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Abstract We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

Abstract Background The growing understanding of cancer biology and the establishment of new treatment modalities has not yielded the expected results in terms of survival for Laryngeal Squamous Cell Cancer (LSCC). Early diagnosis, as well as prompt identification of patients with high risk of relapse would ensure greater chance of therapeutic success. However, this goal remains a challenge due to the absence of specific biomarkers for this neoplasm. Methods Serum samples from 45 LSCC patients and 23 healthy donors were collected for miRNA expression profiling by TaqMan Array analysis. Additional 20 patients and 42 healthy volunteers were included for the validation set, reaching an equal number of clinical samples for each group. The potential diagnostic ability of the such identified three-miRNA signature was confirmed by ROC analysis. Moreover, each miRNA was analyzed for the possible correlation with HNSCC patients’ survival and TNM status by online databases Kaplan–Meier (KM) plotter and OncomiR. In silico analysis of common candidate targets and their network relevance to predict shared biological functions was finally performed by PANTHER and GeneMANIA software. Results We characterized serum miRNA profile of LSCC patients identifying a novel molecular signature, including miR-223, miR-93 and miR-532, as circulating marker endowed with high selectivity and specificity. The oncogenic effect and the prognostic significance of each miRNA was investigated by bioinformatic analysis, denoting significant correlation with OS. To analyse the molecular basis underlying the pro-tumorigenic role of the signature, we focused on the simultaneously regulated gene targets—IL6ST, GTDC1, MAP1B, CPEB3, PRKACB, NFIB, PURB, ATP2B1, ZNF148, PSD3, TBC1D15, PURA, KLF12—found by prediction tools and deepened for their functional role by pathway enrichment analysis. The results showed the involvement of 7 different biological processes, among which inflammation, proliferation, migration, apoptosis and angiogenesis. Conclusions In conclusion, we have identified a possible miRNA signature for early LSCC diagnosis and we assumed that miR-93, miR-223 and miR-532 could orchestrate the regulation of multiple cancer-related processes. These findings encourage the possibility to deepen the molecular mechanisms underlying their oncogenic role, for the desirable development of novel therapeutic opportunities based on the use of short single-stranded oligonucleotides acting as non-coding RNA antagonists in cancer.

Abstract Background Inflammatory breast cancer (IBC) is the most pro-metastatic form of BC. Better understanding of its enigmatic pathophysiology is crucial. We report here the largest whole-exome sequencing (WES) study of clinical IBC samples. Methods We retrospectively applied WES to 54 untreated IBC primary tumor samples and matched normal DNA. The comparator samples were 102 stage-matched non-IBC samples from TCGA. We compared the somatic mutational profiles, spectra and signatures, copy number alterations (CNAs), HRD and heterogeneity scores, and frequencies of actionable genomic alterations (AGAs) between IBCs and non-IBCs. The comparisons were adjusted for the molecular subtypes. Results The number of somatic mutations, TMB, and mutational spectra were not different between IBCs and non-IBCs, and no gene was differentially mutated or showed differential frequency of CNAs. Among the COSMIC signatures, only the age-related signature was more frequent in non-IBCs than in IBCs. We also identified in IBCs two new mutational signatures not associated with any environmental exposure, one of them having been previously related to HIF pathway activation. Overall, the HRD score was not different between both groups, but was higher in TN IBCs than TN non-IBCs. IBCs were less frequently classified as heterogeneous according to heterogeneity H-index than non-IBCs (21% vs 33%), and clonal mutations were more frequent and subclonal mutations less frequent in IBCs. More than 50% of patients with IBC harbored at least one high-level of evidence (LOE) AGA (OncoKB LOE 1–2, ESCAT LOE I–II), similarly to patients with non-IBC. Conclusions We provide the largest mutational landscape of IBC. Only a few subtle differences were identified with non-IBCs. The most clinically relevant one was the higher HRD score in TN IBCs than in TN non-IBCs, whereas the most intriguing one was the smaller intratumor heterogeneity of IBCs.

Abstract Background Osteoarthritis (OA) is a multifactorial, hypertrophic, and degenerative condition involving the whole joint and affecting a high percentage of middle-aged people. It is due to a combination of factors, although the pivotal mechanisms underlying the disease are still obscure. Moreover, current treatments are still poorly effective, and patients experience a painful and degenerative disease course. Methods We used an integrative approach that led us to extract a consensus signature from a meta-analysis of three different OA cohorts. We performed a network-based drug prioritization to detect the most relevant drugs targeting these genes and validated in vitro the most promising candidates. We also proposed a risk score based on a minimal set of genes to predict the OA clinical stage from RNA-Seq data. Results We derived a consensus signature of 44 genes that we validated on an independent dataset. Using network analysis, we identified Resveratrol, Tenoxicam, Benzbromarone, Pirinixic Acid, and Mesalazine as putative drugs of interest for therapeutics in OA for anti-inflammatory properties. We also derived a list of seven gene-targets validated with functional RT-qPCR assays, confirming the in silico predictions. Finally, we identified a predictive subset of genes composed of DNER, TNFSF11, THBS3, LOXL3, TSPAN2, DYSF, ASPN and HTRA1 to compute the patient’s risk score. We validated this risk score on an independent dataset with a high AUC (0.875) and compared it with the same approach computed using the entire consensus signature (AUC 0.922). Conclusions The consensus signature highlights crucial mechanisms for disease progression. Moreover, these genes were associated with several candidate drugs that could represent potential innovative therapeutics. Furthermore, the patient’s risk scores can be used in clinical settings.

Abstract Background Glioblastoma multiforme (GBM) is a highly aggressive primary brain tumor, that is refractory to standard treatment and to immunotherapy with immune-checkpoint inhibitors (ICI). Noteworthy, melanoma brain metastases (MM-BM), that share the same niche as GBM, frequently respond to current ICI therapies. Epigenetic modifications regulate GBM cellular proliferation, invasion, and prognosis and may negatively regulate the cross-talk between malignant cells and immune cells in the tumor milieu, likely contributing to limit the efficacy of ICI therapy of GBM. Thus, manipulating the tumor epigenome can be considered a therapeutic opportunity in GBM. Methods Microarray transcriptional and methylation profiles, followed by gene set enrichment and IPA analyses, were performed to study the differences in the constitutive expression profiles of GBM vs MM-BM cells, compared to the extracranial MM cells and to investigate the modulatory effects of the DNA hypomethylating agent (DHA) guadecitabine among the different tumor cells. The prognostic relevance of DHA-modulated genes was tested by Cox analysis in a TCGA GBM patients’ cohort. Results The most striking differences between GBM and MM-BM cells were found to be the enrichment of biological processes associated with tumor growth, invasion, and extravasation with the inhibition of MHC class II antigen processing/presentation in GBM cells. Treatment with guadecitabine reduced these biological differences, shaping GBM cells towards a more immunogenic phenotype. Indeed, in GBM cells, promoter hypomethylation by guadecitabine led to the up-regulation of genes mainly associated with activation, proliferation, and migration of T and B cells and with MHC class II antigen processing/presentation. Among DHA-modulated genes in GBM, 7.6% showed a significant prognostic relevance. Moreover, a large set of immune-related upstream-regulators (URs) were commonly modulated by DHA in GBM, MM-BM, and MM cells: DHA-activated URs enriched for biological processes mainly involved in the regulation of cytokines and chemokines production, inflammatory response, and in Type I/II/III IFN-mediated signaling; conversely, DHA-inhibited URs were involved in metabolic and proliferative pathways. Conclusions Epigenetic remodeling by guadecitabine represents a promising strategy to increase the efficacy of cancer immunotherapy of GBM, supporting the rationale to develop new epigenetic-based immunotherapeutic approaches for the treatment of this still highly deadly disease.

Abstract Background Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti–PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. Methods Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. Results A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression–based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. Conclusions This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. Trial registration: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Abstract Glioblastoma (GBM) comprises 45.6% of all primary malignant brain cancers and is one of the most common and aggressive intracranial tumors in adults. Intratumoral heterogeneity with a wide range of proteomic, genetic, and epigenetic dysregulation contributes to treatment resistance and poor prognosis, thus demanding novel therapeutic approaches. To date, numerous clinical trials have been developed to target the proteome and epigenome of high-grade gliomas with promising results. However, studying RNA modifications, or RNA epitranscriptomics, is a new frontier within neuro-oncology. RNA epitranscriptomics was discovered in the 1970s, but in the last decade, the extent of modification of mRNA and various non-coding RNAs has emerged and been implicated in transposable element activation and many other oncogenic processes within the tumor microenvironment. This review provides background information and discusses the therapeutic potential of agents modulating epitranscriptomics in high-grade gliomas. A particular emphasis will be placed on how combination therapies that include immune agents targeting hERV-mediated viral mimicry could improve the treatment of GBM.

MicroRNAs (miRNAs) are involved in post-transcriptional gene expression regulation and in mechanisms of cancer growth and metastases. In this light, miRNAs could be promising therapeutic targets and biomarkers in clinical practice. Therefore, we investigated if specific miRNAs and their target genes contribute to laryngeal squamous cell carcinoma (LSCC) development. We found a significant decrease of miR-449a in LSCC patients with nodal metastases (63.3%) compared with patients without nodal involvement (44%). The AmpliSeq Transcriptome of HNO-210 miR-449a-transfected cell lines allowed the identification of IL6-R as a potential target. Moreover, the downregulation of IL6-R and the phosphorylation reduction of the downstream signaling effectors, suggested the inhibition of the IL-6 trans-signaling pathway. These biochemical effects were paralleled by a significant inhibition of invasion and migration in vitro and in vivo, supporting an involvement of epithelial-mesenchymal transition. These findings indicate that miR-449a contributes to suppress the metastasization of LSCC by the IL-6 trans-signaling block and affects sensitivity to external stimuli that mimic pro-inflammatory conditions.

Background and objectiveGlioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome.MethodsWe proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity.ResultsWSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes.ConclusionsThis study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.

Abstract Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.

Abstract Association with hypomethylating agents is a promising strategy to improve the efficacy of immune checkpoint inhibitors-based therapy. The NIBIT-M4 was a phase Ib, dose-escalation trial in patients with advanced melanoma of the hypomethylating agent guadecitabine combined with the anti-CTLA-4 antibody ipilimumab that followed a traditional 3 + 3 design (NCT02608437). Patients received guadecitabine 30, 45 or 60 mg/m2/day subcutaneously on days 1 to 5 every 3 weeks starting on week 0 for a total of four cycles, and ipilimumab 3 mg/kg intravenously starting on day 1 of week 1 every 3 weeks for a total of four cycles. Primary outcomes of safety, tolerability, and maximum tolerated dose of treatment were previously reported. Here we report the 5-year clinical outcome for the secondary endpoints of overall survival, progression free survival, and duration of response, and an exploratory integrated multi-omics analysis on pre- and on-treatment tumor biopsies. With a minimum follow-up of 45 months, the 5-year overall survival rate was 28.9% and the median duration of response was 20.6 months. Re-expression of immuno-modulatory endogenous retroviruses and of other repetitive elements, and a mechanistic signature of guadecitabine are associated with response. Integration of a genetic immunoediting index with an adaptive immunity signature stratifies patients/lesions into four distinct subsets and discriminates 5-year overall survival and progression free survival. These results suggest that coupling genetic immunoediting with activation of adaptive immunity is a relevant requisite for achieving long term clinical benefit by epigenetic immunomodulation in advanced melanoma patients.

Abstract Background Anti-PD1/PDL1 immune checkpoint inhibitors (ICI) transformed the prognosis of patients with advanced non-small cell lung cancer (NSCLC). However, the response rate remains disappointing and toxicity may be life-threatening, making urgent identification of biomarkers predictive for efficacy. Immunologic Constant of Rejection signature (ICR) is a 20-gene expression signature of cytotoxic immune response with prognostic value in some solid cancers. Our objective was to assess its predictive value for benefit from anti-PD1/PDL1 in patients with advanced NSCLC. Methods We retrospectively profiled 44 primary tumors derived from NSCLC patients treated with ICI as single-agent in at least the second-line metastatic setting. Transcriptomic analysis was performed using the nCounter® analysis system and the PanCancer Immune Profiling Panel. We then pooled our data with clinico-biological data from four public gene expression data sets, leading to a total of 162 NSCLC patients treated with single-agent anti-PD1/PDL1. ICR was applied to all samples and correlation was searched between ICR classes and the Durable Clinical Benefit (DCB), defined as stable disease or objective response according to RECIST 1.1 for a minimum of 6 months after the start of ICI. Results The DCB rate was 29%; 22% of samples were classified as ICR1, 30% ICR2, 22% ICR3, and 26% ICR4. These classes were not associated with the clinico-pathological variables, but showed enrichment from ICR1 to ICR4 in quantitative/qualitative markers of immune response. ICR2-4 class was associated with a 5.65-fold DCB rate when compared with ICR1 class. In multivariate analysis, ICR classification remained associated with DCB, independently from PDL1 expression and other predictive immune signatures. By contrast, it was not associated with disease-free survival in 556 NSCLC TCGA patients untreated with ICI. Conclusion The 20-gene ICR signature was independently associated with benefit from anti-PD1/PDL1 ICI in patients with advanced NSCLC. Validation in larger retrospective and prospective series is warranted.

Abstract Background Tumor invasiveness reflects numerous biological changes, including tumorigenesis, progression, and metastasis. To decipher the role of transcriptional regulators (TR) involved in tumor invasiveness, we performed a systematic network-based pan-cancer assessment of master regulators of cancer invasiveness. Materials and methods We stratified patients in The Cancer Genome Atlas (TCGA) into invasiveness high (INV-H) and low (INV-L) groups using consensus clustering based on an established robust 24-gene signature to determine the prognostic association of invasiveness with overall survival (OS) across 32 different cancers. We devise a network-based protocol to identify TRs as master regulators (MRs) unique to INV-H and INV-L phenotypes. We validated the activity of MRs coherently associated with INV-H phenotype and worse OS across cancers in TCGA on a series of additional datasets in the Prediction of Clinical Outcomes from the Genomic Profiles (PRECOG) repository. Results Based on the 24-gene signature, we defined the invasiveness score for each patient sample and stratified patients into INV-H and INV-L clusters. We observed that invasiveness was associated with worse survival outcomes in almost all cancers and had a significant association with OS in ten out of 32 cancers. Our network-based framework identified common invasiveness-associated MRs specific to INV-H and INV-L groups across the ten prognostic cancers, including COL1A1, which is also part of the 24-gene signature, thus acting as a positive control. Downstream pathway analysis of MRs specific to INV-H phenotype resulted in the identification of several enriched pathways, including Epithelial into Mesenchymal Transition, TGF-β signaling pathway, regulation of Toll-like receptors, cytokines, and inflammatory response, and selective expression of chemokine receptors during T-cell polarization. Most of these pathways have connotations of inflammatory immune response and feasibility for metastasis. Conclusion Our pan-cancer study provides a comprehensive master regulator analysis of tumor invasiveness and can suggest more precise therapeutic strategies by targeting the identified MRs and downstream enriched pathways for patients across multiple cancers. Graphical Abstract

The inference of clonal architectures in cancer using single-cell RNA-seq data remains challenging. Here, the authors develop SCEVAN, a variational algorithm for copy number-based clonal structure inference in single-cell RNA-seq data that can characterise evolution and heterogeneity in the tumour and the microenvironment.

Abstract Background Somatic alterations in cancer cause dysregulation of signaling pathways that control cell-cycle progression, apoptosis, and cell growth. The effect of individual alterations in these pathways differs between individual tumors and tumor types. Recognizing driver events is a complex task requiring integrating multiple molecular data, including genomics, epigenomics, and functional genomics. A common hypothesis is that these driver events share similar effects on the hallmarks of cancer. The availability of large-scale multi-omics studies allows for inferring these common effects from data. Once these effects are known, one can then deconvolve in every individual patient whether a given genomics alteration is a driver event. Methods Here, we develop a novel data-driven approach to identify shared oncogenic expression signatures among tumors. We aim to identify gene onco-signature for classifying tumor patients in homogeneous subclasses with distinct prognoses and specific genomic alterations. We derive expression pan-cancer onco-signatures from TCGA gene expression data using a discovery set of 9107 primary pan-tumor samples together with respective matched mutational data and a list of known cancer-related genes from COSMIC database. Results We use the derived ono-signatures to state their prognostic significance and apply them to the TCGA breast cancer dataset as proof of principle of our approach. We uncover a “mitochondrial” sub-group of Luminal patients characterized by its biological features and regulated by specific genetic modulators. Collectively, our results demonstrate the effectiveness of onco-signatures-based methodologies, and they also contribute to a comprehensive understanding of the metabolic heterogeneity of Luminal tumors. Conclusions These findings provide novel genomics evidence for developing personalized breast cancer patient treatments. The onco-signature approach, demonstrated here on breast cancer, is general and can be applied to other cancer types.

Abstract Accurately identifying somatic mutations is essential for precision oncology and crucial for calculating tumor-mutational burden (TMB), an important predictor of response to immunotherapy. For tumor-only variant calling (i.e., when the cancer biopsy but not the patient’s normal tissue sample is sequenced), accurately distinguishing somatic mutations from germline variants is a challenging problem that, when unaddressed, results in unreliable, biased, and inflated TMB estimates. Here, we apply machine learning to the task of somatic vs germline classification in tumor-only solid tumor samples using TabNet, XGBoost, and LightGBM, three machine-learning models for tabular data. We constructed a training set for supervised classification using features derived exclusively from tumor-only variant calling and drawing somatic and germline truth labels from an independent pipeline using the patient-matched normal samples. All three trained models achieved state-of-the-art performance on two holdout test datasets: a TCGA dataset including sarcoma, breast adenocarcinoma, and endometrial carcinoma samples (AUC > 94%), and a metastatic melanoma dataset (AUC > 85%). Concordance between matched-normal and tumor-only TMB improves from R 2 = 0.006 to 0.71–0.76 with the addition of a machine-learning classifier, with LightGBM performing best. Notably, these machine-learning models generalize across cancer subtypes and capture kits with a call rate of 100%. We reproduce the recent finding that tumor-only TMB estimates for Black patients are extremely inflated relative to that of white patients due to the racial biases of germline databases. We show that our approach with XGBoost and LightGBM eliminates this significant racial bias in tumor-only variant calling.

Abstract Background Improvement of efficacy of immune checkpoint blockade (ICB) remains a major clinical goal. Association of ICB with immunomodulatory epigenetic drugs is an option. However, epigenetic inhibitors show a heterogeneous landscape of activities. Analysis of transcriptional programs induced in neoplastic cells by distinct classes of epigenetic drugs may foster identification of the most promising agents. Methods Melanoma cell lines, characterized for mutational and differentiation profile, were treated with inhibitors of DNA methyltransferases (guadecitabine), histone deacetylases (givinostat), BET proteins (JQ1 and OTX-015), and enhancer of zeste homolog 2 (GSK126). Modulatory effects of epigenetic drugs were evaluated at the gene and protein levels. Master molecules explaining changes in gene expression were identified by Upstream Regulator (UR) analysis. Gene set enrichment and IPA were used respectively to test modulation of guadecitabine-specific gene and UR signatures in baseline and on-treatment tumor biopsies from melanoma patients in the Phase Ib NIBIT-M4 Guadecitabine + Ipilimumab Trial. Prognostic significance of drug-specific immune-related genes was tested with Timer 2.0 in TCGA tumor datasets. Results Epigenetic drugs induced different profiles of gene expression in melanoma cell lines. Immune-related genes were frequently upregulated by guadecitabine, irrespective of the mutational and differentiation profiles of the melanoma cell lines, to a lesser extent by givinostat, but mostly downregulated by JQ1 and OTX-015. GSK126 was the least active drug. Quantitative western blot analysis confirmed drug-specific modulatory profiles. Most of the guadecitabine-specific signature genes were upregulated in on-treatment NIBIT-M4 tumor biopsies, but not in on-treatment lesions of patients treated only with ipilimumab. A guadecitabine-specific UR signature, containing activated molecules of the TLR, NF-kB, and IFN innate immunity pathways, was induced in drug-treated melanoma, mesothelioma and hepatocarcinoma cell lines and in a human melanoma xenograft model. Activation of guadecitabine-specific UR signature molecules in on-treatment tumor biopsies discriminated responding from non-responding NIBIT-M4 patients. Sixty-five % of the immune-related genes upregulated by guadecitabine were prognostically significant and conferred a reduced risk in the TCGA cutaneous melanoma dataset. Conclusions The DNMT inhibitor guadecitabine emerged as the most promising immunomodulatory agent among those tested, supporting the rationale for usage of this class of epigenetic drugs in combinatorial immunotherapy approaches.

Abstract Background Melanoma is the deadliest form of skin cancer and metastatic disease is associated with a significant survival rate drop. There is an urgent need for consistent tumor biomarkers to scale precision medicine and reduce cancer mortality. Here, we aimed to identify a melanoma-specific circulating microRNA signature and assess its value as a diagnostic tool. Methods The study consisted of a discovery phase and two validation phases. Circulating plasma extracellular vesicles (pEV) associated microRNA profiles were obtained from a discovery cohort of metastatic melanoma patients and normal subjects as controls. A pEV-microRNA signature was obtained using a LASSO penalized logistic regression model. The pEV-microRNA signature was subsequently validated both in a publicly available dataset and in an independent internal cohort. Results We identified and validated in three independent cohorts a panel of melanoma-specific circulating microRNAs that showed high accuracy in differentiating melanoma patients from healthy subjects with an area under the curve (AUC) of 1.00, 0.94 and 0.75 respectively. Investigation of the function of the pEV-microRNA signature evidenced their possible immune suppressive role in melanoma patients. Conclusions We demonstrate that a blood test based on circulating microRNAs can non-invasively detect melanoma, offering a novel diagnostic tool for improving standard care. Moreover, we revealed an immune suppressive role for melanoma pEV-microRNAs.

Summary: Germline genetic variants modulate human immune response. We present analytical pipelines for assessing the contribution of hosts’ genetic background to the immune landscape of solid tumors using harmonized data from more than 9,000 patients in The Cancer Genome Atlas (TCGA). These include protocols for heritability, genome-wide association studies (GWAS), colocalization, and rare variant analyses. These workflows are developed around the structure of TCGA but can be adapted to explore other repositories or in the context of cancer immunotherapy.For complete details on the use and execution of this protocol, please refer to Sayaman et al. (2021). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Summary: Brain-derived neurotrophic factor (BDNF) plays a pivotal role in neuronal growth and differentiation, neuronal plasticity, learning, and memory. Using CRISPR/Cas9 technology, we generated a vital Bdnf null mutant line in zebrafish and carried out its molecular and behavioral characterization. Although no defects are evident on a morphological inspection, 66% of coding genes and 37% of microRNAs turned out to be differentially expressed in bdnf−/− compared with wild type sibling embryos. We deeply investigated the circadian clock pathway and confirmed changes in the rhythmic expression of clock (arntl1a, clock1a and clock2) and clock-controlled (aanat2) genes. The modulatory role of Bdnf on the zebrafish circadian clock was then validated by behavioral tests highlighting the absence of circadian activity rhythms in bdnf−/− larvae. The circadian behavior was partially rescued by pharmacological treatment. The bdnf−/− zebrafish line presented here is the first valuable and stable vertebrate model for the study of BDNF-related neurodevelopmental diseases

Coronavirus disease 2019 (COVID-19) emerged in December 2019 when the first case was reported in Wuhan, China, and turned into a pandemic. Whole-genome sequencing (WGS) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays a crucial role in understanding the disease. For this reason, we performed WGS of 101 SARS-CoV-2 strains obtained from individuals from two districts of Campania (Italy) from January to May 2021. The phylogenetic analysis of sequence data identified five types of clades including 10 different Pango lineages: 20A (Lineages B.1.258.17, B.1.258.14, and B.1.160) (n = 10; 9.9%), 20B (Lineages B.1.1.351 and B.1.374) (n = 5; 4.9%), 20E (EU1) (Lineages B.1.177.53, B.1.177.75, and B.1.177) (n = 5; 4.9%), 20I (Alpha.V1) (B.1.1.7) (n = 60; 59.4%), and 20J (Gamma.V3) (Lineage P.1.1) (n = 21; 20.7%). In the early time of the epidemic (January and February 2021), B.1.1.7 lineage was in 62% of samples only in Benevento district, while this lineage appears in Avellino later in 64% of samples from March to May. The occurrence of P.1.1 lineage spreading from March to the end of the study was recorded in all districts with the same frequencies of ~21%. The highest genomic distance was observed in Lineage P.1.1. Moreover, we identified 219 “known” missense mutations with different frequencies (114 in ORF1a/1b; 12 in ORF3a; 29 in S; 5 in M; 29 in N; and 5 each in ORF7a and ORF8). This report suggests the quickly spreading in Campania of new variants of SARS-CoV-2 and the strict surveillance of the occurrence of genetic variants of SARS-CoV-2.

Abstract Background Glioblastoma (GBM) is a complex disease with extensive molecular and transcriptional heterogeneity. GBM can be subcategorized into four distinct subtypes; tumors that shift towards the mesenchymal phenotype upon recurrence are generally associated with treatment resistance, unfavorable prognosis, and the infiltration of pro-tumorigenic macrophages. Results We explore the transcriptional regulatory networks of mesenchymal-associated tumor-associated macrophages (MA-TAMs), which drive the malignant phenotypic state of GBM, and identify macrophage receptor with collagenous structure (MARCO) as the most highly differentially expressed gene. MARCOhigh TAMs induce a phenotypic shift towards mesenchymal cellular state of glioma stem cells, promoting both invasive and proliferative activities, as well as therapeutic resistance to irradiation. MARCOhigh TAMs also significantly accelerate tumor engraftment and growth in vivo. Moreover, both MA-TAM master regulators and their target genes are significantly correlated with poor clinical outcomes and are often associated with genomic aberrations in neurofibromin 1 (NF1) and phosphoinositide 3-kinases/mammalian target of rapamycin/Akt pathway (PI3K-mTOR-AKT)-related genes. We further demonstrate the origination of MA-TAMs from peripheral blood, as well as their potential association with tumor-induced polarization states and immunosuppressive environments. Conclusions Collectively, our study characterizes the global transcriptional profile of TAMs driving mesenchymal GBM pathogenesis, providing potential therapeutic targets for improving the effectiveness of GBM immunotherapy.

Abstract Background The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. Results We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the features are independent of the accumulated literature. We build models on a training set which consist of targets with approved drugs and a negative set of non-drug targets. The machine learning techniques help to identify the most important combination of features differentiating validated targets from non-targets. We validated our predictions on an independent set of clinical trial drug targets, achieving a high accuracy characterized by an Area Under the Curve (AUC) of 0.89. Our most predictive features included biological function of proteins, network centrality measures, protein essentiality, tissue specificity, localization and solvent accessibility. Our predictions, based on a small set of 102 validated oncology targets, recovered the majority of known drug targets and identifies a novel set of proteins as drug target candidates. Conclusions We developed a machine learning approach to prioritize proteins according to their similarity to approved drug targets. We have shown that the method proposed is highly predictive on a validation dataset consisting of 277 targets of clinical trial drug confirming that our computational approach is an efficient and cost-effective tool for drug target discovery and prioritization. Our predictions were based on oncology targets and cancer relevant biological functions, resulting in significantly higher scores for targets of oncology clinical trial drugs compared to the scores of targets of trial drugs for other indications. Our approach can be used to make indication specific drug-target prediction by combining generic druggability features with indication specific biological functions.

Abstract Tumor immunology has changed the landscape of cancer treatment. Yet, not all patients benefit as cancer immune responsiveness (CIR) remains a limitation in a considerable proportion of cases. The multifactorial determinants of CIR include the genetic makeup of the patient, the genomic instability central to cancer development, the evolutionary emergence of cancer phenotypes under the influence of immune editing, and external modifiers such as demographics, environment, treatment potency, co-morbidities and cancer-independent alterations including immune homeostasis and polymorphisms in the major and minor histocompatibility molecules, cytokines, and chemokines. Based on the premise that cancer is fundamentally a disorder of the genes arising within a cell biologic process, whose deviations from normality determine the rules of engagement with the host’s response, the Society for Immunotherapy of Cancer (SITC) convened a task force of experts from various disciplines including, immunology, oncology, biophysics, structural biology, molecular and cellular biology, genetics, and bioinformatics to address the complexity of CIR from a holistic view. The task force was launched by a workshop held in San Francisco on May 14–15, 2018 aimed at two preeminent goals: 1) to identify the fundamental questions related to CIR and 2) to create an interactive community of experts that could guide scientific and research priorities by forming a logical progression supported by multiple perspectives to uncover mechanisms of CIR. This workshop was a first step toward a second meeting where the focus would be to address the actionability of some of the questions identified by working groups. In this event, five working groups aimed at defining a path to test hypotheses according to their relevance to human cancer and identifying experimental models closest to human biology, which include: 1) Germline-Genetic, 2) Somatic-Genetic and 3) Genomic-Transcriptional contributions to CIR, 4) Determinant(s) of Immunogenic Cell Death that modulate CIR, and 5) Experimental Models that best represent CIR and its conversion to an immune responsive state. This manuscript summarizes the contributions from each group and should be considered as a first milestone in the path toward a more contemporary understanding of CIR. We appreciate that this effort is far from comprehensive and that other relevant aspects related to CIR such as the microbiome, the individual’s recombined T cell and B cell receptors, and the metabolic status of cancer and immune cells were not fully included. These and other important factors will be included in future activities of the taskforce. The taskforce will focus on prioritization and specific actionable approach to answer the identified questions and implementing the collaborations in the follow-up workshop, which will be held in Houston on September 4–5, 2019.

Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed a multilayer network-based computational framework integrating perturbational signatures of the disease as well as intrinsic characteristics of the drugs, such as their mechanism of action and chemical structure. We present five case studies carried out on public data from The Cancer Genome Atlas, including invasive breast cancer, colon adenocarcinoma, lung squamous cell carcinoma, hepatocellular carcinoma and prostate adenocarcinoma. Our results highlight paclitaxel as a suitable drug for combination therapy for many of the considered cancer types. In addition, several non-cancer-related genes representing unusual drug targets were identified as potential candidates for pharmacological treatment of cancer.

Abstract Background Long non-coding RNAs (lncRNAs) represent a novel class of non-coding RNAs having a crucial role in many biological processes. The identification of long non-coding homologs among different species is essential to investigate such roles in model organisms as homologous genes tend to retain similar molecular and biological functions. Alignment–based metrics are able to effectively capture the conservation of transcribed coding sequences and then the homology of protein coding genes. However, unlike protein coding genes the poor sequence conservation of long non-coding genes makes the identification of their homologs a challenging task. Results In this study we compare alignment–based and alignment–free string similarity metrics and look at promoter regions as a possible source of conserved information. We show that promoter regions encode relevant information for the conservation of long non-coding genes across species and that such information is better captured by alignment–free metrics. We perform a genome wide test of this hypothesis in human, mouse, and zebrafish. Conclusions The obtained results persuaded us to postulate the new hypothesis that, unlike protein coding genes, long non-coding genes tend to preserve their regulatory machinery rather than their transcribed sequence. All datasets, scripts, and the prediction tools adopted in this study are available at https://github.com/bioinformatics-sannio/lncrna-homologs.

Copy Number Alterations (CNAs) represent the most common genetic alterations identified in ovarian cancer cells, being responsible for the extensive genomic instability observed in this cancer. Here we report the identification of CNAs in a cohort of Italian patients affected by ovarian cancer performed by SNP-based array.Our analysis allowed the identification of 201 significantly altered chromosomal bands (70 copy number gains; 131 copy number losses). The 3300 genes subjected to CNA identified here were compared to those present in the TCGA dataset. The analysis allowed the identification of 11 genes with increased CN and mRNA expression (PDCD10, EBAG9, NUDCD1, ENY2, CSNK2A1, TBC1D20, ZCCHC3, STARD3, C19orf12, POP4, UQCRFS1). PDCD10 was selected for further studies because of the highest frequency of CNA.PDCD10 was found, by immunostaining of three different Tissue Micro Arrays, to be over-expressed in the majority of ovarian primary cancer samples and in metastatic lesions. Moreover, significant correlations were found in specific subsets of patients, between increased PDCD10 expression and grade (p

Refeeding after caloric restriction induces weight regain and a disproportionate recovering of fat mass rather than lean mass (catch-up fat) that, in humans, associates with higher risks to develop chronic dysmetabolism. Studies in a well-established rat model of semistarvation-refeeding have reported that catch-up fat associates with hyperinsulinemia, glucose redistribution from skeletal muscle to white adipose tissue and suppressed adaptive thermogenesis sustaining a high efficiency for fat deposition. The skeletal muscle of catch-up fat animals exhibits reduced insulin-stimulated glucose utilization, mitochondrial dysfunction, delayed in vivo contraction-relaxation kinetics, increased proportion of slow fibers and altered local thyroid hormone metabolism, with suggestions of a role for iodothyronine deiodinases. To obtain novel insights into the skeletal muscle response during catch-up fat in this rat model, the functional proteomes of tibialis anterior and soleus muscles, harvested after 2 weeks of caloric restriction and 1 week of refeeding, were studied. Furthermore, to assess the implication of thyroid hormone metabolism in catch-up fat, circulatory thyroid hormones as well as liver type 1 (D1) and liver and skeletal muscle type 3 (D3) iodothyronine deiodinase activities were evaluated. The proteomic profiling of both skeletal muscles indicated catch-up fat-induced alterations, reflecting metabolic and contractile adjustments in soleus muscle and changes in glucose utilization and oxidative stress in tibialis anterior muscle. In response to caloric restriction, D3 activity increased in both liver and skeletal muscle, and persisted only in skeletal muscle upon refeeding. In parallel, liver D1 activity decreased during caloric restriction, and persisted during catch-up fat at a time-point when circulating levels of T4, T3 and rT3 were all restored to those of controls. Thus, during catch-up fat, a local hypothyroidism may occur in liver and skeletal muscle despite systemic euthyroidism. The resulting reduced tissue thyroid hormone bioavailability, likely D1- and D3-dependent in liver and skeletal muscle, respectively, may be part of the adaptive thermogenesis sustaining catch-up fat. These results open new perspectives in understanding the metabolic processes associated with the high efficiency of body fat recovery after caloric restriction, revealing new implications for iodothyronine deiodinases as putative biological brakes contributing in suppressed thermogenesis driving catch-up fat during weight regain.

Small non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many biological processes and diseases. The lack of a complete comprehension of their biological functionality, especially in a genome-wide scenario, has demanded new computational approaches to annotate their roles. It is widely known that secondary structure is determinant to know RNA function and machine learning based approaches have been successfully proven to predict RNA function from secondary structure information. Here we show that RNA function can be predicted with good accuracy from a lightweight representation of sequence information without the necessity of computing secondary structure features which is computationally expensive. This finding appears to go against the dogma of secondary structure being a key determinant of function in RNA. Compared to recent secondary structure based methods, the proposed solution is more robust to sequence boundary noise and reduces drastically the computational cost allowing for large data volume annotations. Scripts and datasets to reproduce the results of experiments proposed in this study are available at: https://github.com/bioinformatics-sannio/ncrna-deep.

Embryonic stem cells (ESCs) are derived from inner cell mass (ICM) of the blastocyst. In serum/LIF culture condition, they show variable expression of pluripotency genes that mark cell fluctuation between pluripotency and differentiation metastate. The ESCs subpopulation marked by zygotic genome activation gene (ZGA) signature, including Zscan4, retains a wider differentiation potency than epiblast-derived ESCs. We have recently shown that retinoic acid (RA) significantly enhances Zscan4 cell population. However, it remains unexplored how RA initiates the ESCs to 2-cell like reprogramming. Here we found that RA is decisive for ESCs to 2C-like cell transition, and reconstructed the gene network surrounding Zscan4. We revealed that RA regulates 2C-like population co-activating Dux and Duxbl1. We provided novel evidence that RA dependent ESCs to 2C-like cell transition is regulated by Dux, and antagonized by Duxbl1. Our suggested mechanism could shed light on the role of RA on ESC reprogramming.

Abstract Background Despite extensive molecular characterization, we lack a comprehensive understanding of lineage identity, differentiation, and proliferation in high-grade gliomas (HGGs). Methods We sampled the cellular milieu of HGGs by profiling dissociated human surgical specimens with a high-density microwell system for massively parallel single-cell RNA-Seq. We analyzed the resulting profiles to identify subpopulations of both HGG and microenvironmental cells and applied graph-based methods to infer structural features of the malignantly transformed populations. Results While HGG cells can resemble glia or even immature neurons and form branched lineage structures, mesenchymal transformation results in unstructured populations. Glioma cells in a subset of mesenchymal tumors lose their neural lineage identity, express inflammatory genes, and co-exist with marked myeloid infiltration, reminiscent of molecular interactions between glioma and immune cells established in animal models. Additionally, we discovered a tight coupling between lineage resemblance and proliferation among malignantly transformed cells. Glioma cells that resemble oligodendrocyte progenitors, which proliferate in the brain, are often found in the cell cycle. Conversely, glioma cells that resemble astrocytes, neuroblasts, and oligodendrocytes, which are non-proliferative in the brain, are generally non-cycling in tumors. Conclusions These studies reveal a relationship between cellular identity and proliferation in HGG and distinct population structures that reflects the extent of neural and non-neural lineage resemblance among malignantly transformed cells.

Summary: The molecular characterization of immune subsets is important for designing effective strategies to understand and treat diseases. We characterized 29 immune cell types within the peripheral blood mononuclear cell (PBMC) fraction of healthy donors using RNA-seq (RNA sequencing) and flow cytometry. Our dataset was used, first, to identify sets of genes that are specific, are co-expressed, and have housekeeping roles across the 29 cell types. Then, we examined differences in mRNA heterogeneity and mRNA abundance revealing cell type specificity. Last, we performed absolute deconvolution on a suitable set of immune cell types using transcriptomics signatures normalized by mRNA abundance. Absolute deconvolution is ready to use for PBMC transcriptomic data using our Shiny app (https://github.com/giannimonaco/ABIS). We benchmarked different deconvolution and normalization methods and validated the resources in independent cohorts. Our work has research, clinical, and diagnostic value by making it possible to effectively associate observations in bulk transcriptomics data to specific immune subsets. : Monaco et al. generate an RNA-seq dataset on 29 immune cell types and identify modules of cell type-specific, co-expressed, and housekeeping genes. The mRNA heterogeneity and abundance of the different cell types were examined. Absolute deconvolution of PBMCs was obtained by taking into account mRNA abundance when normalizing the signature matrix. Keywords: immune system, flow cytometry, transcriptome, RNA-seq, gene modules, housekeeping, mRNA composition, mRNA abundance, mRNA heterogeneity, deconvolution

(1) Purpose: The methyl donor S-Adenosylmethionine (AdoMet) has been widely explored as a therapeutic compound, and its application-alone or in combination with other molecules-is emerging as a potential effective strategy for the treatment and chemoprevention of tumours. In this study, we investigated the antitumor activity of AdoMet in Laryngeal Squamous Cell Carcinoma (LSCC), exploring the underlying mechanisms. (2) Results: We demonstrated that AdoMet induced ROS generation and triggered autophagy with a consistent increase in LC3B-II autophagy-marker in JHU-SCC-011 and HNO210 LSCC cells. AdoMet induced ER-stress and activated UPR signaling through the upregulation of the spliced form of XBP1 and CHOP. To gain new insights into the molecular mechanisms underlying the antitumor activity of AdoMet, we evaluated the regulation of miRNA expression profile and we found a downregulation of miR-888-5p. We transfected LSCC cells with miR-888-5p inhibitor and exposed the cells to AdoMet for 48 and 72 h. The combination of AdoMet with miR-888-5p inhibitor synergistically induced both apoptosis and inhibited cell migration paralleled by the up-regulation of MYCBP and CDH1 genes and of their targets. (3) Conclusion: Overall, these data highlighted that epigenetic reprogramming of miRNAs by AdoMet play an important role in inhibiting apoptosis and migration in LSCC cell lines.

Cancer genome instability leads to accumulation of mutations which may result into tumor-specific mutated “neoantigens”, not be affected by central T-cell tolerance. Such neoantigens are considered the optimal target for the patient’s anti-tumor T cell immunity as well as for personalized cancer immunotherapy strategies. However, only a minor fraction of predicted neoantigens are relevant to the clinical outcome. In the present study, a prediction algorithm was applied using datasets of RNA sequencing from all 377 Hepatocellular carcinoma (HCC) patients available at The Cancer Genome Atlas (TCGA), to predict neoantigens to be presented by each patient's autologous HLA molecules. Correlation with patients’ survival was performed on the 115 samples for whom the exact date of death was known. A total of 30 samples were used for the training set, and 85 samples were used for the validation sets. Neither the somatic mutations nor the number nor the quality of the predicted neoantigens correlate as single parameter with survival of HCC patients who do not undergo immunotherapy treatment. Furthermore, the preferential presentation of such neoantigens in the context of one of the major histocompatibility complex MHC class I molecules does not have an impact on the survival. On the contrary, the expression of Granzyme A (GZMA) is significantly correlated with survival and, in the context of high GZMA, a direct correlation between number and quality of neoantigens with survival is observed. This is in striking contrast to results described in cancer patients undergoing immunotherapy, in which a strong correlation between Tumor Mutational Burden (TMB), number of predicted neoantigens and survival has been reported.

Pancreatic organogenesis is a multistep process that requires the cooperation of several signaling pathways. In this context, the role of pancreatic mesenchyme is important to define the epithelium development; nevertheless, the precise space−temporal signaling activation still needs to be clarified. This study reports a dissection of the pancreatic embryogenesis, highlighting the molecular network surrounding the epithelium−mesenchyme interaction. To investigate this crosstalk, pancreatic epithelium and surrounding mesenchyme, at embryonic day 10.5, were collected through laser capture microdissection (LCM) and characterized based on their global gene expression. We performed a bioinformatic analysis to hypothesize crosstalk interactions, validating the most promising genes and verifying the precise localization of their expression in the compartments, by RNA in situ hybridization (ISH). Our analyses pointed out also the c-Met gene, a very well-known factor involved in stimulating motility, morphogenesis, and organ regeneration. We also highlighted the potential crosstalk between Versican (Vcan) and Syndecan4 (Sdc4) since these genes are involved in pancreatic tissue repair, strengthening the concept that the same signaling pathways required during pancreatic embryogenesis are also involved in tissue repair. This finding leads to novel strategies for obtaining functional pancreatic stem cells for cell replacement therapies.

Hyperlipidemic state-associated perturbations in the network of factors controlling mitochondrial functions, i. e., morphogenesis machinery and metabolic sensor proteins, produce metabolic inflexibility, insulin resistance and reduced oxidative capacity in skeletal muscle. Moreover, intramyocellular lipid (IMCL) accumulation leads to tissue damage and inflammation. The administration of the naturally occurring metabolite 3,5-diiodo-L-thyronine (T2) with thyromimetic actions to high fat diet (HFD)-fed rats exerts a systemic hypolipidemic effect, which produces a lack of IMCL accumulation, a shift toward glycolytic fibers and amelioration of insulin sensitivity in gastrocnemius muscle. In this study, an integrated approach combining large-scale expression profile and functional analyses was used to characterize the response of skeletal muscle mitochondria to T2 during a HFD regimen. Long-term T2 administration to HDF rats induced a glycolytic phenotype of gastrocnemius muscle as well as an adaptation of mitochondria to the fiber type, with a decreased representation of enzymes involved in mitochondrial oxidative metabolism. At the same time, T2 stimulated the activity of individual respiratory complex I, IV, and V. Moreover, T2 prevented the HFD-associated increase in the expression of peroxisome proliferative activated receptor γ coactivator-1α and dynamin-1-like protein as well as mitochondrial morphological aberrations, favoring the appearance of tubular and tethered organelles in the intermyofibrillar regions. Remarkably, T2 reverted the HDF-associated expression pattern of proinflammatory factors, such as p65 subunit of NF-kB, and increased the fiber-specific immunoreactivity of adipose differentiation–related protein in lipid droplets. All together, these results further support a role of T2 in counteracting in vivo some of the HFD-induced impairment in structural/metabolic features of skeletal muscle by impacting the mitochondrial phenotype.

The increased application of high-throughput approaches in translational research has expanded the number of publicly available data repositories. Gathering additional valuable information contained in the datasets represents a crucial opportunity in the biomedical field. To facilitate and stimulate utilization of these datasets, we have recently developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB). In this note, we describe a curated compendium of 13 public datasets on human breast cancer, representing a total of 2142 transcriptome profiles. We classified the samples according to different immune based classification systems and integrated this information into the datasets. Annotated and harmonized datasets were uploaded to GXB. Study samples were categorized in different groups based on their immunologic tumor response profiles, intrinsic molecular subtypes and multiple clinical parameters. Ranked gene lists were generated based on relevant group comparisons. In this data note, we demonstrate the utility of GXB to evaluate the expression of a gene of interest, find differential gene expression between groups and investigate potential associations between variables with a specific focus on immunologic classification in breast cancer. This interactive resource is publicly available online at: http://breastcancer.gxbsidra.org/dm3/geneBrowser/list.

Following publication of the original article [1], the author reported that an author name, Roberta Zappasodi, was missed in the authorship list.

Cancer immunotherapy is revolutionizing the clinical management of several tumors, but has demonstrated limited activity in breast cancer. The development of more effective treatments is hindered by incomplete knowledge of the genetic determinant of immune responsiveness. To fill this gap, we mined copy number alteration, somatic mutation, and expression data from The Cancer Genome Atlas (TCGA). By using RNA-sequencing data from 1,004 breast cancers, we defined distinct immune phenotypes characterized by progressive expression of transcripts previously associated with immune-mediated rejection. The T helper 1 (Th-1) phenotype (ICR4), which also displays upregulation of immune-regulatory transcripts such as PDL1, PD1, FOXP3, IDO1, and CTLA4, was associated with prolonged patients' survival. We validated these findings in an independent meta-cohort of 1,954 breast cancer gene expression data. Chromosome segment 4q21, which includes genes encoding for the Th-1 chemokines CXCL9-11, was significantly amplified only in the immune favorable phenotype (ICR4). The mutation and neoantigen load progressively decreased from ICR4 to ICR1 but could not fully explain immune phenotypic differences. Mutations of TP53 were enriched in the immune favorable phenotype (ICR4). Conversely, the presence of MAP3K1 and MAP2K4 mutations were tightly associated with an immune-unfavorable phenotype (ICR1). Using both the TCGA and the validation dataset, the degree of MAPK deregulation segregates breast tumors according to their immune disposition. These findings suggest that mutation-driven perturbations of MAPK pathways are linked to the negative regulation of intratumoral immune response in breast cancer. Modulations of MAPK pathways could be experimentally tested to enhance breast cancer immune sensitivity.

Hyperactivation of the phosphatydil-inositol-3' phosphate kinase (PI3K)/AKT pathway is observed in most NSCLCs, promoting proliferation, migration, invasion and resistance to therapy. AKT can be activated through several mechanisms that include loss of the negative regulator PTEN, activating mutations of the catalytic subunit of PI3K (PIK3CA) and/or mutations of AKT1 itself. However, number and identity of downstream targets of activated PI3K/AKT pathway are poorly defined. To identify the genes that are targets of constitutive PI3K/AKT signalling in lung cancer cells, we performed a comparative transcriptomic analysis of human lung epithelial cells (BEAS-2B) expressing active mutant AKT1 (AKT1-E17K), active mutant PIK3CA (PIK3CA-E545K) or that are silenced for PTEN. We found that, altogether, aberrant PI3K/AKT signalling in lung epithelial cells regulated the expression of 1,960/20,436 genes (9%), though only 30 differentially expressed genes (DEGs) (15 up-regulated, 12 down-regulated and 3 discordant) out of 20,436 that were common among BEAS-AKT1-E17K, BEAS-PIK3CA-E545K and BEAS-shPTEN cells (0.1%). Conversely, DEGs specific for mutant AKT1 were 133 (85 up-regulated; 48 down-regulated), DEGs specific for mutant PIK3CA were 502 (280 up-regulated; 222 down-regulated) and DEGs specific for PTEN loss were 1549 (799 up-regulated, 750 down-regulated). The results obtained from array analysis were confirmed by quantitative RT-PCR on selected up- and down-regulated genes (n = 10). Treatment of BEAS-C cells and the corresponding derivatives with pharmacological inhibitors of AKT (MK2206) or PI3K (LY294002) further validated the significance of our findings. Moreover, mRNA expression of selected DEGs (SGK1, IGFBP3, PEG10, GDF15, PTGES, S100P, respectively) correlated with the activation status of the PI3K/AKT pathway assessed by S473 phosphorylation in NSCLC cell lines (n = 6). Finally, we made use of Ingenuity Pathway Analysis (IPA) to investigate the relevant BioFunctions enriched by the costitutive activation of AKT1-, PI3K- or PTEN-dependent signalling in lung epithelial cells. Expectedly, the analysis of the DEGs common to all three alterations highlighted a group of BioFunctions that included Cell Proliferation of tumor cell lines (14 DEGs), Invasion of cells (10 DEGs) and Migration of tumour cell lines (10 DEGs), with a common core of 5 genes (ATF3, CDKN1A, GDF15, HBEGF and LCN2) that likely represent downstream effectors of the pro-oncogenic activities of PI3K/AKT signalling. Conversely, IPA analysis of exclusive DEGs led to the identification of different downstream effectors that are modulated by mutant AKT1 (TGFBR2, CTSZ, EMP1), mutant PIK3CA (CCND2, CDK2, IGFBP2, TRIB1) and PTEN loss (ASNS, FHL2). These findings not only shed light on the molecular mechanisms that are activated by aberrant signalling through the PI3K/AKT pathway in lung epithelial cells, but also contribute to the identification of previously unrecognised molecules whose regulation takes part in the development of lung cancer.

Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). This workflow introduces the following Bioconductor packages: AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, MINET, RTCGAToolbox, TCGAbiolinks.

When administered to rats receiving a high-fat diet (HFD), 3,5-diiodo-L-thyronine (3,5-T2) [at a dose of 25 μg/100 g body weight (BW)] is known to increase energy expenditure and to prevent HFD-induced adiposity. Here, we investigated which cellular and molecular processes in visceral white adipose tissue (VAT) contributed to the beneficial effect of 3,5-T2 over time (between 1 day and 4 weeks following administration). 3,5-T2 programmed the adipocyte for lipolysis by rapidly inducing hormone sensitive lipase (HSL) phosphorylation at the protein kinase A-responsive site Ser563, accompanied with glycerol release at the 1-week time-point, contributing to the partial normalization of adipocyte volume with respect to control (N) animals. After two weeks, when the adipocyte volumes of HFD-3,5-T2 rats were completely normalized to those of the controls (N), 3,5-T2 consistently induced HSL phosphorylation at Ser563, indicative of a combined effect of 3,5-T2-induced adipose lipolysis and increasing non-adipose oxidative metabolism. VAT proteome analysis after 4 weeks of treatment revealed that 3,5-T2 significantly altered the proteomic profile of HFD rats and produced a marked pro-angiogenic action. This was associated with a reduced representation of proteins involved in lipid storage or related to response to oxidative stress, and a normalization of the levels of those involved in lipogenesis-associated mitochondrial function. In conclusion, the prevention of VAT mass-gain by 3,5-T2 occurred through different molecular pathways that, together with the previously reported stimulation of resting metabolism and liver fatty acid oxidation, are associated with an anti adipogenic/lipogenic potential and positively impact on tissue health.

Pluripotency confers Embryonic Stem Cells (ESCs) the ability to differentiate in ectoderm, endoderm, and mesoderm derivatives, producing the majority of cell types. Although the majority of ESCs divide without losing pluripotency, it has become evident that ESCs culture consists of multiple cell populations with different degrees of potency that are spontaneously induced in regular ESC culture conditions. Zscan4, a key pluripotency factor, marks ESC subpopulation that is referred to as high-level of pluripotency metastate. Here, we report that in ESC cultures treated with retinoic acid (RA), Zscan4 ESCs metastate is strongly enhanced. In particular, we found that induction of Zscan4 metastate is mediated via RA receptors (RAR-alpha, RAR-beta, and RAR-gamma), and it is dependent on phosphoinositide-3-kinase (PI3K) signaling. Remarkably, Zscan4 metastate induced by RA lacks canonical pluripotency genes Oct3/4 and Nanog but retained both self-renewal and pluripotency capabilities. Finally we demonstrated that the conditional ablation of Zscan4 subpopulation is dispensable for both endoderm and mesoderm but is required for ectoderm lineage. In conclusion, our research provides new insights about the role of RA signaling during ESCs high pluripotency metastate fluctuation.

Epidemiological and experimental data highlighted the thyroid-disrupting activity of bisphenol A (BPA). Although pivotal to identify the mechanisms of toxicity, direct low-dose BPA effects on thyrocytes have not been assessed. Here, we report the results of microarray experiments revealing that the transcriptome reacts dynamically to low-dose BPA exposure, adapting the changes in gene expression to the exposure duration. The response involves many genes, enriching specific pathways and biological functions mainly cell death/proliferation or DNA repair. Their expression is only slightly altered but, since they enrich specific pathways, this results in major effects as shown here for transcripts involved in the DNA repair pathway. Indeed, even though no phenotypic changes are induced by the treatment, we show that the exposure to BPA impairs the cell response to further stressors. We experimentally verify that prolonged exposure to low doses of BPA results in a delayed response to UV-C-induced DNA damage, due to impairment of p21-Tp53 axis, with the BPA-treated cells more prone to cell death and DNA damage accumulation. The present findings shed light on a possible mechanism by which BPA, not able to directly cause genetic damage at environmental dose, may exert an indirect genotoxic activity.

Embryonic Stem cells (ESCs) can be differentiated into ectoderm, endoderm, and mesoderm derivatives, producing the majority of cell types. In regular culture conditions, ESCs' self-renewal is maintained through molecules that inhibit spontaneous differentiation enabling long-term cellular expansion. This undifferentiating condition is characterized by multiple metastable states that fluctuate between self-renewal and differentiation balance. Here, we aim to characterize the high-pluripotent ESC metastate marked by the expression of Zscan4 through a supervised machine learning framework based on an ensemble of support vector machine (SVM) classifiers. Our study revealed a leukaemia inhibitor factor (Lif) dependent not-canonical pluripotency signature (AF067063, BC061212, Dub1, Eif1a, Gm12794, Gm13871, Gm4340, Gm4850, Tcstv1/3, and Zfp352), that specifically marks Zscan4 ESCs' fluctuation. This novel ESC metastate is enhanced by high-pluripotency culture conditions obtained through Extracellular signal Regulated-Kinase (ERK) and Glycogen synthase kinase-3 (Gsk-3) signaling inhibition (2i). Significantly, we reported that the conditional ablation of the novel ESC metastate marked by the expression of Gm12794 is required for ESCs self-renewal maintenance. In conclusion, we extend the comprehension of ESCs biology through the identification of a novel molecular signature associated to pluripotency programming.

Over the past decade, growing evidence indicates that the tumor microenvironment (TME) contributes with genomic/epigenomic aberrations of malignant cells to enhance cancer cells survival, invasion, and dissemination. Many factors, produced or de novo synthesized by immune, stromal, or malignant cells, acting in a paracrine and autocrine fashion, remodel TME and the adaptive immune response culminating in metastasis. Taking into account the recent accomplishments in the field of immune oncology and using metastatic colorectal cancer (mCRC) as a model, we propose that the evasion of the immune surveillance and metastatic spread can be achieved through a number of mechanisms that include (a) intrinsic plasticity and adaptability of immune and malignant cells to paracrine and autocrine stimuli or genotoxic stresses; (b) alteration of positional schemes of myeloid-lineage cells, produced by factors controlling the balance between tumour-suppressing and tumour-promoting activities; (c) acquisition by cancer cells of aberrant immune-phenotypic traits (NT5E/CD73, CD68, and CD163) that enhance the interactions among TME components through the production of immune-suppressive mediators. These properties may represent the driving force of metastatic progression and thus clinically exploitable for cancer prevention and therapy. In this review we summarize results and suggest new hypotheses that favour the growing impact of tumor-infiltrating immune cells on tumour progression, metastasis, and therapy resistance.

Nell’ultimo decennio le aziende Agrarie e Vitivinicole in particolare hanno visto aumentare in maniera considerevole le difficoltà di mercato ed al contempo hanno registrato una maturità e sensibilità da parte del consumatore. Questo nuovo contesto ha fatto sì che le esigenze di automazione, informatizzazione e relativa tracciabilità di prodotto sifacessero più pressanti e dettagliate, tanto da richiedere lo sviluppo di veri e propri Sistemi Informativi di Cantina eGIS agrario-vitivinicoli anche partendo da una analisi e un relativo approccio geospaziale delle problematiche presenti. Abstract The new context of agricultural and wine-devoted farm duringlast decades has brought to the needs of automation, computerizationand traceability of its product, requiring the developmentof real Information Systems as a GIS agrarian wine cellar,starting from an analysis and the approach to the geospatialissues.

Embryonic stem cells (ESCs) are characterized by two remarkable peculiarities: the capacity to propagate as undifferentiated cells (self-renewal) and the ability to differentiate in ectoderm, endoderm, and mesoderm derivatives (pluripotency). Although the majority of ESCs divide without losing the pluripotency, it has become evident that ESC cultures consists of multiple cell populations highlighted by the expression of early germ lineage markers during spontaneous differentiation. Hence, the identification and characterization of ESCs subpopulations represents an efficient approach to improve the comprehension of correlation between gene expression and cell specification status. To study markers of ESCs heterogeneity, we developed an analysis pipeline which can automatically process images of stem cell colonies in optical microscopy. The question we try to address is to find out the statistically significant preferred locations of the marked cells. We tested our algorithm on a set of images of stem cell colonies to analyze the expression pattern of the Zscan4 gene, which was an elite candidate gene to be studied because it is specifically expressed in subpopulation of ESCs. To validate the proposed method we analyzed the behavior of control genes whose pattern had been associated to biological status such as differentiation (EndoA), pluripotency (Pou5f1), and pluripotency fluctuation (Nanog). We found that Zscan4 is not uniformly expressed inside a stem cell colony, and that it tends to be expressed towards the center of the colony, moreover cells expressing Zscan4 cluster each other. This is of significant importance because it allows us to hypothesize a biological status where the cells expressing Zscan4 are preferably associated to the inner of colonies suggesting pluripotent cell status features, and the clustering between themselves suggests either a colony paracrine effect or an early phase of cell specification through proliferation. Also, the analysis on the control genes showed that they behave as expected.