Cell Lineage-Based Stratification for Glioblastoma.
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ABSTRACT:
SUBMITTER: Wang Z
PROVIDER: S-EPMC7494533 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:While the predominant elderly and the pediatric glioblastomas have been extensively investigated, young adult glioblastomas were understudied. In this study, we sought to stratify young adult glioblastomas by BRAF, H3F3A and IDH1 mutations and examine the clinical relevance of the biomarkers. In 107 glioblastomas aged from 17 to 35 years, mutually exclusive BRAF-V600E (15%), H3F3A-K27M (15.9%), H3F3A-G34R/V (2.8%) and IDH1-R132H (16.8%) mutations were identified in over half of the cases. EGFR amplification and TERTp mutation were only detected in 3.7% and 8.4% in young adult glioblastomas, respectively. BRAF-V600E identified a clinically favorable subset of glioblastomas with younger age, frequent CDKN2A homozygous deletion, and was more amendable to surgical resection. H3F3A-K27M mutated glioblastomas were tightly associated with midline locations and showed dismal prognosis. IDH1-R132H was associated with older age and favorable outcome. Interestingly, tumors with positive PDGFRA immunohistochemical expression exhibited poorer prognosis and identified an aggressive subset of tumors among K27M mutated glioblastomas. Combining BRAF, H3F3A and IDH1 mutations allowed stratification of young adult glioblastomas into four prognostic subgroups. In summary, our study demonstrates the clinical values of stratifying young adult glioblastomas with BRAF, H3F3A and IDH1 mutations, which has important implications in refining prognostic classification of glioblastomas.
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Project description:Several classification systems have been proposed to address genomic heterogeneity of glioblastoma multiforme, but they either showed limited prognostic value and/or are difficult to implement in routine diagnostics. Here we propose a prognostic stratification model for these primary tumors based on tumor gene amplification profiles, that might be easily implemented in routine diagnostics, and potentially improve the patients management. Gene amplification profiles were prospectively evaluated in 80 primary glioblastoma multiforme tumors using single-nucleotide polymorphism arrays and the results obtained validated in publicly available data from 267/347 cases. Gene amplification was detected in 45% of patients, and chromosome 7p11.2 including the EGFR gene, was the most frequently amplified chromosomal region - either alone (18%) or in combination with amplification of DNA sequences in other chromosomal regions (10% of cases). Other frequently amplified DNA sequences included regions in chromosomes 12q(10%), 4q12(7%) and 1q32.1(4%). Based on their gene amplification profiles, glioblastomas were subdivided into: i) tumors with no gene amplification (55%); ii) tumors with chromosome 7p/EGFR gene amplification (with or without amplification of other chromosomal regions) (38%); and iii) glioblastoma multiforme with a single (11%) or multiple (6%) amplified DNA sequences in chromosomal regions other than chromosome 7p. From the prognostic point of view, these amplification profiles showed a significant impact on overall survival of glioblastoma multiforme patients (p>0.001). Based on these gene amplification profiles, a risk-stratification scoring system was built for prognostic stratification of glioblastoma which might be easily implemented in routine diagnostics, and potentially contribute to improved patient management.
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Project description:The contribution of lineage identity and differentiation state to malignant transformation is controversial. We have previously shown that adult neural stem and early progenitor cells give origin to glioblastoma. Here we systematically assessed the tumor-initiating potential of adult neural populations at various stages of lineage progression. Cell type-specific tamoxifen-inducible Cre recombinase transgenes were used to target glioblastoma-relevant tumor suppressors Nf1, Trp53 and Pten in late-stage neuronal progenitors, neuroblasts and differentiated neurons. Mutant mice showed cellular and molecular defects demonstrating the impact of tumor suppressor loss, with mutant neurons being the most resistant to early changes associated with tumor development. However, we observed no evidence of glioma formation. These studies show that increasing lineage restriction is accompanied by decreasing susceptibility to malignant transformation, indicating a glioblastoma cell-of-origin hierarchy in which stem cells sit at the apex and differentiated cell types are least susceptible to tumorigenesis.
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Project description:Stem cells that adopt distinct lineages cannot be distinguished based on traditional cell shape. This study reports that higher-order variations in cell shape and cytoskeletal organization that occur within hours of stimulation forecast the lineage commitment fates of human mesenchymal stem cells (hMSCs). The unique approach captures numerous early (24 h), quantitative features of actin fluororeporter shapes, intensities, textures, and spatial distributions (collectively termed morphometric descriptors). The large number of descriptors are reduced into "combinations" through which distinct subpopulations of cells featuring unique combinations are identified. We demonstrate that hMSCs cultured on fibronectin-treated glass substrates under environments permissive to bone lineage induction could be readily discerned within the first 24 h from those cultured in basal- or fat-inductive conditions by such cytoskeletal feature groupings. We extend the utility of this approach to forecast osteogenic stem cell lineage fates across a series of synthetic polymeric materials of diverse physicochemical properties. Within the first 24 h following stem cell seeding, we could successfully "profile" the substrate responsiveness prospectively in terms of the degree of bone versus nonbone predisposition. The morphometric methodology also provided insights into how substrates may modulate the pace of osteogenic lineage specification. Cells on glass substrates deficient in fibronectin showed a similar divergence of lineage fates, but delayed beyond 48 h. In summary, this high-content imaging and single cell modeling approach offers a framework to elucidate and manipulate determinants of stem cell behaviors, as well as to screen stem cell lineage modulating materials and environments.
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Project description:BackgroundTumors can evolve and adapt to therapeutic pressure by acquiring genetic and epigenetic alterations that may be transient or stable. A precise understanding of how such events contribute to intratumoral heterogeneity, dynamic subpopulations, and overall tumor fitness will require experimental approaches to prospectively label, track, and characterize resistant or otherwise adaptive populations at the single-cell level. In glioblastoma, poor efficacy of receptor tyrosine kinase (RTK) therapies has been alternatively ascribed to genetic heterogeneity or to epigenetic transitions that circumvent signaling blockade.ResultsWe combine cell lineage barcoding and single-cell transcriptomics to trace the emergence of drug resistance in stem-like glioblastoma cells treated with RTK inhibitors. Whereas a broad variety of barcoded lineages adopt a Notch-dependent persister phenotype that sustains them through early drug exposure, rare subclones acquire genetic changes that enable their rapid outgrowth over time. Single-cell analyses reveal that these genetic subclones gain copy number amplifications of the insulin receptor substrate-1 and substrate-2 (IRS1 or IRS2) loci, which activate insulin and AKT signaling programs. Persister-like cells and genomic amplifications of IRS2 and other loci are evident in primary glioblastomas and may underlie the inefficacy of targeted therapies in this disease.ConclusionsA method for combined lineage tracing and scRNA-seq reveals the interplay between complementary genetic and epigenetic mechanisms of resistance in a heterogeneous glioblastoma tumor model.
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Project description:ADC as a marker of tumor cellularity has been promising for evaluating the response to therapy in patients with glioblastoma but does not successfully stratify patients according to outcomes, especially in the upfront setting. Here we investigate whether restriction spectrum imaging, an advanced diffusion imaging model, performed after an operation but before radiation therapy, could improve risk stratification in patients with newly diagnosed glioblastoma relative to ADC.Pre-radiation therapy diffusion-weighted and structural imaging of 40 patients with glioblastoma were examined retrospectively. Restriction spectrum imaging and ADC-based hypercellularity volume fraction (restriction spectrum imaging-FLAIR volume fraction, restriction spectrum imaging-contrast-enhanced volume fraction, ADC-FLAIR volume fraction, ADC-contrast-enhanced volume fraction) and intensities (restriction spectrum imaging-FLAIR 90th percentile, restriction spectrum imaging-contrast-enhanced 90th percentile, ADC-FLAIR 10th percentile, ADC-contrast-enhanced 10th percentile) within the contrast-enhanced and FLAIR hyperintensity VOIs were calculated. The association of diffusion imaging metrics, contrast-enhanced volume, and FLAIR hyperintensity volume with progression-free survival and overall survival was evaluated by using Cox proportional hazards models.Among the diffusion metrics, restriction spectrum imaging-FLAIR volume fraction was the strongest prognostic metric of progression-free survival (P = .036) and overall survival (P = .007) in a multivariate Cox proportional hazards analysis, with higher values indicating earlier progression and shorter survival. Restriction spectrum imaging-FLAIR 90th percentile was also associated with overall survival (P = .043), with higher intensities, indicating shorter survival. None of the ADC metrics were associated with progression-free survival/overall survival. Contrast-enhanced volume exhibited a trend toward significance for overall survival (P = .063).Restriction spectrum imaging-derived cellularity in FLAIR hyperintensity regions may be a more robust prognostic marker than ADC and conventional imaging for early progression and poorer survival in patients with glioblastoma. However, future studies with larger samples are needed to explore its predictive ability.
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Project description:Glioblastoma multiforme (GBM) is the most common primary brain tumour in adults. The standard therapy for GBM is maximal surgical resection followed by radiotherapy with concurrent and adjuvant temozolomide (TMZ). In spite of the extensive treatment, the disease is associated with poor clinical outcome. Further intensification of the standard treatment is limited by the infiltrating growth of the GBM in normal brain areas, the expected neurological toxicities with radiation doses >60?Gy and the dose-limiting toxicities induced by systemic therapy. To improve the outcome of patients with GBM, alternative treatment modalities which add low or no additional toxicities to the standard treatment are needed. Many Phase II trials on new chemotherapeutics or targeted drugs have indicated potential efficacy but failed to improve the overall or progression-free survival in Phase III clinical trials. In this review, we will discuss contemporary issues related to recent technical developments and new metabolic strategies for patients with GBM including MR (spectroscopy) imaging, (amino acid) positron emission tomography (PET), amino acid PET, surgery, radiogenomics, particle therapy, radioimmunotherapy and diets.
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Project description:Evading T cell surveillance is a hallmark of cancer. Patients with solid tissue malignancy, such as glioblastoma (GBM), have multiple forms of immune dysfunction, including defective T cell function. T cell dysfunction is exacerbated by standard treatment strategies such as steroids, chemotherapy, and radiation. Reinvigoration of T cell responses can be achieved by utilizing adoptively transferred T cells, including CAR T cells. However, these cells are at risk for depletion and dysfunction as well. This review will discuss adoptive T cell transfer strategies and methods to avoid T cell dysfunction for the treatment of brain cancer.
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Project description:PurposeTo (i) create a survival risk score using radiomic features from the tumor habitat on routine MRI to predict progression-free survival (PFS) in glioblastoma and (ii) obtain a biological basis for these prognostic radiomic features, by studying their radiogenomic associations with molecular signaling pathways.Experimental designTwo hundred three patients with pretreatment Gd-T1w, T2w, T2w-FLAIR MRI were obtained from 3 cohorts: The Cancer Imaging Archive (TCIA; n = 130), Ivy GAP (n = 32), and Cleveland Clinic (n = 41). Gene-expression profiles of corresponding patients were obtained for TCIA cohort. For every study, following expert segmentation of tumor subcompartments (necrotic core, enhancing tumor, peritumoral edema), 936 3D radiomic features were extracted from each subcompartment across all MRI protocols. Using Cox regression model, radiomic risk score (RRS) was developed for every protocol to predict PFS on the training cohort (n = 130) and evaluated on the holdout cohort (n = 73). Further, Gene Ontology and single-sample gene set enrichment analysis were used to identify specific molecular signaling pathway networks associated with RRS features.ResultsTwenty-five radiomic features from the tumor habitat yielded the RRS. A combination of RRS with clinical (age and gender) and molecular features (MGMT and IDH status) resulted in a concordance index of 0.81 (P < 0.0001) on training and 0.84 (P = 0.03) on the test set. Radiogenomic analysis revealed associations of RRS features with signaling pathways for cell differentiation, cell adhesion, and angiogenesis, which contribute to chemoresistance in GBM.ConclusionsOur findings suggest that prognostic radiomic features from routine Gd-T1w MRI may also be significantly associated with key biological processes that affect response to chemotherapy in GBM.
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Project description:A complex organ contains a variety of cell types, each with its own distinct lineage and function. Understanding the lineage and differentiation state of each cell is fundamentally important for the ultimate delineation of organ formation and function. We developed SLICE, a novel algorithm that utilizes single-cell RNA-seq (scRNA-seq) to quantitatively measure cellular differentiation states based on single cell entropy and predict cell differentiation lineages via the construction of entropy directed cell trajectories. We validated our approach using three independent data sets with known lineage and developmental time information from both Homo sapiens and Mus musculus. SLICE successfully measured the differentiation states of single cells and reconstructed cell differentiation trajectories that have been previously experimentally validated. We then applied SLICE to scRNA-seq of embryonic mouse lung at E16.5 to identify lung mesenchymal cell lineage relationships that currently remain poorly defined. A two-branched differentiation pathway of five fibroblastic subtypes was predicted using SLICE. The present study demonstrated the general applicability and high predictive accuracy of SLICE in determining cellular differentiation states and reconstructing cell differentiation lineages in scRNA-seq analysis.
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