Invasion and proliferation kinetics in enhancing gliomas predict IDH1 mutation status

Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma

Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of glioblastoma (GBM). This heterogeneity is further exacerbated during GBM recurrence, as treatment-induced reactive changes produce additional intratumoral heterogeneity that is ambiguous to differentiate on clinical imaging. There is an urgent need to develop non-invasive approaches to map the heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We propose to predictively fuse Magnetic Resonance Imaging (MRI) with the underlying intratumoral heterogeneity in recurrent GBM using machine learning (ML) by leveraging image-localized biopsies with their associated locoregional MRI features. To this end, we develop BioNet, a biologically-informed neural network model, to predict regional distributions of three tissue-specific gene modules: proliferating tumor, reactive/inflammatory cells, and infiltrated brain tissue. BioNet offers valuable insights into the integration of multiple implicit and qualitative biological domain knowledge, which are challenging to describe in mathematical formulations. BioNet performs significantly better than a range of existing methods on cross-validation and blind test datasets. Voxel-level prediction maps of the gene modules by BioNet help reveal intratumoral heterogeneity, which can improve surgical targeting of confirmatory biopsies and evaluation of neuro-oncological treatment effectiveness. The non-invasive nature of the approach can potentially facilitate regular monitoring of the gene modules over time, and making timely therapeutic adjustment. These results also highlight the emerging role of ML in precision medicine.

Epigenetic Reprogramming of Autophagy Drives Mutant IDH1 Glioma Progression and Response to Radiation

Mutant isocitrate dehydrogenase 1 (mIDH1; IDH1 R132H ) exhibits a gain of function mutation enabling 2-hydroxyglutarate (2HG) production. 2HG inhibits DNA and histone demethylases, inducing epigenetic reprogramming and corresponding changes to the transcriptome. We previously demonstrated 2HG-mediated epigenetic reprogramming enhances DNA-damage response and confers radioresistance in mIDH1 gliomas harboring p53 and ATRX loss of function mutations. In this study, RNA-seq and ChIP-seq data revealed human and mouse mIDH1 glioma neurospheres have downregulated gene ontologies related to mitochondrial metabolism and upregulated autophagy. Further analysis revealed that the decreased mitochondrial metabolism was paralleled by a decrease in glycolysis, rendering autophagy as a source of energy in mIDH1 glioma cells. Analysis of autophagy pathways showed that mIDH1 glioma cells exhibited increased expression of pULK1-S555 and enhanced LC3 I/II conversion, indicating augmented autophagy activity. This dependence is reflected by increased sensitivity of mIDH1 glioma cells to autophagy inhibition. Blocking autophagy selectively impairs the growth of cultured mIDH1 glioma cells but not wild-type IDH1 (wtIDH1) glioma cells. Targeting autophagy by systemic administration of synthetic protein nanoparticles packaged with siRNA targeting Atg7 (SPNP-siRNA-Atg7) sensitized mIDH1 glioma cells to radiation-induced cell death, resulting in tumor regression, long-term survival, and immunological memory, when used in combination with IR. Our results indicate autophagy as a critical pathway for survival and maintenance of mIDH1 glioma cells, a strategy that has significant potential for future clinical translation.

One Sentence Summary

The inhibition of autophagy sensitizes mIDH1 glioma cells to radiation, thus creating a promising therapeutic strategy for mIDH1 glioma patients.

Graphical abstract

Our genetically engineered mIDH1 mouse glioma model harbors IDH1 R132H in the context of ATRX and TP53 knockdown. The production of 2-HG elicited an epigenetic reprogramming associated with a disruption in mitochondrial activity and an enhancement of autophagy in mIDH1 glioma cells. Autophagy is a mechanism involved in cell homeostasis related with cell survival under energetic stress and DNA damage protection. Autophagy has been associated with radio resistance. The inhibition of autophagy thus radio sensitizes mIDH1 glioma cells and enhances survival of mIDH1 glioma-bearing mice, representing a novel therapeutic target for this glioma subtype with potential applicability in combined clinical strategies.

Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol

Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma.

MRI-based deep learning techniques for the prediction of isocitrate dehydrogenase and 1p/19q status in grade 2-4 adult gliomas

Molecular biomarkers are becoming increasingly important in the classification of intracranial gliomas. While tissue sampling remains the gold standard, there is growing interest in the use of deep learning (DL) techniques to predict these markers. This narrative review with a systematic approach identifies and synthesises the current published data on DL techniques using conventional MRI sequences for predicting isocitrate dehydrogenase (IDH) and 1p/19q-codeletion status in World Health Organisation grade 2-4 gliomas. Three databases were searched for relevant studies. In all, 13 studies met the inclusion criteria after exclusions. Key results, limitations and discrepancies between studies were synthesised. High accuracy has been reported in some studies, but the existing literature has several limitations, including generally small cohort sizes, a paucity of studies with independent testing cohorts and a lack of studies assessing IDH and 1p/19q together. While DL shows promise as a non-invasive means of predicting glioma genotype, addressing these limitations in future research will be important for facilitating clinical translation.

Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images

OBJECTIVE

To investigate the value of a nomogram based on multiparametric and multiregional MR images to predict Isocitrate Dehydrogenase-1 (IDH1) gene mutations in glioma.

DATA AND METHODS

The authors performed a retrospective analysis of 110 MR images of surgically confirmed pathological gliomas; 33 patients with IDH1 gene Mutation (IDH1-M) and 77 patients with Wild-type IDH1 (IDH1-W) were divided into training and validation sets in a 7:3 ratio. The clinical features were statistically analyzed using SPSS and R software. Three glioma regions (rCET, rE, rNEC) were outlined using ITK-SNAP software and projected to four conventional sequences (T1, T2, Flair, T1C) for feature extraction using AI-Kit software. The extracted features were screened using R software. A logistic regression model was established, and a nomogram was generated using the selected clinical features. Eight models were developed based on different sequences and ROIs, and Receiver Operating Characteristic (ROC) curves were used to evaluate the predictive efficacy. Decision curve analysis was performed to assess the clinical usefulness.

RESULTS

Age was selected with Radscore to construct the nomogram. The Model 1 AUC values based on four sequences and three ROIs were the highest in these models, at 0.93 and 0.89, respectively. Decision curve analysis indicated that the net benefit of model 1 was higher than that of the other models for most Pt-values.

CONCLUSION

A nomogram based on multiparametric and multiregional MR images can predict the mutation status of the IDH1 gene accurately.

Mutant IDH in Gliomas: Role in Cancer and Treatment Options

Altered metabolism is a common feature of many cancers and, in some cases, is a consequence of mutation in metabolic genes, such as the ones involved in the TCA cycle. Isocitrate dehydrogenase (IDH) is mutated in many gliomas and other cancers. Physiologically, IDH converts isocitrate to α-ketoglutarate (α-KG), but when mutated, IDH reduces α-KG to D2-hydroxyglutarate (D2-HG). D2-HG accumulates at elevated levels in IDH mutant tumours, and in the last decade, a massive effort has been made to develop small inhibitors targeting mutant IDH. In this review, we summarise the current knowledge about the cellular and molecular consequences of IDH mutations and the therapeutic approaches developed to target IDH mutant tumours, focusing on gliomas.

MRI-Based Radiomics Combined with Deep Learning for Distinguishing IDH-Mutant WHO Grade 4 Astrocytomas from IDH-Wild-Type Glioblastomas

This study aimed to investigate the potential of quantitative radiomic data extracted from conventional MR images in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type glioblastomas (GBMs). A cohort of 57 treatment-naïve patients with IDH-mutant grade 4 astrocytomas (n = 23) and IDH-wild-type GBMs (n = 34) underwent anatomical imaging on a 3T MR system with standard parameters. Post-contrast T1-weighted and T2-FLAIR images were co-registered. A semi-automatic segmentation approach was used to generate regions of interest (ROIs) from different tissue components of neoplasms. A total of 1050 radiomic features were extracted from each image. The data were split randomly into training and testing sets. A deep learning-based data augmentation method (CTGAN) was implemented to synthesize 200 datasets from the training sets. A total of 18 classifiers were used to distinguish two genotypes of grade 4 astrocytomas. From generated data using 80% training set, the best discriminatory power was obtained from core tumor regions overlaid on post-contrast T1 using the K-best feature selection algorithm and a Gaussian naïve Bayes classifier (AUC = 0.93, accuracy = 0.92, sensitivity = 1, specificity = 0.86, PR_AUC = 0.92). Similarly, high diagnostic performances were obtained from original and generated data using 50% and 30% training sets. Our findings suggest that conventional MR imaging-based radiomic features combined with machine/deep learning methods may be valuable in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type GBMs.

The sinuous, wave-like intratumoral-wall sign is a sensitive and specific radiological biomarker for oligodendrogliomas

OBJECTIVES

The purpose of this study was to investigate the clinical utility of the sinuous, wave-like intratumoral-wall (SWITW) sign on T2WI in diagnosing isocitrate dehydrogenase (IDH) mutant and 1p/19q codeleted (IDHmut-Codel) oligodendrogliomas, for which a relatively conservative resection strategy might be sufficient due to a better response to chemoradiotherapy and favorable prognosis.

METHODS

Imaging data from consecutive adult patients with diffuse lower-grade gliomas (LGGs, histological grades 2-3) in Beijing Tiantan Hospital (December 1, 2013, to October 31, 2021, BTH set, n = 711) and the Cancer Imaging Archive (TCIA) LGGs set (n = 117) were used to develop and validate our findings. Two independent observers assessed the SWITW sign and some well-reported discriminative radiological features to establish a practical diagnostic strategy.

RESULTS

The SWITW sign showed satisfying sensitivity (0.684 and 0.722 for BTH and TCIA sets) and specificity (0.938 and 0.914 for BTH and TCIA sets) in defining IDHmut-Codels, and the interobserver agreement was substantial (κ 0.718 and 0.756 for BTH and TCIA sets). Compared to calcification, the SWITW sign improved the sensitivity by 0.28 (0.404 to 0.684) in the BTH set, and 81.0% (277/342) of IDHmut-Codel cases demonstrated SWITW and/ or calcification positivity. Combining the SWITW sign, calcification, low ADC values, and other discriminative features, we established a concise and reliable diagnostic protocol for IDHmut-Codels.

CONCLUSIONS

The SWITW sign was a sensitive and specific imaging biomarker for IDHmut-Codels. The integrated protocol provided an explicable, efficient, and reproducible method for precise preoperative diagnosis, which was essential to guide individualized surgical plan-making.

KEY POINTS

• The SWITW sign was a sensitive and specific imaging biomarker for IDHmut-Codel oligodendrogliomas. • The SWITW sign was more sensitive than calcification and an integrated strategy could improve diagnostic sensitivity for IDHmut-Codel oligodendrogliomas. • Combining SWITW, calcification, low ADC values, and other discriminative features could make a precise preoperative diagnosis for IDHmut-Codel oligodendrogliomas.

NMI: a potential biomarker for tumor prognosis and immunotherapy

N-Myc and STAT Interactor protein (NMI) is an interferon inducible protein participating in various cellular activities, and is widely involved in the process of tumorigenesis and progression. Studies have shown that the loss of NMI expression in breast cancer can promote its progression by inducing epithelial-mesenchymal transition (EMT). However, the expression level of NMI in other tumors and its impact on immune cell infiltration, patient prognosis, and drug treatment are still unclear. Here, we analyzed the role of NMI in pan-cancer through multiple omics data. We found that NMI was abnormally expressed in a variety of tumor tissues. The expression of NMI was closely related to the unique molecular and immunotyping, diagnosis and prognosis of various tumor tissues. In addition, we identified the main proteins that interact with NMI, and focused on the relationship between the clinical parameters of lower grade glioma (LGG) and NMI expression. Subsequently, we found that the expression of NMI was correlated with the infiltration of multiple immune cells and the expression of immune checkpoints. Finally, we also found that the expression of NMI was correlated with the sensitivity to multiple antitumor drugs. In conclusion, our comprehensive pan-cancer analysis of NMI revealed that it is a potential molecular marker for tumor diagnosis and treatment, plays an important role in tumor immunity, and is a promising molecular target for cancer treatment.

Radiological Prediction of Isocitrate Dehydrogenase (IDH) Mutational Status and Pathological Verification for Lower-Grade Astrocytomas

Background and objective

The isocitrate dehydrogenase (IDH) status of patients with World Health Organization (WHO) grade II or III astrocytoma is essential for understanding its biological features and determining therapeutic strategies. This study aimed to use radiological analysis to predict the IDH status of patients with lower-grade astrocytomas and to verify the pathological implications.

Methods

In this study, 47 patients with grade II (17 cases) or III astrocytomas (30 cases), based on 2016 WHO Classification, underwent methionine (MET) positron emission tomography (PET) and magnetic resonance spectroscopy (MRS) on the same day between January 2013 and June 2020. The patients were retrospectively assessed. Immunohistochemistry showed 23 cases of IDH-mutant and 24 of IDH-wildtype. Based on fluid-attenuated recovery inversion (FLAIR)/T2 imaging, three doctors blinded to clinical data independently allocated 18 patients to the clear boundary group between the tumor and the normal brain and 29 to the unclear boundary group. The peak ratios of N-acetylaspartate (NAA)/creatine (Cr), choline (Cho)/Cr, and Cho/NAA and the tumor-to-normal region (T/N) ratio for maximum accumulation in MET-PET were calculated. For statistical analysis, Fisher’s exact test was used to assess associations between two variables, and the Mann-Whitney U test to compare the values between the IDH-wildtype and IDH-mutant groups. The optimal cut-off values of MET T/N ratio and MRS parameters for discriminating IDH-wildtype from IDH-mutant were obtained using receiver operating characteristics curves.

Results

The unclear boundary group had significantly more IDH-wildtype cases than the clear boundary group (P<0.001). The IDH-wildtype group had significantly lower Cho/Cr (<1.84) and Cho/NAA (<1.62) ratios (P=0.02 and P=0.047, respectively) and a higher MET T/N ratio (>1.44, P=0.02) than the IDH-mutant group. The odds for the IDH-wildtype were 0.22 for patients who fulfilled none of the four criteria, including boundary status and three ratios, and 0.9 for all four criteria.

Conclusions

These results suggest that the combination of MRI, MRS, and MET-PET examination could be helpful for the prediction of IDH status in WHO grade II/III gliomas.

IDH-wild-type glioblastoma cell density and infiltration distribution influence on supramarginal resection and its impact on overall survival: a mathematical model

OBJECTIVE

Recent studies have proposed resection of the T2 FLAIR hyperintensity beyond the T1 contrast enhancement (supramarginal resection [SMR]) for IDH-wild-type glioblastoma (GBM) to further improve patients' overall survival (OS). GBMs have significant variability in tumor cell density, distribution, and infiltration. Advanced mathematical models based on patient-specific radiographic features have provided new insights into GBM growth kinetics on two important parameters of tumor aggressiveness: proliferation rate (ρ) and diffusion rate (D). The aim of this study was to investigate OS of patients with IDH-wild-type GBM who underwent SMR based on a mathematical model of cell distribution and infiltration profile (tumor invasiveness profile).

METHODS

Volumetric measurements were obtained from the selected regions of interest from pre- and postoperative MRI studies of included patients. The tumor invasiveness profile (proliferation/diffusion [ρ/D] ratio) was calculated using the following formula: ρ/D ratio = (4π/3)2/3 × (6.106/[VT21/1 - VT11/1])2, where VT2 and VT1 are the preoperative FLAIR and contrast-enhancing volumes, respectively. Patients were split into subgroups based on their tumor invasiveness profiles. In this analysis, tumors were classified as nodular, moderately diffuse, or highly diffuse.

RESULTS

A total of 101 patients were included. Tumors were classified as nodular (n = 34), moderately diffuse (n = 34), and highly diffuse (n = 33). On multivariate analysis, increasing SMR had a significant positive correlation with OS for moderately and highly diffuse tumors (HR 0.99, 95% CI 0.98-0.99; p = 0.02; and HR 0.98, 95% CI 0.96-0.99; p = 0.04, respectively). On threshold analysis, OS benefit was seen with SMR from 10% to 29%, 10% to 59%, and 30% to 90%, for nodular, moderately diffuse, and highly diffuse, respectively.

CONCLUSIONS

The impact of SMR on OS for patients with IDH-wild-type GBM is influenced by the degree of tumor invasiveness. The authors' results show that increasing SMR is associated with increased OS in patients with moderate and highly diffuse IDH-wild-type GBMs. When grouping SMR into 10% intervals, this benefit was seen for all tumor subgroups, although for nodular tumors, the maximum beneficial SMR percentage was considerably lower than in moderate and highly diffuse tumors.

Noninvasive differentiation of molecular subtypes of adult non-enhancing glioma using MRI perfusion and diffusion parameters

Background

Nonenhancing glioma typically have a favorable outcome, but approximately 19–44% have a highly aggressive course due to a glioblastoma genetic profile. The aim of this retrospective study is to use physiological MRI parameters of both perfusion and diffusion to distinguish the molecular profiles of glioma without enhancement at presentation.

Methods

Ninety-nine patients with nonenhancing glioma were included, in whom molecular status (including 1p/19q codeletion status and IDH mutation) and preoperative MRI (T2w/FLAIR, dynamic susceptibility-weighted, and diffusion-weighted imaging) were available. Tumors were segmented semiautomatically using ITK-SNAP to derive whole tumor histograms of relative Cerebral Blood Volume (rCBV) and Apparent Diffusion Coefficient (ADC). Tumors were divided into three clinically relevant molecular profiles: IDH mutation (IDHmt) with (n = 40) or without (n = 41) 1p/19q codeletion, and (n = 18) IDH-wildtype (IDHwt). ANOVA, Kruskal-Wallis, and Chi-Square analyses were performed using SPSS.

Results

rCBV (mean, median, 75^th and 85^th percentile) and ADC (mean, median, 15^th and 25^th percentile) showed significant differences across molecular profiles (P < .01). Posthoc analyses revealed that IDHwt and IDHmt 1p/19q codeleted tumors showed significantly higher rCBV compared to IDHmt 1p/19q intact tumors: mean rCBV (mean, SD) 1.46 (0.59) and 1.35 (0.39) versus 1.08 (0.31), P < .05. Also, IDHwt tumors showed significantly lower ADC compared to IDHmt 1p/19q codeleted and IDHmt 1p/19q intact tumors: mean ADC (mean, SD) 1.13 (0.23) versus 1.27 (0.15) and 1.45 (0.20), P < .001).

Conclusions

A combination of low ADC and high rCBV, reflecting high cellularity and high perfusion respectively, separates IDHwt from in particular IDHmt 1p/19q intact glioma.

Is It Worth Considering Multicentric High-Grade Glioma a Surgical Disease? Analysis of Our Clinical Experience and Literature Review

INTRODUCTION

The simultaneous presence of multiple foci of high-grade glioma is a rare condition with a poor prognosis. By definition, if an anatomical connection through white matter bundles cannot be hypothesized, multiple lesions are defined as multicentric glioma (MC); on the other hand, when this connection exists, it is better defined as multifocal glioma (MF). Whether surgery can be advantageous for these patients has not been established yet. The aim of our study was to critically review our experience and to compare it to the existing literature.

MATERIALS AND METHODS

Retrospective analysis of patients operated on for MC HGG in two Italian institutions was performed. Distinction between MC and MF was achieved through revision of MR FLAIR images. Clinical and radiological preoperative and postoperative data were analyzed through chart revision and phone interviews. The same data were extracted from literature review. Univariate and multivariate analyses were conducted for the literature review only, and the null hypothesis was rejected for a p-value ≥ 0.05.

RESULTS

Sixteen patients met the inclusion criteria; male predominance and an average age of 66.5 years were detected. Sensory/motor deficit was the main onset symptom both in clinical study and literature review. A tendency to operate on the largest symptomatic lesion was reported and GTR was reached in the majority of cases. GBM was the histological diagnosis in most part of the patients. OS was 8.7 months in our series compared to 7.5 months from the literature review. Age ≤ 70 years, a postoperative KPS ≥ 70, a GTR/STR, a second surgery and adjuvant treatment were shown to be significantly associated with a better prognosis. Pathological examination revealed that MC HGG did not originate by LGG.

CONCLUSIONS

MC gliomas are rare conditions with high malignancy and a poor prognosis. A maximal safe resection should be attempted whenever possible, especially in younger patients with life-threatening large mass.

Comprehensive Molecular Characterization of Chinese Patients with Glioma by Extensive Next-Generation Sequencing Panel Analysis

Background

Tremendous efforts have been made to explore biomarkers for classifying and grading glioma. However, the majority of the current understanding is based on public databases that might not accurately reflect the Asian population. Here, we investigated the genetic landscape of Chinese glioma patients using a validated multigene next-generation sequencing (NGS) panel to provide a strong rationale for the future classification and prognosis of glioma in this population.

Methods

We analyzed 83 samples, consisting of 71 initial treatments and 12 recurrent surgical tumors, from 81 Chinese patients with gliomas by performing multigene NGS with an Acornmed panel targeting 808 cancer-related hotspot genes, including genes related to glioma (hotspots, selected exons or complete coding sequences) and full-length SNPs located on chromosomes 1 and 19.

Results

A total of 76 (91.57%) glioma samples had at least one somatic mutation. The most commonly mutated genes were TP53, TERT, IDH1, PTEN, ATRX, and EGFR. Approximately one-third of cases exhibited more than one copy number variation. Of note, this study identified the amplification of genes, such as EGFR and PDGFRA, which were significantly associated with glioblastoma but had not been previously used for clinical classification (P<0.05). Significant differences in genomic profiles between different pathological subtypes and WHO grade were observed. Compared to the MSKCC database primarily comprised of Caucasians, H3F3A mutations and MET amplifications exhibited higher mutation rates, whereas TERT mutations and EGFR and CDKN2A/B copy number variations presented a lower mutation rate in Chinese patients with glioma (P<0.05).

Conclusion

Our multigene NGS in the simultaneous evaluation of multiple relevant markers revealed several novel genetic alterations in Chinese patients with glioma. NGS-based molecular analysis is a reliable and effective method for diagnosing brain tumors, assisting clinicians in evaluating additional potential therapeutic options, such as targeted therapy, for glioma patients in different racial/ethnic groups.

Increased Tau Expression Correlates With IDH Mutation in Infiltrating Gliomas and Impairs Cell Migration

Although the microtubule-associated protein tau is well studied in human neurodegeneration, the role of tau in neoplastic brain diseases is not well understood. Recently, studies have shown tau mRNA expression is associated with improved survival in human infiltrating gliomas. However, the biologic basis of this association is largely unexplored. Using 2 independent publicly available mRNA databases, we show that high tau mRNA expression is associated with improved patient survival in infiltrating gliomas. Higher tau protein expression is also associated with improved patient prognosis in infiltrating gliomas by immunohistochemical staining of tissue microarrays. This prognostic association is in part due to higher tau mRNA and protein expression in IDH-mutant infiltrating astrocytomas. Expression of tau in an IDH-wildtype glioblastoma cell line selectively impairs cell migration in assays designed to mimic tumor invasion. These findings suggest that tau expression is not only associated with IDH mutation status but also may contribute to improved patient outcomes by impairing tumor invasion.

Liquid biopsy in central nervous system tumors: the potential roles of circulating miRNA and exosomes

The Central nervous system (CNS) tumor still remains the most lethal cancer, and It is hard to diagnose at an earlier stage on most occasions. It is found that recurrent disease is finally observed in patients who occurred chemo-resistance after completely primary treatment. It is a challenge that monitoring treatment efficacy and tumor recurrence of CNS tumors are full of risks and difficulties by brain biopsies. However, the brain biopsies are considered as an invasive technique with low specificity and low sensitivity. In contrast, the liquid biopsy is based on blood and cerebrospinal fluid (CSF) test, which is going to acceptable among the patients through it's minimally invasive and serial bodily fluids. The advantages of liquid biopsy are to follow the development of tumors, provide new insights in real time, and accurate medical care. The major analytical constituents of liquid biopsy contain the Circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating cell-free microRNAs (cfmiRNAs), and circulating exosomes. Liquid biopsy has been widely utilized in CNS tumors in recent years, and the CTCs and ctDNA have become the hot topics for researching. In this review, we are going to explain the clinical potential of liquid biopsy biomarkers in CNS tumor by testing circulating miRNAs and exosomes to evaluate diagnose, prognosis, and response to treatment.

Tumor Volume Dynamics as an Early Biomarker for Patient-Specific Evolution of Resistance and Progression in Recurrent High-Grade Glioma

Recurrent high-grade glioma (HGG) remains incurable with inevitable evolution of resistance and high inter-patient heterogeneity in time to progression (TTP). Here, we evaluate if early tumor volume response dynamics can calibrate a mathematical model to predict patient-specific resistance to develop opportunities for treatment adaptation for patients with a high risk of progression. A total of 95 T1-weighted contrast-enhanced (T1post) MRIs from 14 patients treated in a phase I clinical trial with hypo-fractionated stereotactic radiation (HFSRT; 6 Gy × 5) plus pembrolizumab (100 or 200 mg, every 3 weeks) and bevacizumab (10 mg/kg, every 2 weeks; NCT02313272) were delineated to derive longitudinal tumor volumes. We developed, calibrated, and validated a mathematical model that simulates and forecasts tumor volume dynamics with rate of resistance evolution as the single patient-specific parameter. Model prediction performance is evaluated based on how early progression is predicted and the number of false-negative predictions. The model with one patient-specific parameter describing the rate of evolution of resistance to therapy fits untrained data ( R 2 = 0.70 ). In a leave-one-out study, for the nine patients that had T1post tumor volumes ≥1 cm3, the model was able to predict progression on average two imaging cycles early, with a median of 9.3 (range: 3-39.3) weeks early (median progression-free survival was 27.4 weeks). Our results demonstrate that early tumor volume dynamics measured on T1post MRI has the potential to predict progression following the protocol therapy in select patients with recurrent HGG. Future work will include testing on an independent patient dataset and evaluation of the developed framework on T2/FLAIR-derived data.

Quantifying Uncertainty and Robustness in a Biomathematical Model-Based Patient-Specific Response Metric for Glioblastoma

Purpose

Glioblastomas, lethal primary brain tumors, are known for their heterogeneity and invasiveness. A growing body of literature has been developed demonstrating the clinical relevance of a biomathematical model, the proliferation-invasion model, of glioblastoma growth. Of interest here is the development of a treatment response metric, days gained (DG). This metric is based on individual tumor kinetics estimated through segmented volumes of hyperintense regions on T1-weighted gadolinium-enhanced and T2-weighted magnetic resonance images. This metric was shown to be prognostic of time to progression. Furthermore, it was shown to be more prognostic of outcome than standard response metrics. Although promising, the original article did not account for uncertainty in the calculation of the DG metric, leaving the robustness of this cutoff in question.

Methods

We harnessed the Bayesian framework to consider the impact of two sources of uncertainty: (1) image acquisition and (2) interobserver error in image segmentation. We first used synthetic data to characterize what nonerror variants are influencing the final uncertainty in the DG metric. We then considered the original patient cohort to investigate clinical patterns of uncertainty and to determine how robust this metric is for predicting time to progression and overall survival.

Results

Our results indicate that the key clinical variants are the time between pretreatment images and the underlying tumor growth kinetics, matching our observations in the clinical cohort. Finally, we demonstrated that for this cohort, there was a continuous range of cutoffs between 94 and 105 for which the prediction of the time to progression was over 80% reliable.

Conclusion

Although additional validation must be performed, this work represents a key step in ascertaining the clinical utility of this metric.

Sex-specific impact of patterns of imageable tumor growth on survival of primary glioblastoma patients

Background

Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences.

Methods

Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females).

Results

Among males, tumor (T1Gd) radius was a predictor of overall survival (HR = 1.027, p = 0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR = 1.011, p < 0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p = 0.010 t-test), but tumor size was not correlated with female overall survival (p = 0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p = 0.004, F p = 0.001, t-test).

Conclusion

Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes.

Speed Switch in Glioblastoma Growth Rate due to Enhanced Hypoxia-Induced Migration

We analyze the wave speed of the Proliferation Invasion Hypoxia Necrosis Angiogenesis (PIHNA) model that was previously created and applied to simulate the growth and spread of glioblastoma (GBM), a particularly aggressive primary brain tumor. We extend the PIHNA model by allowing for different hypoxic and normoxic cell migration rates and study the impact of these differences on the wave-speed dynamics. Through this analysis, we find key variables that drive the outward growth of the simulated GBM. We find a minimum tumor wave-speed for the model; this depends on the migration and proliferation rates of the normoxic cells and is achieved under certain conditions on the migration rates of the normoxic and hypoxic cells. If the hypoxic cell migration rate is greater than the normoxic cell migration rate above a threshold, the wave speed increases above the predicted minimum. This increase in wave speed is explored through an eigenvalue and eigenvector analysis of the linearized PIHNA model, which yields an expression for this threshold. The PIHNA model suggests that an inherently faster-diffusing hypoxic cell population can drive the outward growth of a GBM as a whole, and that this effect is more prominent for faster-proliferating tumors that recover relatively slowly from a hypoxic phenotype. The findings presented here act as a first step in enabling patient-specific calibration of the PIHNA model.

A minimal modeling framework of radiation and immune system synergy to assist radiotherapy planning

Recent evidence indicates the ability of radiotherapy to induce local and systemic tumor-specific immune responses as a result of immunogenic cell death. However, fractionation regimes routinely used in clinical practice typically ignore the synergy between radiation and the immune system, and instead attempt to completely eradicate tumors by the direct lethal effect of radiation on cancer cells. This paradigm is expected to change in the near future due to the potential benefits of considering radiation-induced antitumor immunity during treatment planning. Towards this goal, we propose a minimal modeling framework based on key aspects of the tumor-immune system interplay to simulate the effects of radiation on tumors and the immunological consequences of radiotherapy. The impacts of tumor-associated vasculature and intratumoral oxygen-mediated heterogeneity on treatment outcomes are ininvestigated. The model provides estimates of the minimum radiation doses required for tumor eradication given a certain number of treatment fractions. Moreover, estimates of treatment duration for disease control given predetermined fractional radiation doses can be also obtained. Although theoretical in nature, this study motivates the development and establishment of immune-based decision-support tools in radiotherapy planning.

Multiparameter MRI Predictors of Long-Term Survival in Glioblastoma Multiforme

Standard-of-care multiparameter magnetic resonance imaging (MRI) scans of the brain were used to objectively subdivide glioblastoma multiforme (GBM) tumors into regions that correspond to variations in blood flow, interstitial edema, and cellular density. We hypothesized that the distribution of these distinct tumor ecological "habitats" at the time of presentation will impact the course of the disease. We retrospectively analyzed initial MRI scans in 2 groups of patients diagnosed with GBM, a long-term survival group comprising subjects who survived >36 month postdiagnosis, and a short-term survival group comprising subjects who survived ≤19 month postdiagnosis. The single-institution discovery cohort contained 22 subjects in each group, while the multi-institution validation cohort contained 15 subjects per group. MRI voxel intensities were calibrated, and tumor voxels clustered on contrast-enhanced T1-weighted and fluid-attenuated inversion-recovery (FLAIR) images into 6 distinct "habitats" based on low- to medium- to high-contrast enhancement and low-high signal on FLAIR scans. Habitat 6 (high signal on calibrated contrast-enhanced T1-weighted and FLAIR sequences) comprised a significantly higher volume fraction of tumors in the long-term survival group (discovery cohort, 35% ± 6.5%; validation cohort, 34% ± 4.8%) compared with tumors in the short-term survival group (discovery cohort, 17% ± 4.5%, P < .03; validation cohort, 16 ± 4.0%, P < .007). Of the 6 distinct MRI-defined habitats, the fractional tumor volume of habitat 6 at diagnosis was significantly predictive of long- or short-term survival. We discuss a possible mechanistic basis for this association and implications for habitat-driven adaptive therapy of GBM.

From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response

Glioblastomas are aggressive primary brain tumors known for their inter- and intratumor heterogeneity. This disease is uniformly fatal, with intratumor heterogeneity the major reason for treatment failure and recurrence. Just like the nature vs nurture debate, heterogeneity can arise from intrinsic or environmental influences. Whilst it is impossible to clinically separate observed behavior of cells from their environmental context, using a mathematical framework combined with multiscale data gives us insight into the relative roles of variation from different sources. To better understand the implications of intratumor heterogeneity on therapeutic outcomes, we created a hybrid agent-based mathematical model that captures both the overall tumor kinetics and the individual cellular behavior. We track single cells as agents, cell density on a coarser scale, and growth factor diffusion and dynamics on a finer scale over time and space. Our model parameters were fit utilizing serial MRI imaging and cell tracking data from ex vivo tissue slices acquired from a growth-factor driven glioblastoma murine model. When fitting our model to serial imaging only, there was a spectrum of equally-good parameter fits corresponding to a wide range of phenotypic behaviors. When fitting our model using imaging and cell scale data, we determined that environmental heterogeneity alone is insufficient to match the single cell data, and intrinsic heterogeneity is required to fully capture the migration behavior. The wide spectrum of in silico tumors also had a wide variety of responses to an application of an anti-proliferative treatment. Recurrent tumors were generally less proliferative than pre-treatment tumors as measured via the model simulations and validated from human GBM patient histology. Further, we found that all tumors continued to grow with an anti-migratory treatment alone, but the anti-proliferative/anti-migratory combination generally showed improvement over an anti-proliferative treatment alone. Together our results emphasize the need to better understand the underlying phenotypes and tumor heterogeneity present in a tumor when designing therapeutic regimens.

Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors

Tumor growth curves are classically modeled by means of ordinary differential equations. In analyzing the Gompertz model several studies have reported a striking correlation between the two parameters of the model, which could be used to reduce the dimensionality and improve predictive power. We analyzed tumor growth kinetics within the statistical framework of nonlinear mixed-effects (population approach). This allowed the simultaneous modeling of tumor dynamics and inter-animal variability. Experimental data comprised three animal models of breast and lung cancers, with 833 measurements in 94 animals. Candidate models of tumor growth included the exponential, logistic and Gompertz models. The exponential and-more notably-logistic models failed to describe the experimental data whereas the Gompertz model generated very good fits. The previously reported population-level correlation between the Gompertz parameters was further confirmed in our analysis (R2 > 0.92 in all groups). Combining this structural correlation with rigorous population parameter estimation, we propose a reduced Gompertz function consisting of a single individual parameter (and one population parameter). Leveraging the population approach using Bayesian inference, we estimated times of tumor initiation using three late measurement timepoints. The reduced Gompertz model was found to exhibit the best results, with drastic improvements when using Bayesian inference as compared to likelihood maximization alone, for both accuracy and precision. Specifically, mean accuracy (prediction error) was 12.2% versus 78% and mean precision (width of the 95% prediction interval) was 15.6 days versus 210 days, for the breast cancer cell line. These results demonstrate the superior predictive power of the reduced Gompertz model, especially when combined with Bayesian estimation. They offer possible clinical perspectives for personalized prediction of the age of a tumor from limited data at diagnosis. The code and data used in our analysis are publicly available at https://github.com/cristinavaghi/plumky.

Clinical Applications of Cerebrospinal Fluid Circulating Tumor DNA as a Liquid Biopsy for Central Nervous System Tumors

Central nervous system (CNS) malignancies are associated with poor prognosis, as well as exceptional morbidity and mortality, likely as a result of low rates of early diagnosis and limited knowledge of the tumor growth and resistance mechanisms, dissemination, and evolution in the CNS. Monitoring patients with CNS malignancies for treatment response and tumor recurrence can be challenging because of the difficulty and risks of brain biopsies and the low specificity and sensitivity of the less invasive methodologies that are currently available. Therefore, there is an urgent need to detect and validate reliable and minimally invasive biomarkers for CNS tumors that can be used separately or in combination with current clinical practices. The circulating tumor DNA (ctDNA) of cerebrospinal fluid (CSF) samples can outline the genetic landscape of entire CNS tumors effectively and is a promising, suitable biomarker, though its role in managing CNS malignancies has not been studied extensively. This review summarizes recent studies that explore the diagnostic, prognostic, and predictive roles of CSF-ctDNA as a liquid biopsy with primary and metastatic CNS malignancies.

Androgen receptor reverses the oncometabolite R-2-hydroxyglutarate-induced prostate cancer cell invasion via suppressing the circRNA-51217/miRNA-646/TGFβ1/p-Smad2/3 signaling

IDH1 (Isocitrate dehydrogenase 1) mutation occurring at codon 132 (R132) in prostate cancer (PCa) is considered as a classifier for a subgroup of PCas with accumulation of oncometabolite R-2HG (R-2-hydroxyglutarate). Here we found that adding R-2HG or the mutant IDH1 R132H could promote PCa cell invasion in androgen receptor (AR)-negative PC3 cells or suppressing the AR in AR-positive C4-2 cells. Mechanism dissection revealed that R-2HG could increase circRNA-51217 expression to sponge miRNA-646, which might then lead to increase TGFβ1 expression and thus induce TGFβ1/p-Smad2/3 signaling to increase PCa cell invasion. AR can suppress this R-2HG/circRNA-51217/miRNA-646/TGFβ1/p-Smad2/3 signaling-increased PCa cell invasion via repressing TGFβ1 transcription and inhibiting circRNA-51217 expression through regulating ADAR2 expression. Preclinical studies with an in vivo xenograft mouse model also revealed that PCa cells with the IDH1 R132H mutation have more invasive metastasis. This study demonstrates that IDH1 R132H mutation with increased oncometabolite R-2HG in PCa cells may play important roles to increase PCa cell invasion.

Molecular and Clinical Insights into the Invasive Capacity of Glioblastoma Cells

The invasive capacity of GBM is one of the key tumoral features associated with treatment resistance, recurrence, and poor overall survival. The molecular machinery underlying GBM invasiveness comprises an intricate network of signaling pathways and interactions with the extracellular matrix and host cells. Among them, PI3k/Akt, Wnt, Hedgehog, and NFkB play a crucial role in the cellular processes related to invasion. A better understanding of these pathways could potentially help in developing new therapeutic approaches with better outcomes. Nevertheless, despite significant advances made over the last decade on these molecular and cellular mechanisms, they have not been translated into the clinical practice. Moreover, targeting the infiltrative tumor and its significance regarding outcome is still a major clinical challenge. For instance, the pre- and intraoperative methods used to identify the infiltrative tumor are limited when trying to accurately define the tumor boundaries and the burden of tumor cells in the infiltrated parenchyma. Besides, the impact of treating the infiltrative tumor remains unclear. Here we aim to highlight the molecular and clinical hallmarks of invasion in GBM.

Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p < 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy.

Improved model prediction of glioma growth utilizing tissue-specific boundary effects

Kinetic parameter estimates for mathematical models of glioblastoma multiforme (GBM), derived from clinical scans, have been used to predict the occurrence of hypoxia, necrosis, response to radiation therapy, and overall survival. Modeling GBM growth in a cerebral model encounters anatomical boundaries that interfere with model calibration from clinical measurements.

METHODS

The effect of boundaries is examined on both spherically symmetric and anatomical models of tumor growth. This effect is incorporated into a method that updates kinetic parameters. The efficacy of this method in reproducing clinical image-derived subject data is evaluated.

RESULTS

Spherically symmetric simulations of tumor growth with simple boundaries behave predictably when in a linear phase of growth. Anatomic simulations of eleven out of twenty subjects demonstrated improved fit to subject data with the new method. When only subjects exhibiting linear growth are considered, eight out of nine subject demonstrate improved fit to the data.

CONCLUSION

Anatomical boundaries to tumor growth measurably deflect progression and affect estimates of kinetic parameters. The presented method reliably updates kinetic parameters to fit anatomic computational models to clinically derived subject data when those data are in a linear regime.

A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery

OBJECTIVES

To develop and validate a radiomics nomogram to preoperative prediction of isocitrate dehydrogenase (IDH) genotype for astrocytomas, which might contribute to the pretreatment decision-making and prognosis evaluating.

METHODS

One hundred five astrocytomas (Grades II-IV) with contrast-enhanced T1-weighted imaging (CE-T1WI), T2 fluid-attenuated inversion recovery (T2FLAIR), and apparent diffusion coefficient (ADC) map were enrolled in this study (training cohort: n = 74; validation cohort: n = 31). IDH1/2 genotypes were determined using Sanger sequencing. A total of 3882 radiomics features were extracted. Support vector machine algorithm was used to build the radiomics signature on the training cohort. Incorporating radiomics signature and clinico-radiological risk factors, the radiomics nomogram was developed. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess these models. Kaplan-Meier survival analysis and log rank test were performed to assess the prognostic value of the radiomics nomogram.

RESULTS

The radiomics signature was built by six selected radiomics features and yielded AUC values of 0.901 and 0.888 in the training and validation cohorts. The radiomics nomogram based on the radiomics signature and age performed better than the clinico-radiological model (training cohort, AUC = 0.913 and 0.817; validation cohort, AUC = 0.900 and 0.804). Additionally, the survival analysis showed that prognostic values of the radiomics nomogram and IDH genotype were similar (log rank test, p < 0.001; C-index = 0.762 and 0.687; z-score test, p = 0.062).

CONCLUSIONS

The radiomics nomogram might be a useful supporting tool for the preoperative prediction of IDH genotype for astrocytoma, which could aid pretreatment decision-making.

KEY POINTS

• The radiomics signature based on multiparametric and multiregional MRI images could predict IDH genotype of Grades II-IV astrocytomas. • The radiomics nomogram performed better than the clinico-radiological model, and it might be an easy-to-use supporting tool for IDH genotype prediction. • The prognostic value of the radiomics nomogram was similar with that of the IDH genotype, which might contribute to prognosis evaluating.

Updates in the Neuoroimaging and WHO Classification of Primary CNS Gliomas: A Review of Current Terminology, Diagnosis, and Clinical Relevance From a Radiologic Prospective

As new advances in the genomics and imaging of CNS tumors continues to evolve, a standardized system for classification is increasingly essential to diagnosis and management. The molecular markers introduced in the 2016 WHO classification of CNS tumors bring both practical and conceptual advances to the characterization of gliomas, strengthening the prognostic and predictive value of terminology while shedding light on the underlying mechanisms that drive biologic behavior. The purpose of this article is to provide a succinct overview of primary intracranial gliomas from a neuroradiologic prospective and according to the 5th edition WHO classification that was revised in 2016. An update of the molecular markers pertinent to defining the major lineages of brain gliomas will be provided, followed by discussion of the terminology, grading and imaging features associated with individual entities. Neuroradiologists should be aware of the key genomic and radiomic features of common brain gliomas, and familiar with an integrated approach to their diagnosis and grading.

Isocitrate dehydrogenase 1-mutated human gliomas depend on lactate and glutamate to alleviate metabolic stress

Diffuse gliomas often carry point mutations in isocitrate dehydrogenase ( IDH1^mut), resulting in metabolic stress. Although IDH^mut gliomas are difficult to culture in vitro, they thrive in the brain via diffuse infiltration, suggesting brain-specific tumor-stroma interactions that can compensate for IDH-1 deficits. To elucidate the metabolic adjustments in clinical IDH^mut gliomas that contribute to their malignancy, we applied a recently developed method of targeted quantitative RNA next-generation sequencing to 66 clinical gliomas and relevant orthotopic glioma xenografts, with and without the endogenous IDH-1^R132H mutation. Datasets were analyzed in R using Manhattan plots to calculate distance between expression profiles, Ward's method to perform unsupervised agglomerative clustering, and the Mann Whitney U test and Fisher's exact tests for supervised group analyses. The significance of transcriptome data was investigated by protein analysis, in situ enzymatic activity mapping, and in vivo magnetic resonance spectroscopy of orthotopic IDH1^mut- and IDH^wt-glioma xenografts. Gene set enrichment analyses of clinical IDH1^mut gliomas strongly suggest a role for catabolism of lactate and the neurotransmitter glutamate, whereas, in IDH^wt gliomas, processing of glucose and glutamine are the predominant metabolic pathways. Further evidence of the differential metabolic activity in these cancers comes from in situ enzymatic mapping studies and preclinical in vivo magnetic resonance spectroscopy imaging. Our data support an evolutionary model in which IDH^mut glioma cells exist in symbiosis with supportive neuronal cells and astrocytes as suppliers of glutamate and lactate, possibly explaining the diffuse nature of these cancers. The dependency on glutamate and lactate opens the way for novel approaches in the treatment of IDH^mut gliomas.-Lenting, K., Khurshed, M., Peeters, T. H., van den Heuvel, C. N. A. M., van Lith, S. A. M., de Bitter, T., Hendriks, W., Span, P. N., Molenaar, R. J., Botman, D., Verrijp, K., Heerschap, A., ter Laan, M., Kusters, B., van Ewijk, A., Huynen, M. A., van Noorden, C. J. F., Leenders, W. P. J. Isocitrate dehydrogenase 1-mutated human gliomas depend on lactate and glutamate to alleviate metabolic stress.

2-Hydroxyglutarate-Mediated Autophagy of the Endoplasmic Reticulum Leads to an Unusual Downregulation of Phospholipid Biosynthesis in Mutant IDH1 Gliomas

Tumor metabolism is reprogrammed to meet the demands of proliferating cancer cells. In particular, cancer cells upregulate synthesis of the membrane phospholipids phosphatidylcholine (PtdCho) and phosphatidylethanolamine (PtdE) in order to allow for rapid membrane turnover. Nonetheless, we show here that, in mutant isocitrate dehydrogenase 1 (IDHmut) gliomas, which produce the oncometabolite 2-hydroxyglutarate (2-HG), PtdCho and PtdE biosynthesis is downregulated and results in lower levels of both phospholipids when compared with wild-type IDH1 cells. 2-HG inhibited collagen-4-prolyl hydroxylase activity, leading to accumulation of misfolded procollagen-IV in the endoplasmic reticulum (ER) of both genetically engineered and patient-derived IDHmut glioma models. The resulting ER stress triggered increased expression of FAM134b, which mediated autophagic degradation of the ER (ER-phagy) and a reduction in the ER area. Because the ER is the site of phospholipid synthesis, ER-phagy led to reduced PtdCho and PtdE biosynthesis. Inhibition of ER-phagy via pharmacological or molecular approaches restored phospholipid biosynthesis in IDHmut glioma cells, triggered apoptotic cell death, inhibited tumor growth, and prolonged the survival of orthotopic IDHmut glioma-bearing mice, pointing to a potential therapeutic opportunity. Glioma patient biopsies also exhibited increased ER-phagy and downregulation of PtdCho and PtdE levels in IDHmut samples compared with wild-type, clinically validating our observations. Collectively, this study provides detailed and clinically relevant insights into the functional link between oncometabolite-driven ER-phagy and phospholipid biosynthesis in IDHmut gliomas.Significance: Downregulation of phospholipid biosynthesis via ER-phagy is essential for proliferation and clonogenicity of mutant IDH1 gliomas, a finding with immediate therapeutic implications. Cancer Res; 78(9); 2290-304. ©2018 AACR.

Pharmacometabolomics Informs Quantitative Radiomics for Glioblastoma Diagnostic Innovation

Applications of omics systems biology technologies have enormous promise for radiology and diagnostics in surgical fields. In this context, the emerging fields of radiomics (a systems scale approach to radiology using a host of technologies, including omics) and pharmacometabolomics (use of metabolomics for patient and disease stratification and guiding precision medicine) offer much synergy for diagnostic innovation in surgery, particularly in neurosurgery. This synthesis of omics fields and applications is timely because diagnostic accuracy in central nervous system tumors still challenges decision-making. Considering the vast heterogeneity in brain tumors, disease phenotypes, and interindividual variability in surgical and chemotherapy outcomes, we believe that diagnostic accuracy can be markedly improved by quantitative radiomics coupled to pharmacometabolomics and related health information technologies while optimizing economic costs of traditional diagnostics. In this expert review, we present an innovation analysis on a systems-level multi-omics approach toward diagnostic accuracy in central nervous system tumors. For this, we suggest that glioblastomas serve as a useful application paradigm. We performed a literature search on PubMed for articles published in English between 2006 and 2016. We used the search terms "radiomics," "glioblastoma," "biomarkers," "pharmacogenomics," "pharmacometabolomics," "pharmacometabonomics/pharmacometabolomics," "collaborative informatics," and "precision medicine." A list of the top 4 insights we derived from this literature analysis is presented in this study. For example, we found that (i) tumor grading needs to be better refined, (ii) diagnostic precision should be improved, (iii) standardization in radiomics is lacking, and (iv) quantitative radiomics needs to prove clinical implementation. We conclude with an interdisciplinary call to the metabolomics, pharmacy/pharmacology, radiology, and surgery communities that pharmacometabolomics coupled to information technologies (chemoinformatics tools, databases, collaborative systems) can inform quantitative radiomics, thus translating Big Data and information growth to knowledge growth, rational drug development and diagnostics innovation for glioblastomas, and possibly in other brain tumors.

In silico gene expression analysis reveals glycolysis and acetate anaplerosis in IDH1 wild-type glioma and lactate and glutamate anaplerosis in IDH1-mutated glioma

Hotspot mutations in isocitrate dehydrogenase 1 (IDH1) initiate low-grade glioma and secondary glioblastoma and induce a neomorphic activity that converts α-ketoglutarate (α-KG) to the oncometabolite D-2-hydroxyglutarate (D-2-HG). It causes metabolic rewiring that is not fully understood. We investigated the effects of IDH1 mutations (IDH1^MUT) on expression of genes that encode for metabolic enzymes by data mining The Cancer Genome Atlas. We analyzed 112 IDH1 wild-type (IDH1^WT) versus 399 IDH1^MUT low-grade glioma and 157 IDH1^WT versus 9 IDH1^MUT glioblastoma samples. In both glioma types, IDH1^WT was associated with high expression levels of genes encoding enzymes that are involved in glycolysis and acetate anaplerosis, whereas IDH1^MUT glioma overexpress genes encoding enzymes that are involved in the oxidative tricarboxylic acid (TCA) cycle. In vitro, we observed that IDH1^MUT cancer cells have a higher basal respiration compared to IDH1^WT cancer cells and inhibition of the IDH1^MUT shifts the metabolism by decreasing oxygen consumption and increasing glycolysis. Our findings indicate that IDH1^WT glioma have a typical Warburg phenotype whereas in IDH1^MUT glioma the TCA cycle, rather than glycolytic lactate production, is the predominant metabolic pathway. Our data further suggest that the TCA in IDH1^MUT glioma is driven by lactate and glutamate anaplerosis to facilitate production of α-KG, and ultimately D-2-HG. This metabolic rewiring may be a basis for novel therapies for IDH1^MUT and IDH1^WT glioma.

Epigenetic regulation of cell fate reprogramming in aging and disease: A predictive computational model

Understanding the control of epigenetic regulation is key to explain and modify the aging process. Because histone-modifying enzymes are sensitive to shifts in availability of cofactors (e.g. metabolites), cellular epigenetic states may be tied to changing conditions associated with cofactor variability. The aim of this study is to analyse the relationships between cofactor fluctuations, epigenetic landscapes, and cell state transitions. Using Approximate Bayesian Computation, we generate an ensemble of epigenetic regulation (ER) systems whose heterogeneity reflects variability in cofactor pools used by histone modifiers. The heterogeneity of epigenetic metabolites, which operates as regulator of the kinetic parameters promoting/preventing histone modifications, stochastically drives phenotypic variability. The ensemble of ER configurations reveals the occurrence of distinct epi-states within the ensemble. Whereas resilient states maintain large epigenetic barriers refractory to reprogramming cellular identity, plastic states lower these barriers, and increase the sensitivity to reprogramming. Moreover, fine-tuning of cofactor levels redirects plastic epigenetic states to re-enter epigenetic resilience, and vice versa. Our ensemble model agrees with a model of metabolism-responsive loss of epigenetic resilience as a cellular aging mechanism. Our findings support the notion that cellular aging, and its reversal, might result from stochastic translation of metabolic inputs into resilient/plastic cell states via ER systems.

Differential expression of the TWEAK receptor Fn14 in IDH1 wild-type and mutant gliomas

The TNF receptor superfamily member Fn14 is overexpressed by many solid tumor types, including glioblastoma (GBM), the most common and lethal form of adult brain cancer. GBM is notable for a highly infiltrative growth pattern and several groups have reported that high Fn14 expression levels can increase tumor cell invasiveness. We reported previously that the mesenchymal and proneural GBM transcriptomic subtypes expressed the highest and lowest levels of Fn14 mRNA, respectively. Given the recent histopathological re-classification of human gliomas by the World Health Organization based on isocitrate dehydrogenase 1 (IDH1) gene mutation status, we extended this work by comparing Fn14 gene expression in IDH1 wild-type (WT) and mutant (R132H) gliomas and in cell lines engineered to overexpress the IDH1 R132H enzyme. We found that both low-grade and high-grade (i.e., GBM) IDH1 R132H gliomas exhibit low Fn14 mRNA and protein levels compared to IDH1 WT gliomas. Forced overexpression of the IDH1 R132H protein in glioma cells reduced Fn14 expression, while treatment of IDH1 R132H-overexpressing cells with the IDH1 R132H inhibitor AGI-5198 or the DNA demethylating agent 5-aza-2'-deoxycytidine increased Fn14 expression. These results support a role for Fn14 in the more aggressive and invasive phenotype associated with IDH1 WT tumors and indicate that the low levels of Fn14 gene expression noted in IDH1 R132H mutant gliomas may be due to epigenetic regulation via changes in DNA methylation.

Molecular and Genetic Determinants of Glioma Cell Invasion

A diffusely invasive nature is a major obstacle in treating a malignant brain tumor, "diffuse glioma", which prevents neurooncologists from surgically removing the tumor cells even in combination with chemotherapy and radiation. Recently updated classification of diffuse gliomas based on distinct genetic and epigenetic features has culminated in a multilayered diagnostic approach to combine histologic phenotypes and molecular genotypes in an integrated diagnosis. However, it is still a work in progress to decipher how the genetic aberrations contribute to the aggressive nature of gliomas including their highly invasive capacity. Here we depict a set of recent discoveries involving molecular genetic determinants of the infiltrating nature of glioma cells, especially focusing on genetic mutations in receptor tyrosine kinase pathways and metabolic reprogramming downstream of common cancer mutations. The specific biology of glioma cell invasion provides an opportunity to explore the genotype-phenotype correlation in cancer and develop novel glioma-specific therapeutic strategies for this devastating disease.

The Temporal Pattern of a Lesion Modulates the Functional Network Topology of Remote Brain Regions

Focal brain lesions can alter the morphology and function of remote brain areas. When the damage is inflicted more slowly, the functional compensation by and structural reshaping of these areas seem to be more effective. It remains unclear, however, whether the momentum of lesion development also modulates the functional network topology of the remote brain areas. In this study, we compared resting-state functional connectivity data of patients with a slowly growing low-grade glioma (LGG) with that of patients with a faster-growing high-grade glioma (HGG). Using graph theory, we examined whether the tumour growth velocity modulated the functional network topology of remote areas, more specifically of the hemisphere contralateral to the lesion. We observed that the contralesional network topology characteristics differed between patient groups. Based only on the connectivity of the hemisphere contralateral to the lesion, patients could be classified in the correct tumour-grade group with 70% accuracy. Additionally, LGG patients showed smaller contralesional intramodular connectivity, smaller contralesional ratio between intra- and intermodular connectivity, and larger contralesional intermodular connectivity than HGG patients. These results suggest that, in the hemisphere contralateral to the lesion, there is a lower capacity for local, specialized information processing coupled to a higher capacity for distributed information processing in LGG patients. These results underline the utility of a network perspective in evaluating effects of focal brain injury.

Imaging Correlates of Adult Glioma Genotypes

Primary brain tumors, most commonly gliomas, are histopathologically typed and graded as World Health Organization (WHO) grades I-IV according to increasing degrees of malignancy. These grades provide prognostic information and guidance on treatment such as radiation therapy and chemotherapy after surgery. Despite the confirmed value of the WHO grading system, results of a multitude of studies and prospective interventional trials now indicate that tumors with identical morphologic criteria can have highly different outcomes. Molecular markers can allow subtypes of tumors of the same morphologic type and WHO grade to be distinguished and are, therefore, of great interest in personalization of brain tumor treatment. Recent genomic-wide studies have resulted in a far more comprehensive understanding of the genomic alterations in gliomas and provide suggestions for a new molecularly based classification. Magnetic resonance (MR) imaging phenotypes can serve as noninvasive surrogates for tumor genotypes and can provide important information for diagnosis, prognosis, and, eventually, personalized treatment. The newly emerged field of radiogenomics allows specific MR imaging phenotypes to be linked with gene expression profiles. In this article, the authors review the conventional and advanced imaging features of three tumoral genotypes with prognostic and therapeutic consequences: (a) isocitrate dehydrogenase mutation; (b) the combined loss of the short arm of chromosome 1 and the long arm of chromosome 19, or 1p19q codeletion; and (c) methylguanine methyltransferase promoter methylation. ^© RSNA, 2017.

Shedding Light on the 2016 World Health Organization Classification of Tumors of the Central Nervous System in the Era of Radiomics and Radiogenomics

The new World Health Organization classification of brain tumors depends on combining the histologic light microscopy features of central nervous system (CNS) tumors with canonical genetic alterations. This integrated diagnosis is redrawing the pedigree chart of brain tumors with rearrangement of tumor groups on the basis of geno-phenotypical behaviors into meaningful groups. Multiple radiogenomic studies provide a bridge between imaging features and tumor microenvironment. An overlap that can be integrated within the genophenotypical classification of CNS tumors for a better understanding of different clinically relevant entities.

Study protocol of a phase IB/II clinical trial of metformin and chloroquine in patients with IDH1-mutated or IDH2-mutated solid tumours

INTRODUCTION

High-grade chondrosarcoma, high-grade glioma and intrahepatic cholangiocarcinoma are aggressive types of cancer with a dismal outcome. This is due to the lack of effective treatment options, emphasising the need for novel therapies. Mutations in the genes IDH1 and IDH2 (isocitrate dehydrogenase 1 and 2) occur in 60% of chondrosarcoma, 80% of WHO grade II-IV glioma and 20% of intrahepatic cholangiocarcinoma. IDH1/2-mutated cancer cells produce the oncometabolite D-2-hydroxyglutarate (D-2HG) and are metabolically vulnerable to treatment with the oral antidiabetic metformin and the oral antimalarial drug chloroquine.

METHODS AND ANALYSIS

We describe a dose-finding phase Ib/II clinical trial, in which patients with IDH1/2-mutated chondrosarcoma, glioma and intrahepatic cholangiocarcinoma are treated with a combination of metformin and chloroquine. Dose escalation is performed according to a 3+3 dose-escalation scheme. The primary objective is to determine the maximum tolerated dose to establish the recommended dose for a phase II clinical trial. Secondary objectives of the study include (1) determination of pharmacokinetics and toxic effects of the study therapy, for which metformin and chloroquine serum levels will be determined over time; (2) investigation of tumour responses to metformin plus chloroquine in IDH1/2-mutated cancers using CT/MRI scans; and (3) whether tumour responses can be measured by non-invasive D-2HG measurements (mass spectrometry and magnetic resonance spectroscopy) of tumour tissue, serum, urine, and/or bile or next-generation sequencing of circulating tumour DNA (liquid biopsies). This study may open a novel treatment avenue for IDH1/2-mutated high-grade chondrosarcoma, glioma and intrahepatic cholangiocarcinoma by repurposing the combination of two inexpensive drugs that are already approved for other indications.

ETHICS AND DISSEMINATION

This study has been approved by the medical-ethical review committee of the Academic Medical Center, Amsterdam, The Netherlands. The report will be submitted to a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

This article was registered at ClinicalTrials.gov identifier (NCT02496741): Pre-results.

Role of Imaging in the Era of Precision Medicine

Precision medicine is an emerging approach for treating medical disorders, which takes into account individual variability in genetic and environmental factors. Preventive or therapeutic interventions can then be directed to those who will benefit most from targeted interventions, thereby maximizing benefits and minimizing costs and complications. Precision medicine is gaining increasing recognition by clinicians, healthcare systems, pharmaceutical companies, patients, and the government. Imaging plays a critical role in precision medicine including screening, early diagnosis, guiding treatment, evaluating response to therapy, and assessing likelihood of disease recurrence. The Association of University Radiologists Radiology Research Alliance Precision Imaging Task Force convened to explore the current and future role of imaging in the era of precision medicine and summarized its finding in this article. We review the increasingly important role of imaging in various oncological and non-oncological disorders. We also highlight the challenges for radiology in the era of precision medicine.

Does Pretreatment Tumor Growth Hold Prognostic Information for Patients with Glioblastoma?

BACKGROUND

Glioblastomas are highly aggressive and heterogeneous tumors, both in terms of patient outcome and molecular profile. Magnetic resonance imaging of tumor growth could potentially reveal new insights about tumor biology noninvasively. The aim of this exploratory retrospective study was to investigate the prognostic potential of pretreatment growth rate of glioblastomas, after controlling for known prognostic factors.

METHODS

A growth model derived from clinical pretreatment postcontrast T₁-weighted magnetic resonance imaging images was used to divide 106 glioblastoma patients into 2 groups. The "faster growth" group had tumors growing faster than expected based on their volume at diagnosis, whereas the "slower growth" group had tumors growing slower than expected. Associations between tumor growth and survival were examined by the use of multivariable Cox regression and logistic regression.

RESULTS

None of the known prognostic factors were significantly associated with tumor growth. An extended multivariable Cox model showed that during the first 12 months of follow-up, there was no significant difference in survival between faster and slower growing tumors. Beyond 12 months' follow-up, however, there was a significant, independent survival benefit in having a tumor with slower pretreatment growth. In a multiple logistic regression model including patients receiving both radiotherapy and chemotherapy (n = 82), slower pre-treatment growth of the tumor was shown to be a significant predictor of 2-year survival (odds ratio 4.4).

CONCLUSIONS

Pretreatment glioblastoma growth harbors prognostic information. Patients with slower growing tumors have higher odds of survival beyond 2 years, adjusted for other prognostic factors.

Personalized Radiotherapy Planning Based on a Computational Tumor Growth Model

In this article, we propose a proof of concept for the automatic planning of personalized radiotherapy for brain tumors. A computational model of glioblastoma growth is combined with an exponential cell survival model to describe the effect of radiotherapy. The model is personalized to the magnetic resonance images (MRIs) of a given patient. It takes into account the uncertainty in the model parameters, together with the uncertainty in the MRI segmentations. The computed probability distribution over tumor cell densities, together with the cell survival model, is used to define the prescription dose distribution, which is the basis for subsequent Intensity Modulated Radiation Therapy (IMRT) planning. Depending on the clinical data available, we compare three different scenarios to personalize the model. First, we consider a single MRI acquisition before therapy, as it would usually be the case in clinical routine. Second, we use two MRI acquisitions at two distinct time points in order to personalize the model and plan radiotherapy. Third, we include the uncertainty in the segmentation process. We present the application of our approach on two patients diagnosed with high grade glioma. We introduce two methods to derive the radiotherapy prescription dose distribution, which are based on minimizing integral tumor cell survival using the maximum a posteriori or the expected tumor cell density. We show how our method allows the user to compute a patient specific radiotherapy planning conformal to the tumor infiltration. We further present extensions of the method in order to spare adjacent organs at risk by re-distributing the dose. The presented approach and its proof of concept may help in the future to better target the tumor and spare organs at risk.