Prognostic stratification for IDH-wild-type lower-grade astrocytoma by Sanger sequencing and copy-number alteration analysis with MLPA

Intraoperative rapid molecular diagnosis aids glioma subtyping and guides precise surgical resection

OBJECTIVE

The molecular era of glioma diagnosis and treatment has arrived, and a single rapid histopathology is no longer sufficient for surgery. This study sought to present an automatic integrated gene detection system (AIGS), which enables rapid intraoperative detection of IDH/TERTp mutations.

METHODS

A total of 78 patients with gliomas were included in this study. IDH/TERTp mutations were detected intraoperatively using AIGS in 41 of these patients, and they were guided to surgical resection (AIGS detection group). The remaining 37 underwent histopathology-guided conventional surgical resection (non-AIGS detection group). The clinical utility of this technique was evaluated by comparing the accuracy of glioma subtype diagnosis before and after TERTp mutation results were obtained by pathologists and the extent of resection (EOR) and patient prognosis for molecular pathology-guided glioma surgery.

RESULTS

With NGS/Sanger sequencing and chromosome detection as the gold standard, the accuracy of AIGS results was 100%. And the timing was well matched to the intraoperative rapid pathology report. After obtaining the TERTp mutation detection results, the accuracy of the glioma subtype diagnosis made by the pathologists increased by 19.51%. Molecular pathology-guided surgical resection of gliomas significantly increased EOR (99.06% vs. 93.73%, p < 0.0001) and also improved median OS (26.77 vs. 13.47 months, p = 0.0289) and median PFS (15.90 vs. 10.57 months, p = 0.0181) in patients with glioblastoma.

INTERPRETATION

Using AIGS intraoperatively to detect IDH/TERTp mutations to accurately diagnose glioma subtypes can help achieve maximum safe resection of gliomas, which in turn improves the survival prognosis of patients.

Clinical and imaging characteristics of supratentorial glioma with IDH2 mutation

PURPOSE

The rarity of IDH2 mutations in supratentorial gliomas has led to gaps in understanding their radiological characteristics, potentially resulting in misdiagnosis based solely on negative IDH1 immunohistochemical staining. We aimed to investigate the clinical and imaging characteristics of IDH2-mutant gliomas.

METHODS

We analyzed imaging data from adult patients with pathologically confirmed diffuse lower-grade gliomas and known IDH1/2 alteration and 1p/19q codeletion statuses obtained from the records of our institute (January 2011 to August 2022, Cohort 1) and The Cancer Imaging Archive (TCIA, Cohort 2). Two radiologists evaluated clinical information and radiological findings using standardized methods. Furthermore, we compared the data for IDH2-mutant and IDH-wildtype gliomas. Multivariate logistic regression was used to identify the predictors of IDH2 mutation status, and receiver operating characteristic curve analysis was employed to assess the predictive performance of the model.

RESULTS

Of the 20 IDH2-mutant supratentorial gliomas, 95% were in the frontal lobes, with 75% classified as oligodendrogliomas. Age and the T2-FLAIR discordance were independent predictors of IDH2 mutations. Receiver operating characteristic curve analysis for the model using age and T2-FLAIR discordance demonstrated a strong potential for discriminating between IDH2-mutant and IDH-wildtype gliomas, with an area under the curve of 0.96 (95% CI, 0.91-0.98, P = .02).

CONCLUSION

A high frequency of oligodendrogliomas with 1p/19q codeletion was observed in IDH2-mutated gliomas. Younger age and the presence of the T2-FLAIR discordance were associated with IDH2 mutations and these findings may help with precise diagnoses and treatment decisions in clinical practice.

Diagnostic utility of genetic alterations in distinguishing IDH-wildtype glioblastoma from lower-grade gliomas: Insight from next-generation sequencing analysis of 479 cases

The accurate diagnosis and classification of gliomas are essential for appropriate treatment planning and prognosis prediction. This study aimed to investigate the molecular diagnostics of IDH-wildtype diffuse astrocytic gliomas and identify potential genetic variants that could differentiate glioblastoma (GBM) from lower-grade gliomas when DNA methylation analysis is not feasible. In total, 479 H3-and IDH-wildtype diffuse astrocytic gliomas were included in this study. All the cases were diagnosed according to the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors. Panel sequencing data were collected, and clinicopathological information was retrieved from medical records. Genetic alterations and histological findings were analyzed to determine their diagnostic utility and prognostic implications. Out of 479 cases, 439 (91.6%) were diagnosed with GBM, including 28 cases that were molecularly diagnosed as GBM. However, 40 (8.4%) cases could not be classified according to the 2021 WHO classification and were diagnosed as lower-grade diffuse astrocytic glioma, IDH-wildtype, not elsewhere classified (LGNEC). In addition to the three genetic alterations included in the diagnostic criteria of GBM, PTEN and EGFR mutations were found to be enriched in GBM. Patients harboring mTOR pathway mutations demonstrated a more favorable prognosis and often exhibited morphology resembling subependymal giant cell astrocytoma, along with a high tumor mutational burden. Among patients with mTOR pathway mutations, those lacking molecular diagnostic features of GBM exhibited outstanding survival outcomes, even in the presence of grade 4 histology. Integration of molecular features enhanced the diagnostic accuracy of IDH-wildtype gliomas. Some molecular alterations enriched in GBM offer valuable insights for molecular diagnosis and glioma classification. Furthermore, high-grade diffuse astrocytic gliomas featuring mTOR pathway mutations in the absence of molecular diagnostic features of GBM could represent more favorable tumor types distinct from GBM.

Somatic POLE Mutation and Ultra-Hypermutated Genotype in a De Novo High-Grade, Isocitrate Dehydrogenase Wild-Type Glioma: Treatment Implications

Pembrolizumab leads to a durable response in ultra-hypermutated, high-grade, glioma.

Optimizing the risk stratification of astrocytic tumors by applying the cIMPACT-NOW Update 3 signature: real-word single center experience

Our work reports implementation of a useful genetic diagnosis for the clinical managment of patients with astrocytic tumors. We investigated 313 prospectively recruited diffuse astrocytic tumours by applying the cIMPACT-NOW Update 3 signature. The cIMPACT-NOW Update 3 (cIMPACT-NOW 3) markers, i.e., alterations of TERT promoter, EGFR, and/or chromosome 7 and 10, characterized 96.4% of IDHwt cases. Interestingly, it was also found in 48,5% of IDHmut cases. According to the genomic profile, four genetic subgroups could be distinguished: (1) IDwt/cIMPACT-NOW 3 (n = 270); (2) IDHwt/cIMPACT-NOW 3 negative (= 10); (3) IDHmut/cIMPACT-NOW 3 (n = 16); and 4) IDHmut/cIMPACT-NOW 3 negative (n = 17). Multivariate analysis confirmed that IDH1/2 mutations confer a favorable prognosis (IDHwt, HR 2.91 95% CI 1.39-6.06), and validated the prognostic value of the cIMPACT-NOW 3 signature (cIMPACT-NOW 3, HR 2.15 95% CI 1.15-4.03). To accurately identify relevant prognostic categories, overcoming the limitations of histopathology and immunohistochemistry, molecular-cytogenetic analyses must be fully integrated into the diagnostic work-up of astrocytic tumors.

IDH wild-type lower-grade gliomas with glioblastoma molecular features: a systematic review and meta-analysis

The WHO 2021 classification defines IDH wild type (IDHw) histologically lower-grade glioma (hLGG) as molecular glioblastoma (mGBM) if TERT promoter mutation (pTERTm), EGFR amplification or chromosome seven gain and ten loss aberrations are indicated. We systematically reviewed articles of IDHw hLGGs studies (49 studies, N = 3748) and meta-analyzed mGBM prevalence and overall survival (OS) according to the PRISMA statement. mGBM rates in IDHw hLGG were significantly lower in Asian regions (43.7%, 95% confidence interval [CI: 35.8-52.0]) when compared to non-Asian regions (65.0%, [CI: 52.9-75.4]) (P = 0.005) and were significantly lower in fresh-frozen specimen when compared to formalin-fixed paraffin-embedded samples (P = 0.015). IDHw hLGGs without pTERTm rarely expressed other molecular markers in Asian studies when compared to non-Asian studies. Patients with mGBM had significantly longer OS times when compared to histological GBM (hGBM) (pooled hazard ratio (pHR) 0.824, [CI: 0.694-0.98], P = 0.03)). In patients with mGBM, histological grade was a significant prognostic factor (pHR 1.633, [CI: 1.09-2.447], P = 0.018), as was age (P = 0.001) and surgical extent (P = 0.018). Although bias risk across studies was moderate, mGBM with grade II histology showed better OS rates when compared to hGBM.

Easy-to-use machine learning system for the prediction of IDH mutation and 1p/19q codeletion using MRI images of adult-type diffuse gliomas

Adult-type diffuse gliomas are divided into Astrocytoma, IDH-mutant, Oligodendroglioma, IDH-mutant and 1p/19q-codeleted and Glioblastoma, IDH-wildtype based on the IDH mutation, and 1p/19q codeletion status. To determine the treatment strategy for these tumors, pre-operative prediction of IDH mutation and 1p/19q codeletion status might be effective. Computer-aided diagnosis (CADx) systems using machine learning have been noted as innovative diagnostic methods. However, it is difficult to promote the clinical application of machine learning systems at each institute because the support of various specialists is essential. In this study, we established an easy-to-use computer-aided diagnosis system using Microsoft Azure Machine Learning Studio (MAMLS) to predict these statuses. We constructed an analysis model using 258 adult-type diffuse glioma cases from The Cancer Genome Atlas (TCGA) cohort. Using MRI T2-weighted images, the overall accuracy, sensitivity, and specificity for the prediction of IDH mutation and 1p/19q codeletion were 86.9%, 80.9%, and 92.0%, and 94.7%, 94.1%, and 95.1%, respectively. We also constructed an reliable analysis model for the prediction of IDH mutation and 1p/19q codeletion using an independent Nagoya cohort including 202 cases. These analysis models were established within 30 min. This easy-to-use CADx system might be useful for the clinical application of CADx in various institutes.

Telomerase reverse transcriptase promoter mutation and histologic grade in IDH wild-type histological lower-grade gliomas: The value of perfusion-weighted image, diffusion-weighted image, and 18F-FDG-PET

PURPOSE

The telomerase reverse transcriptase promoter (TERTp) mutation is an unfavorable prognostic factor in isocitrate dehydrogenase-wildtype (IDHwt) histologically lower-grade astrocytoma (LGA), which was incorporated as a key component in the WHO 2021 classification of IDHwt LGA, replacing histologic grades in the WHO 2016 classification. The purpose of this study was to identify the imaging characteristics predictive of TERTp mutations in IDHwt LGA.

METHODS

This retrospective study was approved by our institutional review board. This single-center study retrospectively included 59 patients with pathologically confirmed IDHwt LGA with known TERTp mutation status. In addition to clinical information and morphological characteristics, semi-quantitative imaging biomarkers such as the tumor-to-normal ratio (T/N ratio) on ¹⁸F-FDG-PET, normalized apparent diffusion coefficient (nADC), and histogram parameters from normalized relative cerebral blood volume (nrCBV) maps were compared between (a) TERTp-wildtype and TERTp-mutant tumors or (b) grade II and grade III astrocytoma. A p value < 0.05 was considered significant.

RESULTS

There were no significant differences in the conventional imaging findings, T/N ratio on FDG-PET, nrCBV or ADC histogram metrics between IDHwt LGA with TERTp mutations and those without. Grade III IDHwt astrocytomas exhibited significantly higher nrCBV values, T/N ratio and lower ADC parameters than grade II IDHwt astrocytoma.

CONCLUSIONS

In patients with IDHwt LGA, T/N ratio, nrCBV values and nADC may be surrogate markers for predicting histologic grade, but are not useful for predicting TERTp mutations.

Co-amplified with PDGFRA, IGFBP7 is a prognostic biomarker correlated with the immune infiltrations of glioma

BACKGROUND

A subgroup of glioma carry genetic 4q12 amplification including platelet derived growth factor receptor α (PDGFRA) and insulin like growth factor binding protein 7 (IGFBP7). However, the prognosis of PDGFRA and IGFBP7 in glioma is unclear.

METHODS

The prognosis of PDGFRA and IGFBP7 was determined using cox regression and Kaplan-Meier survival analysis. Pathways associated with IGFBP7 were analyzed through gene set enrichment analysis (GSEA). Immune profiling of glioma was determined using "ESTIMATE" and "TIMER" database.

RESULTS

PDGFRA amplification or expression was not correlated with the outcomes of glioblastoma (GBM). IGFBP7 but not PDGFRA was over-expressed in GBM. IGFBP7 over-expression was correlated with the unfavorable outcomes of GBM. In lower grade glioma (LGG), PDGFRA over-expression was not correlated with the unfavorable prognosis of LGG, while, IGFBP7 was a prognostic biomarker of LGG. LGG patients with IGFBP7 lower expressions had prolonged clinical overall survival. Combination of IDH mutation, LGG grade and IGFBP7 achieved even better prognostic effects in LGG. Moreover, IGFBP7 was over-expressed in glioma patients with wild type IDH or with high grades. IGFBP7 over-expression was correlated with the unfavorable outcomes of glioma. Furthermore, IGFBP7 was hypo-methylated in GBM or LGG patients without IDH mutations. IGFBP7 hyper-methylation was correlated with the lower overall survival of GBM or LGG. LGG patients with wild type IDH and with IGFBP7 hypo-methylation demonstrated even worse prognosis. IGFBP7 was associated with multiple immune-related signaling pathways in GBM or LGG. The stromal score, immune score and the infiltrations of immune cells were also correlated with IGFBP7 and the prognosis of LGG.

CONCLUSIONS

IGFBP7 but not PDGFRA served an ideal prognostic marker and therapeutic target of glioma.

Correlation between brain functional connectivity and neurocognitive function in patients with left frontal glioma

The association between neurocognitive function (NCF) impairment and brain cortical functional connectivity in glioma patients remains unclear. The correlations between brain oscillatory activity or functional connectivity and NCF measured by the Wechsler Adult Intelligence Scale full-scale intelligence quotient scores (WAIS FSIQ), the Wechsler Memory Scale-revised general memory scores (WMS-R GM), and the Western aphasia battery aphasia quotient scores (WAB AQ) were evaluated in 18 patients with left frontal glioma using resting-state electroencephalography (EEG). Current source density (CSD) and lagged phase synchronization (LPS) were analyzed using exact low-resolution electromagnetic tomography (eLORETA). Although 2 and 2 patients scored in the borderline range of WAIS FSIQ and WMS-R GM, respectively, the mean WAIS FSIQ, WMS-R GM, and WAB AQ values of all patients were within normal limits, and none had aphasia. In the correlation analysis, lower WMS-R GM was associated with a higher LPS value between the right anterior prefrontal cortex and the left superior parietal lobule in the beta1 band (13-20 Hz, R = - 0.802, P = 0.012). These findings suggest that LPS evaluated by scalp EEG is associated with memory function in patients with left frontal glioma and mild NCF disorders.

T2-fluid-attenuated inversion recovery mismatch sign in lower grade gliomas: correlation with pathological and molecular findings

After the new molecular-based classification was reported to be useful for predicting prognosis, the T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign has gained interest as one of the promising methods for detecting lower grade gliomas (LGGs) with isocitrate dehydrogenase (IDH) mutations and chromosome 1p/19q non-codeletion (IDH mut-Noncodel) with high specificity. Although all institutions could use T2-FLAIR mismatch sign without any obstacles, this sign was not completely helpful because of its low sensitivity. In this study, we attempted to uncover the mechanism of T2-FLAIR mismatch sign for clarifying the cause of this sign's low sensitivity. Among 99 patients with LGGs, 22 were T2-FLAIR mismatch sign-positive (22%), and this sign as a marker of IDH mut-Noncodel showed a sensitivity of 55.6% and specificity of 96.8%. Via pathological analyses, we could provide evidence that not only microcystic changes but the enlarged intercellular space was associated with T2-FLAIR mismatch sign (p = 0.017). As per the molecular analyses, overexpression of mTOR-related genes (m-TOR, RICTOR) were detected as the molecular events correlated with T2-FLAIR mismatch sign (p = 0.020, 0.030. respectively). Taken together, we suggested that T2-FLAIR mismatch sign could pick up the IDH mut-Noncodel LGGs with enlarged intercellular space or that with overexpression of mTOR-related genes.

Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks

Brain tumors are a pernicious cancer with one of the lowest five-year survival rates. Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain tumor. Automated computer-assisted tools can help them speed up the diagnosis process and reduce the burden on the health care systems. Recent advances in deep learning for medical imaging have shown remarkable results, especially in the automatic and instant diagnosis of various cancers. However, we need a large amount of data (images) to train the deep learning models in order to obtain good results. Large public datasets are rare in medicine. This paper proposes a framework based on unsupervised deep generative neural networks to solve this limitation. We combine two generative models in the proposed framework: variational autoencoders (VAEs) and generative adversarial networks (GANs). We swap the encoder–decoder network after initially training it on the training set of available MR images. The output of this swapped network is a noise vector that has information of the image manifold, and the cascaded generative adversarial network samples the input from this informative noise vector instead of random Gaussian noise. The proposed method helps the GAN to avoid mode collapse and generate realistic-looking brain tumor magnetic resonance images. These artificially generated images could solve the limitation of small medical datasets up to a reasonable extent and help the deep learning models perform acceptably. We used the ResNet50 as a classifier, and the artificially generated brain tumor images are used to augment the real and available images during the classifier training. We compared the classification results with several existing studies and state-of-the-art machine learning models. Our proposed methodology noticeably achieved better results. By using brain tumor images generated artificially by our proposed method, the classification average accuracy improved from 72.63% to 96.25%. For the most severe class of brain tumor, glioma, we achieved 0.769, 0.837, 0.833, and 0.80 values for recall, specificity, precision, and F1-score, respectively. The proposed generative model framework could be used to generate medical images in any domain, including PET (positron emission tomography) and MRI scans of various parts of the body, and the results show that it could be a useful clinical tool for medical experts.