Discrete class I molecules on brain endothelium differentially regulate neuropathology in experimental cerebral malaria.

Cerebral malaria is the deadliest complication that can arise from Plasmodium infection. CD8 T-cell engagement of brain vasculature is a putative mechanism of neuropathology in cerebral malaria. To define contributions of brain endothelial cell major histocompatibility complex (MHC) class I antigen-presentation to CD8 T cells in establishing cerebral malaria pathology, we developed novel H-2Kb LoxP and H-2Db LoxP mice crossed with Cdh5-Cre mice to achieve targeted deletion of discrete class I molecules, specifically from brain endothelium. This strategy allowed us to avoid off-target effects on iron homeostasis and class I-like molecules, which are known to perturb Plasmodium infection. This is the first endothelial-specific ablation of individual class-I molecules enabling us to interrogate these molecular interactions. In these studies, we interrogated human and mouse transcriptomics data to compare antigen presentation capacity during cerebral malaria. Using the Plasmodium berghei ANKA model of experimental cerebral malaria (ECM), we observed that H-2Kb and H-2Db class I molecules regulate distinct patterns of disease onset, CD8 T-cell infiltration, targeted cell death and regional blood-brain barrier disruption. Strikingly, ablation of either molecule from brain endothelial cells resulted in reduced CD8 T-cell activation, attenuated T-cell interaction with brain vasculature, lessened targeted cell death, preserved blood-brain barrier integrity and prevention of ECM and the death of the animal. We were able to show that these events were brain-specific through the use of parabiosis and created the novel technique of dual small animal MRI to simultaneously scan conjoined parabionts during infection. These data demonstrate that interactions of CD8 T cells with discrete MHC class I molecules on brain endothelium differentially regulate development of ECM neuropathology. Therefore, targeting MHC class I interactions therapeutically may hold potential for treatment of cases of severe malaria.

Strategies, considerations, and recent advancements in the development of liquid biopsy for glioblastoma: a step towards individualized medicine in glioblastoma.

OBJECTIVE: Glioblastoma (GBM) is a devasting primary brain tumor with less than a 5% 5-year survival. Treatment response assessment can be challenging because of inflammatory pseudoprogression that mimics true tumor progression clinically and on imaging. Developing additional noninvasive assays is critical. In this article, the authors review various biomarkers that could be used in developing liquid biopsies for GBM, along with strengths, limitations, and future applications. In addition, they present a potential liquid biopsy design based on the use of an extracellular vesicle-based liquid biopsy targeting nonneoplastic extracellular vesicles. METHODS: The authors conducted a current literature review of liquid biopsy in GBM by searching the PubMed, Scopus, and Google Scholar databases. Articles were assessed for type of biomarker, isolation methodology, analytical techniques, and clinical relevance. RESULTS: Recent work has shown that liquid biopsies of plasma, blood, and/or CSF hold promise as noninvasive clinical tools that can be used to diagnose recurrence, assess treatment response, and predict patient outcomes in GBM. Liquid biopsy in GBM has focused primarily on extracellular vesicles, cell-free tumor nucleic acids, and whole-cell isolates as focal biomarkers. GBM tumor signatures have been generated via analysis of tumor gene mutations, unique RNA expression, and metabolic and proteomic alterations. Liquid biopsies capture tumor heterogeneity, identifying alterations in GBM tumors that may be undetectable via surgical biopsy specimens. Finally, biomarker burden can be used to assess treatment response and recurrence in GBM. CONCLUSIONS: Liquid biopsy offers a promising avenue for monitoring treatment response and recurrence in GBM without invasive procedures. Although additional steps must be taken to bring liquid biopsy into the clinic, proof-of-principle studies and isolation methodologies are promising. Ultimately, CSF and/or plasma-based liquid biopsy is likely to be a powerful tool in the neurosurgeon's arsenal in the near future for the treatment and management of GBM patients.

The Relationship Between IDH1 Mutation Status and Metabolic Imaging in Nonenhancing Supratentorial Diffuse Gliomas: A 11C-MET PET Study.

PURPOSE: We evaluated the relationship between isocitrate dehydrogenase 1 (IDH1) mutation status and metabolic imaging in patients with nonenhancing supratentorial diffuse gliomas using 11C-methionine positron emission tomography (11C-MET PET). MATERIALS AND METHODS: Between June 2012 and November 2017, we enrolled 86 (38 women and 48 men; mean age, 41.9 ± 13.1 years [range, 8-67 years]) patients with newly diagnosed supratentorial diffuse gliomas. All patients underwent preoperative 11C-MET PET. Tumor samples were obtained and immunohistochemically analyzed for IDH1 mutation status. RESULTS: The mutant and wild-type IDH1 diffuse gliomas had significantly different mean maximum standardized uptake value values (2.73 [95% confidence interval, CI: 2.32-3.16] vs 3.85 [95% CI: 3.22-4.51], respectively; P = .004) and mean tumor-to-background ratio (1.90 [95% CI: 1.65-2.16] vs 2.59 [95% CI: 2.17-3.04], respectively; P = .007). CONCLUSIONS: 11C-methionine PET can noninvasively evaluate the IDH1 mutation status of patients with nonenhancing supratentorial diffuse gliomas.

Pediatric low-grade gliomas can be molecularly stratified for risk.

Pediatric low-grade gliomas (PLGGs) consist of a number of entities with overlapping histological features. PLGGs have much better prognosis than the adult counterparts, but a significant proportion of PLGGs suffers from tumor progression and recurrence. It has been shown that pediatric and adult low-grade gliomas are molecularly distinct. Yet the clinical significance of some of newer biomarkers discovered by genomic studies has not been fully investigated. In this study, we evaluated in a large cohort of 289 PLGGs a list of biomarkers and examined their clinical relevance. TERT promoter (TERTp), H3F3A and BRAF V600E mutations were detected by direct sequencing. ATRX nuclear loss was examined by immunohistochemistry. CDKN2A deletion, KIAA1549-BRAF fusion, and MYB amplification were determined by fluorescence in situ hybridization (FISH). TERTp, H3F3A, and BRAF V600E mutations were identified in 2.5, 6.4, and 7.4% of PLGGs, respectively. ATRX loss was found in 4.9% of PLGGs. CDKN2A deletion, KIAA1549-BRAF fusion and MYB amplification were detected in 8.8, 32.0 and 10.6% of PLGGs, respectively. Survival analysis revealed that TERTp mutation, H3F3A mutation, and ATRX loss were significantly associated with poor PFS (p < 0.0001, p < 0.0001, and p = 0.0002) and OS (p < 0.0001, p < 0.0001, and p < 0.0001). BRAF V600E was associated with shorter PFS (p = 0.011) and OS (p = 0.032) in a subset of PLGGs. KIAA1549-BRAF fusion was a good prognostic marker for longer PFS (p = 0.0017) and OS (p = 0.0029). MYB amplification was also a favorable marker for a longer PFS (p = 0.040). Importantly, we showed that these molecular biomarkers can be used to stratify PLGGs into low- (KIAA1549-BRAF fusion or MYB amplification), intermediate-I (BRAF V600E and/or CDKN2A deletion), intermediate-II (no biomarker), and high-risk (TERTp or H3F3A mutation or ATRX loss) groups with distinct PFS (p < 0.0001) and OS (p < 0.0001). This scheme should aid in clinical decision-making.

Oligodendrogliomas in pediatric and teenage patients only rarely exhibit molecular markers and patients have excellent survivals.

Although oligodendrogliomas appear histologically similar in adult and pediatric patients, the latter have only been rarely studied and most of those studies did not have long follow-up. We examined 55 oligodendroglial tumors from pediatric and teenage patients for their biomarkers with formalin-fixed paraffin-embedded tissues and studied their survival status. None of the tumors harbored 1p/19q codeletion or IDH mutation. Mutations in TERTp (4%), BRAF (11%), FGFR1 (3%) and H3F3A (5%), fusions of BRAF (8%) and FGFR1 (8%) were found sparingly and almost all in a mutually exclusive manner. Molecular events were exclusively found in tumors with classic oligodendroglial histology. Survival analysis showed remarkably excellent prognosis compared to the adult counterparts. 5-year overall survival was 95% in our cohort with median follow-up of 8.1 years and in nine patients with follow-up more than 10 years, the 10-year overall survival was 100%. The 5-year and 10-year progression-free survivals of our cohort were 89 and 77%, respectively. FGFR1 fusion seemed to confer a poor prognosis in pediatric oligodendrogliomas. Patients receiving adjuvant chemotherapy (p = 0.046) or harboring Grade II histology (p < 0.001) had longer interval to recurrence. Our study demonstrated the distinct indolent clinical course of pediatric and teenage oligodendrogliomas compared to the adult tumors. Molecular markers commonly seen in adult oligodendrogliomas and other pediatric low-grade gliomas were only rarely seen. Since there is no clinical or molecular evidence suggesting that pediatric "oligodendrogliomas" are the same as adult oligodendrogliomas albeit histologic similarity, a case can be made for their separation from adult oligodendrogliomas in the next WHO classification.

The kinesin KIF14 is overexpressed in medulloblastoma and downregulation of KIF14 suppressed tumor proliferation and induced apoptosis.

Medulloblastoma (MB) is the most common malignant brain tumor in childhood. At present, there is no well-established targeted drug for majority of patients. The kinesin family member 14 (KIF14) is a novel oncogene located on chromosome 1q and is dysregulated in multiple cancers. The objectives of this study were to evaluate KIF14 expression and chromosome 1q copy number in MB, and to delineate its biological functions in MB pathogenesis. By quantitative RT-PCR and immunohistochemistry, we found KIF14 was overexpressed in MB. Increased KIF14 expression at protein level was strongly associated with shorter progression-free survival (P=0.0063) and overall survival (P=0.0083). Fluorescence in situ hybridization (FISH) analysis confirmed genomic gain of chromosome 1q in 17/93 (18.3%) of MB. Combined genetic and immunohistochemical analyses revealed that 76.5% of MB with 1q gain showed consistent overexpression of KIF14, and a tight link between chromosome 1q gain and KIF14 overexpression (P=0.03). Transient, siRNAs-mediated downregulation of KIF14 suppressed cell proliferation and induced apoptosis in two MB cell lines. Stably KIF14 knockdown by shRNAs inhibited cell viability, colony formation, migration and invasion, and tumor sphere formation in MB cells. We conclude that KIF14 is dysregulated in MB and is an adverse prognostic factor for survival. Furthermore, KIF14 is part of MB biology and is a potential therapeutic target for MB.

Establishment and maintenance of a standardized glioma tissue bank: Huashan experience.

Cerebral glioma is the most common brain tumor as well as one of the top ten malignant tumors in human beings. In spite of the great progress on chemotherapy and radiotherapy as well as the surgery strategies during the past decades, the mortality and morbidity are still high. One of the major challenges is to explore the pathogenesis and invasion of glioma at various "omics" levels (such as proteomics or genomics) and the clinical implications of biomarkers for diagnosis, prognosis or treatment of glioma patients. Establishment of a standardized tissue bank with high quality biospecimens annotated with clinical information is pivotal to the solution of these questions as well as the drug development process and translational research on glioma. Therefore, based on previous experience of tissue banks, standardized protocols for sample collection and storage were developed. We also developed two systems for glioma patient and sample management, a local database for medical records and a local image database for medical images. For future set-up of a regional biobank network in Shanghai, we also founded a centralized database for medical records. Hence we established a standardized glioma tissue bank with sufficient clinical data and medical images in Huashan Hospital. By September, 2013, tissues samples from 1,326 cases were collected. Histological diagnosis revealed that 73 % were astrocytic tumors, 17 % were oligodendroglial tumors, 2 % were oligoastrocytic tumors, 4 % were ependymal tumors and 4 % were other central nervous system neoplasms.

Effect of high-field iMRI guided resection in cerebral glioma surgery: A randomized clinical trial.

BACKGROUND: Extent of resection (EOR) in glioma contributes to longer survival. The purpose of NCT01479686 was to prove whether intraoperative magnetic resonance imaging (iMRI) increases EOR in glioma surgery and benefit survival. METHODS: Patients were randomized (1:1) to receive the iMRI (n = 161) or the conventional neuronavigation (n = 160). The primary endpoint was gross total resection (GTR); secondary outcomes reported were progression-free survival (PFS), overall survival (OS), and safety. RESULTS: 188 high-grade gliomas (HGGs) and 133 low-grade gliomas (LGGs) were enrolled. GTR was 83.85% in the iMRI group vs. 50.00% in the control group (P < 0.0001). In 321 patients, the median PFS (mPFS) was 65.12 months in the iMRI group and 61.01 months in the control group (P = 0.0202). For HGGs, mPFS was improved in the iMRI group (19.32 vs. 13.34 months, P = 0.0015), and a trend of superior OS compared with control was observed (29.73 vs. 25.33 months, P = 0.1233). In the predefined eloquent area HGG subgroup, mPFS, and mOS were 20.47 months and 33.58 months in the iMRI vs. 12.21 months and 21.16 months in the control group (P = 0.0098; P = 0.0375, respectively). From the exploratory analyses of HGGs, residual tumor volume (TV) < 1.0 cm3 decreased the risk of survival (mPFS: 18.99 vs. 9.43 months, P = 0.0055; mOS: 29.77 vs. 18.10 months, P = 0.0042). LGGs with preoperative (pre-OP) TV > 43.1 cm3 and postoperative (post-OP) TV > 4.6 cm3 showed worse OS (P= 0.0117) CONCLUSIONS: It showed that iMRI significantly increased EOR and indicated survival benefits for HGGs, particularly eloquent HGGs. Residual TV in either HGGs or LGGs is a prognostic factor for survival.

Spectral flow cytometry identifies distinct nonneoplastic plasma extracellular vesicle phenotype in glioblastoma patients.

Background: Glioblastoma (GBM) is the most common malignant brain tumor and has a poor prognosis. Imaging findings at diagnosis and in response to treatment are nonspecific. Developing noninvasive assays to augment imaging would be helpful. Plasma extracellular vesicles (EVs) are a promising biomarker source for this. Here, we develop spectral flow cytometry techniques that demonstrate differences in bulk plasma EV phenotype between GBM patients and normal donors that could serve as the basis of a liquid biopsy. Methods: Plasma EVs were stained for EV-associated tetraspanins (CD9/CD63/CD81), markers indicating cell of origin (CD11b/CD31/CD41a/CD45), and actin/phalloidin (to exclude cell debris). EVs were analyzed using spectral flow cytometry. Multiparametric analysis using t-distributed stochastic neighbor embedding (t-SNE) and self-organizing maps on flow cytometry data (FlowSOM) was performed comparing GBM and normal donor (ND) plasma EVs. Results: Size exclusion chromatography plus spectral-based flow cytometer threshold settings enriched plasma EVs while minimizing background noise. GBM patients had increased CD9+, CD63+, CD81+, and myeloid-derived (CD11b+) EVs. Multiparametric analysis demonstrated distinct surface marker expression profiles in GBM plasma EVs compared to ND EVs. Fifteen plasma EV sub-populations differing in size and surface marker expression were identified, six enriched in GBM patients and two in normal donors. Conclusions: Multiparametric analysis demonstrates that GBM patients have a distinct nonneoplastic plasma EV phenotype compared to ND. This simple rapid analysis can be performed without purifying tumor EVs and may serve as the basis of a liquid biopsy.

Superinduction of immunosuppressive glioblastoma extracellular vesicles by IFN-γ through PD-L1 and IDO1.

Background: Glioblastoma (GBM), the most common primary brain tumor, has a median survival of 15-16 months. Immunotherapy is promising but GBM-mediated immunosuppression remains a barrier. GBMs express the interferon-gamma (IFN-γ)-responsive immunosuppressive molecules programmed cell death ligand 1 (PD-L1) and indoleamine 2,3-dioxygenase 1 (IDO1). Extracellular vesicles (EVs) have also been implicated in GBM-mediated immunosuppression, in part through PD-L1. We therefore sought to determine if GBM IFN-γ exposure increased GBM EV-mediated immunosuppression and mechanisms underlying this. Methods: Human GBM-derived cells were cultured in the presence/absence of IFN-γ. EVs were harvested. PD-L1, IDO1, and EV-associated protein expression was assessed. GBM EVs (+/-IFN-γ) were cultured with healthy donor monocytes. Immunosuppressive myeloid-derived suppressor cell (MDSC) and nonclassical monocyte (NCM) frequency was determined. Impact of GBM (+/-IFN-γ) EV-treated monocytes on CD3/CD28-mediated T cell proliferation was assessed. The impact of PD-L1 and IDO1 knockdown in GBM EVs in this system was evaluated. Results: IFN-γ exposure increased PD-L1 and IDO1 expression in GBM cells and EVs without altering EV size or frequency. IFN-γ-exposed GBM EVs induced more MDSC and NCM differentiation in monocytes and these monocytes caused more T cell inhibition than IFN-γ-naive GBM EVs. PD-L1 and/or IDO1 knockdown in GBM cells abrogated the immunosuppressive effects of IFN-γ-exposed GBM EVs on monocytes. Conclusions: IFN-γ exposure such as might occur during an antitumor immune response results in superinduction of GBM EVs' baseline immunosuppressive effects on monocytes. These effects are mediated by increased PD-L1 and IDO1 expression in GBM EVs. These data highlight mechanisms of GBM EV-mediated immunosuppression and identify therapeutic targets (PD-L1, IDO1) to reverse these effects.

Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers.

BACKGROUND: Pathological diagnosis of glioma subtypes is essential for treatment planning and prognosis. Standard histological diagnosis of glioma is based on postoperative hematoxylin and eosin stained slides by neuropathologists. With advancing artificial intelligence (AI), the aim of this study was to determine whether deep learning can be applied to glioma classification. METHODS: A neuropathological diagnostic platform is designed comprising a slide scanner and deep convolutional neural networks (CNNs) to classify 5 major histological subtypes of glioma to assist pathologists. The CNNs were trained and verified on over 79 990 histological patch images from 267 patients. A logical algorithm is used when molecular profiles are available. RESULTS: A new model of the squeeze-and-excitation block DenseNet with weighted cross-entropy (named SD-Net_WCE) is developed for the glioma classification task, which learns the recognizable features of glioma histology CNN-based independent diagnostic testing on data from 56 patients with 17 262 histological patch images demonstrated patch level accuracy of 86.5% and patient level accuracy of 87.5%. Histopathological classifications could be further amplified to integrated neuropathological diagnosis by 2 molecular markers (isocitrate dehydrogenase and 1p/19q). CONCLUSION: The model is capable of solving multiple classification tasks and can satisfactorily classify glioma subtypes. The system provides a novel aid for the integrated neuropathological diagnostic workflow of glioma.

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients.

Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict overall survival (OS) time based on the pre-operative mono- or multi-modal imaging phenotype. The genotypic information of GBM has been proven to be strongly indicative of the prognosis; however, this has not been considered in the existing imaging-based OS prediction methods. The main reason is that the tumor genotype is unavailable pre-operatively unless deriving from craniotomy. In this paper, we propose a new deep learning-based OS prediction method for GBM patients, which can derive tumor genotype-related features from pre-operative multimodal magnetic resonance imaging (MRI) brain data and feed them to OS prediction. Specifically, we propose a multi-task convolutional neural network (CNN) to accomplish both tumor genotype and OS prediction tasks jointly. As the network can benefit from learning tumor genotype-related features for genotype prediction, the accuracy of predicting OS time can be prominently improved. In the experiments, multimodal MRI brain dataset of 120 GBM patients, with as many as four different genotypic/molecular biomarkers, are used to evaluate our method. Our method achieves the highest OS prediction accuracy compared to other state-of-the-art methods.

IDH mutant lower grade (WHO Grades II/III) astrocytomas can be stratified for risk by CDKN2A, CDK4 and PDGFRA copy number alterations.

In the 2016, WHO classification of tumors of the central nervous system, isocitrate dehydrogenase (IDH) mutation is a main classifier for lower grade astrocytomas and IDH-mutated astrocytomas is now regarded as a single group with longer survival. However, the molecular and clinical heterogeneity among IDH mutant lower grade (WHO Grades II/III) astrocytomas have only rarely been investigated. In this study, we recruited 160 IDH mutant lower grade (WHO Grades II/III) astrocytomas, and examined PDGFRA amplification, CDKN2A deletion and CDK4 amplification by FISH analysis, TERT promoter mutation by Sanger sequencing and ATRX loss and p53 expression by immunohistochemistry. We identified PDGFRA amplification, CDKN2A homozygous deletion and CDK4 amplification in 18.8%, 15.0% and 18.1% of our cohort respectively, and these alterations occurred in a mutually exclusive fashion. PDGFRA amplification was associated with shorter PFS (P = 0.0003) and OS (P < 0.0001). In tumors without PDGFRA amplification, CDKN2A homozygous deletion or CDK4 amplification was associated with a shorter OS (P = 0.035). Tumors were divided into three risk groups based on the presence of molecular alterations: high risk (PDGFRA amplification), intermediate risk (CDKN2A deletion or CDK4 amplification) and low risk (neither CDKN2A deletion and CDK4 amplification nor PDGFRA amplification). These three risk groups were significantly different in overall survival with mean survivals of 40.5, 62.9 and 71.5 months. The high-risk group also demonstrated a shorter PFS compared to intermediate- (P = 0.036) and low-risk (P < 0.0001) groups. One limitation of this study is the relatively short follow-up period, a common confounding factor for studies on low-grade tumors. Our data illustrate that IDH mutant lower grade astrocytomas is not a homogeneous group and should be molecularly stratified for risk.

Pre-operative Overall Survival Time Prediction for Glioblastoma Patients Using Deep Learning on Both Imaging Phenotype and Genotype.

Glioblastoma (GBM) is the most common and deadly malignant brain tumor with short yet varied overall survival (OS) time. Per request of personalized treatment, accurate pre-operative prognosis for GBM patients is highly desired. Currently, many machine learning-based studies have been conducted to predict OS time based on pre-operative multimodal MR images of brain tumor patients. However, tumor genotype, such as MGMT and IDH, which has been proven to have strong relationship with OS, is completely not considered in pre-operative prognosis as the genotype information is unavailable until craniotomy. In this paper, we propose a new deep learning based method for OS time prediction. It can derive genotype related features from pre-operative multimodal MR images of brain tumor patients to guide OS time prediction. Particularly, we propose a multi-task convolutional neural network (CNN) to accomplish tumor genotype and OS time prediction tasks. As the network can benefit from learning genotype related features toward genotype prediction, we verify upon a dataset of 120 GBM patients and conclude that the multi-task learning can effectively improve the accuracy of predicting OS time in personalized prognosis.

Whole-exome sequencing revealed mutational profiles of giant cell glioblastomas.

Giant cell glioblastoma (gcGBM) is a rare histological variant of GBM, accounting for about 1% of all GBM. The prognosis is poor generally though gcGBM does slightly better than the other IDH-wild-type GBM. Because of the rarity of the cases, there has been no comprehensive molecular analysis of gcGBM. Previously, single-gene study identified genetic changes in TP53, PTEN and TERT promoter mutation in gcGBM. In this report, we performed whole-exome sequencing (WES) to identify somatically acquired mutations and copy number variations (CNVs) in 10 gcGBM genomes. We also examined TERT promoter mutation and MGMT methylation in our cohort. On top of the reported mutations, WES revealed ATRX, PIK3R1, RB1 and SETD2 as the recurrent mutations in gcGBM. Notably, one tumor harbored a mutation in MutS homolog 6 (MSH6) that is a key mismatch repair (MMR) gene. This tumor demonstrated hypermutation phenotype and showed an increased number of somatic mutations. TERT promoter mutation and MGMT methylation were observed in 20% and 40% of our samples, respectively. In conclusion, we described relevant mutation profiling for developing future targeted therapies in gcGBM.

Multi-Label Nonlinear Matrix Completion With Transductive Multi-Task Feature Selection for Joint MGMT and IDH1 Status Prediction of Patient With High-Grade Gliomas.

The O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation and isocitrate dehydrogenase 1 (IDH1) mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which has limited their wider clinical implementation. Accurate presurgical prediction of their statuses based on preoperative multimodal neuroimaging is of great clinical value for a better treatment plan. Currently, the available data set associated with this study has several challenges, such as small sample size and complex, nonlinear (image) feature-to-(molecular) label relationship. To address these issues, we propose a novel multi-label nonlinear matrix completion (MNMC) model to jointly predict both MGMT and IDH1 statuses in a multi-task framework. Specifically, we first employ a nonlinear random Fourier feature mapping to improve the linear separability of the data, and then use transductive multi-task feature selection (performed in a nonlinearly transformed feature space) to refine the imputed soft labels, thus alleviating the overfitting problem caused by small sample size. We further design an optimization algorithm with a guaranteed convergence ability based on a block prox-linear method to solve the proposed MNMC model. Finally, by using a single-center, multimodal brain imaging and molecular pathology data set of HGG, we derive brain functional and structural connectomics features to jointly predict MGMT and IDH1 statuses. Results demonstrate that our proposed method outperforms the previously widely used single- and multi-task machine learning methods. This paper also shows the promise of utilizing brain connectomics for HGG prognosis in a non-invasive manner.

Microfluidics for rapid detection of isocitrate dehydrogenase 1 mutation for intraoperative application.

OBJECT Conventional methods for isocitrate dehydrogenase 1 (IDH1) detection, such as DNA sequencing and immunohistochemistry, are time- and labor-consuming and cannot be applied for intraoperative analysis. To develop a new approach for rapid analysis of IDH1 mutation from tiny tumor samples, this study used microfluidics as a method for IDH1 mutation detection. METHODS Forty-seven glioma tumor samples were used; IDH1 mutation status was investigated by immunohistochemistry and DNA sequencing. The microfluidic device was fabricated from polydimethylsiloxane following standard soft lithography. The immunoanalysis was conducted in the microfluidic chip. Fluorescence images of the on-chip microcolumn taken by the charge-coupled device camera were collected as the analytical results readout. Fluorescence signals were analyzed by NIS-Elements software to gather detailed information about the IDH1 concentration in the tissue samples. RESULTS DNA sequencing identified IDH1 R132H mutation in 33 of 47 tumor samples. The fluorescence signal for IDH1-mutant samples was 5.49 ± 1.87 compared with 3.90 ± 1.33 for wild type (p = 0.005). Thus, microfluidics was capable of distinguishing IDH1-mutant tumor samples from wild-type samples. When the cutoff value was 4.11, the sensitivity of microfluidics was 87.9% and the specificity was 64.3%. CONCLUSIONS This new approach was capable of analyzing IDH1 mutation status of tiny tissue samples within 30 minutes using intraoperative microsampling. This approach might also be applied for rapid pathological diagnosis of diffuse gliomas, thus guiding personalized resection.

Not all 1p/19q non-codeleted oligodendroglial tumors are astrocytic.

Although 1p/19q codeletion is the genetic hallmark defining oligodendrogliomas, approximately 30-40% of oligodendroglial tumors have intact 1p/19q in the literature and they demonstrate a worse prognosis. This group of 1p/19q intact oligodendroglial tumors is frequently suggested to be astrocytic in nature with TP53 and ATRX mutations but actually remains under-investigated. In the present study, we provided evidence that not all 1p/19q intact oligodendroglial tumors are astrocytic through histologic and molecular approaches. We examined 1p/19q status by FISH in a large cohort of 337 oligodendroglial tumors and identified 39.8% lacking 1p/19q codeletion which was independently associated with poor prognosis. Among this 1p/19q intact oligodendroglial tumor cohort, 58 cases demonstrated classic oligodendroglial histology which showed older patient age, better prognosis, association with grade III histology, PDGFRA expression, TERTp mutation, as well as frequent IDH mutation. More than half of the 1p/19q intact oligodendroglial tumors showed lack of astrocytic defining markers, p53 expression and ATRX loss. TP53 mutational analysis was additionally conducted in 45 cases of the 1p/19q intact oligodendroglial tumors. Wild-type TP53 was detected in 71.1% of cases which was associated with classic oligodendroglial histology. Importantly, IDH and TERTp co-occurred in 75% of 1p/19q intact, TP53 wild-type oligodendrogliomas, highlighting the potential of the co-mutations in assisting diagnosis of oligodendrogliomas in tumors with clear cell morphology and non-codeleted 1p/19q status. In summary, our study demonstrated that not all 1p/19q intact oligodendroglial tumors are astrocytic and co-evaluation of IDH and TERTp mutation could potentially serve as an adjunct for diagnosing 1p/19q intact oligodendrogliomas.

Methods of glioma sample processing for molecular diagnosis for the glioma tissue bank project.

Genome-wide sequencing in glioma samples provides comprehensive insights into oncogenesis and malignant transformation. Several distinct biomarkers have been proven to have clinical significance and are being widely applied in routine clinical practice. Standard sample processing lays the foundation for successful molecular testing. In this study, we found intraoperative neuronavigation ensured higher tumor purity during sample collection, and an automated device helped improve DNA quality and increased yields. These two technologies are beneficial for glioma tissue bank construction and provide for accurate molecular testing during routine clinical practice.