Brain Tumor Response Research Articles

LGG-25. ADVANCING RESPONSE ASSESSMENT OF LOW-GRADE GLIOMAS: IDENTIFYING VOLUME CORRELATIONS OF BI-PERPENDICULAR DIAMETER CHANGES WITH MACHINE LEARNING

Abstract BACKGROUND Assessing brain tumor response is key for effective management and clinical trial benchmarks. Traditionally, bi-perpendicular diameter (BPD) measurements have been used to estimate tumor size. However, recent studies suggest volumetric assessment yields a more accurate measure of response and estimate of tumor burden. Advances in technology enable rapid volume assessments. Integrating volumetric analysis into clinical settings is challenged by the absence of volume-based standards aligned with traditional RAPNO bi-dimensional criteria. Our goal was to correlate volume change percentages with bi-perpendicular diameter changes using machine learning. METHODS In this project, we compared 2 machine learning models to establish a correlation between percentage change of sum of products of BPD and percent change in volume using a generalized linear model (GLM) and an artificial neural network (ANN). We analyzed 49 patients with low grade glioma, treated with focal irradiation (NCT04065776) (n=27) or mirdametinib (NCT04923126) (n=22). We trained and validated the models at train-test split ratios of 80/20, 75/25, and 70/30, incorporating a proportionate split of tumors from each group. The models were compared on the validation data set using mean-squared error (MSE). RESULTS At percentage changes of +25%, -25%, -50%, and -75% in the sum of BPDs, the GLM predicts changes in volume to be +33%, -31%, -59%, and -83%, respectively. For the ANN, the predicted changes are +20%, -29%, -53%, and -78%. The ANN demonstrated a lower MSE compared to the GLM. The two models showed higher concordance in their volume predictions at negative percentage changes in the sum of BPDs: -25% (p=0.15), -50% (p=0.03), and 75% (p=.08) CONCLUSION We identified volume percentage changes matching BPD changes per RAPNO criteria using two machine learning methods, both agreeing on negative values. Further testing with a larger dataset is needed to reliably correlate percentage changes.

Improving the Generalizability of Deep Learning for T2-Lesion Segmentation of Gliomas in the Post-Treatment Setting.

Although fully automated volumetric approaches for monitoring brain tumor response have many advantages, most available deep learning models are optimized for highly curated, multi-contrast MRI from newly diagnosed gliomas, which are not representative of post-treatment cases in the clinic. Improving segmentation for treated patients is critical to accurately tracking changes in response to therapy. We investigated mixing data from newly diagnosed (n = 208) and treated (n = 221) gliomas in training, applying transfer learning (TL) from pre- to post-treatment imaging domains, and incorporating spatial regularization for T2-lesion segmentation using only T2 FLAIR images as input to improve generalization post-treatment. These approaches were evaluated on 24 patients suspected of progression who had received prior treatment. Including 26% of treated patients in training improved performance by 13.9%, and including more treated and untreated patients resulted in minimal changes. Fine-tuning with treated glioma improved sensitivity compared to data mixing by 2.5% (p < 0.05), and spatial regularization further improved performance when used with TL by 95th HD, Dice, and sensitivity (6.8%, 0.8%, 2.2%; p < 0.05). While training with ≥60 treated patients yielded the majority of performance gain, TL and spatial regularization further improved T2-lesion segmentation to treated gliomas using a single MR contrast and minimal processing, demonstrating clinical utility in response assessment.

DNA damage response in brain tumors: A Society for Neuro-Oncology consensus review on mechanisms and translational efforts in neuro-oncology.

DNA damage response (DDR) mechanisms are critical to maintenance of overall genomic stability, and their dysfunction can contribute to oncogenesis. Significant advances in our understanding of DDR pathways have raised the possibility of developing therapies that exploit these processes. In this expert-driven consensus review, we examine mechanisms of response to DNA damage, progress in development of DDR inhibitors in IDH-wild-type glioblastoma and IDH-mutant gliomas, and other important considerations such as biomarker development, preclinical models, combination therapies, mechanisms of resistance and clinical trial design considerations.

Diagnostic validity and reliability of BT-RADS in the management of recurrent high-grade glioma

Background and purposeBT-RADS is a new framework system for reporting the treatment response of brain tumors. The aim of the study was to assess the diagnostic performance and reliability of the BT-RADS in predicting the recurrence of high-grade glioma (HGG). Materials and MethodsThis prospective single-center study recruited 81 cases with previously operated and pathologically proven HGG. The patients underwent baseline and follow-up contrast-enhanced MRI (CE-MRI). Two neuro-radiologists with ten years-experience in neuroimaging independently analyzed and interpreted the MRI images and assigned a BT-RADS category for each case. To assess the diagnostic accuracy of the BT-RADS for detecting recurrent HGG, the reference standard was the histopathology for BT-RADS categories 3 and 4, while neurological clinical examination and clinical follow up were used as a reference for BT-RADS categories 1 and 2. The inter-reader agreement was assessed using the Cohen's Kappa test. ResultsThe study included 81 cases of HGG, of which 42 were recurrent and 39 were non-recurrent HGG cases based on the reference test. BT-RADS 3B was the best cutoff for predicting recurrent HGG with a sensitivity of 90.5 % to 92.9 %, specificity of 76.9 % to 84.6 %, and accuracy of 83.9 % to 88.9 %, based on both readers. The BT-RADS showed a substantial inter-reader agreement with a K of 0.710 (P = 0.001). ConclusionsThe BT-RADS is a valid and reliable framework for predicting recurrent HGG. Moreover, BT-RADS can help neuro-oncologists make clinical decisions that can potentially improve the patient's outcome.

Whole-brain radiation therapy plus simultaneous integrated boost for brain metastases from breast cancers.

The effect of whole-brain radiation therapy (WBRT) plus simultaneous integrated boost (SIB) in brain metastasis from breast cancers has not been demonstrated. In this single-center retrospective study, we reviewed consecutive breast cancer patients who developed brain metastasis and were treated with hypofractionated radiation therapy plus WBRT using intensity-modulated radiation therapy (IMRT)-SIB approaches. We analyzed clinical outcomes, prognostic factors and patterns of treatment failure. A total of 27 patients were eligible for analysis. Four (14.8%) patients achieved clinical complete response and 14 (51.9%) had partial response of brain lesions. The other nine patients were not evaluated for brain tumor response. The median brain progression-free survival was 8.60 (95% CI [6.43-13.33]) months and the median overall survival was 16.8 (95% CI [13.3-27.7]) months. Three patients had in-field failure, five had out-field failure and two had in-field and out-field failure. WBRT plus SIB led to improved tumor control and clinical outcome in breast cancer patients with brain metastasis.

RBIO-06. COMBINING FLASH RADIOTHERAPY WITH GD2-CAR T CELL IMMUNOTHERAPY IN DIFFUSE INTRINSIC PONTINE GLIOMA

Abstract Diffuse Intrinsic Pontine Glioma (DIPG) is the leading cause of brain tumor related deaths in pediatric cancers. DIPG is completely lethal, and palliative radiation therapy (RT) to control symptoms is currently the standard-of-care treatment for DIPG, therefore effective treatment is urgently needed. In recent phase 1 clinical trials, the promising potential for using chimeric antigen receptor T cell immunotherapy against the disialoganglioside GD2 (GD2-CART) has been established, where 75% of patients exhibited clinical and radiographic improvement. There has also been emerging evidence advocating for the combined use of GD2-CART therapy with conventional radiation (CONV-RT) to enhance the immune response in other solid brain tumors. Ultra-high dose rate radiotherapy, abbreviated as FLASH-RT, is an emerging radiation technique shown to reduce normal brain tissue damage while preserving tumor control, as compared to CONV-RT. It is currently unknown how FLASH-RT will affect CART therapy. We hypothesize that effective treatment for DIPG can be achieved by combining FLASH-RT with GD2-CART immunotherapy. We have shown FLASH-RT to equivocally kill DIPG cells as well as the standard-of-care, CONV-RT; Given the neurological tissue sparing effects of FLASH-RT, this radiotherapy alone could be an improvement for DIPG treatment. Additionally, we have validated that GD2 is highly expressed in our DIPG cell line, suggesting high GD2-CART target specificity. Additionally, we have found that GD2 expression on DIPG tumor cells remains unchanged upon treatment with FLASH-RT. Therefore, we can utilize FLASH-RT to target GD2 CART while sparing normal tissue and minimizing neurotoxicity, allowing for a promising avenue of combination treatment for this devastating pediatric cancer.

Tumor Response Metrics in Neuro-Oncology: A Review

Despite the efforts spent in clinical trials in neuro-oncology during the last few decades, many challenges remain in the radiologic evaluation of brain tumors and metastases. This article aims to explain the current use of tumor metrics, especially in phase II clinical trials, to evaluate response to systemic therapy in neuro-oncology. We will discuss the imaging findings associated with progression and with partial, minor, or complete response to treatment, as well as the method of measurement, the distinction between disease-related and treatment-related changes, and inclusion of clinical symptoms to define treatment response and progression. The evaluation of response in brain tumors requires metrics that account for differences in response between high- and low-grade gliomas and specific tumor behaviors associated with radiation and immunotherapy, among other pitfalls. Advanced brain imaging techniques, such as perfusion and permeability imaging, should be incorporated into clinical trials to measure response to novel therapeutic agents more accurately.Learning Objectives: To understand the role of tumor metrics analysis and the main challenges and pitfalls of applying neuro-oncology imaging criteria

Applying single cell multi-omic analyses to understand treatment resistance in pediatric high grade glioma.

Despite improvements in cancer patient outcomes seen in the past decade, tumor resistance to therapy remains a major impediment to achieving durable clinical responses. Intratumoral heterogeneity related to genetic, epigenetic, transcriptomic, proteomic, and metabolic differences between individual cancer cells has emerged as a driver of therapeutic resistance. This cell to cell heterogeneity can be assessed using single cell profiling technologies that enable the identification of tumor cell clones that exhibit similar defining features like specific mutations or patterns of DNA methylation. Single cell profiling of tumors before and after treatment can generate new insights into the cancer cell characteristics that confer therapeutic resistance by identifying intrinsically resistant sub-populations that survive treatment and by describing new cellular features that emerge post-treatment due to tumor cell evolution. Integrative, single cell analytical approaches have already proven advantageous in studies characterizing treatment-resistant clones in cancers where pre- and post-treatment patient samples are readily available, such as leukemia. In contrast, little is known about other cancer subtypes like pediatric high grade glioma, a class of heterogeneous, malignant brain tumors in children that rapidly develop resistance to multiple therapeutic modalities, including chemotherapy, immunotherapy, and radiation. Leveraging single cell multi-omic technologies to analyze naïve and therapy-resistant glioma may lead to the discovery of novel strategies to overcome treatment resistance in brain tumors with dismal clinical outcomes. In this review, we explore the potential for single cell multi-omic analyses to reveal mechanisms of glioma resistance to therapy and discuss opportunities to apply these approaches to improve long-term therapeutic response in pediatric high grade glioma and other brain tumors with limited treatment options.

Abstract 6720: Using proteasome inhibition to hyperactivate the integrated stress response in aggressive pediatric brain tumors

Abstract H3K27-altered diffuse midline glioma (DMG) and atypical teratoid/rhabdoid tumor (AT/RT) are aggressive pediatric brain tumors with dismal outcomes. DMG and AT/RT have increased baseline activation of the integrated stress response (ISR), an evolutionarily conserved system that enables cells to tolerate various forms of stress. This activation is manifested by elevated levels of ATF4 and NRF2 in DMG and AT/RT, respectively. Transient or low-level expression of these transcription factors protect cells from stress, while sustained, high-level activation leads to cell death. Ixazomib is an orally bioavailable proteasome inhibitor that causes endoplasmic reticulum stress, which is a major upstream activator of the ISR. We hypothesized that the high baseline level of ATF4 in DMG and NRF2 in AT/RT would make these tumors susceptible to ISR activators such as ixazomib. After determining the IC50 of ixazomib, DMG and AT/RT cell lines (JHH-DIPG1, JHH-DIPG16A, and CHLA06) were treated with increasing concentrations of the drug. Cleaved caspase 3 immunofluorescence showed a significant increase of apoptosis in all cell lines (JHH-DIPG16A and CHLA06 p&amp;lt;0.0001, JHH-DIPG1 p=0.0008 by ANOVA). Additionally, western blots for cleaved PARP and phospho-Rb expression detected induction of apoptosis and suppression of cell proliferation in JHH-DIPG1 treated with ixazomib. We are currently testing ixazomib in combination with idarubicin, a brain-penetrant anthracycline, and gemcitabine, a brain-penetrant nucleoside analog. Synergy testing in CHLA06 showed an overall zero-interaction-potency (ZIP) synergy score of 23.464 with inhibitory concentrations of ixazomib and idarubicin in the low nanomolar range (scores of 10 or above indicate synergy). Similarly, ixazomib and gemcitabine synergized to suppress CHLA06 growth and induce apoptosis (67% Annexin V+ cells in combination compared to 14 and 24% Annexin V+ with single agent treatment). In the future, we will determine if ixazomib selectively kills DMG and AT/RT while sparing normal cells in iPSC brain organoids. We will also test ixazomib singly and in combination with traditional chemotherapy in orthotopic xenografts of AT/RT. These results suggest that ixazomib, especially in combination with idarubicin, gemcitabine or other ISR activators, has the potential to serve as an effective therapy for aggressive pediatric brain tumors. Citation Format: Orlandi Valencia Novak, Tyler Findlay, Jeffrey Rubens, Charles Eberhart, Eric H. Raabe. Using proteasome inhibition to hyperactivate the integrated stress response in aggressive pediatric brain tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6720.

Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI.

In the context of brain tumour response assessment, deep learning-based three-dimensional (3D) tumour segmentation has shown potential to enter the routine radiological workflow. The purpose of the present study was to perform an external evaluation of a state-of-the-art deep learning 3D brain tumour segmentation algorithm (HD-GLIO) on an independent cohort of consecutive, post-operative patients. For 66 consecutive magnetic resonance imaging examinations, we compared delineations of contrast-enhancing (CE) tumour lesions and non-enhancing T2/FLAIR hyperintense abnormality (NE) lesions by the HD-GLIO algorithm and radiologists using Dice similarity coefficients (Dice). Volume agreement was assessed using concordance correlation coefficients (CCCs) and Bland-Altman plots. The algorithm performed very well regarding the segmentation of NE volumes (median Dice = 0.79) and CE tumour volumes larger than 1.0 cm3 (median Dice = 0.86). If considering all cases with CE tumour lesions, the performance dropped significantly (median Dice = 0.40). Volume agreement was excellent with CCCs of 0.997 (CE tumour volumes) and 0.922 (NE volumes). The findings have implications for the application of the HD-GLIO algorithm in the routine radiological workflow where small contrast-enhancing tumours will constitute a considerable share of the follow-up cases. Our study underlines that independent validations on clinical datasets are key to asserting the robustness of deep learning algorithms.

Characterization of the Response of 9L and U-251N Orthotopic Brain Tumors to 3D Conformal Radiation Therapy.

In a study employing MRI-guided stereotactic radiotherapy (SRS) in two orthotopic rodent brain tumor models, the radiation dose yielding 50% survival (the TCD50) was sought. Syngeneic 9L cells, or human U-251N cells, were implanted stereotactically in 136 Fischer 344 rats or 98 RNU athymic rats, respectively. At approximately 7 days after implantation for 9L, and 18 days for U-251N, rats were imaged with contrast-enhanced MRI (CE-MRI) and then irradiated using a Small Animal Radiation Research Platform (SARRP) operating at 220 kV and 13 mA with an effective energy of ∼70 keV and dose rate of ∼2.5 Gy per min. Radiation doses were delivered as single fractions. Cone-beam CT images were acquired before irradiation, and tumor volumes were defined using co-registered CE-MRI images. Treatment planning using MuriPlan software defined four non-coplanar arcs with an identical isocenter, subsequently accomplished by the SARRP. Thus, the treatment workflow emulated that of current clinical practice. The study endpoint was animal survival to 200 days. The TCD50 inferred from Kaplan-Meier survival estimation was approximately 25 Gy for 9L tumors and below 20 Gy, but within the 95% confidence interval in U-251N tumors. Cox proportional-hazards modeling did not suggest an effect of sex, with the caveat of wide confidence intervals. Having identified the radiation dose at which approximately half of a group of animals was cured, the biological parameters that accompany radiation response can be examined.

Liquid biopsy in pediatric brain tumors.

Malignant primary brain tumors are the most common cancer in children aged 0-14years, and are the most common cause of death among pediatric cancer patients. Compared to other cancers, pediatric brain tumors have been difficult to diagnose and study given the high risk of intracranial biopsy penetrating through vital midline structures, where the majority of pediatric brain tumors originate (Ostrom et al., 2015). Furthermore, the vast majority of these tumors recur. With limitations in the ability to monitor using clinical and radiographic methods alone, minimally invasive methods such as liquid biopsy will be crucial to our understanding and treatment. Liquid biopsy of blood, urine, and cerebrospinal fluid (CSF) can be used to sample cfDNA, ctDNA, RNA, extracellular vesicles, and tumor-associated proteins. In the past year, four seminal papers have made significant advances in the use of liquid biopsy in pediatric brain tumor patients (Liu et al., 2021; Cantor et al., 2022; Miller et al., 2022; Pagès et al., 2022). In this review, we integrate the results of these studies and others to discuss how the newest technologies in liquid biopsy are being developed for molecular diagnosis and treatment response in pediatric brain tumors.

Protein Kinase Inhibitors as a New Target for Immune System Modulation and Brain Cancer Management

High-grade brain tumors are malignant tumors with poor survival and remain the most difficult tumors to treat. An important contributing factor to the development and progression of brain tumors is their ability to evade the immune system. Several immunotherapeutic strategies including vaccines and checkpoint inhibitors have been studied to improve the effectiveness of the immune system in destroying cancer cells. Recent studies have shown that kinase inhibitors, capable of inhibiting signal transduction cascades that affect cell proliferation, migration, and angiogenesis, have additional immunological effects. In this review, we explain the beneficial therapeutic effects of novel small-molecule kinase inhibitors and explore how, through different mechanisms, they increase the protective antitumor immune response in high-grade brain tumors.

ACT-2 JCOG0911A2 AND ESTABLISHING AN INFRASTRUCTURE FOR RADIOLOGICAL STUDIES FOR JCOG BRAIN TUMOR STUDY GROUP CLINICAL TRIALS

Abstract Background Radiological information is indispensable to understanding the pathology and correctly assessing the treatment response of malignant brain tumors. Clinical trials conducted outside of Japan have vigorously collected and investigated radiological images of the enrolled patients and published many significant findings, such as the RANO criteria. The authors initiated the JCOG0911A2 study, which is an ancillary analysis of JCOG0911, and established an infrastructure for collecting and analyzing raw radiological images of patients enrolled in clinical trials conducted by the JCOG brain tumor study group. JCOG0911A2 study design and infrastructure for radiological image storage: JCOG0911A2 aims to collect all radiological images related to the JCOG0911 study, conduct radiomics analysis, and complete general radiological research. Among 122 registered patients, 2532 series from 118 cases accounting for 237 GB of data were collected as of June 28, 2022. All collected data were anonymized and stored in Osirix MD with triple data backup. JCOG0911A2's treatment response assessment will be performed according to the RANO criteria. Thus, the authors developed a script in MATLAB that enables semi-automatic treatment response assessment according to the investigators' annotations on Osirix MD. Data Management The abovementioned “semi-automatic treatment response assessment script” was not necessary until up to a collection of 30 cases. However, manual management became impossible as more case data arrived, and establishing a sophisticated and systematic data management system was inevitable. The data management system developed for JCOG0911A2 is also used with minor modifications for the ongoing JCOG study. Conclusion JCOG0911A2 study established a reliable infrastructure capable of handling radiological images from patients enrolled in JCOG brain tumor study group clinical trials.

LW-10 PRECLINICAL THERAPEUTIC TESTING FOR BRAIN TUMOR USING PATIENT-DERIVED XENOGRAFT (PDX) MODEL

Abstract I have been working on the brain tumor research about 20 years in the United State. My primary research interests in understanding of the mechanisms of genetic and epigenetic regulation that promote tumor development and progression, and identify novel therapeutic targets for the treatment of human brain cancer. For these purposes, I have developed many unique patient-derived xenograft (PDX) models for human brain tumors in the brains of immunocompromised mice. This animal model system has been shown to better preserve tumor characteristics, as manifested in patients, than tumor cell propagation in cell culture. In association with the development of these models, my laboratory utilizes numerous techniques to investigate brain tumor growth and response to novel agents that are administered singularly or in combinations, including with radiation. My laboratory employs the latest genomic/epigenomic technologies to identify novel genetic and epigenetics alteration that would reveal rational therapeutic targets, and test the anti-tumor activity of the therapeutics using PDX mouse model including diffuse intrinsic pontine glioma (DIPG) harboring histone H3 gene mutation (H3K27M). We have investigated the role of H3K27M mutations and histone modifications, and preclinical therapeutic testing in DIPG PDX models. In addition to the molecular targeted therapy, we recently implement AI machine learning approach and emerging patient genomic data to discover more effective and less toxic candidates from billions of compounds for brain tumor therapy. Moreover, we investigate new drug delivery systems to bypass the blood-brain barrier using convection-enhanced delivery and intranasal delivery. My long-term goal of my work further tests the efficacy of my approach preclinically and, if validated, move this therapeutic approach to clinical trial. In the meeting, I will present current advances in experimental therapeutics in my laboratory.

CTIM-06. EVALUATING THE ASSOCIATION BETWEEN PERIPHERAL BLOOD IMMUNOLOGIC RESPONSE AND THERAPEUTIC RESPONSE TO IMMUNE CHECKPOINT INHIBITION IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA AND GLIOSARCOMA

Abstract Immune checkpoint inhibition (ICI) has failed to demonstrate significant efficacy in glioblastoma. With the underlying hypothesis that generation of an immune response in peripheral is necessary for a brain tumor response, we are enrolling patients with newly diagnosed glioblastoma on an ICI immunotherapy clinical trial with longitudinal monitoring of immune function. The purpose of this work is to improve patient selectivity for response to ICI treatment by identifying peripheral immunologic response via immunophenotyping and functional analyses. Currently, 10 of a planned 48 patients have been treated with combination nivolumab and ipilimumab (NCI-21-C-0015). We analyzed blood samples of the first two patients, from pretreatment through cycle 5 of combination ICI to confirm methods used in this clinical trial. Longitudinal evaluation of immunologic response using comprehensive flow cytometric analysis and an in vitro T cell stimulation paradigm with nivolumab and ipilimumab supplementation, serving as an ex vivo predictor of immune response, are reported. Immunophenotyping via flow cytometry successfully documented patients’ peripheral immune cell phenotypes, and ELISpot and in vitro stimulation assays successfully measured patients’ immune cell functional capacitance. Both patients analyzed have shown increased proportions of effector/memory phenotypes within peripheral T cells, and a decrease in peripheral lymphocyte/monocyte ratio during treatment, potentially indicating lymphocyte sequestration in bone marrow or recruitment to the tumor microenvironment. ELISpot analysis documented interferon-γ production capability of T cell is maintained during treatment. Moreover, preliminary analysis of patients’ T cell proliferative capacity in PBMC utilizing in vitro anti-CD3 stimulation indicated T cells become increased in proliferative capability during treatment. These findings support the feasibility of longitudinal assessments, demonstrating that immunologic responsiveness and functionality can be detected with these blood monitoring strategies. Additional patients’ samples will be tested to allow confirmation of trends in immunologic response, and correlations with patient outcomes will be reported at time of presentation.

Contributional role of susceptibility-weighted imaging and apparent diffusion coefficient in pediatric brain tumor grading

BackgroundCentral nervous system neoplasms are a primary cause of mortality and one of the most frequent solid tumors in children. Magnetic resonance imaging (MRI) plays a key role in the diagnosis and treatment response of pediatric brain tumors as well as providing us with functional information regarding cellularity, metabolism, and neoangiogenesis. This study aimed to evaluate the contributional role of susceptibility-weighted imaging (SWI) and diffusion weight imaging (DWI) in pediatric brain tumor grading by assessing the intratumoral susceptibility signals (ITSS), apparent diffusion coefficient (ADC), and relative ADC (rADC).ResultsA significant correlation was noted between the (ITSS) score and the brain tumor grade (P < 0.001) with a descending trend in the low-grade tumor (ITSS score 0 = 14/29, score 1 = 11/29, and score 2 = 4/29) and an ascending trend in the high-grade tumor (ITSS score 0 = 4/41, score 1 = 9/41 and score 2 = 22/41). No embryonal tumor showed an ITSS score of 0. Apparent diffusion coefficient sensitivity and specificity were 82.9% and 82.8%, respectively, while rADC sensitivity and specificity were 91.4% and 86.2%, respectively. For high-grade tumors, the ITSS score 2 had a significant positive prediction (P = 0.009) and the rADC value had a significant negative prediction (P = 0.031).ConclusionsSusceptibility-weighted imaging ITSS score, ADC, and rADC have a promising role in preoperative pediatric brain tumor grading and should be considered as complementary sequences to routine MRI studies.

Prognostic Neurotransmitter Receptors Genes Are Associated with Immune Response, Inflammation and Cancer Hallmarks in Brain Tumors.

Simple SummaryGlioblastoma multiforme (GBM) is an aggressive form of glioma characterized by poor survival rates. The main cause of the limited efficacy of the current treatments and tumor recurrence is associated with the infiltration of GBM cells into the surrounding brain tissue. Until recently, peripheral nerves were believed to play a passive role in tumorigenesis; however, over the last decade, pioneering studies have highlighted their involvement in cancer initiation and progression by releasing neurotransmitters (NTs). In this study, we hypothesized that dysregulated genes encoding for neurotransmitter receptors (NTRs) could have a different association with patient survival in GBM and low-grade glioma (LGG). We identified 10 prognostic NTR genes that are progressively downregulated across cancer grades and are negatively correlated with genes associated with immune response, inflammation, and brain cancer hallmarks in LGG but not in GBM. We believe our findings shed new light on the role of neurotransmitters and their interactions with inflammation and immune response in the malignant progression of human gliomas.Glioblastoma multiforme (GBM) is one of the most aggressive forms of cancer. Neurotransmitters (NTs) have recently been linked with the uncontrolled proliferation of cancer cells, but the role of NTs in the progression of human gliomas is still largely unexplored. Here, we investigate the genes encoding for neurotransmitter receptors (NTRs) by analyzing public transcriptomic data from GBM and LGG (low-grade glioma) samples. Our results showed that 50 out of the 98 tested NTR genes were dysregulated in brain cancer tissue. Next, we identified and validated NTR-associated prognostic gene signatures for both LGG and GBM. A subset of 10 NTR genes (DRD1, HTR1E, HTR3B, GABRA1, GABRA4, GABRB2, GABRG2, GRIN1, GRM7, and ADRA1B) predicted a positive prognosis in LGG and a negative prognosis in GBM. These genes were progressively downregulated across glioma grades and exhibited a strong negative correlation with genes associated with immune response, inflammasomes, and established cancer hallmarks genes in lower grade gliomas, suggesting a putative role in inhibiting cancer progression. This study might have implications for the development of novel therapeutics and preventive strategies that target regulatory networks associated with the link between the autonomic nervous system, cancer cells, and the tumor microenvironment.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques.

ObjectiveSummarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments.MethodsExperts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics).ResultsHigh-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies.ConclusionConsiderable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.

Federated Learning Approach for Measuring the Response of Brain Tumors to Chemotherapy

Brain tumor is a fatal disease and one of the major causes of rising death rates in adults. Predicting methylation of the O6-Methylguanine-DNA Methyltransferase (MGMT) gene status utilizing Magnetic resonance imaging (MRI) imaging is highly important since it is a predictor of brain tumor responses to chemotherapy, which reduces the number of needed surgeries. Deep Learning (DL) approaches became powerful in extracting meaningful relationships and making accurate predictions. DL-based models require a large database and accessing or transferring patient data to train the model. Federated machine learning has recently gained popularity, as it offers practical solutions for data privacy, centralized computation, and high computing power. This study aims to investigate the feasibility of federated learning (FL) by developing a FL-based approach to predict MGMT promoter methylation status using the BraTs2021 dataset for the four sequence types, (Fluid Attenuated Inversion Recovery (FLAIR), T1-weighted (T1w), T1-weighted Gadolinium Post Contrast (T1wCE/T1Gd), and T2-weighted (T2w)) MRI images. The FL model compared to the DL-based and the experimental results show that even with imbalanced and heterogeneous datasets, the FL approach reached the training model to 99.99% of the model quality achieved with centralized data after 300 communication rounds between 10 institutions using OpenFl framework and the improved EfficentNet-B3 neural network architecture.