Live Detection of Neural Progenitors and Glioblastoma Cells by an Oligothiophene Derivative

There is an urgent need for simple and non-invasive identification of live neural stem/progenitor cells (NSPCs) in the developing and adult brain as well as in disease, such as in brain tumors, due to the potential clinical importance in prognosis, diagnosis, and treatment of diseases of the nervous system. Here, we report a luminescent conjugated oligothiophene (LCO), named p-HTMI, for non-invasive and non-amplified real-time detection of live human patient-derived glioblastoma (GBM) stem cell-like cells and NSPCs. While p-HTMI stained only a small fraction of other cell types investigated, the mere addition of p-HTMI to the cell culture resulted in efficient detection of NSPCs or GBM cells from rodents and humans within minutes. p-HTMI is functionalized with a methylated imidazole moiety resembling the side chain of histidine/histamine, and non-methylated analogues were not functional. Cell sorting experiments of human GBM cells demonstrated that p-HTMI labeled the same cell population as CD271, a proposed marker for stem cell-like cells and rapidly migrating cells in glioblastoma. Our results suggest that the LCO p-HTMI is a versatile tool for immediate and selective detection of neural and glioma stem and progenitor cells.

Inference of glioblastoma migration and proliferation rates using single time-point images

Cancer cell migration is a driving mechanism of invasion in solid malignant tumors. Anti-migratory treatments provide an alternative approach for managing disease progression. However, we currently lack scalable screening methods for identifying novel anti-migratory drugs. To this end, we develop a method that can estimate cell motility from single end-point images in vitro by estimating differences in the spatial distribution of cells and inferring proliferation and diffusion parameters using agent-based modeling and approximate Bayesian computation. To test the power of our method, we use it to investigate drug responses in a collection of 41 patient-derived glioblastoma cell cultures, identifying migration-associated pathways and drugs with potent anti-migratory effects. We validate our method and result in both in silico and in vitro using time-lapse imaging. Our proposed method applies to standard drug screen experiments, with no change needed, and emerges as a scalable approach to screen for anti-migratory drugs.

Global hypo-methylation in a proportion of glioblastoma enriched for an astrocytic signature is associated with increased invasion and altered immune landscape

We describe a subset of glioblastoma, the most prevalent malignant adult brain tumour, harbouring a bias towards hypomethylation at defined differentially methylated regions. This epigenetic signature correlates with an enrichment for an astrocytic gene signature, which together with the identification of enriched predicted binding sites of transcription factors known to cause demethylation and to be involved in astrocytic/glial lineage specification, point to a shared ontogeny between these glioblastomas and astroglial progenitors. At functional level, increased invasiveness, at least in part mediated by SRPX2, and macrophage infiltration characterise this subset of glioblastoma.

MODL-10. SYSTEMATIC CHARACTERIZATION OF GLIOBLASTOMA GROWTH PATTERNS REVEALS INVASION ROUTE-SPECIFIC FUNCTIONAL SIGNATURES AND DRUG VULNERABILITIES

Glioblastomas (GBMs) are malignant brain tumors characterized by uncontrolled, invasive growth along multiple anatomical pathways. GBM is well-studied on a genetic and molecular level, but clinically relevant, experimentally tractable models of invasive growth are largely lacking. We report an integrated study of patient-matched information including clinical and radiological data, patient-derived xenografts, genomics, transcriptomics, epigenomics, and drug response data. Our hypothesis is that joint variation across these levels can expose treatments and biomarkers associated with glioblastoma invasion and recurrence. In total, 64 patient-derived cell lines (PDCLs) were injected into the striatum of n >= 4 mice each, of which 45 PDCLs successfully formed tumors. The tumor-forming PDCLs were each scored with respect to 10 distinct growth characteristics (n= 182 mice). The repertoire of phenotypes was highly divergent and included clear cases of perivascular invasion, white matter invasion, perineuronal satellitosis, and gliosarcoma. We explored if cellular pathways, monitored by RNA sequencing, could account for these differences. DNA repair, Wnt-, and Notch-pathways were predictive of tumor initiation, whereas pathways related to hypoxia, metabolic reprogramming, cellular stress, and inflammation reduced tumor initiation capacity of PDCLs. Transcriptional signatures were also strongly predictive of route-specific invasion. Diffuse invasion was predominantly seen in classical-subtype PDCLs with astrocytic or outer radial glia-like signatures. Proneural PDCLs, in turn, grew as solid tumors with an invasive peripheral region, and mesenchymal tumors were demarcated and invaded around vasculature. To explore the therapeutic implications of our findings, we used our data-driven method TargetTranslator to predict the drug vulnerabilities of different types of invasive glioblastoma. Transcriptional signatures of diffusively growing tumors suggested a vulnerability to neuroactive substrates while perivascular invading tumors would be more susceptible to CDK- and RTK inhibitors. We expect that focusing on drivers of these functional signatures will greatly facilitate the search for effective compounds targeting distinct growth behaviors.

EPCO-16. ESTIMATING THE DIFFERENTIATION POTENTIAL AND PLASTICITY OF GLIOBLASTOMA CELLS USING STATISTICAL MECHANICS

Glioblastoma (GBM) is the most common form of adult brain tumors and patients diagnosed with GBM face a very dismal prognosis with a median survival rate of 15 months. The challenges in treating GBM are many, there is a complex cellular and molecular heterogeneity, both between patients and within individual tumors. In a single tumor, individual cancer cells adopt a variety of stem-like, progenitor-like, and more differentiated phenotypes (a.k.a. cell states). Recent advances in single-cell technology have made it possible to detect this diversity of cellular states, but yet we lack a good explanation for how the states arise and if transitions between states can be exploited to therapeutic ends. We here propose a new computational strategy to measure the differentiation potential of GBM cells, based on the Ising models from statistical mechanics. Originally used to model coupled magnets on a lattice, the Ising models can be generalized to represent the probability of a particular configuration of any system of n variables. We show that the models can be fitted to high-dimensional single cell data in a matter of seconds by solving a convex optimization problem, and that the models help explain cell differentiation in terms of minimization of an energy function of the system. In our on-going work we have fitted the model to single cell data from our previous work on state transitions in GBM (Larsson, Dalmo et al, Molecular Systems Biology 2021) and shown that the predicted differentiation potential of cell states is consistent with the cell state hierarchies derived from lineage tracing experiments. Moving forward, we are now exploring how the Ising models can be used to model state transitions in GBM and to predict the effect of targeted therapy on the cell’s differentiation potential.

STEM-19. LIVE DETECTION OF NEURAL STEM AND GLIOBLASTOMA CELLS BY A LUMINESCENT CONJUGATED OLIGOTHIOPHENE DERIVATIVE

Glioblastoma (GBM) is an aggressive nervous system tumor with a mean survival time of 12-14 months. Cells with neural stem cell-like properties can be derived from GBM tumors. These cells seem to escape conventional irradiation treatment, chemotherapy, and surgery, and may play a crucial role for relapse. It is therefore urgent to develop novel approaches for reliable detection of neural stem cell-like cells in GBM. Here we report a luminescent conjugated oligothiophene (LCO), named GlioStem (p-HTMI), for non-invasive and non-amplified real-time detection of live human patient-derived GBM cells and embryonic neural stem/progenitor cells (NSPCs). Within a maximum of 10 minutes after administration of the molecule in vitro, in the existing media, fluorescence emission was observed without any modulation of the cells or additional vehicle, resulting in efficient detection of cytoplasmic luminescent signal in NSPCs or GBM cells from rodents and humans, detectable at Alexa488/GFP wavelength. GlioStem is functionalized with a methylated imidazole moiety resembling the side chain of histidine/histamine, and non-methylated analogues were not functional. In vitro, GlioStem was shown to identify fetal cortical NSPCs from rat (FGF2-expanded), embryonic stem cell-derived NSPCs from mouse (FGF2/EGF-expanded), and FGF2-exposed C6 glioma cell cultures from rat, but not any other cell types investigated. Cell sorting experiments of patient-derived, FGF2/EGF-expanded GBM cells demonstrated that GlioStem in addition to NSPC-markers like Nestin and Sox2 labeled the same population (overlap > 90%) of cells as CD271, a proposed marker for stem cell-like cells and rapidly migrating cells in glioblastoma. Our results suggest that the LCO GlioStem is a versatile tool for immediate and selective detection of subpopulations of neural stem and glioma cells.

Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference

Accurate inference of gene regulatory networks (GRNs) is important to unravel unknown regulatory mechanisms and processes, which can lead to the identification of treatment targets for genetic diseases. A variety of GRN inference methods have been proposed that, under suitable data conditions, perform well in benchmarks that consider the entire spectrum of false-positives and -negatives. However, it is very challenging to predict which single network sparsity gives the most accurate GRN. Lacking criteria for sparsity selection, a simplistic solution is to pick the GRN that has a certain number of links per gene, which is guessed to be reasonable. However, this does not guarantee finding the GRN that has the correct sparsity or is the most accurate one. In this study, we provide a general approach for identifying the most accurate and sparsity-wise relevant GRN within the entire space of possible GRNs. The algorithm, called SPA, applies a “GRN information criterion” (GRNIC) that is inspired by two commonly used model selection criteria, Akaike and Bayesian Information Criterion (AIC and BIC) but adapted to GRN inference. The results show that the approach can, in most cases, find the GRN whose sparsity is close to the true sparsity and close to as accurate as possible with the given GRN inference method and data. The datasets and source code can be found at https://bitbucket.org/sonnhammergrni/spa/.

MEDB-55. Single-cell transcriptomics reveals progenitor cells expressing a photoreceptor program as putative cells origin of MYC-driven Group 3 Medulloblastoma

Brain tumors are the leading cause of childhood cancer-related death. Medulloblastoma is the most common malignant pediatric brain tumor with about 70% survival. Medulloblastoma comprises four distinct subgroups respective of genomic and molecular drivers influencing tumorigenesis. It has been established that despite being considered a single disease entity, each subgroup arises from a distinct population of cells found within unique compartments of the developing brain. The cell of origin of Group 3 medulloblastoma, the most malignant medulloblastoma subgroups, is currently unknown and remains controversial. Transcriptional profiling has revealed that Group 3 medulloblastomas are characterized by elevated expression of a photoreceptor program, which has not been described in the normal cerebellar development but is well characterized in the developing pineal gland and retinal. By investigating and comparing brain and tumor development between our previously developed medulloblastoma mice model (GMYC), where mice spontaneously develop Group 3 medulloblastoma after 4-6 months of age, and their control counterparts, we found that tumor cells emerged from progenitor cells where MYC overexpression drove the transformation of immature progenitor cells expressing a photoreceptor program. Our data suggest that MYC-driven Group 3 medulloblastoma originates from progenitor cells expressing a photoreceptor program, which has implications for future research and the development of novel treatments targeting this devastating childhood malignancy.

MYCMI-7: A Small MYC-Binding Compound that Inhibits MYC: MAX Interaction and Tumor Growth in a MYC-Dependent Manner

Deregulated expression of MYC family oncogenes occurs frequently in human cancer and is often associated with aggressive disease and poor prognosis. While MYC is a highly warranted target, it has been considered "undruggable," and no specific anti-MYC drugs are available in the clinic. We recently identified molecules named MYCMIs that inhibit the interaction between MYC and its essential partner MAX. Here we show that one of these molecules, MYCMI-7, efficiently and selectively inhibits MYC:MAX and MYCN:MAX interactions in cells, binds directly to recombinant MYC, and reduces MYC-driven transcription. In addition, MYCMI-7 induces degradation of MYC and MYCN proteins. MYCMI-7 potently induces growth arrest/apoptosis in tumor cells in a MYC/MYCN-dependent manner and downregulates the MYC pathway on a global level as determined by RNA sequencing. Sensitivity to MYCMI-7 correlates with MYC expression in a panel of 60 tumor cell lines and MYCMI-7 shows high efficacy toward a collection of patient-derived primary glioblastoma and acute myeloid leukemia (AML) ex vivo cultures. Importantly, a variety of normal cells become G₁ arrested without signs of apoptosis upon MYCMI-7 treatment. Finally, in mouse tumor models of MYC-driven AML, breast cancer, and MYCN-amplified neuroblastoma, treatment with MYCMI-7 downregulates MYC/MYCN, inhibits tumor growth, and prolongs survival through apoptosis with few side effects. In conclusion, MYCMI-7 is a potent and selective MYC inhibitor that is highly relevant for the development into clinically useful drugs for the treatment of MYC-driven cancer.

SIGNIFICANCE

Our findings demonstrate that the small-molecule MYCMI-7 binds MYC and inhibits interaction between MYC and MAX, thereby hampering MYC-driven tumor cell growth in culture and in vivo while sparing normal cells.

MYCMI-7: A Small MYC-Binding Compound that Inhibits MYC: MAX Interaction and Tumor Growth in a MYC-Dependent Manner

Deregulated expression of MYC family oncogenes occurs frequently in human cancer and is often associated with aggressive disease and poor prognosis. While MYC is a highly warranted target, it has been considered "undruggable," and no specific anti-MYC drugs are available in the clinic. We recently identified molecules named MYCMIs that inhibit the interaction between MYC and its essential partner MAX. Here we show that one of these molecules, MYCMI-7, efficiently and selectively inhibits MYC:MAX and MYCN:MAX interactions in cells, binds directly to recombinant MYC, and reduces MYC-driven transcription. In addition, MYCMI-7 induces degradation of MYC and MYCN proteins. MYCMI-7 potently induces growth arrest/apoptosis in tumor cells in a MYC/MYCN-dependent manner and downregulates the MYC pathway on a global level as determined by RNA sequencing. Sensitivity to MYCMI-7 correlates with MYC expression in a panel of 60 tumor cell lines and MYCMI-7 shows high efficacy toward a collection of patient-derived primary glioblastoma and acute myeloid leukemia (AML) ex vivo cultures. Importantly, a variety of normal cells become G₁ arrested without signs of apoptosis upon MYCMI-7 treatment. Finally, in mouse tumor models of MYC-driven AML, breast cancer, and MYCN-amplified neuroblastoma, treatment with MYCMI-7 downregulates MYC/MYCN, inhibits tumor growth, and prolongs survival through apoptosis with few side effects. In conclusion, MYCMI-7 is a potent and selective MYC inhibitor that is highly relevant for the development into clinically useful drugs for the treatment of MYC-driven cancer.

Significance

Our findings demonstrate that the small-molecule MYCMI-7 binds MYC and inhibits interaction between MYC and MAX, thereby hampering MYC-driven tumor cell growth in culture and in vivo while sparing normal cells.

Fast and accurate gene regulatory network inference by normalized least squares regression

MOTIVATION

Inferring an accurate gene regulatory network (GRN) has long been a key goal in the field of systems biology. To do this, it is important to find a suitable balance between the maximum number of true positive and the minimum number of false-positive interactions. Another key feature is that the inference method can handle the large size of modern experimental data, meaning the method needs to be both fast and accurate. The Least Squares Cut-Off (LSCO) method can fulfill both these criteria, however as it is based on least squares it is vulnerable to known issues of amplifying extreme values, small or large. In GRN this manifests itself with genes that are erroneously hyper-connected to a large fraction of all genes due to extremely low value fold changes.

RESULTS

We developed a GRN inference method called Least Squares Cut-Off with Normalization (LSCON) that tackles this problem. LSCON extends the LSCO algorithm by regularization to avoid hyper-connected genes and thereby reduce false positives. The regularization used is based on normalization, which removes effects of extreme values on the fit. We benchmarked LSCON and compared it to Genie3, LASSO, LSCO and Ridge regression, in terms of accuracy, speed and tendency to predict hyper-connected genes. The results show that LSCON achieves better or equal accuracy compared to LASSO, the best existing method, especially for data with extreme values. Thanks to the speed of least squares regression, LSCON does this an order of magnitude faster than LASSO.

AVAILABILITY AND IMPLEMENTATION

Data: https://bitbucket.org/sonnhammergrni/lscon; Code: https://bitbucket.org/sonnhammergrni/genespider.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Real-time evaluation of glioblastoma growth in patient-specific zebrafish xenografts

Background

Patient-derived xenograft (PDX) models of glioblastoma (GBM) are a central tool for neuro-oncology research and drug development, enabling the detection of patient-specific differences in growth, and in vivo drug response. However, existing PDX models are not well suited for large-scale or automated studies. Thus, here, we investigate if a fast zebrafish-based PDX model, supported by longitudinal, AI-driven image analysis, can recapitulate key aspects of glioblastoma growth and enable case-comparative drug testing.

Methods

We engrafted 11 GFP-tagged patient-derived GBM IDH wild-type cell cultures (PDCs) into 1-day-old zebrafish embryos, and monitored fish with 96-well live microscopy and convolutional neural network analysis. Using light-sheet imaging of whole embryos, we analyzed further the invasive growth of tumor cells.

Results

Our pipeline enables automatic and robust longitudinal observation of tumor growth and survival of individual fish. The 11 PDCs expressed growth, invasion and survival heterogeneity, and tumor initiation correlated strongly with matched mouse PDX counterparts (Spearman R = 0.89, p < 0.001). Three PDCs showed a high degree of association between grafted tumor cells and host blood vessels, suggesting a perivascular invasion phenotype. In vivo evaluation of the drug marizomib, currently in clinical trials for GBM, showed an effect on fish survival corresponding to PDC in vitro and in vivo marizomib sensitivity.

Conclusions

Zebrafish xenografts of GBM, monitored by AI methods in an automated process, present a scalable alternative to mouse xenograft models for the study of glioblastoma tumor initiation, growth, and invasion, applicable to patient-specific drug evaluation.

Comparative epigenetic analysis of tumour initiating cells and syngeneic EPSC-derived neural stem cells in glioblastoma

Epigenetic mechanisms which play an essential role in normal developmental processes, such as self-renewal and fate specification of neural stem cells (NSC) are also responsible for some of the changes in the glioblastoma (GBM) genome. Here we develop a strategy to compare the epigenetic and transcriptional make-up of primary GBM cells (GIC) with patient-matched expanded potential stem cell (EPSC)-derived NSC (iNSC). Using a comparative analysis of the transcriptome of syngeneic GIC/iNSC pairs, we identify a glycosaminoglycan (GAG)-mediated mechanism of recruitment of regulatory T cells (Tregs) in GBM. Integrated analysis of the transcriptome and DNA methylome of GBM cells identifies druggable target genes and patient-specific prediction of drug response in primary GIC cultures, which is validated in 3D and in vivo models. Taken together, we provide a proof of principle that this experimental pipeline has the potential to identify patient-specific disease mechanisms and druggable targets in GBM.

Modeling glioblastoma heterogeneity as a dynamic network of cell states

Tumor cell heterogeneity is a crucial characteristic of malignant brain tumors and underpins phenomena such as therapy resistance and tumor recurrence. Advances in single-cell analysis have enabled the delineation of distinct cellular states of brain tumor cells, but the time-dependent changes in such states remain poorly understood. Here, we construct quantitative models of the time-dependent transcriptional variation of patient-derived glioblastoma (GBM) cells. We build the models by sampling and profiling barcoded GBM cells and their progeny over the course of 3 weeks and by fitting a mathematical model to estimate changes in GBM cell states and their growth rates. Our model suggests a hierarchical yet plastic organization of GBM, where the rates and patterns of cell state switching are partly patient-specific. Therapeutic interventions produce complex dynamic effects, including inhibition of specific states and altered differentiation. Our method provides a general strategy to uncover time-dependent changes in cancer cells and offers a way to evaluate and predict how therapy affects cell state composition.

NF1 regulates mesenchymal glioblastoma plasticity and aggressiveness through the AP-1 transcription factor FOSL1

The molecular basis underlying glioblastoma (GBM) heterogeneity and plasticity is not fully understood. Using transcriptomic data of human patient-derived brain tumor stem cell lines (BTSCs), classified based on GBM-intrinsic signatures, we identify the AP-1 transcription factor FOSL1 as a key regulator of the mesenchymal (MES) subtype. We provide a mechanistic basis to the role of the neurofibromatosis type 1 gene (NF1), a negative regulator of the RAS/MAPK pathway, in GBM mesenchymal transformation through the modulation of FOSL1 expression. Depletion of FOSL1 in NF1-mutant human BTSCs and Kras-mutant mouse neural stem cells results in loss of the mesenchymal gene signature and reduction in stem cell properties and in vivo tumorigenic potential. Our data demonstrate that FOSL1 controls GBM plasticity and aggressiveness in response to NF1 alterations.

Inferring the experimental design for accurate gene regulatory network inference

Motivation

Accurate inference of gene regulatory interactions is of importance for understanding the mechanisms of underlying biological processes. For gene expression data gathered from targeted perturbations, gene regulatory network (GRN) inference methods that use the perturbation design are the top performing methods. However, the connection between the perturbation design and gene expression can be obfuscated due to problems, such as experimental noise or off-target effects, limiting the methods’ ability to reconstruct the true GRN.

Results

In this study, we propose an algorithm, IDEMAX, to infer the effective perturbation design from gene expression data in order to eliminate the potential risk of fitting a disconnected perturbation design to gene expression. We applied IDEMAX to synthetic data from two different data generation tools, GeneNetWeaver and GeneSPIDER, and assessed its effect on the experiment design matrix as well as the accuracy of the GRN inference, followed by application to a real dataset. The results show that our approach consistently improves the accuracy of GRN inference compared to using the intended perturbation design when much of the signal is hidden by noise, which is often the case for real data.

Availability and implementation

https://bitbucket.org/sonnhammergrni/idemax.

Supplementary information

Supplementary data are available at Bioinformatics online.

Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data

The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where ~1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets have a very low signal-to-noise ratio (SNR) level causing them to be too uninformative to infer accurate GRNs. We developed a gene reduction pipeline in which we eliminate uninformative genes from the system using a selection criterion based on SNR, until reaching an informative subset. The results show that our pipeline can identify an informative subset in an overall uninformative dataset, allowing inference of accurate subset GRNs. The accurate GRNs were functionally characterized and potential novel cancer-related regulatory interactions were identified.

Abstract 1610: Development and characterization of a novel semi-spontaneous bladder cancer model by pathological evaluation, single cell sequencing and proteomic profiling of urine and serum

Bladder cancer continues to be a healthcare challenge and for many years there have been very few new therapies providing benefit for patients either with non-muscle or muscle invasive bladder cancer. The molecular profiling of bladder cancer has evolved over the last years and different classifications systems have arisen; however, there is still a lack of knowledge on the prognostic or predictive therapeutic value based on these stratification methods. The clinical heterogeneity of the disease impacts the use of the molecular tools; in addition to that, the heterogeneity in the tumor microenvironment may hamper therapeutic strategies based on monotherapy approaches. As check-point blocking antibodies have entered the clinic for the treatment of muscle-invasive bladder cancer, and as novel therapeutic approaches are gaining interest in the non-muscle invasive treatment schedule, we may have novel therapeutic options for these patients ahead of us. But to be successful we also need to understand how to combine these therapies to improve overall survival and to impact relapse and progression of the disease. To enable an improved understanding of the landscape of the bladder cancer therapies we have developed a novel model based on a transgenic mouse with overexpression of Hgf and a dysregulated cell cycle. When exposing this transgenic strain to OH-BBN in drinking water these mice develop a non-muscle invasive bladder cancer that differ from a wt C57BL/6 mouse exposed to OH-BBN. The tumors are of squamous differentiated Tis/T1 type after a course of 10 weeks OH-BBN exposure. After another 5-10 weeks there is a noted muscle invasive squamous-like bladder cancer. The tumors show a strong inflammatory reaction. The muscle-invasive stage also leads to a marked change in the urine proteomic profile (as measured by the Proximity Extension Assay [PEA 92]). We have profiled both the micro and macro-hematuria and the time-course proteomic changes along with the pathology in this model. In addition, we recently performed single-cell sequencing on tumors from the non-muscle and muscle invasive model to dissect the molecular profile, immune landscape along with the heterogeneity. Thus, we can compare this novel model to the frequently used syngeneic MB49 tumor model. We are also currently assessing anti-PD1 therapy in the novel semi-spontaneous tumor model to evaluate the responsiveness to check-point blocking therapy. Ahead we will process the single cell sequencing data and dissect out what type of targeted therapies along with immunotherapies that should be combined, to improve the understanding of how to resolve the progression from a non-muscle invasive to a muscle-invasive deadly disease, with a hope to translate this into a clinically relevant therapeutic strategy.

Citation Format: Iliana Kerzeli, Ramy Elgendy, Ivan Stepanek, Milena Doroszko, Aikaterini Chourlia, Sven Nelander, Per-Uno Malmstrom, Ulrika Segersten, Anca Dragomir, Martin Lord, Sara Mangsbo. Development and characterization of a novel semi-spontaneous bladder cancer model by pathological evaluation, single cell sequencing and proteomic profiling of urine and serum [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1610.

A Patient-Derived Cell Atlas Informs Precision Targeting of Glioblastoma

Glioblastoma (GBM) is a malignant brain tumor with few therapeutic options. The disease presents with a complex spectrum of genomic aberrations, but the pharmacological consequences of these aberrations are partly unknown. Here, we report an integrated pharmacogenomic analysis of 100 patient-derived GBM cell cultures from the human glioma cell culture (HGCC) cohort. Exploring 1,544 drugs, we find that GBM has two main pharmacological subgroups, marked by differential response to proteasome inhibitors and mutually exclusive aberrations in TP53 and CDKN2A/B. We confirm this trend in cell and in xenotransplantation models, and identify both Bcl-2 family inhibitors and p53 activators as potentiators of proteasome inhibitors in GBM cells. We can further predict the responses of individual cell cultures to several existing drug classes, presenting opportunities for drug repurposing and design of stratified trials. Our functionally profiled biobank provides a valuable resource for the discovery of new treatments for GBM.

HCN Channel Activity Balances Quiescence and Proliferation in Neural Stem Cells and Is a Selective Target for Neuroprotection During Cancer Treatment

Children suffering from neurologic cancers undergoing chemotherapy and radiotherapy are at high risk of reduced neurocognitive abilities likely via damage to proliferating neural stem cells (NSC). Therefore, strategies to protect NSCs are needed. We argue that induced cell-cycle arrest/quiescence in NSCs during cancer treatment can represent such a strategy. Here, we show that hyperpolarization-activated cyclic nucleotide-gated (HCN) ion channels are dynamically expressed over the cell cycle in NSCs, depolarize the membrane potential, underlie spontaneous calcium oscillations and are required to maintain NSCs in the actively proliferating pool. Hyperpolarizing pharmacologic inhibition of HCN channels during exposure to ionizing radiation protects NSCs cells in neurogenic brain regions of young mice. In contrast, brain tumor-initiating cells, which also express HCN channels, remain proliferative during HCN inhibition. IMPLICATIONS: Our finding that NSCs can be selectively rescued while cancer cells remain sensitive to the treatment, provide a foundation for reduction of cognitive impairment in children with neurologic cancers.

Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes

Motivation

Medulloblastoma (MB) is a brain cancer predominantly arising in children. Roughly 70% of patients are cured today, but survivors often suffer from severe sequelae. MB has been extensively studied by molecular profiling, but often in small and scattered cohorts. To improve cure rates and reduce treatment side effects, accurate integration of such data to increase analytical power will be important, if not essential.

Results

We have integrated 23 transcription datasets, spanning 1350 MB and 291 normal brain samples. To remove batch effects, we combined the Removal of Unwanted Variation (RUV) method with a novel pipeline for determining empirical negative control genes and a panel of metrics to evaluate normalization performance. The documented approach enabled the removal of a majority of batch effects, producing a large-scale, integrative dataset of MB and cerebellar expression data. The proposed strategy will be broadly applicable for accurate integration of data and incorporation of normal reference samples for studies of various diseases. We hope that the integrated dataset will improve current research in the field of MB by allowing more large-scale gene expression analyses.

Availability and implementation

The RUV-normalized expression data is available through the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) and can be accessed via the GSE series number GSE124814.

Supplementary information

Supplementary data are available at Bioinformatics online.

Growth-Inhibitory Activity of Bone Morphogenetic Protein 4 in Human Glioblastoma Cell Lines Is Heterogeneous and Dependent on Reduced SOX2 Expression

Glioblastoma multiforme continues to have a dismal prognosis. Even though detailed information on the genetic aberrations in cell signaling and cell-cycle checkpoint control is available, no effective targeted treatment has been developed. Despite the advanced molecular defects, glioblastoma cells may have remnants of normal growth-inhibitory pathways, such as the bone morphogenetic protein (BMP) signaling pathway. We have evaluated the growth-inhibitory effect of BMP4 across a broad spectrum of patient samples, using a panel of 40 human glioblastoma initiating cell (GIC) cultures. A wide range of responsiveness was observed. BMP4 sensitivity was positively correlated with a proneural mRNA expression profile, high SOX2 activity, and BMP4-dependent upregulation of genes associated with inhibition of the MAPK pathway, as demonstrated by gene set enrichment analysis. BMP4 response in sensitive cells was mediated by the canonical BMP receptor pathway involving SMAD1/5/9 phosphorylation and SMAD4 expression. SOX2 was consistently downregulated in BMP4-treated cells. Forced expression of SOX2 attenuated the BMP4 sensitivity including a reduced upregulation of MAPK-inhibitory genes, implying a functional relationship between SOX2 downregulation and sensitivity. The results show an extensive heterogeneity in BMP4 responsiveness among GICs and identify a BMP4-sensitive subgroup, in which SOX2 is a mediator of the response. IMPLICATIONS: Development of agonists targeting the BMP signaling pathway in glioblastoma is an attractive avenue toward a better treatment. Our study may help find biomarkers that predict the outcome of such treatment and enable stratification of patients.

Monotherapy efficacy of blood-brain barrier permeable small molecule reactivators of protein phosphatase 2A in glioblastoma

Glioblastoma is a fatal disease in which most targeted therapies have clinically failed. However, pharmacological reactivation of tumour suppressors has not been thoroughly studied as yet as a glioblastoma therapeutic strategy. Tumour suppressor protein phosphatase 2A is inhibited by non-genetic mechanisms in glioblastoma, and thus, it would be potentially amendable for therapeutic reactivation. Here, we demonstrate that small molecule activators of protein phosphatase 2A, NZ-8-061 and DBK-1154, effectively cross the in vitro model of blood–brain barrier, and in vivo partition to mouse brain tissue after oral dosing. In vitro, small molecule activators of protein phosphatase 2A exhibit robust cell-killing activity against five established glioblastoma cell lines, and nine patient-derived primary glioma cell lines. Collectively, these cell lines have heterogeneous genetic background, kinase inhibitor resistance profile and stemness properties; and they represent different clinical glioblastoma subtypes. Moreover, small molecule activators of protein phosphatase 2A were found to be superior to a range of kinase inhibitors in their capacity to kill patient-derived primary glioma cells. Oral dosing of either of the small molecule activators of protein phosphatase 2A significantly reduced growth of infiltrative intracranial glioblastoma tumours. DBK-1154, with both higher degree of brain/blood distribution, and more potent in vitro activity against all tested glioblastoma cell lines, also significantly increased survival of mice bearing orthotopic glioblastoma xenografts. In summary, this report presents a proof-of-principle data for blood–brain barrier—permeable tumour suppressor reactivation therapy for glioblastoma cells of heterogenous molecular background. These results also provide the first indications that protein phosphatase 2A reactivation might be able to challenge the current paradigm in glioblastoma therapies which has been strongly focused on targeting specific genetically altered cancer drivers with highly specific inhibitors. Based on demonstrated role for protein phosphatase 2A inhibition in glioblastoma cell drug resistance, small molecule activators of protein phosphatase 2A may prove to be beneficial in future glioblastoma combination therapies.

Integrative discovery of treatments for high-risk neuroblastoma

Despite advances in the molecular exploration of paediatric cancers, approximately 50% of children with high-risk neuroblastoma lack effective treatment. To identify therapeutic options for this group of high-risk patients, we combine predictive data mining with experimental evaluation in patient-derived xenograft cells. Our proposed algorithm, TargetTranslator, integrates data from tumour biobanks, pharmacological databases, and cellular networks to predict how targeted interventions affect mRNA signatures associated with high patient risk or disease processes. We find more than 80 targets to be associated with neuroblastoma risk and differentiation signatures. Selected targets are evaluated in cell lines derived from high-risk patients to demonstrate reversal of risk signatures and malignant phenotypes. Using neuroblastoma xenograft models, we establish CNR2 and MAPK8 as promising candidates for the treatment of high-risk neuroblastoma. We expect that our method, available as a public tool (targettranslator.org), will enhance and expedite the discovery of risk-associated targets for paediatric and adult cancers.

We lack effective treatment for half of children with high-risk neuroblastoma. Here, the authors introduce an algorithm that can predict the effect of interventions on gene expression signatures associated with high disease processes and risk, and identify and validate promising drug targets.

BET and Aurora Kinase A inhibitors synergize against MYCN-positive human glioblastoma cells

Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults. Patients usually undergo surgery followed by aggressive radio- and chemotherapy with the alkylating agent temozolomide (TMZ). Still, median survival is only 12–15 months after diagnosis. Many human cancers including GBMs demonstrate addiction to MYC transcription factor signaling and can become susceptible to inhibition of MYC downstream genes. JQ1 is an effective inhibitor of BET Bromodomains, a class of epigenetic readers regulating expression of downstream MYC targets. Here, we show that BET inhibition decreases viability of patient-derived GBM cell lines. We propose a distinct expression signature of MYCN-elevated GBM cells that correlates with significant sensitivity to BET inhibition. In tumors showing JQ1 sensitivity, we found enrichment of pathways regulating cell cycle, DNA damage response and repair. As DNA repair leads to acquired chemoresistance to TMZ, JQ1 treatment in combination with TMZ synergistically inhibited proliferation of MYCN-elevated cells. Bioinformatic analyses further showed that the expression of MYCN correlates with Aurora Kinase A levels and Aurora Kinase inhibitors indeed showed synergistic efficacy in combination with BET inhibition. Collectively, our data suggest that BET inhibitors could potentiate the efficacy of either TMZ or Aurora Kinase inhibitors in GBM treatment.

COMP-05. A DRUG ASSOCIATION MAP OF GLIOBLASTOMA INFORMS PRECISION TARGETING OF p53-DEPENDENT METABOLIC STATES

Glioblastoma (GBM) is a malignant brain tumor with few therapeutic options. Because GBMs are genetically and functionally diverse, there is little systematic understanding of how hundreds of approved drugs may be active against GBM subpopulations. To identify new pharmacological subgroups of GBM, we studied comprehensively the variation in drug response across 100 well-characterised patient-derived GBM cell cultures and used statistical models to explain patient specific cellular drug response in terms of molecular markers, pathway signatures and drug mechanism-of-action. We find that the response to proteasome inhibitors, currently under clinical development for GBM, shows extreme variation across cases, which is phenocopied \emph{in vivo} and predicted by p53 pathway activity. The resistance to proteasome inhibitor response is explained by a subset of p53 functional GBM cases, which exhibit more efficient scavenging of reactive oxygen species (ROS), thereby protecting against ROS-dependent apoptosis. By interference against this protective feedback loop using secondary inhibitors, synergistic killing of GBM cells is achieved. Our results define a sizable metabolic subclass of GBM cells, with implications for precision and combination targeting.

PDTM-43. THERAPEUTIC TARGETS FROM BIG DATA: INTEGRATIVE DISCOVERY OF TREATMENTS FOR HIGH-RISK NEUROBLASTOMA

Despite major advances in the molecular exploration of pediatric cancers, approximately 50 % of children with high-risk neuroblastoma lack effective treatment. To identify new therapeutic options for this group of high-risk patients, we have combined integrative data analysis with experimental evaluation in patient-derived xenograft cells. We propose a new algorithm, TargetTranslator, which combines data from tumor biobanks, pharmacological databases, and cellular networks, to predict how particular targeted interventions will affect mRNA signatures associated with high patient risk. We find more than 80 known and novel targets to be associated with neuroblastoma risk and differentiation signatures. To evaluate these predictions, we performed RNA sequencing of drug-treated cell lines derived from high-risk patients and show that predicted compounds suppress risk signatures and malignant phenotypes. Using a xenograft model, we establish CNR2 and MAPK8 as promising candidates for the treatment of high-risk neuroblastoma. We expect that our method (made available as a public tool, targettranslator.org) will enhance and expedite the discovery of risk-associated targets for pediatric and adult cancers.

Primary glioblastoma cells for precision medicine: a quantitative portrait of genomic (in)stability during the first 30 passages

Background

Primary glioblastoma cell (GC) cultures have emerged as a key model in brain tumor research, with the potential to uncover patient-specific differences in therapy response. However, there is limited quantitative information about the stability of such cells during the initial 20–30 passages of culture.

Methods

We interrogated 3 patient-derived GC cultures at dense time intervals during the first 30 passages of culture. Combining state-of-the-art signal processing methods with a mathematical model of growth, we estimated clonal composition, rates of change, affected pathways, and correlations between altered gene dosage and transcription.

Results

We demonstrate that GC cultures undergo sequential clonal takeovers, observed through variable proportions of specific subchromosomal lesions, variations in aneuploid cell content, and variations in subpopulation cell cycling times. The GC cultures also show significant transcriptional drift in several metabolic and signaling pathways, including ribosomal synthesis, telomere packaging and signaling via the mammalian target of rapamycin, Wnt, and interferon pathways, to a high degree explained by changes in gene dosage. In addition to these adaptations, the cultured GCs showed signs of shifting transcriptional subtype. Compared with chromosomal aberrations and gene expression, DNA methylations remained comparatively stable during passaging, and may be favorable as a biomarker.

Conclusion

Taken together, GC cultures undergo significant genomic and transcriptional changes that need to be considered in functional experiments and biomarker studies that involve primary glioblastoma cells.

Astrocytes enhance glioblastoma growth

Glioblastoma (GBM) is a deadly disease with a need for deeper understanding and new therapeutic approaches. The microenvironment of glioblastoma has previously been shown to guide glioblastoma progression. In this study, astrocytes were investigated with regard to their effect on glioblastoma proliferation through correlative analyses of clinical samples and experimental in vitro and in vivo studies. Co-culture techniques were used to investigate the GBM growth enhancing potential of astrocytes. Cell sorting and RNA sequencing were used to generate a GBM-associated astrocyte signature and to investigate astrocyte-induced GBM genes. A NOD scid GBM mouse model was used for in vivo studies. A gene signature reflecting GBM-activated astrocytes was associated with poor prognosis in the TCGA GBM dataset. Two genes, periostin and serglycin, induced in GBM cells upon exposure to astrocytes were expressed at higher levels in cases with high "astrocyte signature score". Astrocytes were shown to enhance glioblastoma cell growth in cell lines and in a patient-derived culture, in a manner dependent on cell-cell contact and involving increased cell proliferation. Furthermore, co-injection of astrocytes with glioblastoma cells reduced survival in an orthotopic GBM model in NOD scid mice. In conclusion, this study suggests that astrocytes contribute to glioblastoma growth and implies this crosstalk as a candidate target for novel therapies.

Human Mesenchymal glioblastomas are characterized by an increased immune cell presence compared to Proneural and Classical tumors

Glioblastoma (GBM) is the most aggressive malignant primary brain tumor in adults, with a median survival of 14.6 months. Recent efforts have focused on identifying clinically relevant subgroups to improve our understanding of pathogenetic mechanisms and patient stratification. Concurrently, the role of immune cells in the tumor microenvironment has received increasing attention, especially T cells and tumor-associated macrophages (TAM). The latter are a mixed population of activated brain-resident microglia and infiltrating monocytes/monocyte-derived macrophages, both of which express ionized calcium-binding adapter molecule 1 (IBA1). This study investigated differences in immune cell subpopulations among distinct transcriptional subtypes of GBM. Human GBM samples were molecularly characterized and assigned to Proneural, Mesenchymal or Classical subtypes as defined by NanoString nCounter Technology. Subsequently, we performed and analyzed automated immunohistochemical stainings for TAM as well as specific T cell populations. The Mesenchymal subtype of GBM showed the highest presence of TAM, CD8⁺, CD3⁺ and FOXP3⁺ T cells, as compared to Proneural and Classical subtypes. High expression levels of the TAM-related gene AIF1, which encodes the TAM-specific protein IBA1, correlated with a worse prognosis in Proneural GBM, but conferred a survival benefit in Mesenchymal tumors. We used our data to construct a mathematical model that could reliably identify Mesenchymal GBM with high sensitivity using a combination of the aforementioned cell-specific IHC markers. In conclusion, we demonstrated that molecularly distinct GBM subtypes are characterized by profound differences in the composition of their immune microenvironment, which could potentially help to identify tumors amenable to immunotherapy.

Abstract 2949: The impact of drug perturbations on neuroblastoma disease pathways: A pharmaco-transcriptomics approach

Neuroblastoma (NB) cells exhibit a complex spectrum of pathway changes associated with oncogene activation, chromosome events, tumor micro-environment and super-enhancer states. So far, elucidating which pharmaceutical compounds could modulate the activation level of each known pathway in NB cells has not been feasible. To solve this problem, we have combined transcriptome profiling of drug-perturbed NB cells with mathematical modeling of public data to create a first map of drug- and NB-specific transcriptional signatures for more than 7000 pharmaceutical compounds. We treated 2 patient-derived xenograft (PDX) NB cell lines with different chemical compounds, at 3 different doses (IC50, IC20, and IC10) and 2 time-points (6 and 24h). The whole-transcriptome of the treated cells was analyzed by a cost-effective hybrid ‘PLATE-Seq/SMART-Seq2’ method. Using factor analysis and supervised machine learning, the data was used to build a model that predicts the NB-specific outcome of 7035 drugs studied in other (non-NB) cell lines in the L1000/LINCS project. Analysis of 768 NB-specific transcriptome profiles, showed that our method accurately detects dose-dependent drug-induced pathway changes in the NB cells, including alteration of characteristic pathways and risk signatures. From the 768 profiles, our best-fitted machine learning model was then extended to 7035 compounds by in silico extrapolation, providing a first panel of estimated drug effects in NB cells. Our constructed NB pharmacological map provides a detailed view of both predicted and in vitro-validated drug-NB interactions. Effective treatments for high-risk NB are currently lacking. Using a new and cost-effective strategy, we could build an accurate map of drug-induced pathway alterations, for network pharmacology and drug repositioning for NB patients.

Citation Format: Ramy Elgendy, Elin Almstedt, Michael Vanlandewijck, Sven Nelander. The impact of drug perturbations on neuroblastoma disease pathways: A pharmaco-transcriptomics approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2949.

Abstract 3395: TargetTranslator: Big data identifies non-canonical targets for high risk neuroblastoma

Despite the many advances in the molecular characterization of neuroblastoma, effective treatments for high-risk patients are currently lacking. Using publically available data, integrated computational analysis offers new opportunities to uncover druggable subgroups. In the TargetTranslator project, we have combined state-of-the-art Big Data techniques to identify new targets in high-risk subgroups of neuroblastoma. Starting with clinical or genomic risk factors, TargetTranslator builds a consensus molecular signature across cohorts. This signature is scored against a massive amount of data from (i) childhood tumor biobanks (TARGET, R2), (ii) drug profiling data from cancer cell models (NIH-LINCS), and (iii) drug targets and pathways (STITCH, STRING, MSIGDB). As output, the system provides ranked lists of compounds, molecular targets and pathways for each risk factor, as well as an aggregated probability score. In a proof-of-concept study, we used TargetTranslator to recommend treatments for high-risk neuroblastoma subgroups, including COG high-risk, MYCN amplification, 11q deletion, and signatures of differentiation and ALK activation. Depending on risk group stratification, we detected between 21 and 680 substances (p&lt;0.001), most of which were strongly associated to approximately 10 key targets. In addition to confirming a key role for the PI3K/mTOR and MAPK pathways, we detected high scoring compounds in non-canonical pathways, including neurotransmission, GLI/SMO, ROCK and PKA. Experimental validation of 11 predicted drugs in patient-derived neuroblastoma lines confirmed our signatures and reduced viability. We also identified four new compounds that significantly (p&lt;0.05) suppressed MYCN protein levels (ROCK inhibitor fasudil, CNR2 agonist GW405833, CDK inhibitor AZD5438, lovastatin) at concentrations non-toxic in zebrafish embryos. By integrating data from patients, drugs, and drug-protein networks, we establish a new computational pipeline that predicts protein targets in specific patient subgroups. The TargetTranslator introduces a way to bridge drug effects with patient data and will be available as a user-friendly web tool on www.targettranslator.org in 2018.

Citation Format: Elin Almstedt, Caroline Wärn, Ramy Elgendy, Neda Hekmati, Emil Rosén, Ida Larsson, Rebecka Jörnsten, Cecilia Crona, Sven Nelander. TargetTranslator: Big data identifies non-canonical targets for high risk neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3395.

Integrin α10, a Novel Therapeutic Target in Glioblastoma, Regulates Cell Migration, Proliferation, and Survival

New, effective treatment strategies for glioblastomas (GBMs), the most malignant and invasive brain tumors in adults, are highly needed. In this study, we investigated the potential of integrin α10β1 as a therapeutic target in GBMs. Expression levels and the role of integrin α10β1 were studied in patient-derived GBM tissues and cell lines. The effect of an antibody–drug conjugate (ADC), an integrin α10 antibody conjugated to saporin, on GBM cells and in a xenograft mouse model was studied. We found that integrin α10β1 was strongly expressed in both GBM tissues and cells, whereas morphologically unaffected brain tissues showed only minor expression. Partial or no overlap was seen with integrins α3, α6, and α7, known to be expressed in GBM. Further analysis of a subpopulation of GBM cells selected for high integrin α10 expression demonstrated increased proliferation and sphere formation. Additionally, siRNA-mediated knockdown of integrin α10 in GBM cells led to decreased migration and increased cell death. Furthermore, the ADC reduced viability and sphere formation of GBM cells and induced cell death both in vitro and in vivo. Our results demonstrate that integrin α10β1 has a functional role in GBM cells and is a novel, potential therapeutic target for the treatment of GBM.

LGR5 promotes tumorigenicity and invasion of glioblastoma stem-like cells and is a potential therapeutic target for a subset of glioblastoma patients

Glioblastoma (GBM) is the most common and lethal primary malignant brain tumor which lacks efficient treatment and predictive biomarkers. Expression of the epithelial stem cell marker Leucine-rich repeat-containing G-protein coupled receptor 5 (LGR5) has been described in GBM, but its functional role has not been conclusively elucidated. Here, we have investigated the role of LGR5 in a large repository of patient-derived GBM stem cell (GSC) cultures. The consequences of LGR5 overexpression or depletion have been analyzed using in vitro and in vivo methods, which showed that, among those with highest LGR5 expression (LGR5^high ), there were two phenotypically distinct groups: one that was dependent on LGR5 for its malignant properties and another that was unaffected by changes in LGR5 expression. The LGR5-responding cultures could be identified by their significantly higher self-renewal capacity as measured by extreme limiting dilution assay (ELDA), and these LGR5^high -ELDA^high cultures were also significantly more malignant and invasive compared to the LGR5^high -ELDA^low cultures. This showed that LGR5 expression alone would not be a strict marker of LGR5 responsiveness. In a search for additional biomarkers, we identified LPAR4, CCND2, and OLIG2 that were significantly upregulated in LGR5-responsive GSC cultures, and we found that OLIG2 together with LGR5 were predictive of GSC radiation and drug response. Overall, we show that LGR5 regulates the malignant phenotype in a subset of patient-derived GSC cultures, which supports its potential as a predictive GBM biomarker. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Increasing the accuracy of glioblastoma subtypes: Factoring in the tumor's cell of origin

The transcriptional classification of glioblastoma has proven to be a complex issue. In the absence of strong correlations between underlying genomic lesions and transcriptional subtype, there is a need to systematically understand the origins of the glioblastoma subtypes. A recent integrated analysis of data from both mouse models and patient-derived cells supports that the glioblastoma's cell of origin is important in shaping transcriptional diversity and tumor cell malignancy.

Image-Based Detection of Patient-Specific Drug-Induced Cell-Cycle Effects in Glioblastoma

Image-based analysis is an increasingly important tool to characterize the effect of drugs in large-scale chemical screens. Herein, we present image and data analysis methods to investigate population cell-cycle dynamics in patient-derived brain tumor cells. Images of glioblastoma cells grown in multiwell plates were used to extract per-cell descriptors, including nuclear DNA content. We reduced the DNA content data from per-cell descriptors to per-well frequency distributions, which were used to identify compounds affecting cell-cycle phase distribution. We analyzed cells from 15 patient cases representing multiple subtypes of glioblastoma and searched for clusters of cell-cycle phase distributions characterizing similarities in response to 249 compounds at 11 doses. We show that this approach applied in a blind analysis with unlabeled substances identified drugs that are commonly used for treating solid tumors as well as other compounds that are well known for inducing cell-cycle arrest. Redistribution of nuclear DNA content signals is thus a robust metric of cell-cycle arrest in patient-derived glioblastoma cells.

Case-specific potentiation of glioblastoma drugs by pterostilbene

Glioblastoma multiforme (GBM, astrocytoma grade IV) is the most common malignant primary brain tumor in adults. Addressing the shortage of effective treatment options for this cancer, we explored repurposing of existing drugs into combinations with potent activity against GBM cells. We report that the phytoalexin pterostilbene is a potentiator of two drugs with previously reported anti-GBM activity, the EGFR inhibitor gefitinib and the antidepressant sertraline. Combinations of either of these two compounds with pterostilbene suppress cell growth, viability, sphere formation and inhibit migration in tumor GBM cell (GC) cultures. The potentiating effect of pterostilbene was observed to a varying degree across a panel of 41 patient-derived GCs, and correlated in a case specific manner with the presence of missense mutation of EGFR and PIK3CA and a focal deletion of the chromosomal region 1p32. We identify pterostilbene-induced cell cycle arrest, synergistic inhibition of MAPK activity and induction of Thioredoxin interacting protein (TXNIP) as possible mechanisms behind pterostilbene's effect. Our results highlight a nontoxic stilbenoid compound as a modulator of anticancer drug response, and indicate that pterostilbene might be used to modulate two anticancer compounds in well-defined sets of GBM patients.

c-Jun-N-terminal phosphorylation regulates DNMT1 expression and genome wide methylation in gliomas

High-grade gliomas (HGG) are the most common brain tumors, with an average survival time of 14 months. A glioma-CpG island methylator phenotype (G-CIMP), associated with better clinical outcome, has been described in low and high-grade gliomas. Mutation of IDH1 is known to drive the G-CIMP status. In some cases, however, the hypermethylation phenotype is independent of IDH1 mutation, suggesting the involvement of other mechanisms. Here, we demonstrate that DNMT1 expression is higher in low-grade gliomas compared to glioblastomas and correlates with phosphorylated c-Jun. We show that phospho-c-Jun binds to the DNMT1 promoter and causes DNA hypermethylation. Phospho-c-Jun activation by Anisomycin treatment in primary glioblastoma-derived cells attenuates the aggressive features of mesenchymal glioblastomas and leads to promoter methylation and downregulation of key mesenchymal genes (CD44, MMP9 and CHI3L1). Our findings suggest that phospho-c-Jun activates an important regulatory mechanism to control DNMT1 expression and regulate global DNA methylation in Glioblastoma.

Glioblastoma Cell Malignancy and Drug Sensitivity Are Affected by the Cell of Origin

The identity of the glioblastoma (GBM) cell of origin and its contributions to disease progression and treatment response remain largely unknown. We have analyzed how the phenotypic state of the initially transformed cell affects mouse GBM development and essential GBM cell (GC) properties. We find that GBM induced in neural stem-cell-like glial fibrillary acidic protein (GFAP)-expressing cells in the subventricular zone of adult mice shows accelerated tumor development and produces more malignant GCs (mGC1_GFAP) that are less resistant to cancer drugs, compared with those originating from more differentiated nestin- (mGC2_NES) or 2,'3'-cyclic nucleotide 3'-phosphodiesterase (mGC3_CNP)-expressing cells. Transcriptome analysis of mouse GCs identified a 196 mouse cell origin (MCO) gene signature that was used to partition 61 patient-derived GC lines. Human GC lines that clustered with the mGC1_GFAP cells were also significantly more self-renewing, tumorigenic, and sensitive to cancer drugs compared with those that clustered with mouse GCs of more differentiated origin.

Origin of the U87MG glioma cell line: Good news and bad news

Human tumor-derived cell lines are indispensable tools for basic and translational oncology. They have an infinite life span and are easy to handle and scalable, and results can be obtained with high reproducibility. However, a tumor-derived cell line may not be authentic to the tumor of origin. Two major questions emerge: Have the identity of the donor and the actual tumor origin of the cell line been accurately determined? To what extent does the cell line reflect the phenotype of the tumor type of origin? The importance of these questions is greatest in translational research. We have examined these questions using genetic profiling and transcriptome analysis in human glioma cell lines. We find that the DNA profile of the widely used glioma cell line U87MG is different from that of the original cells and that it is likely to be a bona fide human glioblastoma cell line of unknown origin.

FC1000: normalized gene expression changes of systematically perturbed human cells

The systematic study of transcriptional responses to genetic and chemical perturbations in human cells is still in its early stages. The largest available dataset to date is the newly released L1000 compendium. With its 1.3 million gene expression profiles of treated human cells it offers many opportunities for biomedical data mining, but also data normalization challenges of new dimensions. We developed a novel and practical approach to obtain accurate estimates of fold change response profiles from L1000, based on the RUV (Remove Unwanted Variation) statistical framework. Extending RUV to a big data setting, we propose an estimation procedure, in which an underlying RUV model is tuned by feedback through dataset specific statistical measures, reflecting p-value distributions and internal gene knockdown controls. Applying these metrics - termed evaluation endpoints - to disjoint data splits and integrating the results to select an optimal normalization, the procedure reduces bias and noise in the L1000 data, which in turn broadens the potential of this resource for pharmacological and functional genomic analyses. Our pipeline and normalization results are distributed as an R package (nelanderlab.org/FC1000.html).

Epigenetic Regulation of ZBTB18 Promotes Glioblastoma Progression

Glioblastoma (GBM) comprises distinct subtypes characterized by their molecular profile. Mesenchymal identity in GBM has been associated with a comparatively unfavorable prognosis, primarily due to inherent resistance of these tumors to current therapies. The identification of molecular determinants of mesenchymal transformation could potentially allow for the discovery of new therapeutic targets. Zinc Finger and BTB Domain Containing 18 (ZBTB18/ZNF238/RP58) is a zinc finger transcriptional repressor with a crucial role in brain development and neuronal differentiation. Here, ZBTB18 is primarily silenced in the mesenchymal subtype of GBM through aberrant promoter methylation. Loss of ZBTB18 contributes to the aggressive phenotype of glioblastoma through regulation of poor prognosis-associated signatures. Restitution of ZBTB18 expression reverses the phenotype and impairs tumor-forming ability. These results indicate that ZBTB18 functions as a tumor suppressor in GBM through the regulation of genes associated with phenotypically aggressive properties.Implications: This study characterizes the role of the putative tumor suppressor ZBTB18 and its regulation by promoter hypermethylation, which appears to be a common mechanism to silence ZBTB18 in the mesenchymal subtype of GBM and provides a new mechanistic opportunity to specifically target this tumor subclass. Mol Cancer Res; 15(8); 998-1011. ©2017 AACR.