Data from Glioblastoma TCGA Mesenchymal and IGS 23 Tumors are Identifiable by IHC and have an Immune-phenotype Indicating a Potential Benefit from Immunotherapy (original) (raw)
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Clinical Cancer Research, 2020
Purpose: Molecular subtype classifications in glioblastoma may detect therapy sensitivities. IHC would potentially allow the identification of molecular subtypes in routine clinical practice. Experimental Design: Formalin-fixed, paraffin-embedded tumor samples of 124 uniformly treated, newly diagnosed patients with glioblastoma were submitted to RNA sequencing, IHC, and immune-phenotyping to identify differences in molecular subtypes associated with treatment sensitivities. Results: We detected high molecular and IHC overlapping of the The Cancer Genome Atlas (TCGA) mesenchymal subtype with instrinsic glioma subtypes (IGS) cluster 23 and of the TCGA classical subtype with IGS cluster 18. IHC patterns, gene fusion profiles, and immune-phenotypes varied across subtypes. IHC revealed that the TCGA classical subtype was identified by high expression of EGFR and low expression of PTEN, while the mesenchymal subtype was identified by low expression of SOX2 and high expression of two antib...
Acta Neuropathologica Communications, 2022
The tumor micro-environment (TME) plays an important role in various cancers, including gliomas. We estimated immune cell type-specific gene expression profiles in 3 large clinically annotated glioma datasets using CIBERSORTx and LM22/LM10 blood-based immune signatures and found that the proportions and estimated gene expression patterns of specific immune cells significantly varied according to IDH mutation status. When IDH-WT and IDH-MUT tumors were considered separately, cluster-of-cluster analyses of immune cell gene expression identified groups with distinct survival outcomes. We confirmed and extended these findings by applying a signature matrix derived from single-cell RNA-sequencing data derived from 19 glioma tumor samples to the bulk profiling data, validating findings from the LM22/LM10 results. To link immune cell signatures with outcomes in checkpoint therapy, we then showed a significant association of monocytic lineage cell gene expression clusters with patient survi...
Immune landscapes associated with different glioblastoma molecular subtypes
Acta Neuropathologica Communications, 2019
Recent work has highlighted the tumor microenvironment as a central player in cancer. In particular, interactions between tumor and immune cells may help drive the development of brain tumors such as glioblastoma multiforme (GBM). Despite significant research into the molecular classification of glioblastoma, few studies have characterized in a comprehensive manner the immune infiltrate in situ and within different GBM subtypes.In this study, we use an unbiased, automated immunohistochemistry-based approach to determine the immune phenotype of the four GBM subtypes (classical, mesenchymal, neural and proneural) in a cohort of 98 patients. Tissue Micro Arrays (TMA) were stained for CD20 (B lymphocytes), CD5, CD3, CD4, CD8 (T lymphocytes), CD68 (microglia), and CD163 (bone marrow derived macrophages) antibodies. Using automated image analysis, the percentage of each immune population was calculated with respect to the total tumor cells. Mesenchymal GBMs displayed the highest percentag...
Therapeutic Advances in Medical Oncology
Introduction: Glioblastoma (GBM), isocitrate dehydrogenase ( IDH) wild-type ( IDHwt), and grade 4 astrocytomas, IDH mutant ( IDHmut), are the most common and aggressive primary malignant brain tumors in adults. A better understanding of the tumor immune microenvironment may provide new biomarkers and therapeutic opportunities. Objectives: We aimed to evaluate the expression profile of 730 immuno-oncology-related genes in patients with IDHwt GBM and IDHmut tumors and identify prognostic biomarkers and a gene signature associated with patient survival. Methods: RNA was isolated from formalin-fixed, paraffin-embedded sections of 99 tumor specimens from patients treated with standard therapy. Gene expression profile was assessed using the Pan-Cancer Immune Profiling Panel (Nanostring Technologies, Inc., Seattle, WA, USA). Data analysis was performed using nSolverSoftware and validated in The Cancer Genome Atlas. In addition, we developed a prognostic signature using the cox regression a...
Immune genes are associated with human glioblastoma pathology and patient survival
2012
Background: Glioblastoma (GBM) is the most common and lethal primary brain tumor in adults. Several recent transcriptomic studies in GBM have identified different signatures involving immune genes associated with GBM pathology, overall survival (OS) or response to treatment. Methods: In order to clarify the immune signatures found in GBM, we performed a co-expression network analysis that grouped 791 immune-associated genes (IA genes) in large clusters using a combined dataset of 161 GBM specimens from published databases. We next studied IA genes associated with patient survival using 3 different statistical methods. We then developed a 6-IA gene risk predictor which stratified patients into two groups with statistically significantly different survivals. We validated this risk predictor on two other Affymetrix data series, on a local Agilent data series, and using RT-Q-PCR on a local series of GBM patients treated by standard chemo-radiation therapy. Results: The co-expression network analysis of the immune genes disclosed 6 powerful modules identifying innate immune system and natural killer cells, myeloid cells and cytokine signatures. Two of these modules were significantly enriched in genes associated with OS. We also found 108 IA genes linked to the immune system significantly associated with OS in GBM patients. The 6-IA gene risk predictor successfully distinguished two groups of GBM patients with significantly different survival (OS low risk: 22.3 months versus high risk: 7.3 months; p < 0.001). Patients with significantly different OS could even be identified among those with known good prognosis (methylated MGMT promoter-bearing tumor) using Agilent (OS 25 versus 8.1 months; p < 0.01) and RT-PCR (OS 21.8 versus 13.9 months; p < 0.05) technologies. Interestingly, the 6-IA gene risk could also distinguish proneural GBM subtypes. Conclusions: This study demonstrates the immune signatures found in previous GBM genomic analyses and suggests the involvement of immune cells in GBM biology. The robust 6-IA gene risk predictor should be helpful in establishing prognosis in GBM patients, in particular in those with a proneural GBM subtype, and even in the well-known good prognosis group of patients with methylated MGMT promoter-bearing tumors.
Relevance of a TCGA-derived Glioblastoma Subtype Gene-Classifier among Patient Populations
Scientific Reports
Glioblastoma multiforme (GBM), a deadly cancer, is the most lethal and common malignant brain tumor, and the leading cause of death in adult brain tumors. While genomic data continues to rocket, clinical application and translation to patient care are lagging behind. Big data now deposited in the TCGA network offers a window to generate novel clinical hypotheses. We hypothesized that a TCGAderived gene-classifier can be applied across different gene profiling platforms and population groups. This gene-classifier validated three robust GBM-subtypes across six different platforms, among Caucasian, Korean and Chinese populations: Three Caucasian-predominant TCGA-cohorts (Affymetrix U133A = 548, Agilent Custom-Array = 588, RNA-seq = 168), and three Asian-cohorts (Affymetrix Human Gene 1.0ST-Array = 61, Illumina = 52, Agilent 4 × 44 K = 60). To understand subtype-relevance in patient therapy, we investigated retrospective TCGA patient clinical sets. Subtype-specific patient survival outcome was similarly poor and reflected the net result of a mixture of treatment regimens with/without surgical resection. As a proof-of-concept, in subtype-specific patient-derived orthotopic xenograft (PDOX) mice, Classical-subtype demonstrated no survival difference comparing radiationtherapy versus temozolomide monotherapies. Though preliminary, a PDOX model of Proneural/Neuralsubtype demonstrated significantly improved survival with temozolomide compared to radiationtherapy. A larger scale study using this gene-classifier may be useful in clinical outcome prediction and patient selection for trials based on subtyping. Glioblastoma (GBM), a deadly brain cancer, is the most lethal and common malignant brain tumor and the leading cause of death in adult brain tumors. Despite the advances in genomics and molecular classification 1-8 , the survival of GBM patients has not improved over the last decade 9-11. The median survival of GBM patients is less than 16 months despite a multitude of therapies 9-11. Standard of care is radiation therapy and temozolomide, which gives the best 2-year overall survival of about 25% 9-11. While genomic data continues to rocket, clinical application and translation to patient care are lagging behind. Big data now deposited in The Cancer Genome Atlas (TCGA) network offers a window to generate novel clinical hypotheses. One overarching theme is how
Cancer Cell, 2009
The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM). We describe a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of EGFR, NF1, and PDGFRA/IDH1 each define the Classical, Mesenchymal, and Proneural subtypes, respectively. Gene signatures of normal brain cell types show a strong relationship between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype, with the greatest benefit in the Classical subtype and no benefit in the Proneural subtype. We provide a framework that unifies transcriptomic and genomic dimensions for GBM molecular stratification with important implications for future studies.
2016
We leveraged IDH wild type glioblastomas and derivative neurospheres to define tumor-intrinsic transcription phenotypes. Transcriptomic multiplicity correlated with increased intratumoral heterogeneity and tumor microenvironment presence. In silico cell sorting demonstrated that M2 macrophages/microglia are the most frequent type of immune cells in the glioma microenvironment, followed by CD4 T lymphocytes and neutrophils. Hypermutation associated with CD8+ T cell enrichment. Longitudinal transcriptome analysis of 124 pairs of primary and recurrent gliomas showed expression subtype is retained in 53% of cases with no proneural to mesenchymal transition being apparent. Inference of the tumor microenvironment through gene signatures revealed a decrease in invading monocytes but a subtype dependent increase in M2 macrophages/microglia cells after disease recurrence. All expression datasets are accessible through http://recur.bioinfo.cnio.es/.
Subclassification of Newly Diagnosed Glioblastomas through an Immunohistochemical Approach
PLOS ONE, 2014
Molecular signatures in Glioblastoma (GBM) have been described that correlate with clinical outcome and response to therapy. The Proneural (PN) and Mesenchymal (MES) signatures have been identified most consistently, but others including Classical (CLAS) have also been reported. The molecular signatures have been detected by array techniques at RNA and DNA level, but these methods are costly and cannot take into account individual contributions of different cells within a tumor. Therefore, the aim of this study was to investigate whether subclasses of newly diagnosed GBMs could be assessed and assigned by application of standard pathology laboratory procedures. 123 newly diagnosed GBMs were analyzed for the tumor cell expression of 23 pre-identified proteins and EGFR amplification, together allowing for the subclassification of 65% of the tumors. Immunohistochemistry (IHC)-based profiling was found to be analogous to transcription-based profiling using a 9-gene transcriptional signature for PN and MES subclasses. Based on these data a novel, minimal IHC-based scheme for subclass assignment for GBMs is proposed. Positive staining for IDH1 R132H can be used for PN subclass assignment, high EGFR expression for the CLAS subtype and a combined high expression of PTEN, VIM and/or YKL40 for the MES subclass. The application of the proposed scheme was evaluated in an independent tumor set, which resulted in similar subclass assignment rates as those observed in the training set. The IHC-based subclassification scheme
Neuro-Oncology, 2017
Glioblastoma expression subtypes have been previously been associated with genomic abnormalities, treatment response, and differences in tumor microenvironment. We leveraged IDH wild-type glioblastomas, derivative neurospheres, and single cell gene expression profiles to define three tumorintrinsic transcriptional subtypes designated as proneural, mesenchymal, and classical, a revision of the previously reported TCGA subtypes. Transcriptomic subtype multiplicity correlated with increased intratumoral heterogeneity and the presence of tumor microenvironment. In silico cell sorting identified macrophages/microglia, CD4 + T lymphocytes, and neutrophils in the glioma microenvironment. NF1 deficiency resulted in increased tumorassociated macrophages/microglia infiltration. Comparison of matching primary and recurrent gliomas elucidated treatment-induced phenotypic tumor evolution, including expression subtype switching, in 45% of our cohort as well as associations between microenvironmental components and treatment response. Gene signature-based tumor microenvironment inference revealed a decrease in invading monocytes and a subtype-dependent increase in macrophages/microglia cells upon disease recurrence. Hypermutation at diagnosis or at recurrence was associated with CD8 + T cell enrichment. Frequency of M2 macrophage detection was associated with short-term relapse after radiation therapy. Our study provides a comprehensive transcriptional and cellular landscape of IDH wild-type glioblastoma during treatment modulated tumor evolution. Characterization of the evolving glioblastoma transcriptome and tumor microenvironment aids in designing more effective immunotherapy trials.