Glioblastoma multiforme (GBM) is the most common brain tumor with very aggressive and infiltrative. Extracellular matrix (ECM) plays pivotal roles in the infiltrative characteristics of GBM. To ...understand the invasive characteristic of GBM, it is necessary to study cell-ECM interaction in the physiologically relevant biomimetic model that recapitulates the GBM-specific ECM microenvironment. Here, we propose biomimetic GBM-specific ECM microenvironment for studying mode and dynamics of glioblastoma cell invasion. Using tissue decellularization process, we constructed a patient tissue-derived ECM (pdECM)-based three-dimensional in vitro model. In our model, GBM cells exhibited heterogeneous morphology and altered the invasion routes in a microenvironment-adaptive manner. We further elucidate the effects of inhibition of ECM remodeling-related enzymatic activity (Matrix metalloproteinase (MMP) 2/9, hyaluronan synthase (HAS)) on GBM cell invasion. Interestingly, after blocking both enzyme activity, GBM cells underwent morphological transition and switch the invasion mode. Such adaptability could render cell invasion resistant to anti-cancer target therapy. There results provide insight of how organ-specific matrix differentially regulates cancer cell phenotype, and have significant implications for the design of matrix with appropriate physiologically relevant properties for in vitro tumor model.
Gastric cancer is a considerable burden for worldwide patients. And diffuse gastric cancer is the most insidious subgroup with poor survival. The phenotypic characterization of the diffuse gastric ...cancer cell line can be useful for gastric cancer researchers. In this article, we aimed to characterize the diffuse gastric cancer cells with MRI and transcriptomic data. We hypothesized that gene expression pattern is associated with the phenotype of the cells and that the heterogeneous enhancement pattern and the high tumorigenicity of SNU484 can be modulated by the perturbation of the highly expressed gene.
We evaluated the 9.4 T magnetic resonance imaging and transcriptomic data of the orthotopic mice models from diffuse gastric cancer cells such as SNU484, Hs746T, SNU668, and KATO III. We included MKN74 as an intestinal cancer control cell. After comprehensive analysis integrating MRI and transcriptomic data, we selected CD34 and validated the effect by shRNA in the BALB/c nude mice models.
SNU484, SNU668, Hs746T, and MKN74 formed orthotopic tumors by the 5 weeks after cell injection. The diffuse phenotype was found in the SNU484 and Hs746T. SNU484 was the only tumor showing the heterogeneous enhancement pattern on T2 images with a high level of CD34 expression. Knockdown of CD34 decreased the round-void shape in the H&E staining (P = 0.028), the heterogeneous T2 enhancement, and orthotopic tumorigenicity (100% vs 66.7%). The RNAseq showed that the suppressed CD34 is associated with the downregulated gene-sets of the extracellular matrix remodeling.
Suppression of CD34 in the human-originated gastric cancer cell suggests that it is important for the round-void histologic shape, heterogeneous enhancement pattern on MRI, and the growth of gastric cancer cell line.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Objectives
To evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine-learning algorithms in differentiating primary central nervous system lymphoma (PCNSL) from ...non-necrotic atypical glioblastoma (GBM).
Methods
Seventy-seven patients (54 individuals with PCNSL and 23 with non-necrotic atypical GBM), diagnosed from January 2009 to April 2017, were enrolled in this retrospective study. A total of 6,366 radiomics features, including shape, volume, first-order, texture, and wavelet-transformed features, were extracted from multi-parametric (post-contrast T1- and T2-weighted, and fluid attenuation inversion recovery images) and multiregional (enhanced and non-enhanced) tumour volumes. These features were subjected to recursive feature elimination and random forest (RF) analysis with nested cross-validation. The diagnostic abilities of a radiomics machine-learning classifier, apparent diffusion coefficient (ADC), and three readers, who independently classified the tumours based on conventional MR sequences, were evaluated using receiver operating characteristic (ROC) analysis. Areas under the ROC curves (AUC) of the radiomics classifier, ADC value, and the radiologists were compared.
Results
The mean AUC of the radiomics classifier was 0.921 (95 % CI 0.825–0.990). The AUCs of the three readers and ADC were 0.707 (95 % CI 0.622–0.793), 0.759 (95 %CI 0.656–0.861), 0.695 (95 % CI 0.590–0.800) and 0.684 (95 % CI0.560–0.809), respectively. The AUC of the radiomics-based classifier was significantly higher than those of the three readers and ADC (
p
< 0.001 for all).
Conclusions
Large-scale radiomics with a machine-learning algorithm can be useful for differentiating PCNSL from atypical GBM, and yields a better diagnostic performance than human radiologists and ADC values.
Key Points
• Machine-learning algorithm radiomics can help to differentiate primary central PCNSL from GBM.
• This approach yields a higher diagnostic accuracy than visual analysis by radiologists.
• Radiomics can strengthen radiologists’ diagnostic decisions whenever conventional MRI sequences are available.
Although mesenchymal stem cells (MSCs) have been implicated as stromal components of several cancers, their ultimate contribution to tumorigenesis and their potential to drive cancer stem cells, ...particularly in the unique microenvironment of human brain tumors, remain largely undefined. Consequently, using established criteria, we isolated glioma‐associated‐human MSCs (GA‐hMSCs) from fresh human glioma surgical specimens for the first time. We show that these GA‐hMSCs are nontumorigenic stromal cells that are phenotypically similar to prototypical bone marrow‐MSCs. Low‐passage genomic sequencing analyses comparing GA‐hMSCs with matched tumor‐initiating glioma stem cells (GSCs) suggest that most GA‐hMSCs (60%) are normal cells recruited to the tumor (group 1 GA‐hMSCs), although, rarely (10%), GA‐hMSCs may differentiate directly from GSCs (group 2 GA‐hMSCs) or display genetic patterns intermediate between these groups (group 3 GA‐hMSCs). Importantly, GA‐hMSCs increase proliferation and self‐renewal of GSCs in vitro and enhance GSC tumorigenicity and mesenchymal features in vivo, confirming their functional significance within the GSC niche. These effects are mediated by GA‐hMSC‐secreted interleukin‐6, which activates STAT3 in GSCs. Our results establish GA‐hMSCs as a potentially new stromal component of gliomas that drives the aggressiveness of GSCs, and point to GA‐hMSCs as a novel therapeutic target within gliomas. Stem Cells 2015;33:2400–2415
Background and purpose
Recent studies have highlighted the importance of isocitrate dehydrogenase (
IDH
) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed ...to evaluate whether MRI-based radiomic features could improve the accuracy of survival predictions for lower grade gliomas over clinical and
IDH
status.
Materials and methods
Radiomic features (
n
= 250) were extracted from preoperative MRI data of 296 lower grade glioma patients from databases at our institutional (
n
= 205) and The Cancer Genome Atlas (TCGA)/The Cancer Imaging Archive (TCIA) (
n
= 91) datasets. For predicting overall survival, random survival forest models were trained with radiomic features; non-imaging prognostic factors including age, resection extent, WHO grade, and
IDH
status on the institutional dataset, and validated on the TCGA/TCIA dataset. The performance of the random survival forest (RSF) model and incremental value of radiomic features were assessed by time-dependent receiver operating characteristics.
Results
The radiomics RSF model identified 71 radiomic features to predict overall survival, which were successfully validated on TCGA/TCIA dataset (iAUC, 0.620; 95% CI, 0.501–0.756). Relative to the RSF model from the non-imaging prognostic parameters, the addition of radiomic features significantly improved the overall survival prediction accuracy of the random survival forest model (iAUC, 0.627 vs. 0.709; difference, 0.097; 95% CI, 0.003–0.209).
Conclusion
Radiomic phenotyping with machine learning can improve survival prediction over clinical profile and genomic data for lower grade gliomas.
Key Points
• Radiomics analysis with machine learning can improve survival prediction over the non-imaging factors (clinical and molecular profiles) for lower grade gliomas, across different institutions.
Focal cortical dysplasia type II (FCDII) is a sporadic developmental malformation of the cerebral cortex characterized by dysmorphic neurons, dyslamination and medically refractory epilepsy. It has ...been hypothesized that FCD is caused by somatic mutations in affected regions. Here, we used deep whole-exome sequencing (read depth, 412-668×) validated by site-specific amplicon sequencing (100-347,499×) in paired brain-blood DNA from four subjects with FCDII and uncovered a de novo brain somatic mutation, mechanistic target of rapamycin (MTOR) c.7280T>C (p.Leu2427Pro) in two subjects. Deep sequencing of the MTOR gene in an additional 73 subjects with FCDII using hybrid capture and PCR amplicon sequencing identified eight different somatic missense mutations found in multiple brain tissue samples of ten subjects. The identified mutations accounted for 15.6% of all subjects with FCDII studied (12 of 77). The identified mutations induced the hyperactivation of mTOR kinase. Focal cortical expression of mutant MTOR by in utero electroporation in mice was sufficient to disrupt neuronal migration and cause spontaneous seizures and cytomegalic neurons. Inhibition of mTOR with rapamycin suppressed cytomegalic neurons and epileptic seizures. This study provides, to our knowledge, the first evidence that brain somatic activating mutations in MTOR cause FCD and identifies mTOR as a treatment target for intractable epilepsy in FCD.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SBMB, UILJ, UKNU, UL, UM, UPUK
Purpose
The TruSight Tumor 170 (TST-170) panel consists of a DNA workflow for the identification of single-nucleotide variants, small insertions and deletions, and copy number variation, as well as a ...panel of 55 genes for a RNA workflow for the identification of splice variants and gene fusions. To date, the application of TST-170 in diffuse gliomas (DGs) has not been described.
Methods
We analyzed 135 samples of DG, which were diagnosed by WHO criteria based on histological features and conventional molecular tests including immunostaining, 1p/19q FISH, and analysis of
MGMT
methylation and
TERT
promoter mutation.
Results
A total of 135 cases consisted of 38
IDH
-mutant 17 astrocytoma (AC), 13 oligodendroglioma (OD) and eight glioblastoma (GBM), 87
IDH
-wildtype (six AC, three OD and 78 GBM), and 10 diffuse midline glioma,
H3K27M-mutant
. DNA analysis enabled the detection of all mutations identified in these samples by conventional techniques, and the results were highly comparable to the known mutations in each subtype. RNA analysis detected four fusion genes including
PTPRZ1
–
MET, FGFR3
–
TACC3, FAM131B
–
BRAF
, and
RET
–
CCDC6
and one splicing variant (
EGFR
vIII mutant). Clustered copy number loss in 1p and 19q loci genes were detected in 1p/19q-codeleted OD.
Conclusions
The application of TST-170 panel based NGS in clinical and laboratory setting is expected to improve diagnostic accuracy and prognostication. Most benefits are expected in
IDH
-wildtype DG, a group of genetically heterogenous tumors harboring DNA sequence changes, copy number alterations, and fusions in a large number of oncogenes and tumor suppressor genes.
Objectives
Epidermal growth factor receptor (EGFR) amplification and telomerase reverse transcriptase promoter (TERTp) mutation status of isocitrate dehydrogenase-wildtype (IDHwt) lower-grade gliomas ...(LGGs; grade II/III) are crucial for identifying IDHwt LGG with an aggressive clinical course. The purpose of this study was to assess whether parameters from diffusion tensor imaging, dynamic susceptibility contrast (DSC), and diffusion tensor imaging, dynamic contrast-enhanced imaging can predict the EGFR amplification and TERTp mutation status of IDHwt LGGs.
Methods
A total of 49 patients with IDHwt LGGs with either known EGFR amplification (39 non-amplified, 10 amplified) or TERTp mutation (19 wildtype, 21 mutant) statuses underwent MRI. The mean ADC, fractional anisotropy (FA), normalized cerebral blood volume (nCBV), normalized cerebral blood flow (nCBF), volume transfer constant (
K
trans
), rate transfer coefficient (
K
ep
), extravascular extracellular volume fraction (
V
e
), and plasma volume fraction (
V
p
) values were assessed. Univariate and multivariate logistic regression models were constructed.
Results
EGFR-amplified tumors showed lower mean ADC values than EGFR-non-amplified tumors (
p
= 0.019). Mean ADC was an independent predictor of EGFR amplification, with an AUC of 0.75. TERTp mutant tumors showed higher mean nCBV (
p
= 0.020), higher mean nCBF (
p
= 0.017), and higher mean
V
p
(
p
= 0.002) than TERTp wildtype tumors. With multivariate logistic regression, mean
V
p
was the independent predictor of TERTp mutation status, with an AUC of 0.85.
Conclusion
This exploratory pilot study shows that lower ADC values may be useful for prediction of EGFR amplification, whereas higher
V
p
values may be useful for prediction of the TERTp mutation status of IDHwt LGGs.
Key Points
•
EGFR amplification and TERTp mutation are key molecular markers that predict an aggressive clinical course of IDHwt LGGs
.
•
EGFR-amplified tumors showed lower ADC values than EGFR-non-amplified tumors, suggesting higher cellularity
.
•
TERTp mutant tumors showed a higher plasma volume fraction than TERTp wildtype tumors, suggesting higher vascular proliferation and tumor angiogenesis
.
The high mortality in glioblastoma multiforme (GBM) patients is primarily caused by extensive infiltration into adjacent tissue and subsequent rapid recurrence. There are no clear therapeutic ...strategies that target the infiltrative subpopulation of GBM mass. Using mesenchymal mode of invasion, the GBM is known to widely infiltrate by interacting with various unique components within brain microenvironment such as hyaluronic acid (HA)-rich matrix and white matter tracts. However, it is unclear how these GBM microenvironments influence the strategies of mesenchymal invasion. We hypothesize that GBM has different strategies to facilitate such invasion through adaptation to their local microenvironment. Using our in vitro biomimetic microenvironment platform for three-dimensional GBM tumorspheres (TSs), we found that the strategies of GBM invasion were predominantly regulated by the HA-rich ECM microenvironment, showing marked phenotypic changes in the presence of HA, which were mainly mediated by HA synthase (HAS). Interestingly, after inhibition of the HAS gene, GBM switched their invasion strategies to a focal adhesion (FA)-mediated invasion. These results demonstrate that the microenvironmental adaptation allowed a flexible invasion strategy for GBM. Using our model, we suggest a new inhibitory pathway for targeting infiltrative GBM and propose an importance of multi-target therapy for GBM, which underwent microenvironmental adaptation.
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