer context provides a good example of how challenging the problem is in Information Systems terms. In fact, it is one of the basic problems faced by clinicians who work in this area. However, this problem is even bigger when clinicians and biologists try to relate these data to biological data obtained from research studies about this disease. This paper shows how Conceptual Modeling practices provide reliable working environments where clinical and biological information are clearly linked. We focus on miRNAs expression studies in breast cancer patients as a representative study of biological data in this environment. Representing all these biological data using Conceptual Modeling techniques and connecting it with the clinical information properly modeled using the same technique, creates the appropriate background to develop an information system able to manage efficiently and analyze all this information.

Background Human pigmentation is a polygenic quantitative trait with high heritability. In addition to genetic factors, it has been shown that pigmentation can be modulated by oestrogens and androgens via up- or down-regulation of melanin synthesis. Our aim was to identify possible sex differences in pigmentation phenotype as well as in melanoma association in a melanoma case-control population of Spanish origin. Methods Five hundred and ninety-nine females (316 melanoma cases and 283 controls) and 458 males (234 melanoma cases and 224 controls) were analysed. We genotyped 363 polymorphisms (single nucleotide polymorphisms (SNPs)) from 65 pigmentation gene regions. Results When samples were stratified by sex, we observed more SNPs associated with dark pigmentation and good sun tolerance in females than in males (107 versus 75; P = 2.32 × 10−6), who were instead associated with light pigmentation and poor sun tolerance. Furthermore, six SNPs in TYR, SILV/CDK2, GPR143, and F2RL1 showed strong differences in melanoma risk by sex (P

Introduction Breast cancer in very young women (BCVY), (≤35 years old) exhibits aggressive tumour characteristics associated with poorer prognosis, accounting for the leading cause of death by cancer in women of this age. Indeed, BCVY might arise as a different molecular entity derived from different underlying mechanisms. Gene expression can be used to estimate functional cell activities in order to reveal disease mechanisms and to predict clinical outcomes. The aim of this work is to evaluate the differences in gene expression on BCVY patients using TCGA (The Cancer Genome Atlas) data and to estimate the activation of cell mechanisms via effector genes. Material and methods We obtained RNASeq gene expression data of breast cancer patients from TCGA project. We then performed gene expression profiling and evaluated statistical differences between 36 samples from young women (≤35) and 256 from older patients (≥65) using R/Bioconductor tools. In order to evaluate the activity of cell mechanisms, we obtained the pathways’ activation values for each of the samples using Hipathia R package and performed a Wilcoxon test to evaluate activity differences in BCVY samples compared with both the remaining of BC samples in groups of age and with paired normal tissue (10 samples). We focused in pathways related to cancer and selected the most relevant effector genes. Results and discussions The statistical analysis of the expression of mRNAs in BCVY, revealed a differential gene expression profile characteristic of BCVY in TCGA dataset. Moreover, the analysis of effector genes revealed activated mechanisms in BCVY tumours that would lead to cell proliferation, more aggressive behaviour and metastatic capacity, features usually displayed by young breast cancer patients. Conclusion Our study suggests that BCVY has a gene expression profile characteristic of this group of patients. The underlying mechanisms leading to the development of tumour in BCVY seem to be different than those occurring in their older counterparts. These findings would lead to a more efficient targeted therapy in these patients.

BACKGROUND: Breast cancer is rarely diagnosed in very young women (35 years old or younger), and it often presents with distinct clinical-pathological features related to a more aggressive phenotype and worse prognosis when diagnosed at this early age. A pending question is whether breast cancer in very young women arises from the deregulation of different underlying mechanisms, something that will make this disease an entity differentiated from breast cancer diagnosed in older patients. METHODS: We performed a comprehensive study of miRNA expression using miRNA Affymetrix2.0 array on paraffin-embedded tumour tissue of 42 breast cancer patients 35 years old or younger, 17 patients between 45 and 65 years old and 29 older than 65 years. Data were statistically analyzed by t-test and a hierarchical clustering via average linkage method was conducted. Results were validated by qRT-PCR. Putative targeted pathways were obtained using DIANA miRPath online software. RESULTS: The results show a differential and unique miRNA expression profile of 121 miRNAs (p-value