Search

Numerous patient-based studies have highlighted the protective role of immunoglobulin E-mediated allergic diseases on glioblastoma (GBM) susceptibility and prognosis. However, the mechanisms behind this observation remain elusive. Our objective was to establish a preclinical model able to recapitulate this phenomenon and investigate the role of immunity underlying such protection.An immunocompetent mouse model of allergic airway inflammation (AAI) was initiated before intracranial implantation of mouse GBM cells (GL261). RAG1-KO mice served to assess tumor growth in a model deficient for adaptive immunity. Tumor development was monitored by MRI. Microglia were isolated for functional analyses and RNA-sequencing. Peripheral as well as tumor-associated immune cells were characterized by flow cytometry. The impact of allergy-related microglial genes on patient survival was analyzed by Cox regression using publicly available datasets.We found that allergy establishment in mice delayed tumor engraftment in the brain and reduced tumor growth resulting in increased mouse survival. AAI induced a transcriptional reprogramming of microglia towards a pro-inflammatory-like state, uncovering a microglia gene signature, which correlated with limited local immunosuppression in glioma patients. AAI increased effector memory T-cells in the circulation as well as tumor-infiltrating CD4Our results demonstrate that AAI limits both tumor take and progression in mice, providing a preclinical model to study the impact of allergy on GBM susceptibility and prognosis, respectively. We identify a potentiation of local and adaptive systemic immunity, suggesting a reciprocal crosstalk that orchestrates allergy-induced immune protection against GBM.

Small extracellular vesicle (sEV, or exosome) communication among cells in the tumor microenvironment has been modeled mainly in cell culture, whereas their relevance in cancer pathogenesis and progression in vivo is less characterized. Here we investigated cancer–microenvironment interactions in vivo using mouse models of chronic lymphocytic leukemia (CLL). sEVs isolated directly from CLL tissue were enriched in specific miRNA and immune-checkpoint ligands. Distinct molecular components of tumor-derived sEVs altered CD8+ T-cell transcriptome, proteome, and metabolome, leading to decreased functions and cell exhaustion ex vivo and in vivo. Using antagomiRs and blocking antibodies, we defined specific cargo-mediated alterations on CD8+ T cells. Abrogating sEV biogenesis by Rab27a/b knockout dramatically delayed CLL pathogenesis. This phenotype was rescued by exogenous leukemic sEV or CD8+ T-cell depletion. Finally, high expression of sEV-related genes correlated with poor outcomes in CLL patients, suggesting sEV profiling as a prognostic tool. In conclusion, sEVs shape the immune microenvironment during CLL progression. Significance: sEVs produced in the leukemia microenvironment impair CD8+ T-cell mediated antitumor immune response and are indispensable for leukemia progression in vivo in murine preclinical models. In addition, high expression of sEV-related genes correlated with poor survival and unfavorable clinical parameters in CLL patients. See related commentary by Zhong and Guo, p. 5. This article is highlighted in the In This Issue feature, p. 1

By generating protein diversity, alternative splicing provides an important oncogenic pathway. Isocitrate dehydrogenase (IDH) 1 and 2 mutations and 1p/19q co-deletion have become crucial for the novel molecular classification of diffuse gliomas, which also incorporates DNA methylation profiling. In this study, we have carried out a bioinformatics analysis to examine the impact of the IDH mutation, as well as the 1p/19q co-deletion and the glioma CpG island methylator phenotype (G-CIMP) status on alternative splicing in a cohort of 662 diffuse gliomas from The Cancer Genome Atlas (TCGA). We identify the biological processes and molecular functions affected by alternative splicing in the various glioma subgroups and provide evidence supporting the important contribution of alternative splicing in modulating epigenetic regulation in diffuse gliomas. Targeting the genes and pathways affected by alternative splicing might provide novel therapeutic opportunities against gliomas.

Dysregulation of mRNA translation, including preferential translation of mRNA with complex 5'-UTRs such as the MYC oncogene, is recognized as an important mechanism in cancer. In this study, we show that both human and murine chronic lymphocytic leukemia (CLL) cells display a high translation rate, which can be inhibited by the synthetic flavagline FL3, a prohibitin (PHB)-binding drug. A multiomics analysis consisting of pulsed SILAC, RNA sequencing and polysome profiling performed in CLL patient samples and cell lines treated with FL3 revealed the decreased translation of the MYC oncogene and of proteins involved in cell cycle and metabolism. Furthermore, inhibition of translation was associated with a block of proliferation and a profound rewiring of MYC-driven metabolism. Interestingly, contrary to other models, the RAS-RAF-(PHBs)-MAPK pathway is neither impaired by FL3 nor implicated in translation regulation in CLL cells. Here, we rather show that PHBs are directly associated with the translation initiation complex and can be targeted by FL3. Knock-down of PHBs resembled FL3 treatment. Importantly, inhibition of translation was efficient in controlling CLL development in vivo either alone or combined with immunotherapy. Finally, high expression of translation initiation-related genes and PHBs genes correlated with poor survival and unfavorable clinical parameters in CLL patients. In conclusion, we demonstrated that translation inhibition is a valuable strategy to control CLL development by blocking the translation of several oncogenic pathways including MYC. We also unraveled a new and direct role of PHBs in translation initiation, thus creating new therapeutic opportunities for CLL patients.

Supplemental Table S3 - Patient clinical information

Supplementary Methods and Figures

Supplemental Table S4 - Flow cytometry antibody list

Supplemental Table S1 - Proteins detected in sEV preparations or cells treated with sEV

Supplemental Table S2 - Differential expression analysis of genes in leukemic or immune cells treated with sEV

This document contains Supplementary Table S1, which provides the expression data pertaining to the 27-gene signature and relevant clinical data of the Heidelberg cohort.

Inevitable tumor recurrence and a poor median survival are frustrating reminders of the inefficacy of our current standard of care for patients with newly diagnosed glioblastoma (GBM), which includes surgery followed by radiotherapy and chemotherapy with the DNA alkylating agent temozolomide. Because resistance to genotoxic damage is achieved mainly through execution of the DNA damage response (DDR) and DNA repair pathways, knowledge of the changes in DNA repair and cell-cycle gene expression that occur during tumor development might help identify new targets and improve treatment. Here, we performed a gene expression analysis targeting components of the DNA repair and cell-cycle machineries in cohorts of paired tumor samples (i.e., biopsies from the same patient obtained at the time of primary tumor operation and at recurrence) from patients treated with radiotherapy or radiotherapy plus temozolomide. We identified and validated a 27-gene signature that resulted in the classification of GBM specimens into three groups, two of which displayed inverse expression profiles. Each group contained primary and recurrent samples, and the tumor at relapse frequently displayed a gene expression profile different from that of the matched primary biopsy. Within the groups that exhibited opposing gene expression profiles, the expression pattern of the gene signature at relapse was linked to progression-free survival. We provide experimental evidence that our signature exposes group-specific vulnerabilities against genotoxicants and inhibitors of the cell cycle and DDR, with the prospect of personalized therapeutic strategies.Significance: These findings suggest that classification of GBM tumors based on a DNA repair and cell-cycle gene expression signature exposes vulnerabilities to standard-of-care therapies and offers the potential for personalized therapeutic strategies.

This document contains Supplementary Figures S1-S6 as well as their Legends. Specifically, Fig. S1: characterization of the 52 gene signature; Fig. S2: Validation of the 27 DNA repair and cell cycle gene signature; Fig. S3: Analysis of the Murat cohort; Fig. S4: Analysis of the TCGA dataset; Fig. S5: Identification of differentially expressed genes and gene ontology analysis of the G1 and G3 groups; Fig. S6: DNA repair and cell-cycle-based strategies for personalized management of GBM".

This document details the preliminary differential gene expression analysis carried out using the Köln dataset

This document contains Supplementary Table S4, which regroups the differentially expressed genes identified between the G1 and G3 groups of the Wang dataset and the gene ontology terms associated.

Small noncoding microRNAs (miRNA) regulate the expression of target mRNAs by repressing their translation or orchestrating their sequence-specific degradation. In this study, we investigated miRNA and miRNA target gene expression patterns in melanoma to identify candidate biomarkers for early and progressive disease. Because data presently available on miRNA expression in melanoma are inconsistent thus far, we applied several different miRNA detection and profiling techniques on a panel of 10 cell lines and 20 patient samples representing nevi and primary or metastatic melanoma. Expression of selected miRNAs was inconsistent when comparing cell line–derived and patient-derived data. Moreover, as expected, some discrepancies were also detected when miRNA microarray data were correlated with qPCR-measured expression levels. Nevertheless, we identified miRNA-200c to be consistently downregulated in melanocytes, melanoma cell lines, and patient samples, whereas miRNA-205 and miRNA-23b were markedly reduced only in patient samples. In contrast, miR-146a and miR-155 were upregulated in all analyzed patients but none of the cell lines. Whole-genome microarrays were performed for analysis of selected melanoma cell lines to identify potential transcriptionally regulated miRNA target genes. Using Ingenuity pathway analysis, we identified a deregulated gene network centered around microphthalmia-associated transcription factor, a transcription factor known to play a key role in melanoma development. Our findings define miRNAs and miRNA target genes that offer candidate biomarkers in human melanoma. Cancer Res; 70(10); 4163–73. ©2010 AACR.

Supplementary Figure 1 from Signatures of MicroRNAs and Selected MicroRNA Target Genes in Human Melanoma

Supplementary Figure 2 from Signatures of MicroRNAs and Selected MicroRNA Target Genes in Human Melanoma

Supplementary Table 1 from Signatures of MicroRNAs and Selected MicroRNA Target Genes in Human Melanoma

BackgroundGlioblastoma (GBM) evades the immune system by creating an immune-suppressive tumor microenvironment (TME), where GBM-associated myeloid cells are geared towards tumor-supportive roles. However, it is unclear whether recruited myeloid cells are phenotypically and functionally identical. Here, we aim to understand the TME heterogeneity in GBM patients recapitulated in patient-derived orthotopic xenografts (PDOXs) and systematically characterize myeloid cell type identities at the molecular and functional level.MethodsWe applied single-cell RNA-sequencing and spatial transcriptomics, multicolor flow cytometry, immunohistochemistry and functional assays to examine the heterogeneity of the TME in GBM. Various GBM PDOXs representing different tumor phenotypes were analyzed and compared to the patient tumors, normal brain and mouse GL261 glioma model.ResultsPDOX models recapitulate the major components of the TME detected in human GBM, where tumor cells reciprocally interact with host cells to create a GBM-specific TME. We detect the most prominent transcriptomic adaptations in myeloid cells, which are largely of microglial origin. We reveal intra-tumoral heterogeneity of microglia and identify diverse phenotypic states across distinct GBM landscapes and tumor niches. GBM-educated microglia acquire dendritic cell-like features, displaying increased migration and phagocytosis. We further find novel microglial states expressing astrocytic and endothelial markers. Lastly, we show that temozolomide (TMZ) treatment leads to transcriptomic plasticity of both GBM tumor cells and adjacent TME components.ConclusionsOur data provide insight into the phenotypic adaptation of the heterogeneous TME instructed by GBM. We uncover that GBM-educated microglia are represented by various concomitant states, both in patients and recapitulated in PDOXs, displaying different pro- or anti-tumoral properties that are modulated by anti-neoplastic treatments, such as TMZ.KEY POINTSGBM-educated tumor microenvironment is faithfully recapitulated and modulated in PDOX modelsMicroglia represent an essential myeloid cell population in the GBM microenvironmentGBM-educated microglia acquire heterogeneous transcriptomic states across distinct tumor nichesGBM-educated microglia subsets display phagocytic and dendritic cell-like gene expression programs, which are modulated upon TMZ treatmentIMPORTANCE OF THE STUDYThis manuscript addresses tumor-immune interactions in GBM, focusing on the molecular changes of the myeloid compartment. We find that myeloid cells, the most abundant immune cell population in brain tumors, undergo the most prominent transcriptional adaptation in the TME. Resident microglia represent the main myeloid cell population in the cellular tumor, while peripheral-derived myeloid cells appear to infiltrate the brain at sites of blood-brain barrier disruption. We identify reactive dendritic cell-like gene expression programs associated with enhanced phagocytic and antigen-presentation features in GBM-educated microglia subsets that might be harnessed for novel immunotherapeutic approaches. Overall, PDOX models faithfully recapitulate the major components of the GBM-educated TME and allow assessment of phenotypic changes in the GBM ecosystem upon treatment.

ObjectiveTo develop a vocal biomarker for fatigue monitoring in people with COVID-19.DesignProspective cohort study.SettingPredi-COVID data between May 2020 and May 2021.ParticipantsA total of 1772 voice recordings were used to train an AI-based algorithm to predict fatigue, stratified by gender and smartphone’s operating system (Android/iOS). The recordings were collected from 296 participants tracked for 2 weeks following SARS-CoV-2 infection.Primary and secondary outcome measuresFour machine learning algorithms (logistic regression, k-nearest neighbours, support vector machine and soft voting classifier) were used to train and derive the fatigue vocal biomarker. The models were evaluated based on the following metrics: area under the curve (AUC), accuracy, F1-score, precision and recall. The Brier score was also used to evaluate the models’ calibrations.ResultsThe final study population included 56% of women and had a mean (±SD) age of 40 (±13) years. Women were more likely to report fatigue (pConclusionsThis study demonstrates the feasibility of identifying and remotely monitoring fatigue thanks to voice. Vocal biomarkers, digitally integrated into telemedicine technologies, are expected to improve the monitoring of people with COVID-19 or Long-COVID.Trial registration numberNCT04380987.

Highlights • Review on tools and databases linking miRNA and its mRNA targetome. • Databases show little overlap in miRNA targetome predictions suggesting strong contextual effects. • Deconvolution and deep learning approaches are promising new approaches to improve miRNA targetome predictions., Advanced sequencing technologies such as RNASeq provide the means for production of massive amounts of data, including transcriptome-wide expression levels of coding RNAs (mRNAs) and non-coding RNAs such as miRNAs, lncRNAs, piRNAs and many other RNA species. In silico analysis of datasets, representing only one RNA species is well established and a variety of tools and pipelines are available. However, attaining a more systematic view of how different players come together to regulate the expression of a gene or a group of genes requires a more intricate approach to data analysis. To fully understand complex transcriptional networks, datasets representing different RNA species need to be integrated. In this review, we will focus on miRNAs as key post-transcriptional regulators summarizing current computational approaches for miRNA:target gene prediction as well as new data-driven methods to tackle the problem of comprehensively and accurately dissecting miRNome-targetome interactions.

Cytokines orchestrate responses to pathogens and in inflammatory processes, but they also play an important role in cancer by shaping the expression levels of cytokine response genes. Here, we conducted a large profiling study comparing miRNome and mRNA transcriptome data generated following different cytokine stimulations. Transcriptomic responses to STAT1- (IFNγ, IL-27) and STAT3-activating cytokines (IL6, OSM) were systematically compared in nine cancerous and non-neoplastic cell lines of different tissue origins (skin, liver and colon). The largest variation in our datasets was seen between cell lines of the three different tissues rather than stimuli. Notably, the variability in miRNome datasets was a lot more pronounced than in mRNA data. Our data also revealed that cells of skin, liver and colon tissues respond very differently to cytokines and that the cell signaling networks activated or silenced in response to STAT1- or STAT3-activating cytokines are specific to the tissue and the type of cytokine. However, globally, STAT1-activating cytokines had stronger effects than STAT3-inducing cytokines with most significant responses in liver cells, showing more genes upregulated and with higher fold change. A more detailed analysis of gene regulations upon cytokine stimulation in these cells provided insights into STAT1- versus STAT3-driven processes in hepatocarcinogenesis. Finally, independent component analysis revealed interconnected transcriptional networks distinct between cancer cells and their healthy counterparts.

Background “Long COVID” is characterized by a variety of symptoms and an important burden for affected people. Our objective was to describe long COVID symptomatology according to initial coronavirus disease 2019 (COVID-19) severity. Methods Predi-COVID cohort study participants, recruited at the time of acute COVID-19 infection, completed a detailed 12-month symptom and quality of life questionnaire. Frequencies and co-occurrences of symptoms were assessed. Results Among the 289 participants who fully completed the 12-month questionnaire, 59.5% reported at least 1 symptom, with a median of 6 symptoms. Participants with an initial moderate or severe acute illness declared more frequently 1 or more symptoms (82.6% vs 38.6%, P Conclusions Frequencies and burden of symptoms present 12 months after acute COVID-19 infection increased with the severity of the acute illness. Long COVID likely consists of multiple subcategories rather than a single entity. This work will contribute to the better understanding of long COVID and to the definition of precision health strategies. Clinical Trials Registration NCT04380987.

ObjectiveTo develop a vocal biomarker for fatigue monitoring in people with COVID-19.DesignProspective cohort study.SettingPredi-COVID data between May 2020 and May 2021.ParticipantsA total of 1772 voice recordings was used to train an AI-based algorithm to predict fatigue, stratified by gender and smartphone’s operating system (Android/iOS). The recordings were collected from 296 participants tracked for two weeks following SARS-CoV-2 infection.primary and secondary outcome measuresFour machine learning algorithms (Logistic regression, k-nearest neighbors, support vector machine, and soft voting classifier) were used to train and derive the fatigue vocal biomarker. A t-test was used to evaluate the distribution of the vocal biomarker between the two classes (Fatigue and No fatigue).ResultsThe final study population included 56% of women and had a mean (±SD) age of 40 (±13) years. Women were more likely to report fatigue (P<.001). We developed four models for Android female, Android male, iOS female, and iOS male users with a weighted AUC of 79%, 85%, 86%, 82%, and a mean Brier Score of 0.15, 0.12, 0.17, 0.12, respectively. The vocal biomarker derived from the prediction models successfully discriminated COVID-19 participants with and without fatigue (t-test P<.001).ConclusionsThis study demonstrates the feasibility of identifying and remotely monitoring fatigue thanks to voice. Vocal biomarkers, digitally integrated into telemedicine technologies, are expected to improve the monitoring of people with COVID-19 or Long-COVID.

We developed BIODICA, an integrated computational environment for application of Independent Component Analysis (ICA) to bulk and single-cell molecular profiles, interpretation of the results in terms of biological functions and correlation with metadata. The computational core is the novel Python package stabilized-ica which provides interface to several ICA algorithms, a stabilization procedure, meta-analysis and component interpretation tools. BIODICA is equipped with a user-friendly graphical user interface, allowing non-experienced users to perform the ICA-based omics data analysis. The results are provided in interactive ways, thus facilitating communication with biology experts.BIODICA is implemented in Java, Python and JavaScript. The source code is freely available on GitHub under the MIT and the GNU LGPL licenses. BIODICA is supported on all major operating systems.https://sysbio-curie.github.io/biodica-environment/.

DNA methylation profiling offers unique insights into human development and diseases. Often the analysis of complex tissues and cell mixtures is the only feasible option to study methylation changes across large patient cohorts. Since DNA methylomes are highly cell type specific, deconvolution methods can be used to recover cell type-specific information in the form of latent methylation components (LMCs) from such 'bulk' samples. Reference-free deconvolution methods retrieve these components without the need for DNA methylation profiles of purified cell types. Currently no integrated and guided procedure is available for data preparation and subsequent interpretation of deconvolution results. Here, we describe a three-stage protocol for reference-free deconvolution of DNA methylation data comprising: (i) data preprocessing, confounder adjustment using independent component analysis (ICA) and feature selection using DecompPipeline, (ii) deconvolution with multiple parameters using MeDeCom, RefFreeCellMix or EDec and (iii) guided biological inference and validation of deconvolution results with the R/Shiny graphical user interface FactorViz. Our protocol simplifies the analysis and guides the initial interpretation of DNA methylation data derived from complex samples. The harmonized approach is particularly useful to dissect and evaluate cell heterogeneity in complex systems such as tumors. We apply the protocol to lung cancer methylomes from The Cancer Genome Atlas (TCGA) and show that our approach identifies the proportions of stromal cells and tumor-infiltrating immune cells, as well as associations of the detected components with clinical parameters. The protocol takes slightly >3 d to complete and requires basic R skills.

Background The COVID-19 disease has multiple symptoms, with anosmia and ageusia being the most prevalent, varying from 75% to 95% and from 50% to 80% of infected patients, respectively. An automatic assessment tool for these symptoms will help monitor the disease in a fast and noninvasive manner. Objective We hypothesized that people with COVID-19 experiencing anosmia and ageusia had different voice features than those without such symptoms. Our objective was to develop an artificial intelligence pipeline to identify and internally validate a vocal biomarker of these symptoms for remotely monitoring them. Methods This study used population-based data. Participants were assessed daily through a web-based questionnaire and asked to register 2 different types of voice recordings. They were adults (aged >18 years) who were confirmed by a polymerase chain reaction test to be positive for COVID-19 in Luxembourg and met the inclusion criteria. Statistical methods such as recursive feature elimination for dimensionality reduction, multiple statistical learning methods, and hypothesis tests were used throughout this study. The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Prediction Model Development checklist was used to structure the research. Results This study included 259 participants. Younger (aged Conclusions This study demonstrates that people with COVID-19 who have anosmia and ageusia have different voice features from those without these symptoms. Upon further validation, these vocal biomarkers could be nested in digital devices to improve symptom assessment in clinical practice and enhance the telemonitoring of COVID-19–related symptoms. Trial Registration Clinicaltrials.gov NCT04380987; https://clinicaltrials.gov/ct2/show/NCT04380987

Simple Summary Endometrial cancer (EC) mortality is directly associated with the presence of poor prognostic factors. Molecular prognostic factors have been identified, but none are used in clinical practice due to lack of validation studies. This study aims to validate a set of 255 prognostic biomarkers previously identified in an extensive literature review and explore new prognostic applications by analyzing them in The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases. A total of 30 biomarkers were validated and associated to a histological type (n = 15), histological grade (n = 6), FIGO stage (n = 1), molecular classification (n = 16), overall survival (n = 11), and recurrence-free survival (n = 5). Our results encourage further studies of understudied biomarkers such as TPX2, and validates already broadly studied biomarkers such as MSH6, MSH2, or L1CAM, among others. Finally, our results present a significant step to advance the quest for biomarkers to accurately assess the risk of EC patients. Abstract Endometrial cancer (EC) mortality is directly associated with the presence of prognostic factors. Current stratification systems are not accurate enough to predict the outcome of patients. Therefore, identifying more accurate prognostic EC biomarkers is crucial. We aimed to validate 255 prognostic biomarkers identified in multiple studies and explore their prognostic application by analyzing them in TCGA and CPTAC datasets. We analyzed the mRNA and proteomic expression data to assess the statistical prognostic performance of the 255 proteins. Significant biomarkers related to overall survival (OS) and recurrence-free survival (RFS) were combined and signatures generated. A total of 30 biomarkers were associated either to one or more of the following prognostic factors: histological type (n = 15), histological grade (n = 6), FIGO stage (n = 1), molecular classification (n = 16), or they were associated to OS (n = 11), and RFS (n = 5). A prognostic signature composed of 11 proteins increased the accuracy to predict OS (AUC = 0.827). The study validates and identifies new potential applications of 30 proteins as prognostic biomarkers and suggests to further study under-studied biomarkers such as TPX2, and confirms already used biomarkers such as MSH6, MSH2, or L1CAM. These results are expected to advance the quest for biomarkers to accurately assess the risk of EC patients.

Replication-associated single-ended DNA double-strand breaks (seDSBs) are repaired predominantly through RAD51-mediated homologous recombination (HR). Removal of the non-homologous end-joining (NHEJ) factor Ku from resected seDSB ends is crucial for HR. The coordinated actions of MRE11-CtIP nuclease activities orchestrated by ATM define one pathway for Ku eviction. Here, we identify the pre-mRNA splicing protein XAB2 as a factor required for resistance to seDSBs induced by the chemotherapeutic alkylator temozolomide. Moreover, we show that XAB2 prevents Ku retention and abortive HR at seDSBs induced by temozolomide and camptothecin, via a pathway that operates in parallel to the ATM-CtIP-MRE11 axis. Although XAB2 depletion preserved RAD51 focus formation, the resulting RAD51-ssDNA associations were unproductive, leading to increased NHEJ engagement in S/G2 and genetic instability. Overexpression of RAD51 or RAD52 rescued the XAB2 defects and XAB2 loss was synthetically lethal with RAD52 inhibition, providing potential perspectives in cancer therapy., Graphical Abstract Graphical AbstractXAB2 promotes Ku eviction from single-ended DNA double-strand breaks independently of the ATM kinase.

BACKGROUND Numerous epidemiological studies have highlighted the protective role of immunoglobulin E-mediated allergic diseases on glioblastoma (GBM) susceptibility and prognosis. However, the mechanistic explanations behind these phenomena remain unexplored. Our objective was to set up a preclinical model and investigate the mechanisms underlying such protection to improve our understanding of the crosstalk between immune system and brain tumor development. MATERIAL AND METHODS A mouse model of allergic airway inflammation (AAI) induced by repeated nasal instillation of House Dust Mite extract was initiated before intracranial implantation of GL261 glioma cells, in both immunocompetent (C57BL/6) and immunodeficient (RAG-KO) mice. Tumor take and tumor growth were monitored by MRI. Central (microglia) and peripheral (spleen, bone marrow) immune cells were characterized by flow cytometry. The response of microglia was further assessed by RNA sequencing. Impact of candidate genes on patient survival was characterized by Cox regression analysis using data from TCGA and CGGA. RESULTS Following AAI induction in C57BL/6 mice, engraftment of GL261 cells in the brain was delayed and tumor growth rate was reduced. This correlated with an increase in survival of the mice and was accompanied by increased effector memory T-cells in the circulation. Of note, the survival benefit was lost in RAG-KO mice devoid of adaptive immunity. At the level of the brain, we observed enhanced secretion of TNFα and IL6 in microglia ex vivo. AAI induced a transcriptional reprogramming of microglia towards a pro-inflammatory-like state. We identified an allergy-related microglia gene signature that is associated with improved prognosis of glioma patients. CONCLUSION Our results demonstrate that AAI limits both tumor take and GBM progression in mice, providing a preclinical model to study the role of allergic inflammation in GBM susceptibility and prognosis, respectively. At the functional level, we identify a potentiation of microglial and adaptive anti-tumoral immunity. Further investigations are warranted to shed light on the reciprocal crosstalk between microglial reprogramming and peripheral immunity in the context of allergies and brain tumors.

BACKGROUND Glioblastomas are among the most heterogeneous tumors, which hampers patient stratification and development of effective therapies. Glioblastomas create a dynamic ecosystem, where heterogeneous tumor cells interact with the tumor microenvironment to establish different niches. Upon tumor growth, Glioblastoma cells manifest remarkable plasticity and respond flexibly to selective pressures by transiting towards states favorable to the new tumor microenvitonment. How this phenotypic plasticity contributes to treatment resistance is currently less clear. The exact nature of treatment resistant, tolerant and sensitive Glioblastoma cells remains unresolved. Further studies at the single cell level are needed to reveal transient and long-term signatures of the resistant states. MATERIAL AND METHODS To investigate long-term phenotypic changes upon treatment at the single cell level we performed single cell RNA-seq (scRNA-seq) on the longitudinal patient-derived xenograft (PDOX) models derived from Glioblastoma patient tumors prior and after the standard-of-care treatment. In addition, direct treatment of PDOXs with temozolomide combined with scRNA-seq allowed revealing short-term transcriptomic changes both in tumor cells and in the mouse-derived cells forming tumor microenvironment. Advanced computational algorithms, including reference-free deconvolution methods, were applied to reveal treatment resistance signatures and master regulators of the identified treatment-resistant subpopulations. RESULTS We show that PDOX models recapitulate all the major cell types and transcriptional programs reported in Glioblastoma patient samples, providing clinically relevant models for investigating treatment resistance signatures of tumor cells and associated tumor microenvironment. Analysis of treatment naïve and treated Glioblastomas at the single cell level revealed presence of pre-existing treatment resistant states as well as newly established resistant subpopulatons. Certain transcriptomic changes are preserved long term, regardless of the lack of genetic evolution of the tumor cells. CONCLUSION Phenotypic plasticity is an important factor contributing to resistance mechanisms in Glioblastoma. Key molecular regulators of tumor cell plasticity towards treatment resistance states represent novel targets for future combinatory treatments.

Alternative splicing is an essential characteristic of living cells that usually infers a various exon-exon junction governed by different splice sites. The traditional classification based on the mode of use designates splice site to one of the two groups, constitutive or alternative. Here, we considered another criterion and reorganized splice sites into “unisplice” and “multisplice” groups according to the number of undertaken splicing events. This approach provided us with a new insight in the organization and functionality of leukemia cells. We determined features associated with uni- and multisplice sites and found that combinatorics of these sites follows strict rules of the power-law in the t(8;21)-positive leukemia cells. We also found that system splicing characteristics of the transcriptome of leukemia cells remained persistent after drastic changes in the transcript composition caused by knockdown of the RUNX1-RUNX1T1 oncogene. In this work, we show for the first time that leukemia cells possess a sub-set of unisplice sites with a hidden multisplice potential. These findings reveal a new side in organization and functioning of the leukemic cells and open up new perspectives in the study of the t(8;21)-positive leukemia.

Simple Summary Pediatric high-grade gliomas are incurable brain tumors for which there is a critical need for new therapeutic strategies as well as treatment-predictive biomarkers. This study examined the expression of DNA repair and cell cycle genes in pediatric high-grade gliomas with distinct driving mutations. The aim is to propose a novel classification of these tumors based on sub-groups exposing therapeutic vulnerabilities. Several DNA repair factors were identified that might become new diagnostic markers. Abstract Background: Pediatric high-grade gliomas (pHGGs) are the leading cause of mortality in pediatric neuro-oncology, displaying frequent resistance to standard therapies. Profiling DNA repair and cell cycle gene expression has recently been proposed as a strategy to classify adult glioblastomas. To improve our understanding of the DNA damage response pathways that operate in pHGGs and the vulnerabilities that these pathways might expose, we sought to identify and characterize a specific DNA repair and cell-cycle gene expression signature of pHGGs. Methods: Transcriptomic analyses were performed to identify a DNA repair and cell-cycle gene expression signature able to discriminate pHGGs (n = 6) from low-grade gliomas (n = 10). This signature was compared to related signatures already established. We used the pHGG signature to explore already transcriptomic datasets of DIPGs and sus-tentorial pHGGs. Finally, we examined the expression of key proteins of the pHGG signature in 21 pHGG diagnostic samples and nine paired relapses. Functional inhibition of one DNA repair factor was carried out in four patients who derived H3.3 K27M mutant cell lines. Results: We identified a 28-gene expression signature of DNA repair and cell cycle that clustered pHGGs cohorts, in particular sus-tentorial locations, in two groups. Differential protein expression levels of PARP1 and XRCC1 were associated to TP53 mutations and TOP2A amplification and linked significantly to the more radioresistant pHGGs displaying the worst outcome. Using patient-derived cell lines, we showed that the PARP-1/XRCC1 expression balance might be correlated with resistance to PARP1 inhibition. Conclusion: We provide evidence that PARP1 overexpression, associated to XRCC1 expression, TP53 mutations, and TOP2A amplification, is a new theranostic and potential therapeutic target.

Aims: To establish the common rules of exon combinatorics during RNA splicing. Study Design: Inferring a plausible statistical model of exon combinatorics from the annotated models of human genes during RNA splicing. Place and Duration of Study: Department of Genetics (Belarusian State University), Proteome and Genome Research Unit (Luxembourg Institute of Health), Department of Genetics (Lomonosov Moscow State University) and Moscow Center of Experimental Embryology and Reproductive Biotechnologies, between January 2017 and July 2019. Methodology: We used human mRNA and EST sequences from GenBank (1093522 unique records in total) and linear models of the human genes from Ensembl (58051 genes), AceView (72384 genes), ECgene (57172 genes), NCBI RefSeq (54262 genes), UCSC Genome Browser (58037 genes) and VEGA (54950 genes) to calculate a combinatorial index of human exons. We inferred the most plausible statistical model describing the distribution of combinatorial index of human exons using Clauset’s mathematical formalism. Predictors of the combinatorial index values and functional outcomes of the predefined behavior of exons during splicing were also determined. Results: Power-law is the most plausible statistical model describing the combinatorics of exons during RNA splicing. The combinatorial index of human exons is defined by more than 90% by the 138 features that have different importance. The most important of these features are the abundance of exon in transcripts, the strength of splice sites, the rank of exon in transcripts and the type of exon. Analysis of the marginal effects shows that different values of the same feature have unequal influence on the combinatorial index of human exons. Power-law behavior of exons during RNA splicing pre-determines structural diversity of transcripts, low sensitivity of splicing process to random perturbations and its high vulnerability to manipulation with highly combinative exons. Conclusion: Exons widely involved in alternative splicing are a part of the common power-law phenomenon in human cells. The power-law behavior of exons during RNA splicing gives the unique characteristics to human genes.

Inevitable tumor recurrence and a poor median survival are frustrating reminders of the inefficacy of our current standard of care for patients with newly diagnosed glioblastoma (GBM), which includes surgery followed by radiotherapy and chemotherapy with the DNA alkylating agent temozolomide. Because resistance to genotoxic damage is achieved mainly through execution of the DNA damage response (DDR) and DNA repair pathways, knowledge of the changes in DNA repair and cell-cycle gene expression that occur during tumor development might help identify new targets and improve treatment. Here, we performed a gene expression analysis targeting components of the DNA repair and cell-cycle machineries in cohorts of paired tumor samples (i.e., biopsies from the same patient obtained at the time of primary tumor operation and at recurrence) from patients treated with radiotherapy or radiotherapy plus temozolomide. We identified and validated a 27-gene signature that resulted in the classification of GBM specimens into three groups, two of which displayed inverse expression profiles. Each group contained primary and recurrent samples, and the tumor at relapse frequently displayed a gene expression profile different from that of the matched primary biopsy. Within the groups that exhibited opposing gene expression profiles, the expression pattern of the gene signature at relapse was linked to progression-free survival. We provide experimental evidence that our signature exposes group-specific vulnerabilities against genotoxicants and inhibitors of the cell cycle and DDR, with the prospect of personalized therapeutic strategies. Significance: These findings suggest that classification of GBM tumors based on a DNA repair and cell-cycle gene expression signature exposes vulnerabilities to standard-of-care therapies and offers the potential for personalized therapeutic strategies.

MotivationModern methods of whole transcriptome sequencing accurately recover nucleotide sequences of RNA molecules present in cells and allow for determining their quantitative abundances. The coding potential of such molecules can be estimated using open reading frames (ORF) finding algorithms, implemented in a number of software packages. However, these algorithms show somewhat limited accuracy, are intended for single-molecule analysis and do not allow selecting proper ORFs in the case of long mRNAs containing multiple ORF candidates.ResultsWe developed a computational approach, corresponding machine learning model and a package, dedicated to automatic identification of the ORFs in large sets of human mRNA molecules. It is based on vectorization of nucleotide sequences into features, followed by classification using a random forest. The predictive model was validated on sets of human mRNA molecules from the NCBI RefSeq and Ensembl databases and demonstrated almost 95% accuracy in detecting true ORFs. The developed methods and pre-trained classification model were implemented in a powerful ORFhunteR computational tool that performs an automatic identification of true ORFs among large set of human mRNA molecules.Availability and implementationThe developed open-source R package ORFhunteR is available for the community at GitHub repository (https://github.com/rfctbio-bsu/ORFhunteR), from Bioconductor (https://bioconductor.org/packages/devel/bioc/html/ORFhunteR.html) and as a web application (http://orfhunter.bsu.by).

The pioneering transcription factor C/EBPα coordinates cell fate and cell differentiation. C/EBPα represents an intrinsically disordered protein with multiple short linear motifs and extensive post-translational side chain modifications (PTM), reflecting its modularity and functional plasticity. Here, we combined arrayed peptide matrix screening (PRISMA) with biotin ligase proximity labeling proteomics (BioID) to generate a linear, isoform specific and PTM-dependent protein interaction map of C/EBPα in myeloid cells. The C/EBPα interactome comprises promiscuous and PTM-regulated interactions with protein machineries involved in gene expression, epigenetics, genome organization, DNA replication, RNA processing, and nuclear transport as the basis of functional C/EBPα plasticity. Protein interaction hotspots were identified that coincide with homologous conserved regions of the C/EBP family and revealed interaction motifs that score as molecular recognition features (MoRF). PTMs alter the interaction spectrum of multi-valent C/EBP-motifs to configure a multimodal transcription factor hub that allows interaction with multiple co-regulatory components, including BAF/SWI-SNF or Mediator complexes. Combining PRISMA and BioID acts as a powerful strategy to systematically explore the interactomes of intrinsically disordered proteins and their PTM-regulated, multimodal capacity.Key pointsIntegration of proximity labeling and arrayed peptide screen proteomics refines the interactome of C/EBPα isoformsHotspots of protein interactions in C/EBPα mostly occur in conserved short linear motifsInteractions of the BAF/SWI-SNF complex with C/EBPα are modulated by arginine methylation and isoform statusThe integrated experimental strategy suits systematic interactome studies of intrinsically disordered proteins

The infiltrative nature of Glioblastoma (GBM), the most aggressive primary brain tumor, critically prevents complete surgical resection and masks tumor cells behind the blood brain barrier reducing the efficacy of systemic treatment. Here, we use a genome-wide interference screen to determine invasion-essential genes and identify the AN1/A20 zinc finger domain containing protein 3 (ZFAND3) as a crucial driver of GBM invasion. Using patient-derived cellular models, we show that loss of ZFAND3 hampers the invasive capacity of GBM, whereas ZFAND3 overexpression increases motility in cells that were initially not invasive. At the mechanistic level, we find that ZFAND3 activity requires nuclear localization and integral zinc-finger domains. Our findings indicate that ZFAND3 acts within a nuclear protein complex to activate gene transcription and regulates the promoter of invasion-related genes such as COL6A2, FN1, and NRCAM. Further investigation in ZFAND3 function in GBM and other invasive cancers is warranted., Glioblastomas (GBMs) are highly invasive brain tumours, but the underlying mechanisms of GBM invasion are unclear. Here, the authors perform an RNA interference screen and identify AN1-Type Zinc Finger protein 3 (ZFAND3) as a regulator of GBM invasion, and find that it acts through the transcriptional regulation of invasion-related genes.

Unrepaired O6-methylguanine lesions induced by the alkylating chemotherapy agent temozolomide lead to replication-associated single-ended DNA double-strand breaks (seDSBs) that are repaired predominantly through RAD51-mediated homologous recombination (HR). Here, we show that loss of the pre-mRNA splicing and DNA repair protein XAB2 leads to increased temozolomide sensitivity in glioblastoma cells, which reflects abortive HR due to Ku retention on resected seDSBs. XAB2-dependent Ku eviction also occurred at seDSBs generated by the topoisomerase I poison campthotecin and operated in parallel to an ATM-dependent pathway previously described. Although Ku retention elicited by loss of XAB2 did not prevent RAD51 focus formation, the resulting RAD51-ssDNA associations were unproductive, leading to increased engagement of non-homologous-end-joining in S/G2 and genetic instability. Overexpression of RAD51 or the single-stranded DNA annealing factor RAD52 rescued the XAB2 defects. RAD52 depletion led to severe temozolomide sensitivity, whereas a synthetic lethality interaction was observed between RAD52 and XAB2.

Patient-derived cancer models are essential tools for studying tumor biology and preclinical interventions. Here, we show that glioma patient-derived orthotopic xenografts (PDOXs) enable long-term propagation of patient tumors and represent clinically relevant patient avatars. We created a large collection of PDOXs from primary and recurrent gliomas with and without mutations in IDH1, which retained histopathological, genetic, epigenetic and transcriptomic features of patient tumors with no mouse-specific clonal evolution. Longitudinal PDOX models recapitulate the limited genetic evolution of gliomas observed in patient tumors following treatment. PDOX-derived standardized tumor organoid cultures enabled assessment of drug responses, which were validated in mice. PDOXs showed clinically relevant responses to Temozolomide and to targeted treatments such as EGFR and CDK4/6 inhibitors in (epi)genetically defined groups, according to MGMT promoter and EGFR/CDK status respectively. Dianhydrogalactitol, a bifunctional alkylating agent, showed promising potential against glioblastoma. Our study underlines the clinical relevance of glioma PDOX models for translational research and personalized treatment studies.

Patient-based cancer models are essential tools for studying tumor biology and for the assessment of drug responses in a translational context. We report the establishment a large cohort of unique organoids and patient-derived orthotopic xenografts (PDOX) of various glioma subtypes, including gliomas with mutations in IDH1, and paired longitudinal PDOX from primary and recurrent tumors of the same patient. We show that glioma PDOXs enable long-term propagation of patient tumors and represent clinically relevant patient avatars that retain histopathological, genetic, epigenetic, and transcriptomic features of parental tumors. We find no evidence of mouse-specific clonal evolution in glioma PDOXs. Our cohort captures individual molecular genotypes for precision medicine including mutations in IDH1, ATRX, TP53, MDM2/4, amplification of EGFR, PDGFRA, MET, CDK4/6, MDM2/4, and deletion of CDKN2A/B, PTCH, and PTEN. Matched longitudinal PDOX recapitulate the limited genetic evolution of gliomas observed in patients following treatment. At the histological level, we observe increased vascularization in the rat host as compared to mice. PDOX-derived standardized glioma organoids are amenable to high-throughput drug screens that can be validated in mice. We show clinically relevant responses to temozolomide (TMZ) and to targeted treatments, such as EGFR and CDK4/6 inhibitors in (epi)genetically defined subgroups, according to MGMT promoter and EGFR/CDK status, respectively. Dianhydrogalactitol (VAL-083), a promising bifunctional alkylating agent in the current clinical trial, displayed high therapeutic efficacy, and was able to overcome TMZ resistance in glioblastoma. Our work underscores the clinical relevance of glioma organoids and PDOX models for translational research and personalized treatment studies and represents a unique publicly available resource for precision oncology.

DNA methylation profiling offers unique insights into human development and diseases. Often the analysis of complex tissues and cell mixtures is the only feasible option to study methylation changes across large patient cohorts. Since DNA methylomes are highly cell type specific, deconvolution methods can be used to recover cell type-specific information in the form of latent methylation components (LMCs) from such 'bulk' samples. Reference-free deconvolution methods retrieve these components without the need for DNA methylation profiles of purified cell types. Currently no integrated and guided procedure is available for data preparation and subsequent interpretation of deconvolution results. Here, we describe a three-stage protocol for reference-free deconvolution of DNA methylation data comprising: (i) data preprocessing, confounder adjustment using independent component analysis (ICA) and feature selection using DecompPipeline, (ii) deconvolution with multiple parameters using MeDeCom, RefFreeCellMix or EDec and (iii) guided biological inference and validation of deconvolution results with the R/Shiny graphical user interface FactorViz. Our protocol simplifies the analysis and guides the initial interpretation of DNA methylation data derived from complex samples. The harmonized approach is particularly useful to dissect and evaluate cell heterogeneity in complex systems such as tumors. We apply the protocol to lung cancer methylomes from The Cancer Genome Atlas (TCGA) and show that our approach identifies the proportions of stromal cells and tumor-infiltrating immune cells, as well as associations of the detected components with clinical parameters. The protocol takes slightly3 d to complete and requires basic R skills.

Interleukin-6 (IL-6)-type cytokines play important roles in liver (patho-)biology. For instance, they regulate the acute phase response to inflammatory signals and are involved in hepatocarcinogenesis. Much is known about the regulation of protein-coding genes by cytokines whereas their effects on the miRNome is less well understood. We performed a microarray screen to identify microRNAs (miRNAs) in human hepatocytes which are modulated by IL-6-type cytokines. Using samples of 2 donors, 27 and 68 miRNAs (out of 1,733) were found to be differentially expressed upon stimulation with hyper-IL-6 (HIL-6) for up to 72 h, with an overlap of 15 commonly regulated miRNAs. qPCR validation revealed that miR-146b-5p was also consistently up-regulated in hepatocytes derived from 2 other donors. Interestingly, miR-146b-5p (but not miR-146a-5p) was induced by IL-6-type cytokines (HIL-6 and OSM) in non-transformed liver-derived PH5CH8 and THLE2 cells and in Huh-7 hepatoma cells, but not in HepG2 or Hep3B hepatoma cells. We did not find evidence for a differential regulation of miR-146b-5p expression by promoter methylation, also when analyzing the TCGA data set on liver cancer samples. Inducible overexpression of miR-146b-5p in PH5CH8 cells followed by RNA-Seq analysis revealed effects on multiple mRNAs, including those encoding IRAK1 and TRAF6 crucial for Toll-like receptor signaling. Indeed, LPS-mediated signaling was attenuated upon overexpression of miR-146b-5p, suggesting a regulatory loop to modulate inflammatory signaling in hepatocytes. Further validation experiments suggest DNAJC6, MAGEE1, MPHOSPH6, PPP2R1B, SLC10A3, SNRNP27, and TIMM17B to be novel targets for miR-146b-5p (and miR-146a-5p). IL-6-type cytokines up-regulate miR-146b-5p expression in liver cells, an anti-inflammatory miRNA with multiple targets shared with miR-146a-5p.

Background Distinguishing lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC) has a tremendous therapeutic implication. Sometimes, the commonly used immunohistochemistry (IHC) markers fail to discriminate between them, urging for the identification of new diagnostic biomarkers. Methods We performed IHC on tissue microarrays from two cohorts of lung cancer patients to analyse the expression of beta-arrestin-1, beta-arrestin-2 and clinically used diagnostic markers in ADC and SCC samples. Logistic regression models were applied for tumour subtype prediction. Parallel reaction monitoring (PRM)-based mass spectrometry was used to quantify beta-arrestin-1 in plasma from cancer patients and healthy donors. Results Beta-arrestin-1 expression was significantly higher in ADC versus SCC samples. Beta-arrestin-1 displayed high sensitivity, specificity and negative predictive value. Its usefulness in an IHC panel was also shown. Plasma beta-arrestin-1 levels were considerably higher in lung cancer patients than in healthy donors and were higher in patients who later experienced a progressive disease than in patients showing complete/partial response following EGFR inhibitor therapy. Conclusions Our data identify beta-arrestin-1 as a diagnostic marker to differentiate ADC from SCC and indicate its potential as a plasma biomarker for non-invasive diagnosis of lung cancer. Its utility to predict response to EGFR inhibitors is yet to be confirmed.

In solid tumors, cancer stem cells (CSCs) or tumor-initiating cells (TICs) are often found in hypoxic niches. Nevertheless, the influence of hypoxia on TICs is poorly understood. Using previously established, TIC-enrichedpatient-derived colorectal cancer (CRC) cultures, we show that hypoxia increases the self-renewal capacity of TICs while inducing proliferation arrest in their more differentiated counterpart cultures. Gene expression data revealed macroautophagy/autophagy as one of the major pathways induced by hypoxia in TICs. Interestingly, hypoxia-induced autophagy was found to induce phosphorylation of EZR (ezrin) at Thr567 residue, which could be reversed by knocking down ATG5, BNIP3, BNIP3L, or BECN1. Furthermore, we identified PRKCA/PKCα as a potential kinase involved in hypoxia-induced autophagy-mediated TIC self-renewal. Genetic targeting of autophagy or pharmacological inhibition of PRKC/PKC and EZR resulted in decreased tumor-initiating potential of TICs. In addition, we observed significantly reduced in vivo tumor initiation and growth after a stable knockdown of ATG5. Analysis of human CRC samples showed that p-EZR is often present in TICs located in the hypoxic and autophagic regions of the tumor. Altogether, our results establish the hypoxia-autophagy-PKC-EZR signaling axis as a novel regulatory mechanism of TIC self-renewal and CRC progression. Autophagy inhibition might thus represent a promising therapeutic strategy for cancer patients. Abbreviations ATG: autophagy related; BECN1: beclin 1; BNIP3: BCL2 interacting protein 3; BNIP3L: BCL2 interacting protein 3 like; CQ: chloroquine; CSC: cancer stem cells; CRC: colorectal cancer; HIF1A/HIF-1α: hypoxia inducible factor 1 subunit alpha; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; PRKC/PKC: protein kinase C; SQSTM1/p62: sequestosome 1; TICs: tumor-initiating cells.

Epigenomic profiling enables unique insights into human development and diseases. Often the analysis of bulk samples remains the only feasible option for studying complex tissues and organs in large patient cohorts, masking the signatures of important cell populations in convoluted signals. DNA methylomes are highly cell type-specific, and enable recovery of hidden components using advanced computational methods without the need for reference profiles. We propose a three-stage protocol for reference-free deconvolution of DNA methylomes comprising: (i) data preprocessing, confounder adjustment and feature selection, (ii) deconvolution with multiple parameters, and (iii) guided biological inference and validation of deconvolution results. Our protocol simplifies the analysis and integration of DNA methylomes derived from complex samples, including tumors. Applying this protocol to lung cancer methylomes from TCGA revealed components linked to stromal cells, tumor-infiltrating immune cells, and associations with clinical parameters. The protocol takes less than four days to complete and requires basic R skills.

BACKGROUND Cellular heterogeneity is a hallmark of numerous cancer types, including Glioblastoma (GBM). Cancer stem cells (CSC) have been accounted for the generation of phenotypic heterogeneity and tumor progression in GBM. Recent data, however, suggest that CSCs may not represent a stable entity and intrinsic plasticity plays a key role in tumor adaptation to changing microenvironments. The question arises whether CSCs are a defined subpopulation of GBM or whether they represent a cellular state that any cancer cell can adopt. METHODS We interrogated intra-tumoral phenotypic heterogeneity at the single cell transcriptomic and proteomic level in GBM biopsies, patient-derived stem-like cultures and orthotopic xenografts (PDOXs). Tumor cell subpopulations, classified based on their expression of four proposed stem cell markers (CD133, CD15, A2B5 and CD44), were FACS isolated and functionally characterized under various microenvironmental conditions. Mathematical Markov modelling was applied to calculate state transitions. RESULTS GBM patient biopsies, PDOXs and stem-like cell cultures displayed remarkable stem cell-associated intra-tumoral heterogeneity. However independent of marker expression, all analysed tumor subpopulations carried stem-cell properties and recreated phenotypic heterogeneity. Mathematical modeling revealed a different propensity in reforming heterogeneity over time, which was independent of the proliferation index but linked to in vivo tumorigenic potential. Although GBM subpopulations varied in their potential to adapt to new environments, all were able to reach a steady state microenvironment-specific equilibrium. CONCLUSIONS Our results suggest that phenotypic heterogeneity in GBM results from intrinsic plasticity allowing tumor cells to adapt to changing microenvironmental conditions. Cellular states are non-hierarchical, reversible and occur via stochastic state transitions, striving towards a microenvironment-instructed equilibrium. Our data provides evidence that CSCs do not represent a defined clonal entity, but rather a cellular state determined by environmental conditions, which has implications for the design of treatment strategies targeting CSC-like states.

Pediatric high-grade gliomas (pHGGs), including diffuse intrinsic pontine gliomas (DIPGs), despite their low incidence, are the leading cause of mortality in pediatric neuro-oncology. Frequently, pHGGs, harboring mainly histone mutations, are or become resistant to standard therapies like irradiation or chemotherapies. Recent insights showed in adult HGG the predominant role of DNA damage repair (DDR) as a way of prognostic classification. Given the recent evidence that transcription conflicts like in histone-mutated gliomas can induce replication stress and be linked to DDR abnormalities, this study is aiming to establish a DDR signature able to classify specifically pHGGs and to cluster among them poor responders to radiation. Transcriptomic analyses were performed to discriminate seven pHGGs comparatively to a cohort of 10 pilocytic astrocytomas with specific DDR deregulations. The specific transcriptomic signature obtained from this differential gene expression analysis was compared to the aHGG signature already established. To strengthen, refine and finalize the DDR signature able to classify and cluster the pHGGs, we explored both signatures and their common genes in already published transcriptomic analyses of DIPGs and sus-tentorial pHGGs. To check DDR protein expressions correlated to loss of trimethylation as well as histone and TP53 mutations, an immunohistochemical assessment of several DDR markers was performed on a collection of 21 pHGG diagnostic samples and 9 paired relapses. To validate the DDR functional inhibition, we used 3 patient-derived cell lines bearing H3.3K27M mutations. A finalized signature of 28 genes involved in DNA repair and cell cycle machineries was used to cluster in two groups the pHGG cohorts. The differential protein expression of PARP1, XRCC1, p53 and stem cell markers was linked significantly to the more resistant pHGGs and the rapid progressions after radiotherapy. Those DDR makers might be used as theranostic and therapeutic targets, which were screened in PDCLs with promising results.

Interleukin-27 (IL27) is a type-I-cytokine of the IL6/IL12 family predominantly secreted by activated macrophages and dendritic cells. In the liver, IL27 expression was observed to be upregulated in patients with hepatitis B, and sera of hepatocellular carcinoma (HCC) patients contain significantly elevated levels of IL27 compared to healthy controls or patients with hepatitis and/or liver cirrhosis. In this study, we show that IL27 induces STAT1 and STAT3 phosphorylation in 5 HCC lines and 3 different types of non-transformed liver cells. We were especially interested in the relevance of the IL27-induced STAT3 activation in liver cells. Thus, we compared the IL27 responses with those induced by IFNγ (STAT1-dominated response) or IL6-type cytokines (IL6, hyper-IL6 (hy-IL6) or OSM) (STAT3-dominated response) by microarray analysis and find that in HCC cells, IL27 induces an IFNγ-like, STAT1-dependent transcriptional response, but we do not find an effective STAT3-dependent response. Validation experiments corroborate the finding from the microarray evaluation. Interestingly, the availability of STAT1 seems critical in the shaping of the IL27 response, as the siRNA knock-down of STAT1 revealed the ability of IL27 to induce the acute-phase protein γ-fibrinogen, a typical IL6 family characteristic. Moreover, we describe a crosstalk between the signaling of IL6-type cytokines and IL27: responses to the gp130-engaging cytokine IL27 (but not those to IFNs) can be inhibited by IL6-type cytokine pre-stimulation, likely by a SOCS3-mediated mechanism. Thus, IL27 recapitulates IFNγ responses in liver cells, but differs from IFNγ by its sensitivity to SOCS3 inhibition.