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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.

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.

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.

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.

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.

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.

Independent component analysis (ICA) is a matrix factorization approach where the signalscaptured by each individual matrix factors are optimized to become as mutually independent aspossible. Initially suggested for solving source blind separation problems in various fields, ICA wasshown to be successful in analyzing functional magnetic resonance imaging (fMRI) and other typesof biomedical data. In the last twenty years, ICA became a part of the standard machine learningtoolbox, together with other matrix factorization methods such as principal component analysis (PCA)and non-negative matrix factorization (NMF). Here, we review a number of recent works where ICAwas shown to be a useful tool for unraveling the complexity of cancer biology from the analysis ofdifferent types of omics data, mainly collected for tumoral samples. Such works highlight the useof ICA in dimensionality reduction, deconvolution, data pre-processing, meta-analysis, and othersapplied to dierent data types (transcriptome, methylome, proteome, single-cell data). We particularlyfocus on the technical aspects of ICA application in omics studies such as using different protocols,determining the optimal number of components, assessing and improving reproducibility of the ICAresults, and comparison with other popular matrix factorization techniques. We discuss the emergingICA applications to the integrative analysis of multi-level omics datasets and introduce a conceptualview on ICA as a tool for defining functional subsystems of a complex biological system and theirinteractions under various conditions. Our review is accompanied by a Jupyter notebook whichillustrates the discussed concepts and provides a practical tool for applying ICA to the analysis ofcancer omics datasets.

Background Targeted approaches for inhibiting epidermal growth factor receptor (EGFR) and other receptor tyrosine kinases (RTKs) in glioblastoma (GBM) have led to therapeutic resistance and little clinical benefit, raising the need for the development of alternative strategies. Endogenous LRIG1 (Leucine-rich Repeats and ImmunoGlobulin-like domains protein 1) is an RTK inhibitory protein required for stem cell maintenance, and we previously demonstrated the soluble ectodomain of LRIG1 (sLRIG1) to potently inhibit GBM growth in vitro and in vivo. Methods Here, we generated a recombinant protein of the ectodomain of LRIG1 (sLRIG1) and determined its activity in various cellular GBM models including patient-derived stem-like cells and patient organoids. We used proliferation, adhesion, and invasion assays, and performed gene and protein expression studies. Proximity ligation assay and NanoBiT complementation technology were applied to assess protein–protein interactions. Results We show that recombinant sLRIG1 downregulates EGFRvIII but not EGFR, and reduces proliferation in GBM cells, irrespective of their EGFR expression status. We find that sLRIG1 targets and downregulates a wide range of RTKs, including AXL, and alters GBM cell adhesion. Mechanistically, we demonstrate that LRIG1 interferes with AXL but not with EGFR dimerization. Conclusions These results identify AXL as a novel sLRIG1 target and show that LRIG1-mediated RTK downregulation depends on direct protein interaction. The pan-RTK inhibitory activity of sLRIG1 warrants further investigation for new GBM treatment approaches.

Cancer stem cells, also known as tumor-initiating cells (TICs), are a population of aggressive and self-renewing cells that are responsible for the initiation and progression of many cancers, including colorectal carcinoma. Intratumoral hypoxia, i.e. reduced oxygen supply following uncontrolled proliferation of cancer cells, is thought to support TIC activity by inducing specific hypoxia-responsive mechanisms that are not yet entirely understood. Using previously established and fully characterized patient-derived TIC cultures, we could observe increased sphere and colony formation under hypoxic conditions. Mechanistically, microRNA (miRNA)-profiling experiments allowed us to identify miR-215 as one of the main hypoxia-induced miRNAs in primary colon TICs. Through stable overexpression of miR-215, followed by a set of functional in vitro and in vivo investigations, miR-215 was pinpointed as a negative feedback regulator, working against the TIC-promoting effects of hypoxia. Furthermore, we could single out LGR5, a bona fide marker of non-neoplastic intestinal stem cells, as a downstream target of hypoxia/miR-215 signaling. The strong tumor- and TIC-suppressor potential of miR-215 and the regulatory role of the hypoxia/miR-215/LGR5 axis may thus represent interesting points of attack for the development of innovative anti-CSC therapy approaches.

The identity and unique capacity of cancer stem cells (CSC) to drive tumor growth and resistance have been challenged in brain tumors. Here we report that cells expressing CSC-associated cell membrane markers in Glioblastoma (GBM) do not represent a clonal entity defined by distinct functional properties and transcriptomic profiles, but rather a plastic state that most cancer cells can adopt. We show that phenotypic heterogeneity arises from non-hierarchical, reversible state transitions, instructed by the microenvironment and is predictable by mathematical modeling. Although functional stem cell properties were similar in vitro, accelerated reconstitution of heterogeneity provides a growth advantage in vivo, suggesting that tumorigenic potential is linked to intrinsic plasticity rather than CSC multipotency. The capacity of any given cancer cell to reconstitute tumor heterogeneity cautions against therapies targeting CSC-associated membrane epitopes. Instead inherent cancer cell plasticity emerges as a novel relevant target for treatment., Cancer stem cells (CSCs) comprise a putative population that can drive growth and resistance. Here, in glioblastoma models the authors show that rather than being a distinct clonal entity, the CSC population represents a plastic state adoptable by most cancer cells via reversible state transitions induced by the microenvironment.

The integration of publicly available and new patient-derived transcriptomic datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels. Here we present a methodology that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large reference datasets. The approach is based on independent component analysis (ICA) – an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes merged new and reference datasets into signals with minimal mutual dependency. By applying the method to a small cohort of primary melanoma and control samples combined with a large public melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. Cancer subtypes, patient survival and activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation were characterized. Additionally, through integration of transcriptomes and miRNomes, the method identified biological functions of miRNAs, which would otherwise not be possible.

Interleukin-27 (IL27) is a type-I cytokine of the IL6/IL12 family and is predominantly secreted by activated macrophages and dendritic cells. We show that IL27 induces STAT factor phosphorylation in cancerous cell lines of different tissue origin. IL27 leads to STAT1 phosphorylation and recapitulates an IFN-γ-like response in the microarray analyses, with up-regulation of genes involved in antiviral defense, antigen presentation, and immune suppression. Like IFN-γ, IL27 leads to an up-regulation of TAP2 and MHC-I proteins, which mediate increased tumor immune clearance. However, both cytokines also upregulate proteins such as PD-L1 (CD274) and IDO-1, which are associated with immune escape of cancer. Interestingly, differential expression of these genes was observed within the different cell lines and when comparing IL27 to IFN-γ. In coculture experiments of hepatocellular carcinoma (HCC) cells with peripheral blood mononuclear cells, pre-treatment of the HCC cells with IL27 resulted in lowered IL2 production by anti-CD3/-CD28 activated T-lymphocytes. Addition of anti-PD-L1 antibody, however, restored IL2 secretion. The levels of other TH1 cytokines were also enhanced or restored upon administration of anti-PD-L1. In addition, we show that the suppression of IL27 signaling by IL6-type cytokine pre-stimulation—mimicking a situation occurring, for example, in IL6-secreting tumors or in tumor inflammation–induced cachexia—can be antagonized by antibodies against IL6-type cytokines or their receptors. Therapeutically, the antitumor effects of IL27 (mediated, e.g., by increased antigen presentation) might thus be increased by combining IL27 with blocking antibodies against PD-L1 or/and IL6-type cytokines. Anti-IL6 or anti-PD-L1 influence IL27 signaling effects in HCC cells by preventing IL6 mediated inhibition of IL27 or by mediating an increase in immunogenicity, respectively.

SummaryThe prediction of anticancer drug response is crucial for achieving a more effective and precise treatment of patients. Models based on the analysis of large cell line collections have shown potential for investigating drug efficacy in a clinically-meaningful, cost-effective manner. Using data from thousands of cancer cell lines and drug response experiments, we propose a drug sensitivity prediction system based on a 47-gene expression profile, which was derived from an unbiased transcriptomic network analysis approach. The profile reflects the molecular activity of a diverse range of cancer-relevant processes and pathways. We validated our model using independent datasets and comparisons with published models. A high concordance between predicted and observed drug sensitivities was obtained, including additional validated predictions for four glioblastoma cell lines and four drugs. Our approach can accurately predict anti-cancer drug sensitivity and will enable further pre-clinical research. In the longer-term, it may benefit patient-oriented investigations and interventions.

Background RNA sequencing (RNA-seq) and microarrays are two transcriptomics techniques aimed at the quantification of transcribed genes and their isoforms. Here we compare the latest Affymetrix HTA 2.0 microarray with Illumina 2000 RNA-seq for the analysis of patient samples - normal lung epithelium tissue and squamous cell carcinoma lung tumours. Protein coding mRNAs and long non-coding RNAs (lncRNAs) were included in the study. Results Both platforms performed equally well for protein-coding RNAs, however the stochastic variability was higher for the sequencing data than for microarrays. This reduced the number of differentially expressed genes and genes with predictive potential for RNA-seq compared to microarray data. Analysis of this variability revealed a lack of reads for short and low abundant genes; lncRNAs, being shorter and less abundant RNAs, were found especially susceptible to this issue. A major difference between the two platforms was uncovered by analysis of alternatively spliced genes. Investigation of differential exon abundance showed insufficient reads for many exons and exon junctions in RNA-seq while the detection on the array platform was more stable. Nevertheless, we identified 207 genes which undergo alternative splicing and were consistently detected by both techniques. Conclusions Despite the fact that the results of gene expression analysis were highly consistent between Human Transcriptome Arrays and RNA-seq platforms, the analysis of alternative splicing produced discordant results. We concluded that modern microarrays can still outperform sequencing for standard analysis of gene expression in terms of reproducibility and cost. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3819-y) contains supplementary material, which is available to authorized users.