Genome-wide CRISPR-Cas9 knockout screens identify DNMT1 as a druggable dependency in sonic hedgehog medulloblastoma.

Sonic hedgehog subgroup of medulloblastoma (SHH-MB) is characterized by aberrant activation of the SHH signaling pathway. An inhibition of the positive SHH regulator Smoothened (SMO) has demonstrated promising clinical efficacy. Yet, primary and acquired resistance to SMO inhibitors limit their efficacy. An understanding of underlying molecular mechanisms of resistance to therapy is warranted to bridge this unmet need. Here, we make use of genome-wide CRISPR-Cas9 knockout screens in murine SMB21 and human DAOY cells, in order to unravel genetic dependencies and drug-related genetic interactors that could serve as alternative therapeutic targets for SHH-MB. Our screens reinforce SMB21 cells as a faithful model system for SHH-MB, as opposed to DAOY cells, and identify members of the epigenetic machinery including DNA methyltransferase 1 (DNMT1) as druggable targets in SHH-dependent tumors. We show that Dnmt1 plays a crucial role in normal murine cerebellar development and is required for SHH-MB growth in vivo. Additionally, DNMT1 pharmacological inhibition alone and in combination with SMO inhibition effectively inhibits tumor growth in murine and human SHH-MB cell models and prolongs survival of SHH-MB mouse models by inhibiting SHH signaling output downstream of SMO. In conclusion, our data highlight the potential of inhibiting epigenetic regulators as a novel therapeutic avenue in SMO-inhibitor sensitive as well as resistant SHH-MBs.

Multimodal Deep Learning Improves Recurrence Risk Prediction in Pediatric Low-Grade Gliomas.

BACKGROUND: Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to predict by conventional clinical, radiographic, and genomic factors. We investigated if deep learning of MRI tumor features could improve postoperative pLGG risk stratification. METHODS: We used pre-trained deep learning (DL) tool designed for pLGG segmentation to extract pLGG imaging features from preoperative T2-weighted MRI from patients who underwent surgery (DL-MRI features). Patients were pooled from two institutions: Dana Farber/Boston Children's Hospital (DF/BCH) and the Children's Brain Tumor Network (CBTN). We trained three DL logistic hazard models to predict postoperative event-free survival (EFS) probabilities with 1) clinical features, 2) DL-MRI features, and 3) multimodal (clinical and DL-MRI features). We evaluated the models with a time-dependent Concordance Index (Ctd) and risk group stratification with Kaplan Meier plots and log-rank tests. We developed an automated pipeline integrating pLGG segmentation and EFS prediction with the best model. RESULTS: Of the 396 patients analyzed (median follow-up: 85 months, range: 1.5-329 months), 214 (54%) underwent gross total resection and 110 (28%) recurred. The multimodal model improved EFS prediction compared to the DL-MRI and clinical models (Ctd: 0.85 (95% CI: 0.81-0.93), 0.79 (95% CI: 0.70-0.88), and 0.72 (95% CI: 0.57-0.77), respectively). The multimodal model improved risk-group stratification (3-year EFS for predicted high-risk: 31% versus low-risk: 92%, p<0.0001). CONCLUSIONS: DL extracts imaging features that can inform postoperative recurrence prediction for pLGG. Multimodal DL improves postoperative risk stratification for pLGG and may guide postoperative decision-making. Larger, multicenter training data may be needed to improve model generalizability.

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.

Molecular profiling of 888 pediatric tumors informs future precision trials and data-sharing initiatives in pediatric cancer.

To inform clinical trial design and real-world precision pediatric oncology practice, we classified diagnoses, assessed the landscape of mutations, and identified genomic variants matching trials in a large unselected institutional cohort of solid tumors patients sequenced at Dana-Farber / Boston Children's Cancer and Blood Disorders Center. Tumors were sequenced with OncoPanel, a targeted next-generation DNA sequencing panel. Diagnoses were classified according to the International Classification of Diseases for Oncology (ICD-O-3.2). Over 6.5 years, 888 pediatric cancer patients with 95 distinct diagnoses had successful tumor sequencing. Overall, 33% (n = 289/888) of patients had at least 1 variant matching a precision oncology trial protocol, and 14% (41/289) were treated with molecularly targeted therapy. This study highlights opportunities to use genomic data from hospital-based sequencing performed either for research or clinical care to inform ongoing and future precision oncology clinical trials. Furthermore, the study results emphasize the importance of data sharing to define the genomic landscape and targeted treatment opportunities for the large group of rare pediatric cancers we encounter in clinical practice.

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.

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.

Unraveling the complexity of the senescence-associated secretory phenotype in adamantinomatous craniopharyngioma using multimodal machine learning analysis.

BACKGROUND: Cellular senescence can have positive and negative effects on the body, including aiding in damage repair and facilitating tumor growth. Adamantinomatous craniopharyngioma (ACP), the most common pediatric sellar/suprasellar brain tumor, poses significant treatment challenges. Recent studies suggest that senescent cells in ACP tumors may contribute to tumor growth and invasion by releasing a senesecence-associated secretory phenotype. However, a detailed analysis of these characteristics has yet to be completed. METHODS: We analyzed primary tissue samples from ACP patients using single-cell, single-nuclei, and spatial RNA sequencing. We performed various analyses, including gene expression clustering, inferred senescence cells from gene expression, and conducted cytokine signaling inference. We utilized LASSO to select essential gene expression pathways associated with senescence. Finally, we validated our findings through immunostaining. RESULTS: We observed significant diversity in gene expression and tissue structure. Key factors such as NFKB, RELA, and SP1 are essential in regulating gene expression, while senescence markers are present throughout the tissue. SPP1 is the most significant cytokine signaling network among ACP cells, while the Wnt signaling pathway predominantly occurs between epithelial and glial cells. Our research has identified links between senescence-associated features and pathways, such as PI3K/Akt/mTOR, MYC, FZD, and Hedgehog, with increased P53 expression associated with senescence in these cells. CONCLUSIONS: A complex interplay between cellular senescence, cytokine signaling, and gene expression pathways underlies ACP development. Further research is crucial to understand how these elements interact to create novel therapeutic approaches for patients with ACP.

Aberrant DNA repair reveals a vulnerability in histone H3.3-mutant brain tumors.

Pediatric high-grade gliomas (pHGG) are devastating and incurable brain tumors with recurrent mutations in histone H3.3. These mutations promote oncogenesis by dysregulating gene expression through alterations of histone modifications. We identify aberrant DNA repair as an independent mechanism, which fosters genome instability in H3.3 mutant pHGG, and opens new therapeutic options. The two most frequent H3.3 mutations in pHGG, K27M and G34R, drive aberrant repair of replication-associated damage by non-homologous end joining (NHEJ). Aberrant NHEJ is mediated by the DNA repair enzyme polynucleotide kinase 3'-phosphatase (PNKP), which shows increased association with mutant H3.3 at damaged replication forks. PNKP sustains the proliferation of cells bearing H3.3 mutations, thus conferring a molecular vulnerability, specific to mutant cells, with potential for therapeutic targeting.

Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma.

A hallmark of high-risk childhood medulloblastoma is the dysregulation of RNA translation. Currently, it is unknown whether medulloblastoma dysregulates the translation of putatively oncogenic non-canonical open reading frames (ORFs). To address this question, we performed ribosome profiling of 32 medulloblastoma tissues and cell lines and observed widespread non-canonical ORF translation. We then developed a stepwise approach using multiple CRISPR-Cas9 screens to elucidate non-canonical ORFs and putative microproteins implicated in medulloblastoma cell survival. We determined that multiple lncRNA-ORFs and upstream ORFs (uORFs) exhibited selective functionality independent of main coding sequences. A microprotein encoded by one of these ORFs, ASNSD1-uORF or ASDURF, was upregulated, associated with MYC-family oncogenes, and promoted medulloblastoma cell survival through engagement with the prefoldin-like chaperone complex. Our findings underscore the fundamental importance of non-canonical ORF translation in medulloblastoma and provide a rationale to include these ORFs in future studies seeking to define new cancer targets.

Indoximod-based chemo-immunotherapy for pediatric brain tumors: A first-in-children phase I trial.

BACKGROUND: Recurrent brain tumors are the leading cause of cancer death in children. Indoleamine 2,3-dioxygenase (IDO) is a targetable metabolic checkpoint that, in preclinical models, inhibits anti-tumor immunity following chemotherapy. METHODS: We conducted a phase I trial (NCT02502708) of the oral IDO-pathway inhibitor indoximod in children with recurrent brain tumors or newly diagnosed diffuse intrinsic pontine glioma (DIPG). Separate dose-finding arms were performed for indoximod in combination with oral temozolomide (200 mg/m2/day x 5 days in 28-day cycles), or with palliative conformal radiation. Blood samples were collected at baseline and monthly for single-cell RNA-sequencing with paired single-cell T cell receptor sequencing. RESULTS: Eighty-one patients were treated with indoximod-based combination therapy. Median follow-up was 52 months (range 39-77 months). Maximum tolerated dose was not reached, and the pediatric dose of indoximod was determined as 19.2 mg/kg/dose, twice daily. Median overall survival was 13.3 months (n = 68, range 0.2-62.7) for all patients with recurrent disease and 14.4 months (n = 13, range 4.7-29.7) for DIPG. The subset of n = 26 patients who showed evidence of objective response (even a partial or mixed response) had over 3-fold longer median OS (25.2 months, range 5.4-61.9, p = 0.006) compared to n = 37 nonresponders (7.3 months, range 0.2-62.7). Four patients remain free of active disease longer than 36 months. Single-cell sequencing confirmed emergence of new circulating CD8 T cell clonotypes with late effector phenotype. CONCLUSIONS: Indoximod was well tolerated and could be safely combined with chemotherapy and radiation. Encouraging preliminary evidence of efficacy supports advancing to Phase II/III trials for pediatric brain tumors.

Pediatric low-grade glioma: State-of-the-art and ongoing challenges.

The most common childhood central nervous system (CNS) tumor is pediatric low-grade glioma (pLGG), representing 30%-40% of all CNS tumors in children. Although there is high associated morbidity, tumor-related mortality is relatively rare. pLGG is now conceptualized as a chronic disease, underscoring the importance of functional outcomes and quality-of-life measures. A wealth of data has emerged about these tumors, including a better understanding of their natural history and their molecular drivers, paving the way for the use of targeted inhibitors. While these treatments have heralded tremendous promise, challenges remain about how to best optimize their use, and the long-term toxicities associated with these inhibitors remain unknown. The International Pediatric Low-Grade Glioma Coalition (iPLGGc) is a global group of physicians and scientists with expertise in pLGG focused on addressing key pLGG issues. Here, the iPLGGc provides an overview of the current state-of-the-art in pLGG, including epidemiology, histology, molecular landscape, treatment paradigms, survival outcomes, functional outcomes, imaging response, and ongoing challenges. This paper also serves as an introduction to 3 other pLGG manuscripts on (1) pLGG preclinical models, (2) consensus framework for conducting early-phase clinical trials in pLGG, and (3) pLGG resistance, rebound, and recurrence.

A road map for the treatment of pediatric diffuse midline glioma.

Recent clinical trials for H3K27-altered diffuse midline gliomas (DMGs) have shown much promise. We present a consensus roadmap and identify three major barriers: (1) refinement of experimental models to include immune and brain-specific components; (2) collaboration among researchers, clinicians, and industry to integrate patient-derived data through sharing, transparency, and regulatory considerations; and (3) streamlining clinical efforts including biopsy, CNS-drug delivery, endpoint determination, and response monitoring. We highlight the importance of comprehensive collaboration to advance the understanding, diagnostics, and therapeutics for DMGs.

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.

Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning.

Purpose: To develop and externally validate a scan-to-prediction deep-learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pLGG. Materials and Methods: We conducted a retrospective study of two pLGG datasets with linked genomic and diagnostic T2-weighted MRI of patients: BCH (development dataset, n=214 [60 (28%) BRAF fusion, 50 (23%) BRAF V600E, 104 (49%) wild-type), and Child Brain Tumor Network (CBTN) (external validation, n=112 [60 (53%) BRAF-Fusion, 17 (15%) BRAF-V600E, 35 (32%) wild-type]). We developed a deep learning pipeline to classify BRAF mutational status (V600E vs. fusion vs. wildtype) via a two-stage process: 1) 3D tumor segmentation and extraction of axial tumor images, and 2) slice-wise, deep learning-based classification of mutational status. We investigated knowledge-transfer and self-supervised approaches to prevent model overfitting with a primary endpoint of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, we developed a novel metric, COMDist, that quantifies the accuracy of 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 macro-average AUC (0.82 [95% CI: 0.70-0.90]) and accuracy (77%) on internal validation, with an AUC improvement of +17.7% and a COMDist improvement of +6.4% versus training from scratch. On external validation, the TransferX model yielded AUC (0.73 [95% CI 0.68-0.88]) and accuracy (75%). Conclusion: Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pLGG mutational status prediction in a limited data scenario.

A Pilot Study Omitting Radiation in the Treatment of Children with Newly Diagnosed Wnt-Activated Medulloblastoma.

PURPOSE: Treatment of wingless (WNT)-activated medulloblastoma (WNT+MB) with surgery, irradiation (XRT), and chemotherapy results in excellent outcomes. We studied the efficacy of therapy de-intensification by omitting XRT entirely in children with WNT+MB. PATIENTS AND METHODS: Tumors were molecularly screened to confirm the diagnosis of WNT+MB. Eligible children were treated within 31 days following surgery with nine cycles of adjuvant chemotherapy per ACNS0331. No XRT was planned. The primary endpoint was the occurrence of relapse, progression, or death in the absence of XRT within the first two years after study enrollment. Four events in the first 10 evaluable patients would result in early study closure. RESULTS: Fourteen children were prescreened, and nine met the protocol definition of WNT+MB. Six of the nine eligible patients consented to protocol therapy, and five completed planned protocol therapy. The first two children enrolled relapsed shortly after therapy completion with local and leptomeningeal recurrences. The study was closed early due to safety concerns. Both children are surviving after XRT and additional chemotherapy. A third child relapsed at completion of therapy but died of progressive disease 35 months from diagnosis. Two children finished treatment but immediately received post-treatment XRT to guard against early relapse. The final child's treatment was aborted in favor of a high-dose therapy/stem cell rescue approach. Although OS at 5 years is 83%, no child received only planned protocol therapy, with all receiving eventual XRT and/or alternative therapy. CONCLUSIONS: Radiotherapy is required to effectively treat children with WNT-altered medulloblastoma. See related commentary by Gottardo and Gajjar, p. 4996.

Genome-wide pooled CRISPR screening in neurospheres.

Spheroid culture systems have allowed in vitro propagation of cells unable to grow in canonical cell culturing conditions, and may capture cellular contexts that model tumor growth better than current model systems. The insights gleaned from genome-wide clustered regularly interspaced short palindromic repeat (CRISPR) screening of thousands of cancer cell lines grown in conventional culture conditions illustrate the value of such CRISPR pooled screens. It is clear that similar genome-wide CRISPR screens of three-dimensional spheroid cultures will be important for future biological discovery. Here, we present a protocol for genome-wide CRISPR screening of three-dimensional neurospheres. While many in-depth protocols and discussions have been published for more typical cell lines, few detailed protocols are currently available in the literature for genome-wide screening in spheroidal cell lines. For those who want to screen such cell lines, and particularly neurospheres, we provide a step-by-step description of assay development tests to be performed before screening, as well as for the screen itself. We highlight considerations of variables that make these screens distinct from, or similar to, typical nonspheroid cell lines throughout. Finally, we illustrate typical outcomes of neurosphere genome-wide screens, and how neurosphere screens typically produce slightly more heterogeneous signal distributions than more canonical cancer cell lines. Completion of this entire protocol will take 8-12 weeks from the initial assay development tests to deconvolution of the sequencing data.

Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma.

A hallmark of high-risk childhood medulloblastoma is the dysregulation of RNA translation. Currently, it is unknown whether medulloblastoma dysregulates the translation of putatively oncogenic non-canonical open reading frames. To address this question, we performed ribosome profiling of 32 medulloblastoma tissues and cell lines and observed widespread non-canonical ORF translation. We then developed a step-wise approach to employ multiple CRISPR-Cas9 screens to elucidate functional non-canonical ORFs implicated in medulloblastoma cell survival. We determined that multiple lncRNA-ORFs and upstream open reading frames (uORFs) exhibited selective functionality independent of the main coding sequence. One of these, ASNSD1-uORF or ASDURF, was upregulated, associated with the MYC family oncogenes, and was required for medulloblastoma cell survival through engagement with the prefoldin-like chaperone complex. Our findings underscore the fundamental importance of non-canonical ORF translation in medulloblastoma and provide a rationale to include these ORFs in future cancer genomics studies seeking to define new cancer targets.

Integrated response analysis of pediatric low-grade gliomas during and after targeted therapy treatment.

Background: Pediatric low-grade gliomas (pLGGs) are the most common central nervous system tumor in children, characterized by RAS/MAPK pathway driver alterations. Genomic advances have facilitated the use of molecular targeted therapies, however, their long-term impact on tumor behavior remains critically unanswered. Methods: We performed an IRB-approved, retrospective chart and imaging review of pLGGs treated with off-label targeted therapy at Dana-Farber/Boston Children's from 2010 to 2020. Response analysis was performed for BRAFV600E and BRAF fusion/duplication-driven pLGG subsets. Results: Fifty-five patients were identified (dabrafenib n = 15, everolimus n = 26, trametinib n = 11, and vemurafenib n = 3). Median duration of targeted therapy was 9.48 months (0.12-58.44). The 1-year, 3-year, and 5-year EFS from targeted therapy initiation were 62.1%, 38.2%, and 31.8%, respectively. Mean volumetric change for BRAFV600E mutated pLGG on BRAF inhibitors was -54.11%; median time to best volumetric response was 8.28 months with 9 of 12 (75%) objective RAPNO responses. Median time to largest volume post-treatment was 2.86 months (+13.49%); mean volume by the last follow-up was -14.02%. Mean volumetric change for BRAF fusion/duplication pLGG on trametinib was +7.34%; median time to best volumetric response was 6.71 months with 3 of 7 (43%) objective RAPNO responses. Median time to largest volume post-treatment was 2.38 months (+71.86%); mean volume by the last follow-up was +39.41%. Conclusions: Our integrated analysis suggests variability in response by pLGG molecular subgroup and targeted therapy, as well as the transience of some tumor growth following targeted therapy cessation.