benchdamic: benchmarking of differential abundance methods for microbiome data.

SUMMARY: Recently, an increasing number of methodological approaches have been proposed to tackle the complexity of metagenomics and microbiome data. In this scenario, reproducibility and replicability have become two critical issues, and the development of computational frameworks for the comparative evaluations of such methods is of utmost importance. Here, we present benchdamic, a Bioconductor package to benchmark methods for the identification of differentially abundant taxa. AVAILABILITY AND IMPLEMENTATION: benchdamic is available as an open-source R package through the Bioconductor project at https://bioconductor.org/packages/benchdamic/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A comprehensive investigation of histotype-specific microRNA and their variants in Stage I epithelial ovarian cancers.

isomiRs, the sequence-variants of microRNA, are known to be tissue and cell type specific but their physiological role is largely unknown. In our study, we explored for the first time the expression of isomiRs across different Stage I epithelial ovarian cancer (EOC) histological subtypes, in order to shed new light on their biological role in tumor growth and progression. In a multicentric retrospective cohort of tumor biopsies (n = 215) we sequenced small RNAs finding 971 expressed miRNAs, 64% of which are isomiRs. Among them, 42 isomiRs showed a clear histotype specific pattern, confirming our previously identified miRNA markers (miR192/194 and miR30a-3p/5p for mucinous and clear cell subtypes, respectively) and uncovering new biomarkers for all the five subtypes. Using integrative models, we found that the 38% of these miRNA expression alterations is the result of copy number variations while the 17% of differential transcriptional activities. Our work represents the first attempt to characterize isomiRs expression in Stage I EOC within and across subtypes and to contextualize their alterations in the framework of the large genomic heterogeneity of this tumor.

NewWave: a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA-seq data.

SUMMARY: We present NewWave, a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA sequencing data. To achieve scalability, NewWave uses mini-batch optimization and can work with out-of-memory data, enabling users to analyze datasets with millions of cells. AVAILABILITY AND IMPLEMENTATION: NewWave is implemented as an open-source R package available through the Bioconductor project at https://bioconductor.org/packages/NewWave/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The power of word-frequency-based alignment-free functions: a comprehensive large-scale experimental analysis.

MOTIVATION: Alignment-free (AF) distance/similarity functions are a key tool for sequence analysis. Experimental studies on real datasets abound and, to some extent, there are also studies regarding their control of false positive rate (Type I error). However, assessment of their power, i.e. their ability to identify true similarity, has been limited to some members of the D2 family. The corresponding experimental studies have concentrated on short sequences, a scenario no longer adequate for current applications, where sequence lengths may vary considerably. Such a State of the Art is methodologically problematic, since information regarding a key feature such as power is either missing or limited. RESULTS: By concentrating on a representative set of word-frequency-based AF functions, we perform the first coherent and uniform evaluation of the power, involving also Type I error for completeness. Two alternative models of important genomic features (CIS Regulatory Modules and Horizontal Gene Transfer), a wide range of sequence lengths from a few thousand to millions, and different values of k have been used. As a result, we provide a characterization of those AF functions that is novel and informative. Indeed, we identify weak and strong points of each function considered, which may be used as a guide to choose one for analysis tasks. Remarkably, of the 15 functions that we have considered, only four stand out, with small differences between small and short sequence length scenarios. Finally, to encourage the use of our methodology for validation of future AF functions, the Big Data platform supporting it is public. AVAILABILITY AND IMPLEMENTATION: The software is available at: https://github.com/pipp8/power_statistics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide profiling of patient-derived glioblastoma stem-like cells reveals recurrent genetic and transcriptomic signatures associated with brain tumors.

PURPOSE: Patient-derived cancer cell lines can be very useful to investigate genetic as well as epigenetic mechanisms of transformation and to test new drugs. In this multi-centric study, we performed genomic and transcriptomic characterization of a large set of patient-derived glioblastoma (GBM) stem-like cells (GSCs). METHODS: 94 (80 I surgery/14 II surgery) and 53 (42 I surgery/11 II surgery) GSCs lines underwent whole exome and trascriptome analysis, respectively. RESULTS: Exome sequencing revealed TP53 as the main mutated gene (41/94 samples, 44%), followed by PTEN (33/94, 35%), RB1 (16/94, 17%) and NF1 (15/94, 16%), among other genes associated to brain tumors. One GSC sample bearing a BRAF p.V600E mutation showed sensitivity in vitro to a BRAF inhibitor. Gene Ontology and Reactome analysis uncovered several biological processes mostly associated to gliogenesis and glial cell differentiation, S - adenosylmethionine metabolic process, mismatch repair and methylation. Comparison of I and II surgery samples disclosed a similar distribution of mutated genes, with an overrepresentation of mutations in mismatch repair, cell cycle, p53 and methylation pathways in I surgery samples, and of mutations in receptor tyrosine kinase and MAPK signaling pathways in II surgery samples. Unsupervised hierarchical clustering of RNA-seq data produced 3 clusters characterized by distinctive sets of up-regulated genes and signaling pathways. CONCLUSION: The availability of a large set of fully molecularly characterized GCSs represents a valuable public resource to support the advancement of precision oncology for the treatment of GBM.

A pan-cancer landscape of pathogenic somatic copy number variations.

OBJECTIVE: Copy number variations (CNVs) play crucial roles in physiological and pathological processes, including cancer. However, the functional implications of somatic CNVs in tumor progression and evolution remain unclear. This study focuses on identifying CNV alterations with high pathogenic potential that drive and sustain tumorigenesis, distinguishing them from passenger alterations that accumulate during tumor growth. Our goal is to explore the variability of CNVs across different tumor types and infer their impact on tumor cell functions. METHODS: Starting from 7352 copy number profiles across 33 different cancer types, we infer the pathogenicity of each CNV and perform both intra- and inter-tumor analyses to predict the functional impact of different genomic patterns. We evaluate the actionability of genes belonging to altered regions and we correlate the presence of pathogenic regions with genome instability patterns and patients' survival. RESULTS: Our analysis uncovered large heterogeneity among different tumors suggesting in many cases distinct genetic drivers of tumorigenesis. Recurrent genomic alterations frequently coincide with dysfunctional homologous recombination pathways and negative regulation of the immune system. In certain tumors, the number of pathogenic CNVs emerged as a prognostic biomarker, highlighting their significance in cancer progression. CONCLUSION: This study contributes to elucidate the functional impact of pathogenic CNVs in tumor progression and sheds light on their potential as prognostic markers in specific cancer types.

PsiNorm: a scalable normalization for single-cell RNA-seq data.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables transcriptome-wide gene expression measurements at single-cell resolution providing a comprehensive view of the compositions and dynamics of tissue and organism development. The evolution of scRNA-seq protocols has led to a dramatic increase of cells throughput, exacerbating many of the computational and statistical issues that previously arose for bulk sequencing. In particular, with scRNA-seq data all the analyses steps, including normalization, have become computationally intensive, both in terms of memory usage and computational time. In this perspective, new accurate methods able to scale efficiently are desirable. RESULTS: Here, we propose PsiNorm, a between-sample normalization method based on the power-law Pareto distribution parameter estimate. Here, we show that the Pareto distribution well resembles scRNA-seq data, especially those coming from platforms that use unique molecular identifiers. Motivated by this result, we implement PsiNorm, a simple and highly scalable normalization method. We benchmark PsiNorm against seven other methods in terms of cluster identification, concordance and computational resources required. We demonstrate that PsiNorm is among the top performing methods showing a good trade-off between accuracy and scalability. Moreover, PsiNorm does not need a reference, a characteristic that makes it useful in supervised classification settings, in which new out-of-sample data need to be normalized. AVAILABILITY AND IMPLEMENTATION: PsiNorm is implemented in the scone Bioconductor package and available at https://bioconductor.org/packages/scone/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Zebrafish Mutant Lines Reveal the Interplay between nr3c1 and nr3c2 in the GC-Dependent Regulation of Gene Transcription.

Glucocorticoids mainly exert their biological functions through their cognate receptor, encoded by the nr3c1 gene. Here, we analysed the glucocorticoids mechanism of action taking advantage of the availability of different zebrafish mutant lines for their receptor. The differences in gene expression patterns between the zebrafish gr knock-out and the grs357 mutant line, in which a point mutation prevents binding of the receptor to the hormone-responsive elements, reveal an intricate network of GC-dependent transcription. Particularly, we show that Stat3 transcriptional activity mainly relies on glucocorticoid receptor GR tethering activity: several Stat3 target genes are induced upon glucocorticoid GC exposure both in wild type and in grs357/s357 larvae, but not in gr knock-out zebrafish. To understand the interplay between GC, their receptor, and the mineralocorticoid receptor, which is evolutionarily and structurally related to the GR, we generated an mr knock-out line and observed that several GC-target genes also need a functional mineralocorticoid receptor MR to be correctly transcribed. All in all, zebrafish mutants and transgenic models allow in vivo analysis of GR transcriptional activities and interactions with other transcription factors such as MR and Stat3 in an in-depth and rapid way.

The CSF Profile Linked to Cortical Damage Predicts Multiple Sclerosis Activity.

OBJECTIVE: Intrathecal inflammation correlates with the grey matter damage since the early stages of multiple sclerosis (MS), but whether the cerebrospinal fluid (CSF) profile can help to identify patients at risk of disease activity is still unclear. METHODS: We evaluated the association between CSF levels of 18 cytokines, previously found to be associated to grey matter damage, and the disease activity, among 99 patients with relapsing-remitting MS, who underwent blinded clinical and 3 T magnetic resonance imaging (MRI) evaluations for 4 years. Groups with evidence of disease activity (EDA) or no evidence of disease activity (NEDA; occurrence of relapses, new white matter lesions, and Expanded Disability Status Scale [EDSS] change) were identified. Cortical lesions and the annualized cortical thinning were also evaluated. RESULTS: Forty-one patients experienced EDA and, compared to the NEDA group, had at diagnosis higher CSF levels of CXCL13, CXCL12, IFNγ, TNF, sCD163, LIGHT, and APRIL (p < 0.001). In the multivariate analysis, CXCL13 (hazard ratio [HR] = 1.35; p = 0.0002), LIGHT (HR = 1.22; p = 0.005) and APRIL (HR = 1.78; p = 0.0001) were the CSF molecules more strongly associated with the risk of EDA. The model, including CSF variables, predicted more accurately the occurrence of disease activity than the model with only clinical/MRI parameters (C-index at 4 years = 71% vs 44%). Finally, higher CSF levels of CXCL13 (ß = 4.7*10-4 ; p < 0.001), TNF (ß = 3.1*10-3 ; p = 0.004), LIGHT (ß = 2.6*10-4 ; p = 0.003), sCD163 (ß = 4.3*10-3 ; p = 0.009), and TWEAK (ß = 3.4*10-3 ; p = 0.024) were associated with more severe cortical thinning. INTERPRETATION: A specific CSF profile, mainly characterized by elevated levels of B-cell related cytokines, distinguishes patients at high risk of disease activity and severe cortical damage. The CSF analysis may allow stratifications of patients at diagnosis for optimizing therapeutic approaches. ANN NEUROL 2020;88:562-573.

A novel prognostic score to assess the risk of progression in relapsing-remitting multiple sclerosis patients.

BACKGROUND: At the patient level, the prognostic value of several features that are known to be associated with an increased risk of converting from relapsing-remitting (RR) to secondary phase (SP) multiple sclerosis (MS) remains limited. METHODS: Among 262 RRMS patients followed up for 10 years, we assessed the probability of developing the SP course based on clinical and conventional and non-conventional magnetic resonance imaging (MRI) parameters at diagnosis and after 2 years. We used a machine learning method, the random survival forests, to identify, according to their minimal depth (MD), the most predictive factors associated with the risk of SP conversion, which were then combined to compute the secondary progressive risk score (SP-RiSc). RESULTS: During the observation period, 69 (26%) patients converted to SPMS. The number of cortical lesions (MD = 2.47) and age (MD = 3.30) at diagnosis, the global cortical thinning (MD = 1.65), the cerebellar cortical volume loss (MD = 2.15) and the cortical lesion load increase (MD = 3.15) over the first 2 years exerted the greatest predictive effect. Three patients' risk groups were identified; in the high-risk group, 85% (46/55) of patients entered the SP phase in 7 median years. The SP-RiSc optimal cut-off estimated was 17.7 showing specificity and sensitivity of 87% and 92%, respectively, and overall accuracy of 88%. CONCLUSIONS: The SP-RiSc yielded a high performance in identifying MS patients with high probability to develop SPMS, which can help improve management strategies. These findings are the premise of further larger prospective studies to assess its use in clinical settings.

MOSClip: multi-omic and survival pathway analysis for the identification of survival associated gene and modules.

Survival analyses of gene expression data has been a useful and widely used approach in clinical applications. But, in complex diseases, such as cancer, the identification of survival-associated cell processes - rather than single genes - provides more informative results because the efficacy of survival prediction increases when multiple prognostic features are combined to enlarge the possibility of having druggable targets. Moreover, genome-wide screening in molecular medicine has rapidly grown, providing not only gene expression but also multi-omic measurements such as DNA mutations, methylation, expression, and copy number data. In cancer, virtually all these aberrations can contribute in synergy to pathological processes, and their measurements can improve a patient's outcome and help in diagnosis and treatment decisions. Here, we present MOSClip, an R package implementing a new topological pathway analysis tool able to integrate multi-omic data and look for survival-associated gene modules. MOSClip tests the survival association of dimensionality-reduced multi-omic data using multivariate models, providing graphical devices for management, browsing and interpretation of results. Using simulated data we evaluated MOSClip performance in terms of false positives and false negatives in different settings, while the TCGA ovarian cancer dataset is used as a case study to highlight MOSClip's potential.

Single cell analysis reveals the involvement of the long non-coding RNA Pvt1 in the modulation of muscle atrophy and mitochondrial network.

Long non-coding RNAs (lncRNAs) are emerging as important players in the regulation of several aspects of cellular biology. For a better comprehension of their function, it is fundamental to determine their tissue or cell specificity and to identify their subcellular localization. In fact, the activity of lncRNAs may vary according to cell and tissue specificity and subcellular compartmentalization. Myofibers are the smallest complete contractile system of skeletal muscle influencing its contraction velocity and metabolism. How lncRNAs are expressed in different myofibers, participate in metabolism regulation and muscle atrophy or how they are compartmentalized within a single myofiber is still unknown. We compiled a comprehensive catalog of lncRNAs expressed in skeletal muscle, associating the fiber-type specificity and subcellular location to each of them, and demonstrating that many lncRNAs can be involved in the biological processes de-regulated during muscle atrophy. We demonstrated that the lncRNA Pvt1, activated early during muscle atrophy, impacts mitochondrial respiration and morphology and affects mito/autophagy, apoptosis and myofiber size in vivo. This work corroborates the importance of lncRNAs in the regulation of metabolism and neuromuscular pathologies and offers a valuable resource to study the metabolism in single cells characterized by pronounced plasticity.

Expression profiles of PRKG1, SDF2L1 and PPP1R12A are predictive and prognostic factors for therapy response and survival in high-grade serous ovarian cancer.

High-grade serous ovarian cancer (HGS-EOCs) is generally sensitive to front-line platinum (Pt)-based chemotherapy although most patients at an advanced stage relapse with progressive resistant disease. Clinical or molecular data to identify primary resistant cases at diagnosis are not yet available. HGS-EOC biopsies from 105 Pt-sensitive (Pt-s) and 89 Pt-resistant (Pt-r) patients were retrospectively selected from two independent tumor tissue collections. Pathway analysis was done integrating miRNA and mRNA expression profiles. Signatures were further validated in silico on a cohort of 838 HGS-EOC cases from a published dataset. In all, 131 mRNAs and 5 miRNAs belonging to different functionally related molecular pathways distinguish Pt-s from Pt-r cases. Then, 17 out of 23 selected elements were validated by orthogonal approaches (SI signature). As resistance to Pt is associated with a short progression-free survival (PFS) and overall survival (OS), the prognostic role of the SI signature was assessed, and 14 genes associated with PFS and OS, in multivariate analyses (SII signature). The prognostic value of the SII signature was validated in a third extensive cohort. The expression profiles of SDF2L1, PPP1R12A and PRKG1 genes (SIII signature) served as independent prognostic biomarkers of Pt-response and survival. The study identified a prognostic molecular signature based on the combined expression profile of three genes which had never been associated with the clinical outcome of HGS-EOC. This may lead to early identification, at the time of diagnosis, of patients who would not greatly benefit from standard chemotherapy and are thus eligible for novel investigational approaches.

metaGraphite-a new layer of pathway annotation to get metabolite networks.

MOTIVATION: Metabolomics is an emerging 'omics' science involving the characterization of metabolites and metabolism in biological systems. Few bioinformatic tools have been developed for the visualization, exploration and analysis of metabolomic data within the context of metabolic pathways: some of them became rapidly obsolete and are no longer supported, others are based on a single database. A systematic collection of existing annotations has the potential of considerably boosting the investigation and contextualization of metabolomic measurements. RESULTS: We have released a major update of our Bioconductor package graphite which explicitly tracks small molecules within pathway topologies and their interactions with proteins. The package gathers the information stored in eight major databases, oriented both at genes and at metabolites, across 14 different species. Depending on user preferences, all pathways can be retrieved as gene-only, gene metabolite or metabolite-only networks. AVAILABILITY AND IMPLEMENTATION: The new graphite version (1.24) is available on Bioconductor. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A TGF-ß associated genetic score to define prognosis and platinum sensitivity in advanced epithelial ovarian cancer.

OBJECTIVE: Epithelial ovarian cancer (EOC) is usually diagnosed at advanced stages with highly variable clinical outcomes, even among patients with similar clinical characteristics and treatments. Host immune system plays a pivotal role in EOC pathogenesis and progression. Here, we assessed the clinical significance of 192 single nucleotide polymorphisms (SNPs) on 34 immune-system related genes in EOC patients. METHODS: Two hundred and thirty advanced EOC patients treated with platinum-based chemotherapy were included. Germ-line DNA was analyzed with Illumina GoldenGate Genotyping Assay. RESULTS: Nineteen polymorphisms were significantly associated with overall survival (OS), 17 with progression free survival (PFS) and 20 with platinum-free interval (PFI). Of the 8 polymorphisms associated with all three outcomes, 7 SNPs belonged to genes involved in the TGF-ß pathway. A genetic score was built considering the unfavourable genotypes (UGs) of these 7 polymorphisms (group 0-2 UGs: presence of 0, 1, or 2 UGs; group 3-4 UGs: 3 or 4 UGs; group 5-7: 5, 6, or 7 UGs). According to this score, OS decreased as the number of UGs increased (median OS: 0-2 UGs = not reached, 3-4 UGs = 44.6 and 5-7 UGs = 19.3 months, p < 0.0001). The same trend was observed also for PFS (median PFS: 0-2 UGs = 21.5, 3-4 UGs = 17.3 and 5-7 UGs = 11 months, p < 0.0001) and PFI (median PFI: 0-2 UGs = 16.6, 3-4 UGs = 9.8 and 5-7 UGs = 3.8 months, p < 0.0001). The score was validated by permutation analysis. CONCLUSIONS: The proposed TGF-ß pathway score could be useful to define prognosis and platinum sensitivity of advanced EOC patients.

SourceSet: A graphical model approach to identify primary genes in perturbed biological pathways.

Topological gene-set analysis has emerged as a powerful means for omic data interpretation. Although numerous methods for identifying dysregulated genes have been proposed, few of them aim to distinguish genes that are the real source of perturbation from those that merely respond to the signal dysregulation. Here, we propose a new method, called SourceSet, able to distinguish between the primary and the secondary dysregulation within a Gaussian graphical model context. The proposed method compares gene expression profiles in the control and in the perturbed condition and detects the differences in both the mean and the covariance parameters with a series of likelihood ratio tests. The resulting evidence is used to infer the primary and the secondary set, i.e. the genes responsible for the primary dysregulation, and the genes affected by the perturbation through network propagation. The proposed method demonstrates high specificity and sensitivity in different simulated scenarios and on several real biological case studies. In order to fit into the more traditional pathway analysis framework, SourceSet R package also extends the analysis from a single to multiple pathways and provides several graphical outputs, including Cytoscape visualization to browse the results.

Multisite analysis of high-grade serous epithelial ovarian cancers identifies genomic regions of focal and recurrent copy number alteration in 3q26.2 and 8q24.3.

High-grade serous epithelial ovarian cancer (HGS-EOC) is a systemic disease, with marked intra and interpatient tumor heterogeneity. The issue of spatial and temporal heterogeneity has long been overlooked, hampering the possibility to identify those genomic alterations that persist, before and after therapy, in the genome of all tumor cells across the different anatomical districts. This knowledge is the first step to clarify those molecular determinants that characterize the tumor biology of HGS-EOC and their route toward malignancy. In our study, -omics data were generated from 79 snap frozen matched tumor biopsies, withdrawn before and after chemotherapy from 24 HGS-EOC patients, gathered together from independent cohorts. The landscape of somatic copy number alterations depicts a more homogenous and stable genomic portrait than the single nucleotide variant profile. Genomic identification of significant targets in cancer analysis identified two focal and minimal common regions (FMCRs) of amplification in the cytoband 3q26.2 (region α, 193 kb long) and 8q24.3 (region ß, 495 kb long). Analysis in two external databases confirmed regions α and ß are features of HGS-EOC. The MECOM gene is located in region α, and 15 genes are in region ß. No functional data are yet available for the genes in the ß region. In conclusion, we have identified for the first time two FMCRs of amplification in HGS-EOC, opening up a potential biological role in its etiopathogenesis.

FXYD5 (Dysadherin) upregulation predicts shorter survival and reveals platinum resistance in high-grade serous ovarian cancer patients.

BACKGROUND: High-grade serous ovarian carcinoma (HGSOC) is generally associated with a very dismal prognosis. Nevertheless, patients with similar clinicopathological characteristics can have markedly different clinical outcomes. Our aim was the identification of novel molecular determinants influencing survival. METHODS: Gene expression profiles of extreme HGSOC survivors (training set) were obtained by microarray. Differentially expressed genes (DEGs) and enriched signalling pathways were determined. A prognostic signature was generated and validated on curatedOvarianData database through a meta-analysis approach. The best prognostic biomarker from the signature was confirmed by RT-qPCR and by immunohistochemistry on an independent validation set. Cox regression model was chosen for survival analysis. RESULTS: Eighty DEGs and the extracellular matrix-receptor (ECM-receptor) interaction pathway were associated to extreme survival. A 10-gene prognostic signature able to correctly classify patients with 98% of accuracy was identified. By an 'in-silico' meta-analysis, overexpression of FXYD domain-containing ion transport regulator 5 (FXYD5), also known as dysadherin, was confirmed in HGSOC short-term survivors compared to long-term ones. Its prognostic and predictive power was then successfully validated, both at mRNA and protein level, first on training than on validation sample set. CONCLUSION: We demonstrated the possible involvement of FXYD5 and ECM-receptor interaction signal pathway in HCSOC survival and prognosis.

Inflammatory intrathecal profiles and cortical damage in multiple sclerosis.

OBJECTIVE: Gray matter (GM) damage and meningeal inflammation have been associated with early disease onset and a more aggressive disease course in multiple sclerosis (MS), but can these changes be identified in the patient early in the disease course? METHODS: To identify possible biomarkers linking meningeal inflammation, GM damage, and disease severity, gene and protein expression were analyzed in meninges and cerebrospinal fluid (CSF) from 27 postmortem secondary progressive MS and 14 control cases. Combined cytokine/chemokine CSF profiling and 3T magnetic resonance imaging (MRI) were performed at diagnosis in 2 independent cohorts of MS patients (35 and 38 subjects) and in 26 non-MS patients. RESULTS: Increased expression of proinflammatory cytokines (IFNγ, TNF, IL2, and IL22) and molecules related to sustained B-cell activity and lymphoid-neogenesis (CXCL13, CXCL10, LTα, IL6, and IL10) was detected in the meninges and CSF of postmortem MS cases with high levels of meningeal inflammation and GM demyelination. Similar proinflammatory patterns, including increased levels of CXCL13, TNF, IFNγ, CXCL12, IL6, IL8, and IL10, together with high levels of BAFF, APRIL, LIGHT, TWEAK, sTNFR1, sCD163, MMP2, and pentraxin III, were detected in the CSF of MS patients with higher levels of GM damage at diagnosis. INTERPRETATION: A common pattern of intrathecal (meninges and CSF) inflammatory profile strongly correlates with increased cortical pathology, both at the time of diagnosis and at death. These results suggest a role for detailed CSF analysis combined with MRI as a prognostic marker for more aggressive MS. Ann Neurol 2018 Ann Neurol 2018;83:739-755.

rs4143815-PDL1, a New Potential Immunogenetic Biomarker of Biochemical Recurrence in Locally Advanced Prostate Cancer after Radiotherapy.

Up to 30-50% of patients with locally advanced prostate cancer (PCa) undergoing radiotherapy (RT) experience biochemical recurrence (BCR). The immune system affects the RT response. Immunogenetics could define new biomarkers for personalization of PCa patients' treatment. The aim of this study is to define the immunogenetic biomarkers of 10 year BCR (primary aim), 10 year overall survival (OS) and 5 year BCR (secondary aims). In this mono-institutional retrospective study, 549 Caucasian patients (a discovery set n = 418; a replication set n = 131) were affected by locally advanced PCa and homogeneously treated with RT. In the training set, associations were made between 447 SNPs in 77 genes of the immune system; and 10 year BCR and 10 year OS were tested through a multivariate Cox proportional hazard model. Significant SNPs (p-value < 0.05, q-value < 0.15) were analyzed in the replication set. Replicated SNPs were tested for 5 year BCR in both sets of patients. A polymorphism in the PDL1 gene (rs4143815) was the unique potential genetic variant of 10 year BCR (training set: p = 0.003, HR (95% CI) = 0.58 (0.41-0.83); replication set: p = 0.063, HR (95% CI) = 0.52 (0.26-1.04)) that was significantly associated with 5 year BCR (training set: p = 0.009, HR (95% CI) = 0.59 (0.40-0.88); replication set: p = 0.036, HR (95% CI) = 0.39 (0.16-0.94)). No biomarkers of OS were replicated. rs4143815-PDL1 arose as a new immunogenetic biomarker of BCR in PCa, giving new insights into the RT/immune system interaction, which could be potentially useful in new approaches using anti-PDL1 therapies for PCa.