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Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal treatment. DNA methylation profiles, which capture the methylation status of thousands of individual CpG sites, are state-of-the-art data-driven means to enhance diagnostic accuracy but are also time consuming and not widely available. Here, to address these limitations, we developed Deep lEarning from histoPathoLOgy and methYlation (DEPLOY), a deep learning model that classifies CNS tumors to ten major categories from histopathology. DEPLOY integrates three distinct components: the first classifies CNS tumors directly from slide images (‘direct model’), the second initially generates predictions for DNA methylation beta values, which are subsequently used for tumor classification (‘indirect model’), and the third classifies tumor types directly from routinely available patient demographics. First, we find that DEPLOY accurately predicts beta values from histopathology images. Second, using a ten-class model trained on an internal dataset of 1,796 patients, we predict the tumor categories in three independent external test datasets including 2,156 patients, achieving an overall accuracy of 95% and balanced accuracy of 91% on samples that are predicted with high confidence. These results showcase the potential future use of DEPLOY to assist pathologists in diagnosing CNS tumors within a clinically relevant short time frame.

Ganglioglioma (GG) with anaplasia (anaplastic ganglioglioma) is a rare and controversial diagnosis. When present, anaplasia involves the glial component of the tumor, either at presentation or at recurrence. To date, most published cases lack molecular characterization. We describe the histologic and molecular features of 3 patients presenting with BRAFp. V600E-mutant GG (CNS WHO grade 1) with high-grade glial transformation at recurrence. The tumors occurred in pediatric patients (age 9–16 years) with time to recurrence from 20 months to 7 years. At presentation, each tumor was low-grade, with a BRAFV600E-positive ganglion cell component and a glial component resembling pleomorphic xanthoastrocytoma (PXA) or fibrillary astrocytoma. At recurrence, tumors resembled anaplastic PXA or high-grade astrocytomas without neuronal differentiation. CDKN2Ahomozygous deletion (HD) was absent in all primary tumors. At recurrence, 2 cases acquired CDKN2AHD; the third case showed loss of p16 and MTAP immunoexpression, but no CDKN2A/BHD or mutation was identified. By DNA methylation profiling, all primary and recurrent tumors either grouped or definitely matched to different methylation classes. Our findings indicate that malignant progression of the glial component can occur in GG and suggest that CDKN2A/Binactivation plays a significant role in this process.

In this study, Nuechterlein et al. show that the deficiency of phosphodiesterase PDE10A activates PI3K/AKT/mTOR signaling independent of PTEN and promotes proneural-to-mesenchymal transition and gliomagenesis. Their work highlights that PDE10A may function as a potential tumor suppressor and that PDE10A-deficient glioblastomas may be therapeutically vulnerable to PI3K inhibition.

Immune-mediated anti-tumoral responses, elicited by oncolytic viruses and augmented with checkpoint inhibition, may be an effective treatment approach for glioblastoma. Here in this multicenter phase 1/2 study we evaluated the combination of intratumoral delivery of oncolytic virus DNX-2401 followed by intravenous anti-PD-1 antibody pembrolizumab in recurrent glioblastoma, first in a dose-escalation and then in a dose-expansion phase, in 49 patients. The primary endpoints were overall safety and objective response rate. The primary safety endpoint was met, whereas the primary efficacy endpoint was not met. There were no dose-limiting toxicities, and full dose combined treatment was well tolerated. The objective response rate was 10.4% (90% confidence interval (CI) 4.2–20.7%), which was not statistically greater than the prespecified control rate of 5%. The secondary endpoint of overall survival at 12 months was 52.7% (95% CI 40.1–69.2%), which was statistically greater than the prespecified control rate of 20%. Median overall survival was 12.5 months (10.7–13.5 months). Objective responses led to longer survival (hazard ratio 0.20, 95% CI 0.05–0.87). A total of 56.2% (95% CI 41.1–70.5%) of patients had a clinical benefit defined as stable disease or better. Three patients completed treatment with durable responses and remain alive at 45, 48 and 60 months. Exploratory mutational, gene-expression and immunophenotypic analyses revealed that the balance between immune cell infiltration and expression of checkpoint inhibitors may potentially inform on response to treatment and mechanisms of resistance. Overall, the combination of intratumoral DNX-2401 followed by pembrolizumab was safe with notable survival benefit in select patients (ClinicalTrials.gov registration: NCT02798406).

Immature neuroectodermal tissue can be found in the ovary as part of an immature teratoma or as part of a teratoma with malignant neuroectodermal transformation. Such lesions may closely resemble central nervous system tumors, but their biologic similarity is unclear. We describe an 18-yr-old female who presented with abdominal pain caused by an ovarian mass with widespread metastases. Histology showed a primitive, high-grade tumor arising in the background of a mature teratoma. The tumor was SOX10 positive, with focal expression of GFAP, S100, NSE, and synaptophysin. Molecular analysis demonstrated co-amplification of PDGFRAand KIT, alterations common in high-grade gliomas. By whole-genome methylation profiling, it clustered into the “diffuse pediatric-type high-grade glioma, RTK1 subtype, subclass c” group. Despite progressing through 2 lines of chemotherapy with widespread metastatic disease, she achieved an excellent response to chemotherapy directed toward aggressive germ cell tumors. This case emphasizes the importance of immunohistochemical, genomic, and epigenetic analyses to accurately classify these exceedingly rare tumors and determine the optimal therapy.

Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of ‘big data’ in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment.

Despite breakthroughs in cancer immunotherapy, most tumor-reactive T cells cannot persist in solid tumors due to an immunosuppressive environment. We developed Tres (tumor-resilient T cell, https://resilience.ccr.cancer.gov/), a computational model utilizing single-cell transcriptomic data to identify signatures of T cells that are resilient to immunosuppressive signals, such as transforming growth factor-β1, tumor necrosis factor-related apoptosis-inducing ligand and prostaglandin E2. Tres reliably predicts clinical responses to immunotherapy in melanoma, lung cancer, triple-negative breast cancer and B cell malignancies using bulk T cell transcriptomic data from pre-treatment tumors from patients who received immune-checkpoint inhibitors (n= 38), infusion products for chimeric antigen receptor T cell therapies (n= 34) and pre-manufacture samples for chimeric antigen receptor T cell or tumor-infiltrating lymphocyte therapies (n= 84). Further, Tres identified FIBP, whose functions are largely unknown, as the top negative marker of tumor-resilient T cells across many solid tumor types. FIBPknockouts in murine and human donor CD8+T cells significantly enhanced T cell-mediated cancer killing in in vitro co-cultures. Further, Fibpknockout in murine T cells potentiated the in vivo efficacy of adoptive cell transfer in the B16 tumor model. Fibpknockout T cells exhibit reduced cholesterol metabolism, which inhibits effector T cell function. These results demonstrate the utility of Tres in identifying biomarkers of T cell effectiveness and potential therapeutic targets for immunotherapies in solid tumors.

Meningiomas are the most common primary intracranial tumour in adults1. Patients with symptoms are generally treated with surgery as there are no effective medical therapies. The World Health Organization histopathological grade of the tumour and the extent of resection at surgery (Simpson grade) are associated with the recurrence of disease; however, they do not accurately reflect the clinical behaviour of all meningiomas2. Molecular classifications of meningioma that reliably reflect tumour behaviour and inform on therapies are required. Here we introduce four consensus molecular groups of meningioma by combining DNA somatic copy-number aberrations, DNA somatic point mutations, DNA methylation and messenger RNA abundance in a unified analysis. These molecular groups more accurately predicted clinical outcomes compared with existing classification schemes. Each molecular group showed distinctive and prototypical biology (immunogenic, benign NF2wild-type, hypermetabolic and proliferative) that informed therapeutic options. Proteogenomic characterization reinforced the robustness of the newly defined molecular groups and uncovered highly abundant and group-specific protein targets that we validated using immunohistochemistry. Single-cell RNA sequencing revealed inter-individual variations in meningioma as well as variations in intrinsic expression programs in neoplastic cells that mirrored the biology of the molecular groups identified.

Classification of cancers by tissue-of-origin is fundamental to diagnostic pathology. While the combination of clinical data, tissue histology, and immunohistochemistry is usually sufficient, there remains a small but not insignificant proportion of difficult-to-classify cases. These challenging cases provide justification for ancillary molecular testing, including high-throughput DNA methylation array profiling, which promises cell-of-origin information and compatibility with formalin-fixed specimens. While diagnostically powerful, methylation profiling platforms are costly and technically challenging to implement, particularly for less well-resourced laboratories. To address this, we simulated the performance of “minimalist” methylation-based tests for cancer classification using publicly-available and internal institutional profiling data. These analyses showed that small and focused sets of the most informative CpG biomarkers from the arrays are sufficient for accurate diagnoses. As an illustrative example, one classifier, using information from just 53 out of about 450,000 available CpG probes, achieved an accuracy of 94.5% on 2575 fresh primary validation cases across 28 cancer types from The Cancer Genome Atlas Network. By training minimalist classifiers on formalin-fixed primary and metastatic cases, generally high accuracies were also achieved on additional datasets. These results support the potential of minimalist methylation testing, possibly via quantitative PCR and targeted next-generation sequencing platforms, in cancer classification.

Classification of cancers by tissue-of-origin is fundamental to diagnostic pathology. While the combination of clinical data, tissue histology, and immunohistochemistry is usually sufficient, there remains a small but not insignificant proportion of difficult-to-classify cases. These challenging cases provide justification for ancillary molecular testing, including high-throughput DNA methylation array profiling, which promises cell-of-origin information and compatibility with formalin-fixed specimens. While diagnostically powerful, methylation profiling platforms are costly and technically challenging to implement, particularly for less well-resourced laboratories. To address this, we simulated the performance of “minimalist” methylation-based tests for cancer classification using publicly-available and internal institutional profiling data. These analyses showed that small and focused sets of the most informative CpG biomarkers from the arrays are sufficient for accurate diagnoses. As an illustrative example, one classifier, using information from just 53 out of about 450,000 available CpG probes, achieved an accuracy of 94.5% on 2575 fresh primary validation cases across 28 cancer types from The Cancer Genome Atlas Network. By training minimalist classifiers on formalin-fixed primary and metastatic cases, generally high accuracies were also achieved on additional datasets. These results support the potential of minimalist methylation testing, possibly via quantitative PCR and targeted next-generation sequencing platforms, in cancer classification.

Definitive diagnosis of intracranial tumors relies on tissue specimens obtained by invasive surgery. Noninvasive diagnostic approaches provide an opportunity to avoid surgery and mitigate unnecessary risk to patients. In the present study, we show that DNA-methylation profiles from plasma reveal highly specific signatures to detect and accurately discriminate common primary intracranial tumors that share cell-of-origin lineages and can be challenging to distinguish using standard-of-care imaging.

Glioblastoma is the most common primary malignant brain tumor. Although current standard therapy extends median survival to ~15 months, most patients do not have a sustained response to treatment. While O6-methylguanine (O6-MeG)-DNA methyltransferase (MGMT) promoter methylation status is accepted as a prognostic and promising predictive biomarker in glioblastoma, its value in informing treatment decisions for glioblastoma patients remains debatable. Discrepancies between MGMTpromoter methylation status and treatment response in some patients may stem from inconsistencies between MGMTmethylation and expression levels in glioblastoma. Here, we discuss MGMTas a biomarker and elucidate the discordance between MGMTmethylation, expression, and patient outcome, which currently challenges the implementation of this biomarker in clinical practice.

The evolutionary processes that drive universal therapeutic resistance in adult patients with diffuse glioma remain unclear1,2. Here we analysed temporally separated DNA-sequencing data and matched clinical annotation from 222 adult patients with glioma. By analysing mutations and copy numbers across the three major subtypes of diffuse glioma, we found that driver genes detected at the initial stage of disease were retained at recurrence, whereas there was little evidence of recurrence-specific gene alterations. Treatment with alkylating agents resulted in a hypermutator phenotype at different rates across the glioma subtypes, and hypermutation was not associated with differences in overall survival. Acquired aneuploidy was frequently detected in recurrent gliomas and was characterized by IDH mutation but without co-deletion of chromosome arms 1p/19q, and further converged with acquired alterations in the cell cycle and poor outcomes. The clonal architecture of each tumour remained similar over time, but the presence of subclonal selection was associated with decreased survival. Finally, there were no differences in the levels of immunoediting between initial and recurrent gliomas. Collectively, our results suggest that the strongest selective pressures occur during early glioma development and that current therapies shape this evolution in a largely stochastic manner.

Despite decades of research, brain tumours remain among the deadliest of all forms of cancer. The ability of these tumours to resist almost all conventional and novel treatments relates, in part, to the unique cell-intrinsic and microenvironmental properties of neural tissues. In an attempt to encourage progress in our understanding and ability to successfully treat patients with brain tumours, Cancer Research UK convened an international panel of clinicians and laboratory-based scientists to identify challenges that must be overcome if we are to cure all patients with a brain tumour. The seven key challenges summarized in this Position Paper are intended to serve as foci for future research and investment.

The National Cancer Institute-led multidisciplinary Comparative Brain Tumor Consortium (CBTC) convened a glioma pathology board, comprising both veterinarian and physician neuropathologists, and conducted a comprehensive review of 193 cases of canine glioma. The immediate goal was to improve existing glioma classification methods through creation of a histologic atlas of features, thus yielding greater harmonization of phenotypic characterization. The long-term goal was to support future incorporation of clinical outcomes and genomic data into proposed simplified diagnostic schema, so as to further bridge the worlds of veterinary and physician neuropathology and strengthen validity of the dog as a naturally occurring, translationally relevant animal model of human glioma. All cases were morphologically reclassified according to a new schema devised by the entire board, yielding a majority opinion diagnosis of astrocytoma (43, 22.3%), 19 of which were low-grade and 24 high-grade, and oligodendroglioma (134, 69.4%), 35 of which were low-grade and 99 were high-grade. Sixteen cases (8.3%) could not be classified as oligodendroglioma or astrocytoma based on morphology alone and were designated as undefined gliomas. The simplified classification scheme proposed herein provides a tractable means for future addition of molecular data, and also serves to highlight histologic similarities and differences between human and canine glioma.

IMPORTANCE: The initial report of NRG Oncology/Radiation Therapy Oncology Group (RTOG) 0424 demonstrated a 3-year overall survival benefit with the addition of temozolomide to radiotherapy compared with a historical control. However, an important end point of the trial—evaluation of the association between O6-methylgaunine-DNA-methyltransferase (MGMT) promoter methylation and survival outcomes—was not previously reported. OBJECTIVE: To examine the proportion of patients in NRG Oncology/RTOG 0424 with MGMT promoter methylation and its association with survival outcomes. DESIGN, SETTING, AND PARTICIPANTS: Specimens collected were analyzed after trial completion to determine MGMT promoter methylation and IDH1/2 status and the association between MGMT status and survival outcomes. A model derived from logistic regression (MGMT-STP27) was used to calculate MGMT promoter methylation status. Univariate and multivariable analyses were performed using the Cox proportional hazards regression model to determine the association of MGMT status with survival outcomes. Patient pretreatment characteristics were included as covariates in multivariable analyses. MAIN OUTCOMES AND MEASURES: Progression-free survival (PFS) and overall survival (OS). RESULTS: Of all 129 eligible patients in NRG Oncology/RTOG 0424, 75 (58.1%) had MGMT status available (median age, 48 years; age range, 20-76 years; 42 [56.0%] male): 57 (76.0%) methylated and 18 (24.0%) unmethylated. A total of 13 unmethylated patients (72.2%) had astrocytoma as opposed to oligoastrocytoma or oligodendroglioma, whereas 23 methylated patients (40.4%) had astrocytoma. On univariate analyses, an unmethylated MGMT promoter was significantly associated with worse OS (hazard ratio [HR], 3.52; 95% CI, 1.64-7.56; P < .001) and PFS (HR, 3.06; 95% CI, 1.55-6.04; P < .001). The statistical significances were maintained in multimarker multivariable analyses, including IDH1/2 status for both OS (HR, 2.70; 95% CI, 1.02-7.14; P = .045) and PFS (HR, 2.74; 95% CI, 1.19-6.33; P = .02). CONCLUSIONS AND RELEVANCE: In this study, MGMT promoter methylation was an independent prognostic biomarker of high-risk, low-grade glioma treated with temozolomide and radiotherapy. This is the first study, to our knowledge, to validate the prognostic importance of MGMT promoter methylation in patients with grade II glioma treated with combined radiotherapy and temozolomide and highlights its potential prognostic value beyond IDH1/2 mutation status. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00114140

The goal of this study was to develop a methylation-based droplet digital PCR to separate 2 cancer classes that do not have sensitive and specific immunohistochemical stains: gastric/esophageal and pancreatic adenocarcinomas. The assay used methylation-independent primers and methylation-dependent probes to assess a single differentially methylated CpG site; analyses of array data from The Cancer Genome Atlas network showed that high methylation at the cg06118999probe supports the presence of cells originating from the stomach or esophagus (eg, as in gastric metastasis), whereas low methylation suggests that these cells are rare to absent (eg, pancreatic metastasis). On validation using formalin-fixed paraffin-embedded primary and metastatic samples from our institution, methylation-based droplet digital PCR targeting the corresponding CpG dinucleotide generated evaluable data for 60 of the 62 samples (97%) and correctly classified 50 of the 60 evaluable cases (83.3%), mostly adenocarcinomas from the stomach or pancreas. This ddPCR was created to be easy-to-interpret, rapid, inexpensive, and compatible with existing platforms at many clinical laboratories. We suggest that similarly accessible PCRs could be developed for other differentials in pathology that do not have sensitive and specific immunohistochemical stains.

The recent update of the World Health Organization (WHO) classification of tumors of the central nervous system represents a paradigm shift. Previous iterations of the classification relied on morphologic features for classification. In the 2016 update, the definitions of specific neoplastic entities tumors now include precise molecularly defined entities. This article discusses this paradigm shift, and focuses on the refinements in classification criteria, relations to previous editions, and their implication to neuropathology and neuro-oncology practice. The authors distinguish the criteria that were used to determine why molecular changes were included.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging—with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.

Genomic sequencing has driven precision-based oncology therapy; however, the genetic drivers of many malignancies remain unknown or non-targetable, so alternative approaches to the identification of therapeutic leads are necessary. Ependymomas are chemotherapy-resistant brain tumours, which, despite genomic sequencing, lack effective molecular targets. Intracranial ependymomas are segregated on the basis of anatomical location (supratentorial region or posterior fossa) and further divided into distinct molecular subgroups that reflect differences in the age of onset, gender predominance and response to therapy. The most common and aggressive subgroup, posterior fossa ependymoma group A (PF-EPN-A), occurs in young children and appears to lack recurrent somatic mutations. Conversely, posterior fossa ependymoma group B (PF-EPN-B) tumours display frequent large-scale copy number gains and losses but have favourable clinical outcomes. More than 70% of supratentorial ependymomas are defined by highly recurrent gene fusions in the NF-κB subunit gene RELA (ST-EPN-RELA), and a smaller number involve fusion of the gene encoding the transcriptional activator YAP1 (ST-EPN-YAP1). Subependymomas, a distinct histologic variant, can also be found within the supratetorial and posterior fossa compartments, and account for the majority of tumours in the molecular subgroups ST-EPN-SE and PF-EPN-SE. Here we describe mapping of active chromatin landscapes in 42 primary ependymomas in two non-overlapping primary ependymoma cohorts, with the goal of identifying essential super-enhancer-associated genes on which tumour cells depend. Enhancer regions revealed putative oncogenes, molecular targets and pathways; inhibition of these targets with small molecule inhibitors or short hairpin RNA diminished the proliferation of patient-derived neurospheres and increased survival in mouse models of ependymomas. Through profiling of transcriptional enhancers, our study provides a framework for target and drug discovery in other cancers that lack known genetic drivers and are therefore difficult to treat.

Cancer classification in the clinic is primarily based on histological analysis in the proper clinical context, often supplemented by immunohistochemical and molecular studies. Recent genomic studies have shown the potential of integrated multiomics platforms for molecular classification. We performed unsupervised analyses of molecular platforms in The Cancer Genome Atlas data (n = 6,216 samples) in comparison with tumor type. Our data showed that mRNA signatures and DNA methylation signatures mapped to histological diagnosis with high accuracy (95% and 88%, respectively) as individual platforms. The accuracy of mRNA signatures alone for classification and subtype identification was comparable to accuracies reported in the previously published Pan-Cancer 12 analysis. When combined, mRNA and methylation revealed a set of outliers for which the mRNA- and methylation-based molecular signatures concordantly differed from the original histological diagnosis. A subset remained consistent as outliers after using alternative classification and clustering algorithms and analysis of an independent molecular platform (miRNA). Overall, our results indicate that unsupervised approaches with individual genomic platforms, especially gene expression and DNA methylation, provide substantial classification information and identify occasional outlier cases in which the molecular signature is distinct from signatures expected for a given histological diagnosis. Identification of cases in which the molecular signature correlates with a specific histology that differs from initial impressions may prompt reconsideration of tumor classification in specific cases.

IMPORTANCE: There is a need for a more refined, molecularly based classification model for glioblastoma (GBM) in the temozolomide era. OBJECTIVE: To refine the existing clinically based recursive partitioning analysis (RPA) model by incorporating molecular variables. DESIGN, SETTING, AND PARTICIPANTS: NRG Oncology RTOG 0525 specimens (n = 452) were analyzed for protein biomarkers representing key pathways in GBM by a quantitative molecular microscopy-based approach with semiquantitative immunohistochemical validation. Prognostic significance of each protein was examined by single-marker and multimarker Cox regression analyses. To reclassify the prognostic risk groups, significant protein biomarkers on single-marker analysis were incorporated into an RPA model consisting of the same clinical variables (age, Karnofsky Performance Status, extent of resection, and neurologic function) as the existing RTOG RPA. The new RPA model (NRG-GBM-RPA) was confirmed using traditional immunohistochemistry in an independent data set (n = 176). MAIN OUTCOMES AND MEASURES: Overall survival (OS). RESULTS: In 452 specimens, MGMT (hazard ratio [HR], 1.81; 95% CI, 1.37-2.39; P < .001), survivin (HR, 1.36; 95% CI, 1.04-1.76; P = .02), c-Met (HR, 1.53; 95% CI, 1.06-2.23; P = .02), pmTOR (HR, 0.76; 95% CI, 0.60-0.97; P = .03), and Ki-67 (HR, 1.40; 95% CI, 1.10-1.78; P = .007) protein levels were found to be significant on single-marker multivariate analysis of OS. To refine the existing RPA, significant protein biomarkers together with clinical variables (age, Karnofsky Performance Status, extent of resection, and neurological function) were incorporated into a new model. Of 166 patients used for the new NRG-GBM-RPA model, 97 (58.4%) were male (mean [SD] age, 55.7 [12.0] years). Higher MGMT protein level was significantly associated with decreased MGMT promoter methylation and vice versa (1425.1 for methylated vs 1828.0 for unmethylated; P < .001). Furthermore, MGMT protein expression (HR, 1.84; 95% CI, 1.38-2.43; P < .001) had greater prognostic value for OS compared with MGMT promoter methylation (HR, 1.77; 95% CI, 1.28-2.44; P < .001). The refined NRG-GBM-RPA consisting of MGMT protein, c-Met protein, and age revealed greater separation of OS prognostic classes compared with the existing clinically based RPA model and MGMT promoter methylation in NRG Oncology RTOG 0525. The prognostic significance of the NRG-GBM-RPA was subsequently confirmed in an independent data set (n = 176). CONCLUSIONS AND RELEVANCE: This new NRG-GBM-RPA model improves outcome stratification over both the current RTOG RPA model and MGMT promoter methylation, respectively, for patients with GBM treated with radiation and temozolomide and was biologically validated in an independent data set. The revised RPA has the potential to contribute to improving the accurate assessment of prognostic groups in patients with GBM treated with radiation and temozolomide and to influence clinical decision making. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00304031

Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers.

Schwannomas are common peripheral nerve sheath tumors that can cause debilitating morbidities. We performed an integrative analysis to determine genomic aberrations common to sporadic schwannomas. Exome sequence analysis with validation by targeted DNA sequencing of 125 samples uncovered, in addition to expected NF2 disruption, recurrent mutations in ARID1A, ARID1B and DDR1. RNA sequencing identified a recurrent in-frame SH3PXD2A-HTRA1 fusion in 12/125 (10%) cases, and genomic analysis demonstrated the mechanism as resulting from a balanced 19-Mb chromosomal inversion on chromosome 10q. The fusion was associated with male gender predominance, occurring in one out of every six men with schwannoma. Methylation profiling identified distinct molecular subgroups of schwannomas that were associated with anatomical location. Expression of the SH3PXD2A-HTRA1 fusion resulted in elevated phosphorylated ERK, increased proliferation, increased invasion and in vivo tumorigenesis. Targeting of the MEK-ERK pathway was effective in fusion-positive Schwann cells, suggesting a possible therapeutic approach for this subset of tumors.

Emerging evidence has shown that GSK3β plays oncogenic roles in multiple tumour types; however, the underlying mechanisms remain largely unknown. Here, we show that nuclear GSK3β is responsible for the accumulation of the histone demethylase KDM1A and critically regulates histone H3K4 methylation during tumorigenesis. GSK3β phosphorylates KDM1A Ser683 upon priming phosphorylation of KDM1A Ser687 by CK1α. Phosphorylation of KDM1A induces its binding with and deubiquitylation by USP22, leading to KDM1A stabilization. GSK3β- and USP22-dependent KDM1A stabilization is required for the demethylation of histone H3K4, thereby repressing BMP2, CDKN1A and GATA6 transcription, which results in cancer stem cell self-renewal and glioblastoma tumorigenesis. In human glioblastoma specimens, KDM1A levels are correlated with nuclear GSK3β and USP22 levels. Furthermore, a GSK3 inhibitor, tideglusib, sensitizes tumour xenografts to chemotherapy in mice via KDM1A downregulation and improves survival. Our findings demonstrate that nuclear GSK3β- and USP22-mediated KDM1A stabilization is essential for glioblastoma tumorigenesis.

Tumors of the CNS are composed of a complex mixture of neoplastic cells, in addition to vascular, inflammatory and stromal components. Similar to most other tumors, brain tumors contain a heterogeneous population of cells that are found at different stages of differentiation. The cancer stem cell hypothesis suggests that all tumors are composed of subpopulation of cells with stem-like properties, which are capable of self-renewal, display resistance to therapy and lead to tumor recurrence. One of the most important transcription factors that regulate cancer stem cell properties is SOX2. In this review, we focus on SOX2 and the complex network of signaling molecules and transcription factors that regulate its expression and function in brain tumor initiating cells. We also highlight important findings in the literature about the role of SOX2 in glioblastoma and medulloblastoma, where it has been more extensively studied.

Tumors of the CNS are composed of a complex mixture of neoplastic cells, in addition to vascular, inflammatory and stromal components. Similar to most other tumors, brain tumors contain a heterogeneous population of cells that are found at different stages of differentiation. The cancer stem cell hypothesis suggests that all tumors are composed of subpopulation of cells with stem-like properties, which are capable of self-renewal, display resistance to therapy and lead to tumor recurrence. One of the most important transcription factors that regulate cancer stem cell properties is SOX2. In this review, we focus on SOX2 and the complex network of signaling molecules and transcription factors that regulate its expression and function in brain tumor initiating cells. We also highlight important findings in the literature about the role of SOX2 in glioblastoma and medulloblastoma, where it has been more extensively studied.

IMPORTANCE: The molecular-genetic alterations contributing to the pathogenesis of sebaceous carcinoma and sebaceous adenoma remain poorly understood. Given that sebaceous carcinoma is associated with substantial morbidity and mortality, there is a critical need to delineate the pathways driving sebaceous carcinoma and candidate molecules for targeted therapy. OBJECTIVE: To describe differentially expressed microRNAs (miRNAs) in a series of periocular sebaceous carcinomas compared with sebaceous adenomas in order to identify pathways driving the pathogenesis of sebaceous carcinoma. DESIGN, SETTING, AND PARTICIPANTS: Thirty sebaceous carcinomas and 23 sebaceous adenomas (including 11 that were confirmed to be related to Muir-Torre syndrome and 6 that were confirmed to be sporadic) were obtained from archives (from 48 patients) of 2 institutions (University of Texas MD Anderson Cancer Center, Houston, and Siriraj Hospital, Mahidol University, Bangkok, Thailand) and profiled. MAIN OUTCOMES AND MEASURES: Expression of miRNAs was determined using total RNA from formalin-fixed, paraffin-embedded tissue and real-time reverse transcription–polymerase chain reaction performed in a microfluidics card containing 378 unique miRNAs. Fold change was determined using the ΔΔCt method (reference probe, RNU48). Median centering was used to normalize the data. Two-sample t tests were used to identify differentially expressed miRNAs. The false discovery rate was assessed by β-uniform mixture analysis of P values from the t statistics. Significance was defined by this estimated false discovery rate. RESULTS: Serial testing and validation confirmed overexpression of 2 miRNAs previously reported to be oncogenic, miR-486-5p (4.4-fold; P = 2.4 × 10−8) and miR-184 (3.5-fold; P = 1.7 × 10−6), in sebaceous carcinoma compared with sebaceous adenoma and downregulation of 2 miRNAs previously reported to have tumor-suppressive properties, miR-211 (−5.8-fold; P = 2.3 × 10−9) and miR-518d (−4.5-fold; 6.7 × 10−5), in sebaceous carcinoma compared with sebaceous adenoma. CONCLUSIONS AND RELEVANCE: Sebaceous carcinoma exhibits an miRNA expression profile distinct from that of sebaceous adenoma, implicating dysregulation of NF-κB and PTEN (targets of miR-486-5p) and TGF-β signaling (target of miR-211) in the pathogenesis of sebaceous carcinoma. The identification of miRNAs whose expression is altered in sebaceous carcinoma compared with sebaceous adenoma provides a novel entry point for a more comprehensive understanding of the molecular-genetic alterations pivotal to the development of sebaceous carcinoma.

Rhabdoid histology in clear-cell renal cell carcinoma is associated with a poor prognosis. The prognosis of patients with clear-cell renal cell carcinoma may also be influenced by molecular alterations. The aim of this study was to evaluate the association between histologic features and salient molecular changes in rhabdoid clear-cell renal cell carcinoma. We macrodissected the rhabdoid and clear-cell epithelioid components from 12 cases of rhabdoid clear-cell renal cell carcinoma. We assessed cancer-related mutations from eight cases using a clinical next-generation exome-sequencing platform. The transcriptome of rhabdoid clear-cell renal cell carcinoma (n=8) and non-rhabdoid clear-cell renal cell carcinoma (n=37) was assessed by RNA-seq and gene expression microarray. VHL (63%) showed identical mutations in all regions from the same tumor. BAP1 (38%) and PBRM1 (13%) mutations were identified in the rhabdoid but not in the epithelioid component and were mutually exclusive in 3/3 cases and 1 case, respectively. SETD2 (63%) mutations were discordant between different histologic regions in 2/5 cases, with mutations called only in the epithelioid and rhabdoid components, respectively. The transcriptome of rhabdoid clear-cell renal cell carcinoma was distinct from advanced-stage and high-grade clear-cell renal cell carcinoma. The diverse histologic components of rhabdoid clear-cell renal cell carcinoma, however, showed a similar transcriptomic program, including a similar prognostic gene expression signature. Rhabdoid clear-cell renal cell carcinoma is transcriptomically distinct and shows a high rate of SETD2 and BAP1 mutations and a low rate of PBRM1 mutations. Driver mutations in clear-cell renal cell carcinoma are often discordant across different morphologic regions, whereas the gene expression program is relatively stable. Molecular profiling of clear-cell renal cell carcinoma may improve by assessing for gene expression and sampling tumor foci from different histologic regions.