Adult brain tumour research in 2024: Status, challenges and recommendations.

In 2015, a groundswell of brain tumour patient, carer and charity activism compelled the UK Minister for Life Sciences to form a brain tumour research task and finish group. This resulted, in 2018, with the UK government pledging £20m of funding, to be paralleled with £25m from Cancer Research UK, specifically for neuro-oncology research over the subsequent 5 years. Herein, we review if and how the adult brain tumour research landscape in the United Kingdom has changed over that time and what challenges and bottlenecks remain. We have identified seven universal brain tumour research priorities and three cross-cutting themes, which span the research spectrum from bench to bedside and back again. We discuss the status, challenges and recommendations for each one, specific to the United Kingdom.

Single-cell nanobiopsy enables multigenerational longitudinal transcriptomics of cancer cells.

Single-cell RNA sequencing has revolutionized our understanding of cellular heterogeneity, but routine methods require cell lysis and fail to probe the dynamic trajectories responsible for cellular state transitions, which can only be inferred. Here, we present a nanobiopsy platform that enables the injection of exogenous molecules and multigenerational longitudinal cytoplasmic sampling from a single cell and its progeny. The technique is based on scanning ion conductance microscopy (SICM) and, as a proof of concept, was applied to longitudinally profile the transcriptome of single glioblastoma (GBM) brain tumor cells in vitro over 72 hours. The GBM cells were biopsied before and after exposure to chemotherapy and radiotherapy, and our results suggest that treatment either induces or selects for more transcriptionally stable cells. We envision the nanobiopsy will contribute to transforming standard single-cell transcriptomics from a static analysis into a dynamic assay.

IDHwt glioblastomas can be stratified by their transcriptional response to standard treatment, with implications for targeted therapy.

BACKGROUND: Glioblastoma (GBM) brain tumors lacking IDH1 mutations (IDHwt) have the worst prognosis of all brain neoplasms. Patients receive surgery and chemoradiotherapy but tumors almost always fatally recur. RESULTS: Using RNA sequencing data from 107 pairs of pre- and post-standard treatment locally recurrent IDHwt GBM tumors, we identify two responder subtypes based on longitudinal changes in gene expression. In two thirds of patients, a specific subset of genes is upregulated from primary to recurrence (Up responders), and in one third, the same genes are downregulated (Down responders), specifically in neoplastic cells. Characterization of the responder subtypes indicates subtype-specific adaptive treatment resistance mechanisms that are associated with distinct changes in the tumor microenvironment. In Up responders, recurrent tumors are enriched in quiescent proneural GBM stem cells and differentiated neoplastic cells, with increased interaction with the surrounding normal brain and neurotransmitter signaling, whereas Down responders commonly undergo mesenchymal transition. ChIP-sequencing data from longitudinal GBM tumors suggests that the observed transcriptional reprogramming could be driven by Polycomb-based chromatin remodeling rather than DNA methylation. CONCLUSIONS: We show that the responder subtype is cancer-cell intrinsic, recapitulated in in vitro GBM cell models, and influenced by the presence of the tumor microenvironment. Stratifying GBM tumors by responder subtype may lead to more effective treatment.

The Epigenetic Evolution of Glioma Is Determined by the IDH1 Mutation Status and Treatment Regimen.

Tumor adaptation or selection is thought to underlie therapy resistance in glioma. To investigate longitudinal epigenetic evolution of gliomas in response to therapeutic pressure, we performed an epigenomic analysis of 132 matched initial and recurrent tumors from patients with IDH-wildtype (IDHwt) and IDH-mutant (IDHmut) glioma. IDHwt gliomas showed a stable epigenome over time with relatively low levels of global methylation. The epigenome of IDHmut gliomas showed initial high levels of genome-wide DNA methylation that was progressively reduced to levels similar to those of IDHwt tumors. Integration of epigenomics, gene expression, and functional genomics identified HOXD13 as a master regulator of IDHmut astrocytoma evolution. Furthermore, relapse of IDHmut tumors was accompanied by histologic progression that was associated with survival, as validated in an independent cohort. Finally, the initial cell composition of the tumor microenvironment varied between IDHwt and IDHmut tumors and changed differentially following treatment, suggesting increased neoangiogenesis and T-cell infiltration upon treatment of IDHmut gliomas. This study provides one of the largest cohorts of paired longitudinal glioma samples with epigenomic, transcriptomic, and genomic profiling and suggests that treatment of IDHmut glioma is associated with epigenomic evolution toward an IDHwt-like phenotype. SIGNIFICANCE: Standard treatments are related to loss of DNA methylation in IDHmut glioma, resulting in epigenetic activation of genes associated with tumor progression and alterations in the microenvironment that resemble treatment-naïve IDHwt glioma.

Investigating the effects of arginine methylation inhibitors on microdissected brain tumour biopsies maintained in a miniaturised perfusion system.

Arginine methylation is a post-translational modification that consists of the transfer of one or two methyl (CH3) groups to arginine residues in proteins. Several types of arginine methylation occur, namely monomethylation, symmetric dimethylation and asymmetric dimethylation, which are catalysed by different protein arginine methyltransferases (PRMTs). Inhibitors of PRMTs have recently entered clinical trials to target several types of cancer, including gliomas (NCT04089449). People with glioblastoma (GBM), the most aggressive form of brain tumour, are among those with the poorest quality of life and likelihood of survival of anyone diagnosed with cancer. There is currently a lack of (pre)clinical research on the possible application of PRMT inhibitors to target brain tumours. Here, we set out to investigate the effects of clinically-relevant PRMT inhibitors on GBM biopsies. We present a new, low-cost, easy to fabricate perfusion device that can maintain GBM tissue in a viable condition for at least eight days post-surgical resection. The miniaturised perfusion device enables the treatment of GBM tissue with PRMT inhibitors ex vivo, and we observed a two-fold increase in apoptosis in treated samples compared to parallel control experiments. Mechanistically, we show thousands of differentially expressed genes after treatment, and changes in the type of arginine methylation of the RNA binding protein FUS that are consistent with hundreds of differential gene splicing events. This is the first time that cross-talk between different types of arginine methylation has been observed in clinical samples after treatment with PRMT inhibitors.

GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data.

BACKGROUND: Characterizing and quantifying cell types within glioblastoma (GBM) tumors at scale will facilitate a better understanding of the association between the cellular landscape and tumor phenotypes or clinical correlates. We aimed to develop a tool that deconvolutes immune and neoplastic cells within the GBM tumor microenvironment from bulk RNA sequencing data. METHODS: We developed an IDH wild-type (IDHwt) GBM-specific single immune cell reference consisting of B cells, T-cells, NK-cells, microglia, tumor associated macrophages, monocytes, mast and DC cells. We used this alongside an existing neoplastic single cell-type reference for astrocyte-like, oligodendrocyte- and neuronal progenitor-like and mesenchymal GBM cancer cells to create both marker and gene signature matrix-based deconvolution tools. We applied single-cell resolution imaging mass cytometry (IMC) to ten IDHwt GBM samples, five paired primary and recurrent tumors, to determine which deconvolution approach performed best. RESULTS: Marker-based deconvolution using GBM-tissue specific markers was most accurate for both immune cells and cancer cells, so we packaged this approach as GBMdeconvoluteR. We applied GBMdeconvoluteR to bulk GBM RNAseq data from The Cancer Genome Atlas and recapitulated recent findings from multi-omics single cell studies with regards associations between mesenchymal GBM cancer cells and both lymphoid and myeloid cells. Furthermore, we expanded upon this to show that these associations are stronger in patients with worse prognosis. CONCLUSIONS: GBMdeconvoluteR accurately quantifies immune and neoplastic cell proportions in IDHwt GBM bulk RNA sequencing data and is accessible here: https://gbmdeconvoluter.leeds.ac.uk.

Glioma progression is shaped by genetic evolution and microenvironment interactions.

The factors driving therapy resistance in diffuse glioma remain poorly understood. To identify treatment-associated cellular and genetic changes, we analyzed RNA and/or DNA sequencing data from the temporally separated tumor pairs of 304 adult patients with isocitrate dehydrogenase (IDH)-wild-type and IDH-mutant glioma. Tumors recurred in distinct manners that were dependent on IDH mutation status and attributable to changes in histological feature composition, somatic alterations, and microenvironment interactions. Hypermutation and acquired CDKN2A deletions were associated with an increase in proliferating neoplastic cells at recurrence in both glioma subtypes, reflecting active tumor growth. IDH-wild-type tumors were more invasive at recurrence, and their neoplastic cells exhibited increased expression of neuronal signaling programs that reflected a possible role for neuronal interactions in promoting glioma progression. Mesenchymal transition was associated with the presence of a myeloid cell state defined by specific ligand-receptor interactions with neoplastic cells. Collectively, these recurrence-associated phenotypes represent potential targets to alter disease progression.

SpheroidAnalyseR-an online platform for analyzing data from 3D spheroids or organoids grown in 96-well plates.

Spheroids and organoids are increasingly popular three-dimensional (3D) cell culture models. Spheroid models are more physiologically relevant to a tumor compared to two-dimensional (2D) cultures and organoids are a simplified version of an organ with similar composition. Spheroids are often only formed from a single cell type which does not represent the situation in vivo. However, despite this, both spheroids and organoids can be used in cell migration studies, disease modelling and drug discovery. A drawback of these models is, however, the lack of appropriate analytical tools for high throughput imaging and analysis over a time course. To address this, we have developed an R Shiny app called SpheroidAnalyseR: a simple, fast, effective open-source app that allows the analysis of spheroid or organoid size data generated in a 96-well format. SpheroidAnalyseR processes and analyzes datasets of image measurements that can be obtained via a bespoke software, described herein, that automates spheroid imaging and quantification using the Nikon A1R Confocal Laser Scanning Microscope. However, templates are provided to enable users to input spheroid image measurements obtained by user-preferred methods. SpheroidAnalyseR facilitates outlier identification and removal followed by graphical visualization of spheroid measurements across multiple predefined parameters such as time, cell-type and treatment(s). Spheroid imaging and analysis can, thus, be reduced from hours to minutes, removing the requirement for substantial manual data manipulation in a spreadsheet application. The combination of spheroid generation in 96-well ultra-low attachment microplates, imaging using our bespoke software, and analysis using SpheroidAnalyseR toolkit allows high throughput, longitudinal quantification of 3D spheroid growth whilst minimizing user input and significantly improving the efficiency and reproducibility of data analysis. Our bespoke imaging software is available from https://github.com/GliomaGenomics. SpheroidAnalyseR is available at https://spheroidanalyser.leeds.ac.uk, and the source code found at https://github.com/GliomaGenomics.

Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data.

Intratumour heterogeneity provides tumours with the ability to adapt and acquire treatment resistance. The development of more effective and personalised treatments for cancers, therefore, requires accurate characterisation of the clonal architecture of tumours, enabling evolutionary dynamics to be tracked. Many methods exist for achieving this from bulk tumour sequencing data, involving identifying mutations and performing subclonal deconvolution, but there is a lack of systematic benchmarking to inform researchers on which are most accurate, and how dataset characteristics impact performance. To address this, we use the most comprehensive tumour genome simulation tool available for such purposes to create 80 bulk tumour whole exome sequencing datasets of differing depths, tumour complexities, and purities, and use these to benchmark subclonal deconvolution pipelines. We conclude that i) tumour complexity does not impact accuracy, ii) increasing either purity or purity-corrected sequencing depth improves accuracy, and iii) the optimal pipeline consists of Mutect2, FACETS and PyClone-VI. We have made our benchmarking datasets publicly available for future use.

Patient-derived organoids and orthotopic xenografts of primary and recurrent gliomas represent relevant patient avatars for precision oncology.

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.

Identification of candidate mediators of chemoresponse in breast cancer through therapy-driven selection of somatic variants.

PURPOSE: More than a third of primary breast cancer patients are treated with cytotoxic chemotherapy, typically without guidance from predictive markers. Increased use of neoadjuvant chemotherapy provides opportunities for identification of molecules associated with treatment response, by comparing matched tumour samples before and after therapy. Our hypothesis was that somatic variants of increased prevalence after therapy promote resistance, while variants with reduced prevalence cause sensitivity. METHODS: We performed systematic analyses of matched pairs of cancer exomes from primary oestrogen receptor-positive/HER2-negative breast cancers (n = 6) treated with neoadjuvant epirubicin/cyclophosphamide. We identified candidate genes as mediators of chemotherapy response by consistent subclonal changes in somatic variant prevalence through therapy, predicted variant impact on gene function, and enrichment of specific functional pathways. Influence of candidate genes on breast cancer outcome was tested using publicly available breast cancer expression data (n = 1903). RESULTS: We identified 14 genes as the strongest candidate mediators of chemoresponse: TCHH, MUC17, ARAP2, FLG2, ABL1, CENPF, COL6A3, DMBT1, ITGA7, PLXNA1, S100PBP, SYNE1, ZFHX4, and CACNA1C. Genes contained somatic variants showing prevalence changes in up to 4 patients, with up to 3 being predicted as damaging. Genes coding for extra-cellular matrix components or related signalling pathways were significantly over-represented among variants showing prevalence changes. Expression of 5 genes (TCHH, ABL1, CENPF, S100PBP, and ZFHX4) was significantly associated with patient survival. CONCLUSIONS: Genomic analysis of paired pre- and post-therapy samples resulting from neoadjuvant therapy provides a powerful method for identification of mediators of response. Genes we identified should be assessed as predictive markers or targets in chemo-sensitization.

EGFRvIII upregulates DNA mismatch repair resulting in increased temozolomide sensitivity of MGMT promoter methylated glioblastoma.

The oncogene epidermal growth factor receptor variant III (EGFRvIII) is frequently expressed in glioblastomas (GBM) but its impact on therapy response is still under controversial debate. Here we wanted to test if EGFRvIII influences the sensitivity towards the alkylating agent temozolomide (TMZ). Therefore, we retrospectively analyzed the survival of 336 GBM patients, demonstrating that under standard treatment, which includes TMZ, EGFRvIII expression is associated with prolonged survival, but only in patients with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylated tumors. Using isogenic GBM cell lines with endogenous EGFRvIII expression we could demonstrate that EGFRvIII increases TMZ sensitivity and results in enhanced numbers of DNA double-strand breaks and a pronounced S/G2-phase arrest after TMZ treatment. We observed a higher expression of DNA mismatch repair (MMR) proteins in EGFRvIII+ cells and patient tumor samples, which was most pronounced for MSH2 and MSH6. EGFRvIII-specific knockdown reduced MMR protein expression thereby increasing TMZ resistance. Subsequent functional kinome profiling revealed an increased activation of p38- and ERK1/2-dependent signaling in EGFRvIII expressing cells, which regulates MMR protein expression downstream of EGFRvIII. In summary, our results demonstrate that the oncoprotein EGFRvIII sensitizes a fraction of GBM to current standard of care treatment through the upregulation of DNA MMR.

Genomic and Expression Analyses Define MUC17 and PCNX1 as Predictors of Chemotherapy Response in Breast Cancer.

Poor-prognosis breast cancers are treated with cytotoxic chemotherapy, but often without any guidance from therapy predictive markers because universally accepted markers are not currently available. Treatment failure, in the form of recurrences, is relatively common. We aimed to identify chemotherapy predictive markers and resistance pathways in breast cancer. Our hypothesis was that tumor cells remaining after neoadjuvant chemotherapy (NAC) contain somatic variants causing therapy resistance, while variants present pre-NAC but lost post-NAC cause sensitivity. Whole-exome sequencing was performed on matched pre- and post-NAC cancer cells, which were isolated by laser microdissection, from 6 cancer cases, and somatic variants selected for or against by NAC were identified. Somatic variant diversity was significantly reduced after therapy (P < 0.05). MUC17 variants were identified in 3 tumors and were selected against by NAC in each case, while PCNX1 variants were identified in 2 tumors and were selected for in both cases, implicating the function of these genes in defining chemoresponse. In vitro knockdown of MUC17 or PCNX1 was associated with significantly increased or decreased chemotherapy sensitivity, respectively (P < 0.05), further supporting their roles in chemotherapy response. Expression was tested for predictive value in two independent cohorts of chemotherapy-treated breast cancers (n = 53, n = 303). Kaplan-Meier analyses revealed that low MUC17 expression was significantly associated with longer survival after chemotherapy, whereas low PCNX1 was significantly associated with reduced survival. We concluded that therapy-driven selection of somatic variants allows identification of chemotherapy response genes. With respect to MUC17 and PCNX1, therapy-driven selection acting on somatic variants, in vitro knockdown data concerning drug sensitivity, and survival analysis of expression levels in patient cohorts all define the genes as mediators of and predictive markers for chemotherapy response in breast cancer.

Chemically induced neurite-like outgrowth reveals a multicellular network function in patient-derived glioblastoma cells.

Tumor stem cells and malignant multicellular networks have been separately implicated in the therapeutic resistance of glioblastoma multiforme (GBM), the most aggressive type of brain cancer in adults. Here, we show that small-molecule inhibition of RHO-associated serine/threonine kinase proteins (ROCKi) significantly promoted the outgrowth of neurite-like cell projections in cultures of heterogeneous patient-derived GBM stem-like cells. These projections formed de novo-induced cellular network (iNet) 'webs', which regressed after withdrawal of ROCKi. Connected cells within the iNet web exhibited long range Ca2+ signal transmission, and significant lysosomal and mitochondrial trafficking. In contrast to their less-connected vehicle control counterparts, iNet cells remained viable and proliferative after high-dose radiation. These findings demonstrate a link between ROCKi-regulated cell projection dynamics and the formation of radiation-resistant multicellular networks. Our study identifies means to reversibly induce iNet webs ex vivo, and may thereby accelerate future studies into the biology of GBM cellular networks.

Doomed from the TERT? A Two-Stage Model of Tumorigenesis in IDH-Wild-Type Glioblastoma.

Using longitudinal molecular profiling, Körber et al. propose in this issue of Cancer Cell that IDH-wild-type glioblastomas initiate years pre-diagnosis with chromosome-level alterations that drive cell proliferation but require survival-promoting mutations, commonly in the TERT promoter, to form a detectable tumor. Multiple subclones drive disease progression, creating a therapeutic challenge.

Simulation of heterogeneous tumour genomes with HeteroGenesis and in silico whole exome sequencing.

SUMMARY: Tumour evolution results in progressive cancer phenotypes such as metastatic spread and treatment resistance. To better treat cancers, we must characterize tumour evolution and the genetic events that confer progressive phenotypes. This is facilitated by high coverage genome or exome sequencing. However, the best approach by which, or indeed whether, these data can be used to accurately model and interpret underlying evolutionary dynamics is yet to be confirmed. Establishing this requires sequencing data from appropriately heterogeneous tumours in which the exact trajectory and combination of events occurring throughout its evolution are known. We therefore developed HeteroGenesis: a tool to generate realistically evolved tumour genomes, which can be sequenced using weighted-Wessim (w-Wessim), an in silico exome sequencing tool that we have adapted from previous methods. HeteroGenesis simulates more complex and realistic heterogeneous tumour genomes than existing methods, can model different evolutionary dynamics, and enables the creation of multi-region and longitudinal data. AVAILABILITY AND IMPLEMENTATION: HeteroGenesis and w-Wessim are freely available under the GNU General Public Licence from https://github.com/GeorgetteTanner, implemented in Python and supported on linux and MS Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Intravenous delivery of oncolytic reovirus to brain tumor patients immunologically primes for subsequent checkpoint blockade.

Immune checkpoint inhibitors, including those targeting programmed cell death protein 1 (PD-1), are reshaping cancer therapeutic strategies. Evidence suggests, however, that tumor response and patient survival are determined by tumor programmed death ligand 1 (PD-L1) expression. We hypothesized that preconditioning of the tumor immune microenvironment using targeted, virus-mediated interferon (IFN) stimulation would up-regulate tumor PD-L1 protein expression and increase cytotoxic T cell infiltration, improving the efficacy of subsequent checkpoint blockade. Oncolytic viruses (OVs) represent a promising form of cancer immunotherapy. For brain tumors, almost all studies to date have used direct intralesional injection of OV, because of the largely untested belief that intravenous administration will not deliver virus to this site. We show, in a window-of-opportunity clinical study, that intravenous infusion of oncolytic human Orthoreovirus (referred to herein as reovirus) leads to infection of tumor cells subsequently resected as part of standard clinical care, both in high-grade glioma and in brain metastases, and increases cytotoxic T cell tumor infiltration relative to patients not treated with virus. We further show that reovirus up-regulates IFN-regulated gene expression, as well as the PD-1/PD-L1 axis in tumors, via an IFN-mediated mechanism. Finally, we show that addition of PD-1 blockade to reovirus enhances systemic therapy in a preclinical glioma model. These results support the development of combined systemic immunovirotherapy strategies for the treatment of both primary and secondary tumors in the brain.

How to analyse the spatiotemporal tumour samples needed to investigate cancer evolution: A case study using paired primary and recurrent glioblastoma.

Many traits of cancer progression (e.g., development of metastases or resistance to therapy) are facilitated by tumour evolution: Darwinian selection of subclones with distinct genotypes or phenotypes that enable such progression. Characterising these subclones provide an opportunity to develop drugs to better target their specific properties but requires the accurate identification of somatic mutations shared across multiple spatiotemporal tumours from the same patient. Current best practices for calling somatic mutations are optimised for single samples, and risk being too conservative to identify shared mutations with low prevalence in some samples. We reasoned that datasets from multiple matched tumours can be used for mutual validation and thus propose an adapted two-stage approach: (1) low-stringency mutation calling to identify mutations shared across samples irrespective of the weight of evidence in a single sample; (2) high-stringency mutation calling to further characterise mutations present in a single sample. We applied our approach to three-independent cohorts of paired primary and recurrent glioblastoma tumours, two of which have previously been analysed using existing approaches, and found that it significantly increased the amount of biologically relevant shared somatic mutations identified. We also found that duplicate removal was detrimental when identifying shared somatic mutations. Our approach is also applicable when multiple datasets e.g. DNA and RNA are available for the same tumour.

Genomic Subtypes of Non-invasive Bladder Cancer with Distinct Metabolic Profile and Female Gender Bias in KDM6A Mutation Frequency.

Bladder cancer incurs a higher lifetime treatment cost than other cancers due to frequent recurrence of non-invasive disease. Improved prognostic biomarkers and localized therapy are needed for this large patient group. We defined two major genomic subtypes of primary stage Ta tumors. One of these was characterized by loss of 9q including TSC1, increased KI67 labeling index, upregulated glycolysis, DNA repair, mTORC1 signaling, features of the unfolded protein response, and altered cholesterol homeostasis. Comparison with muscle-invasive bladder cancer mutation profiles revealed lower overall mutation rates and more frequent mutations in RHOB and chromatin modifier genes. More mutations in the histone lysine demethylase KDM6A were present in non-invasive tumors from females than males.