Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort

Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis

Common variants in Alzheimer's disease and risk stratification by polygenic risk scores.

Funder: Funder: Fundación bancaria ‘La Caixa’ Number: LCF/PR/PR16/51110003 Funder: Grifols SA Number: LCF/PR/PR16/51110003 Funder: European Union/EFPIA Innovative Medicines Initiative Joint Number: 115975 Funder: JPco-fuND FP-829-029 Number: 733051061Genetic discoveries of Alzheimer's disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer's disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer's disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer's disease

Morbi-mortalidad de los tumores cerebrales

Available from Centro de Informacion y Documentacion Cientifica CINDOC. Joaquin Costa, 22. 28002 Madrid. SPAIN / CINDOC - Centro de Informaciòn y Documentaciòn CientìficaSIGLEESSpai

STAT3 labels a subpopulation of reactive astrocytes required for brain metastasis.

The brain microenvironment imposes a particularly intense selective pressure on metastasis-initiating cells, but successful metastases bypass this control through mechanisms that are poorly understood. Reactive astrocytes are key components of this microenvironment that confine brain metastasis without infiltrating the lesion. Here, we describe that brain metastatic cells induce and maintain the co-option of a pro-metastatic program driven by signal transducer and activator of transcription 3 (STAT3) in a subpopulation of reactive astrocytes surrounding metastatic lesions. These reactive astrocytes benefit metastatic cells by their modulatory effect on the innate and acquired immune system. In patients, active STAT3 in reactive astrocytes correlates with reduced survival from diagnosis of intracranial metastases. Blocking STAT3 signaling in reactive astrocytes reduces experimental brain metastasis from different primary tumor sources, even at advanced stages of colonization. We also show that a safe and orally bioavailable treatment that inhibits STAT3 exhibits significant antitumor effects in patients with advanced systemic disease that included brain metastasis. Responses to this therapy were notable in the central nervous system, where several complete responses were achieved. Given that brain metastasis causes substantial morbidity and mortality, our results identify a novel treatment for increasing survival in patients with secondary brain tumors.We want to thank the CNIO Core Facilities for their excellent assistance. We also thank F.X. Real, O. Marin, M. Serrano, O. Fernandez-Capetillo and M. Soengas for critically reading the manuscript, P. Bos for advice with CD8+ T cell experiments, J. Massague (MSKCC) for the BrM cell lines, MEDA for Legasil, M. A. Perez (University of Copenhagen), H. Peinado (CNIO), M. Soengas (CNIO) and M. Squatrito (CNIO) for reagents. This work was supported by MINECO grants MINECO-Retos SAF201457243-R (M.V.), MINECO-Europa Excelencia SAF2015-62547-ERC (M.V.), FERO Grant for Research in Oncology (M.V.), Melanoma Research Alliance Young Investigator Award (M.V.), AECC Coordinated Translational Groups (M.V., E.M.-S. and S.R.y.C), SEOM (J.B.-B.), Pfizer WI190764 (J.B.-B.), Meda Pharma (J.B.-B.), Armangue Family Fund (JA.M. and J.B.-B.), La Caixa-Severo Ochoa International PhD Program Fellowship (L.Z.), FCT PhD Fellowship SFRH/BD/100089/2014 (C.M.), the Fulbright Program (W.B.). M.V. is a Ramon y Cajal Investigator (RYC-2013-13365). This work is dedicated to the memory of Maria Jesus Cortes Garin.S

ICO amplicon NGS data analysis: a web tool for variant detection in common high-risk hereditary cancer genes analyzed by amplicon GS junior next-Generation Sequencing

Next-generation sequencing (NGS) has revolutionized genomic research and is set to have a major impact on genetic diagnostics thanks to the advent of benchtop sequencers and flexible kits for targeted libraries. Among the main hurdles in NGS are the difficulty of performing bioinformatic analysis of the huge volume of data generated and the high number of false positive calls that could be obtained, depending on the NGS technology and the analysis pipeline. Here, we present the development of a free and user-friendly Web data analysis tool that detects and filters sequence variants, provides coverage information, and allows the user to customize some basic parameters. The tool has been developed to provide accurate genetic analysis of targeted sequencing of common high-risk hereditary cancer genes using amplicon libraries run in a GS Junior System. The Web resource is linked to our own mutation database, to assist in the clinical classification of identified variants. We believe that this tool will greatly facilitate the use of the NGS approach in routine laboratories.Peer ReviewedPostprint (published version

ICO amplicon NGS data analysis: a web tool for variant detection in common high-risk hereditary cancer genes analyzed by amplicon GS junior next-Generation Sequencing

Next-generation sequencing (NGS) has revolutionized genomic research and is set to have a major impact on genetic diagnostics thanks to the advent of benchtop sequencers and flexible kits for targeted libraries. Among the main hurdles in NGS are the difficulty of performing bioinformatic analysis of the huge volume of data generated and the high number of false positive calls that could be obtained, depending on the NGS technology and the analysis pipeline. Here, we present the development of a free and user-friendly Web data analysis tool that detects and filters sequence variants, provides coverage information, and allows the user to customize some basic parameters. The tool has been developed to provide accurate genetic analysis of targeted sequencing of common high-risk hereditary cancer genes using amplicon libraries run in a GS Junior System. The Web resource is linked to our own mutation database, to assist in the clinical classification of identified variants. We believe that this tool will greatly facilitate the use of the NGS approach in routine laboratories.Peer Reviewe