Marrow-ablative consolidation chemotherapy and molecular targeted therapy delivered in a risk-adapted manner for newly diagnosed children with choroid plexus carcinoma: A work in progress.

Choroid plexus carcinomas (CPC) are early childhood cancers characterized by loss of TP53 function and poor survival. We are analyzing data on TP53 status, survival, and second cancers from the largest cohort of CPC receiving chemotherapy followed by consolidation with marrow-ablative chemotherapy (HDCx). Additionally, we discuss the rationale for targeted therapies for CPC patients. Currently, 8 of the 13 with Li-Fraumeni Syndrome-associated CPC were treated and continued CPC-free, indicating that HDCx improves CPC-free survival in young children with TP53-mutated CPC. These data justify the inclusion of HDCx in the planned prospective international trial for children with TP53-mutated CPC.

Dynamic governance: A new era for consent for stem cell research.

Governance infrastructures streamline scientific and ethical provenance verification of human pluripotent stem cell (SC) lines. Yet, scientific developments (e.g., SC-derived embryo models, organoids) challenge research governance approaches to stored biospecimens, questioning the validity of informed consent (IC) models. Likewise, e-health platforms are driving major transformations in data processing, prompting a reappraisal of IC. Given these developments, participatory research platforms are identified as effective tools to promote longitudinal engagement, interactive decision-making, and dynamic governance. Learning from European initiatives piloting dynamic IC for biobanking and SC research, this Perspective explores the benefits and challenges of implementing dynamic IC and governance for SC.

GABA production induced by imipridones is a targetable and imageable metabolic alteration in diffuse midline gliomas.

Diffuse midline gliomas (DMGs) are lethal primary brain tumors in children. The imipridones ONC201 and ONC206 induce mitochondrial dysfunction and have emerged as promising therapies for DMG patients. However, efficacy as monotherapy is limited, identifying a need for strategies that enhance response. Another hurdle is the lack of biomarkers that report on drug-target engagement at an early timepoint after treatment onset. Here, using 1 H-magnetic resonance spectroscopy, which is a non-invasive method of quantifying metabolite pool sizes, we show that accumulation of ψ-aminobutyric acid (GABA) is an early metabolic biomarker that can be detected within a week of ONC206 treatment, when anatomical alterations are absent, in mice bearing orthotopic xenografts. Mechanistically, imipridones activate the mitochondrial protease ClpP and upregulate the stress-responsive transcription factor ATF4. ATF4, in turn, upregulates glutamate decarboxylase, which synthesizes GABA, and downregulates ABAT , which degrades GABA, leading to GABA accumulation in DMG cells and tumors. Functionally, GABA secreted by imipridone-treated cells acts in an autocrine manner via the GABAB receptor to induce expression of superoxide dismutase (SOD1), which mitigates imipridone-induced oxidative stress and, thereby, curbs apoptosis. Importantly, blocking autocrine GABA signaling using the clinical stage GABAB receptor antagonist SGS-742 exacerbates oxidative stress and synergistically induces apoptosis in combination with imipridones in DMG cells and orthotopic tumor xenografts. Collectively, we identify GABA as a unique metabolic adaptation to imipridones that can be leveraged for non-invasive assessment of drug-target engagement and therapy. Clinical translation of our studies has the potential to enable precision metabolic therapy and imaging for DMG patients. One Sentence Summary: Imipridones induce GABA accumulation in diffuse midline gliomas, an effect that can be leveraged for therapy and non-invasive imaging.

Rational combination platform trial design for children and young adults with diffuse midline glioma: A report from PNOC.

Background Diffuse midline glioma (DMG) is a devastating pediatric brain tumor unresponsive to hundreds of clinical trials. Approximately 80% of DMGs harbor H3K27M oncohistones, which reprogram the epigenome to increase the metabolic profile of the tumor cells. Methods We have previously shown preclinical efficacy of targeting both oxidative phosphorylation and glycolysis through treatment with ONC201, which activates the mitochondrial protease ClpP, and paxalisib, which inhibits PI3K/mTOR, respectively. Results ONC201 and paxalisib combination treatment aimed at inducing metabolic distress led to the design of the first DMG-specific platform trial PNOC022 (NCT05009992). Conclusions Here, we expand on the PNOC022 rationale and discuss various considerations, including liquid biome, microbiome, and genomic biomarkers, quality-of-life endpoints, and novel imaging modalities, such that we offer direction on future clinical trials in DMG.

ACTION: a randomized phase 3 study of ONC201 (dordaviprone) in patients with newly diagnosed H3 K27M-mutant diffuse glioma.

BACKGROUND: H3 K27M-mutant diffuse glioma primarily affects children and young adults, is associated with a poor prognosis, and no effective systemic therapy is currently available. ONC201 (dordaviprone) has previously demonstrated efficacy in patients with recurrent disease. This phase 3 trial evaluates ONC201 in patients with newly diagnosed H3 K27M-mutant glioma. METHODS: ACTION (NCT05580562) is a randomized, double-blind, placebo-controlled, parallel-group, international phase 3 study of ONC201 in newly diagnosed H3 K27M-mutant diffuse glioma. Patients who have completed standard frontline radiotherapy are randomized 1:1:1 to receive placebo, once-weekly dordaviprone, or twice-weekly dordaviprone on 2 consecutive days. Primary efficacy endpoints are overall survival (OS) and progression-free survival (PFS); PFS is assessed by response assessment in neuro-oncology high-grade glioma criteria (RANO-HGG) by blind independent central review. Secondary objectives include safety, additional efficacy endpoints, clinical benefit, and quality of life. Eligible patients have histologically confirmed H3 K27M-mutant diffuse glioma, a Karnofsky/Lansky performance status ≥70, and completed first-line radiotherapy. Eligibility is not restricted by age; however, patients must be ≥10 kg at time of randomization. Patients with a primary spinal tumor, diffuse intrinsic pontine glioma, leptomeningeal disease, or cerebrospinal fluid dissemination are not eligible. ACTION is currently enrolling in multiple international sites.

Comparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment of BRAF V600E-Mutant Pediatric Gliomas in the Pacific Pediatric Neuro-Oncology Consortium Clinical Trial.

BACKGROUND AND PURPOSE: Response on imaging is widely used to evaluate treatment efficacy in clinical trials of pediatric gliomas. While conventional criteria rely on 2D measurements, volumetric analysis may provide a more comprehensive response assessment. There is sparse research on the role of volumetrics in pediatric gliomas. Our purpose was to compare 2D and volumetric analysis with the assessment of neuroradiologists using the Brain Tumor Reporting and Data System (BT-RADS) in BRAF V600E-mutant pediatric gliomas. MATERIALS AND METHODS: Manual volumetric segmentations of whole and solid tumors were compared with 2D measurements in 31 participants (292 follow-up studies) in the Pacific Pediatric Neuro-Oncology Consortium 002 trial (NCT01748149). Two neuroradiologists evaluated responses using BT-RADS. Receiver operating characteristic analysis compared classification performance of 2D and volumetrics for partial response. Agreement between volumetric and 2D mathematically modeled longitudinal trajectories for 25 participants was determined using the model-estimated time to best response. RESULTS: Of 31 participants, 20 had partial responses according to BT-RADS criteria. Receiver operating characteristic curves for the classification of partial responders at the time of first detection (median = 2 months) yielded an area under the curve of 0.84 (95% CI, 0.69-0.99) for 2D area, 0.91 (95% CI, 0.80-1.00) for whole-volume, and 0.92 (95% CI, 0.82-1.00) for solid volume change. There was no significant difference in the area under the curve between 2D and solid (P = .34) or whole volume (P = .39). There was no significant correlation in model-estimated time to best response (ρ = 0.39, P >.05) between 2D and whole-volume trajectories. Eight of the 25 participants had a difference of ≥90 days in transition from partial response to stable disease between their 2D and whole-volume modeled trajectories. CONCLUSIONS: Although there was no overall difference between volumetrics and 2D in classifying partial response assessment using BT-RADS, further prospective studies will be critical to elucidate how the observed differences in tumor 2D and volumetric trajectories affect clinical decision-making and outcomes in some individuals.

Safety and pharmacokinetics of ONC201 (dordaviprone) administered two consecutive days per week in pediatric patients with H3 K27M-mutant glioma.

BACKGROUND: This study evaluated the safety and pharmacokinetics (PK) of oral ONC201 administered twice-weekly on consecutive days (D1D2) in pediatric patients with newly diagnosed DIPG and/or recurrent/refractory H3 K27M glioma. METHODS: This phase 1 dose-escalation and expansion study included pediatric patients with H3 K27M-mutant glioma and/or DIPG following ≥1 line of therapy (NCT03416530). ONC201 was administered D1D2 at 3 dose levels (DLs; -1, 1, and 2). The actual administered dose within DLs was dependent on weight. Safety was assessed in all DLs; PK analysis was conducted in DL2. Patients receiving once-weekly ONC201 (D1) served as a PK comparator. RESULTS: Twelve patients received D1D2 ONC201 (DL1, n = 3; DL1, n = 3; DL2, n = 6); no dose-limiting toxicities or grade ≥3 treatment-related adverse events occurred. PK analyses at DL2 (D1-250 mg, n = 3; D1-625 mg, n = 3; D1D2-250 mg, n = 2; D1D2-625 mg, n = 2) demonstrated variability in Cmax, AUC0-24, and AUC0-48, with comparable exposures across weight groups. No accumulation occurred with D1D2 dosing; the majority of ONC201 cleared before administration of the second dose. Cmax was variable between groups but did not appear to increase with D1D2 dosing. AUC0-48 was greater with D1D2 than once-weekly. CONCLUSIONS: ONC201 given D1D2 was well tolerated at all DLs and associated with greater AUC0-48.

A Standardized Nomenclature Design for Systematic Referencing and Identification of Animal Cellular Material.

The documentation, preservation and rescue of biological diversity increasingly uses living biological samples. Persistent associations between species, biosamples, such as tissues and cell lines, and the accompanying data are indispensable for using, exchanging and benefiting from these valuable materials. Explicit authentication of such biosamples by assigning unique and robust identifiers is therefore required to allow for unambiguous referencing, avoid identification conflicts and maintain reproducibility in research. A predefined nomenclature based on uniform rules would facilitate this process. However, such a nomenclature is currently lacking for animal biological material. We here present a first, standardized, human-readable nomenclature design, which is sufficient to generate unique and stable identifying names for animal cellular material with a focus on wildlife species. A species-specific human- and machine-readable syntax is included in the proposed standard naming scheme, allowing for the traceability of donated material and cultured cells, as well as data FAIRification. Only when it is consistently applied in the public domain, as publications and inter-institutional samples and data are exchanged, distributed and stored centrally, can the risks of misidentification and loss of traceability be mitigated. This innovative globally applicable identification system provides a standard for a sustainable structure for the long-term storage of animal bio-samples in cryobanks and hence facilitates current as well as future species conservation and biomedical research.

Consensus framework for conducting phase I/II clinical trials for children, adolescents, and young adults with pediatric low-grade glioma: Guidelines established by the International Pediatric Low-Grade Glioma Coalition Clinical Trial Working Group.

Within the last few decades, we have witnessed tremendous advancements in the study of pediatric low-grade gliomas (pLGG), leading to a much-improved understanding of their molecular underpinnings. Consequently, we have achieved successful milestones in developing and implementing targeted therapeutic agents for treating these tumors. However, the community continues to face many unknowns when it comes to the most effective clinical implementation of these novel targeted inhibitors or combinations thereof. Questions encompassing optimal dosing strategies, treatment duration, methods for assessing clinical efficacy, and the identification of predictive biomarkers remain unresolved. Here, we offer the consensus of the international pLGG coalition (iPLGGc) clinical trial working group on these important topics and comment on clinical trial design and endpoint rationale. Throughout, we seek to standardize the global approach to early clinical trials (phase I and II) for pLGG, leading to more consistently interpretable results as well as enhancing the pace of novel therapy development and encouraging an increased focus on functional endpoints as well and quality of life for children faced with this disease.

Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario.

Purpose To develop, externally test, and evaluate clinical acceptability of a deep learning pediatric brain tumor segmentation model using stepwise transfer learning. Materials and Methods In this retrospective study, the authors leveraged two T2-weighted MRI datasets (May 2001 through December 2015) from a national brain tumor consortium (n = 184; median age, 7 years [range, 1-23 years]; 94 male patients) and a pediatric cancer center (n = 100; median age, 8 years [range, 1-19 years]; 47 male patients) to develop and evaluate deep learning neural networks for pediatric low-grade glioma segmentation using a stepwise transfer learning approach to maximize performance in a limited data scenario. The best model was externally tested on an independent test set and subjected to randomized blinded evaluation by three clinicians, wherein they assessed clinical acceptability of expert- and artificial intelligence (AI)-generated segmentations via 10-point Likert scales and Turing tests. Results The best AI model used in-domain stepwise transfer learning (median Dice score coefficient, 0.88 [IQR, 0.72-0.91] vs 0.812 [IQR, 0.56-0.89] for baseline model; P = .049). With external testing, the AI model yielded excellent accuracy using reference standards from three clinical experts (median Dice similarity coefficients: expert 1, 0.83 [IQR, 0.75-0.90]; expert 2, 0.81 [IQR, 0.70-0.89]; expert 3, 0.81 [IQR, 0.68-0.88]; mean accuracy, 0.82). For clinical benchmarking (n = 100 scans), experts rated AI-based segmentations higher on average compared with other experts (median Likert score, 9 [IQR, 7-9] vs 7 [IQR 7-9]) and rated more AI segmentations as clinically acceptable (80.2% vs 65.4%). Experts correctly predicted the origin of AI segmentations in an average of 26.0% of cases. Conclusion Stepwise transfer learning enabled expert-level automated pediatric brain tumor autosegmentation and volumetric measurement with a high level of clinical acceptability. Keywords: Stepwise Transfer Learning, Pediatric Brain Tumors, MRI Segmentation, Deep Learning Supplemental material is available for this article. © RSNA, 2024.

Longitudinal risk prediction for pediatric glioma with temporal deep learning.

Pediatric glioma recurrence can cause morbidity and mortality; however, recurrence pattern and severity are heterogeneous and challenging to predict with established clinical and genomic markers. Resultingly, almost all children undergo frequent, long-term, magnetic resonance (MR) brain surveillance regardless of individual recurrence risk. Deep learning analysis of longitudinal MR may be an effective approach for improving individualized recurrence prediction in gliomas and other cancers but has thus far been infeasible with current frameworks. Here, we propose a self-supervised, deep learning approach to longitudinal medical imaging analysis, temporal learning, that models the spatiotemporal information from a patient's current and prior brain MRs to predict future recurrence. We apply temporal learning to pediatric glioma surveillance imaging for 715 patients (3,994 scans) from four distinct clinical settings. We find that longitudinal imaging analysis with temporal learning improves recurrence prediction performance by up to 41% compared to traditional approaches, with improvements in performance in both low- and high-grade glioma. We find that recurrence prediction accuracy increases incrementally with the number of historical scans available per patient. Temporal deep learning may enable point-of-care decision-support for pediatric brain tumors and be adaptable more broadly to patients with other cancers and chronic diseases undergoing surveillance imaging.

Towards Consistency in Pediatric Brain Tumor Measurements: Challenges, Solutions, and the Role of AI-Based Segmentation.

MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Several response assessment guidelines have been set forth by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working groups in different tumor histologies; however, the visual delineation of tumor components using MRIs is not always straightforward, and complexities not currently addressed by these criteria can introduce inter- and intra-observer variability in manual assessments. Differentiation of non-enhancing tumor from peritumoral edema, mild enhancement from absence of enhancement, and various cystic components can be challenging; particularly given a lack of sufficient and uniform imaging protocols in clinical practice. Automated tumor segmentation with artificial intelligence (AI) may be able to provide more objective delineations, but rely on accurate and consistent training data created manually (ground truth). Herein, this paper reviews existing challenges and potential solutions to identifying and defining subregions of pediatric brain tumors (PBTs) that are not explicitly addressed by current guidelines. The goal is to assert the importance of defining and adopting criteria for addressing these challenges, as it will be critical to achieving standardized tumor measurements and reproducible response assessment in PBTs, ultimately leading to more precise outcome metrics and accurate comparisons among clinical studies.

Noninvasive Molecular Subtyping of Pediatric Low-Grade Glioma with Self-Supervised Transfer Learning.

Purpose To develop and externally test a scan-to-prediction deep learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pediatric low-grade glioma. Materials and Methods This retrospective study included two pediatric low-grade glioma datasets with linked genomic and diagnostic T2-weighted MRI data of patients: Dana-Farber/Boston Children's Hospital (development dataset, n = 214 [113 (52.8%) male; 104 (48.6%) BRAF wild type, 60 (28.0%) BRAF fusion, and 50 (23.4%) BRAF V600E]) and the Children's Brain Tumor Network (external testing, n = 112 [55 (49.1%) male; 35 (31.2%) BRAF wild type, 60 (53.6%) BRAF fusion, and 17 (15.2%) BRAF V600E]). A deep learning pipeline was developed to classify BRAF mutational status (BRAF wild type vs BRAF fusion vs BRAF V600E) via a two-stage process: (a) three-dimensional tumor segmentation and extraction of axial tumor images and (b) section-wise, deep learning-based classification of mutational status. Knowledge-transfer and self-supervised approaches were investigated to prevent model overfitting, with a primary end point of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, a novel metric, center of mass distance, was developed to quantify the model attention around the tumor. Results A combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest classification performance with an AUC of 0.82 (95% CI: 0.72, 0.91), 0.87 (95% CI: 0.61, 0.97), and 0.85 (95% CI: 0.66, 0.95) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively, on internal testing. On external testing, the pipeline yielded an AUC of 0.72 (95% CI: 0.64, 0.86), 0.78 (95% CI: 0.61, 0.89), and 0.72 (95% CI: 0.64, 0.88) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively. Conclusion Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pediatric low-grade glioma mutational status prediction in a limited data scenario. Keywords: Pediatrics, MRI, CNS, Brain/Brain Stem, Oncology, Feature Detection, Diagnosis, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.

Comparison of volumetric and 2D-based response methods in the PNOC-001 pediatric low-grade glioma clinical trial.

Background: Although response in pediatric low-grade glioma (pLGG) includes volumetric assessment, more simplified 2D-based methods are often used in clinical trials. The study's purpose was to compare volumetric to 2D methods. Methods: An expert neuroradiologist performed solid and whole tumor (including cyst and edema) volumetric measurements on MR images using a PACS-based manual segmentation tool in 43 pLGG participants (213 total follow-up images) from the Pacific Pediatric Neuro-Oncology Consortium (PNOC-001) trial. Classification based on changes in volumetric and 2D measurements of solid tumor were compared to neuroradiologist visual response assessment using the Brain Tumor Reporting and Data System (BT-RADS) criteria for a subset of 65 images using receiver operating characteristic (ROC) analysis. Longitudinal modeling of solid tumor volume was used to predict BT-RADS classification in 54 of the 65 images. Results: There was a significant difference in ROC area under the curve between 3D solid tumor volume and 2D area (0.96 vs 0.78, P = .005) and between 3D solid and 3D whole volume (0.96 vs 0.84, P = .006) when classifying BT-RADS progressive disease (PD). Thresholds of 15-25% increase in 3D solid tumor volume had an 80% sensitivity in classifying BT-RADS PD included in their 95% confidence intervals. The longitudinal model of solid volume response had a sensitivity of 82% and a positive predictive value of 67% for detecting BT-RADS PD. Conclusions: Volumetric analysis of solid tumor was significantly better than 2D measurements in classifying tumor progression as determined by BT-RADS criteria and will enable more comprehensive clinical management.

The oncolytic adenovirus Delta-24-RGD in combination with ONC201 induces a potent antitumor response in pediatric high-grade and diffuse midline glioma models.

BACKGROUND: Pediatric high-grade gliomas (pHGGs), including diffuse midline gliomas (DMGs), are aggressive pediatric tumors with one of the poorest prognoses. Delta-24-RGD and ONC201 have shown promising efficacy as single agents for these tumors. However, the combination of both agents has not been evaluated. METHODS: The production of functional viruses was assessed by immunoblotting and replication assays. The antitumor effect was evaluated in a panel of human and murine pHGG and DMG cell lines. RNAseq, the seahorse stress test, mitochondrial DNA content, and γH2A.X immunofluorescence were used to perform mechanistic studies. Mouse models of both diseases were used to assess the efficacy of the combination in vivo. The tumor immune microenvironment was evaluated using flow cytometry, RNAseq, and multiplexed immunofluorescence staining. RESULTS: The Delta-24-RGD/ONC201 combination did not affect the virus replication capability in human pHGG and DMG models in vitro. Cytotoxicity analysis showed that the combination treatment was either synergistic or additive. Mechanistically, the combination treatment increased nuclear DNA damage and maintained the metabolic perturbation and mitochondrial damage caused by each agent alone. Delta-24-RGD/ONC201 cotreatment extended the overall survival of mice implanted with human and murine pHGG and DMG cells, independent of H3 mutation status and location. Finally, combination treatment in murine DMG models revealed a reshaping of the tumor microenvironment to a proinflammatory phenotype. CONCLUSIONS: The Delta-24-RGD/ONC201 combination improved the efficacy compared to each agent alone in in vitro and in vivo models by potentiating nuclear DNA damage and in turn improving the antitumor (immune) response to each agent alone.

Resistance, rebound, and recurrence regrowth patterns in pediatric low-grade glioma treated by MAPK inhibition: A modified Delphi approach to build international consensus-based definitions-International Pediatric Low-Grade Glioma Coalition.

Pediatric low-grade glioma (pLGG) is the most common childhood brain tumor group. The natural history, when curative resection is not possible, is one of a chronic disease with periods of tumor stability and episodes of tumor progression. While there is a high overall survival rate, many patients experience significant and potentially lifelong morbidities. The majority of pLGGs have an underlying activation of the RAS/MAPK pathway due to mutational events, leading to the use of molecularly targeted therapies in clinical trials, with recent regulatory approval for the combination of BRAF and MEK inhibition for BRAFV600E mutated pLGG. Despite encouraging activity, tumor regrowth can occur during therapy due to drug resistance, off treatment as tumor recurrence, or as reported in some patients as a rapid rebound growth within 3 months of discontinuing targeted therapy. Definitions of these patterns of regrowth have not been well described in pLGG. For this reason, the International Pediatric Low-Grade Glioma Coalition, a global group of physicians and scientists, formed the Resistance, Rebound, and Recurrence (R3) working group to study resistance, rebound, and recurrence. A modified Delphi approach was undertaken to produce consensus-based definitions and recommendations for regrowth patterns in pLGG with specific reference to targeted therapies.

PI3K/mTOR is a therapeutically targetable genetic dependency in diffuse intrinsic pontine glioma.

Diffuse midline glioma (DMG), including tumors diagnosed in the brainstem (diffuse intrinsic pontine glioma; DIPG), are uniformly fatal brain tumors that lack effective treatment. Analysis of CRISPR/Cas9 loss-of-function gene deletion screens identified PIK3CA and MTOR as targetable molecular dependencies across patient derived models of DIPG, highlighting the therapeutic potential of the blood-brain barrier-penetrant PI3K/Akt/mTOR inhibitor, paxalisib. At the human-equivalent maximum tolerated dose, mice treated with paxalisib experienced systemic glucose feedback and increased insulin levels commensurate with patients using PI3K inhibitors. To exploit genetic dependence and overcome resistance while maintaining compliance and therapeutic benefit, we combined paxalisib with the antihyperglycemic drug metformin. Metformin restored glucose homeostasis and decreased phosphorylation of the insulin receptor in vivo, a common mechanism of PI3K-inhibitor resistance, extending survival of orthotopic models. DIPG models treated with paxalisib increased calcium-activated PKC signaling. The brain penetrant PKC inhibitor enzastaurin, in combination with paxalisib, synergistically extended the survival of multiple orthotopic patient-derived and immunocompetent syngeneic allograft models; benefits potentiated in combination with metformin and standard-of-care radiotherapy. Therapeutic adaptation was assessed using spatial transcriptomics and ATAC-Seq, identifying changes in myelination and tumor immune microenvironment crosstalk. Collectively, this study has identified what we believe to be a clinically relevant DIPG therapeutic combinational strategy.

Precision based approach to tailoring radiotherapy in the multidisciplinary management of pediatric central nervous system tumors.

Modern day survivorship from childhood malignancies is estimated to be over 80%. However, central nervous system tumors remain the leading cause of cancer mortality in children and is the most common solid tumor in this population. Improved survivorship is, in part, a result of improved multidisciplinary care, often with a combination of surgery, radiation therapy, and systemic therapy. With improved survival, long term effects of treatment and quality of life impacts have been recognized and pose a challenge to maximize the therapeutic ratio of treatment. It has been increasingly more apparent that precise risk stratification, such as with the inclusion of molecular classification, is instrumental in efforts to tailor radiotherapy for appropriate treatment, generally towards de-intensification for this vulnerable patient population. In addition, advances in radiotherapy techniques have allowed greater conformality and accuracy of treatment for those who do require radiotherapy for tumor control. Ongoing efforts to tailor radiotherapy, including de-escalation, omission, or intensification of radiotherapy, continue to improve as increasing insight into tumor heterogeneity is recognized, coupled with advances in precision medicine employing novel molecularly-targeted therapeutics.

A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network.

Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the wealth of radiology imaging data collected through standard care, however, has significantly lagged other domains. Such data is ripe for use with predictive analytics such as artificial intelligence (AI) methods, which require large datasets. To address this unmet need, we provide a multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric MRI exams acquired through routine care for 1,526 brain tumor patients, as part of the Children's Brain Tumor Network. This includes longitudinal MRIs across various cancer diagnoses, with associated patient-level clinical information, digital pathology slides, as well as tissue genotype and omics data. To facilitate downstream analysis, treatment-naïve images for 370 subjects were processed and released through the NCI Childhood Cancer Data Initiative via the Cancer Data Service. Through ongoing efforts to continuously build these imaging repositories, our aim is to accelerate discovery and translational AI models with real-world data, to ultimately empower precision medicine for children.