AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification.

SUMMARY: With the increasing rates of exome and whole genome sequencing, the ability to classify large sets of germline sequencing variants using up-to-date American College of Medical Genetics-Association for Molecular Pathology (ACMG-AMP) criteria is crucial. Here, we present Automated Germline Variant Pathogenicity (AutoGVP), a tool that integrates germline variant pathogenicity annotations from ClinVar and sequence variant classifications from a modified version of InterVar (PVS1 strength adjustments, removal of PP5/BP6). This tool facilitates large-scale, clinically focused classification of germline sequence variants in a research setting. AVAILABILITY AND IMPLEMENTATION: AutoGVP is an open source dockerized workflow implemented in R and freely available on GitHub at https://github.com/diskin-lab-chop/AutoGVP.

MitoScape: A big-data, machine-learning platform for obtaining mitochondrial DNA from next-generation sequencing data.

The growing number of next-generation sequencing (NGS) data presents a unique opportunity to study the combined impact of mitochondrial and nuclear-encoded genetic variation in complex disease. Mitochondrial DNA variants and in particular, heteroplasmic variants, are critical for determining human disease severity. While there are approaches for obtaining mitochondrial DNA variants from NGS data, these software do not account for the unique characteristics of mitochondrial genetics and can be inaccurate even for homoplasmic variants. We introduce MitoScape, a novel, big-data, software for extracting mitochondrial DNA sequences from NGS. MitoScape adopts a novel departure from other algorithms by using machine learning to model the unique characteristics of mitochondrial genetics. We also employ a novel approach of using rho-zero (mitochondrial DNA-depleted) data to model nuclear-encoded mitochondrial sequences. We showed that MitoScape produces accurate heteroplasmy estimates using gold-standard mitochondrial DNA data. We provide a comprehensive comparison of the most common tools for obtaining mtDNA variants from NGS and showed that MitoScape had superior performance to compared tools in every statistically category we compared, including false positives and false negatives. By applying MitoScape to common disease examples, we illustrate how MitoScape facilitates important heteroplasmy-disease association discoveries by expanding upon a reported association between hypertrophic cardiomyopathy and mitochondrial haplogroup T in men (adjusted p-value = 0.003). The improved accuracy of mitochondrial DNA variants produced by MitoScape will be instrumental in diagnosing disease in the context of personalized medicine and clinical diagnostics.

The InsP7 phosphatase Siw14 regulates inositol pyrophosphate levels to control localization of the general stress response transcription factor Msn2.

The environmental stress response (ESR) is critical for cell survival. Yeast cells unable to synthesize inositol pyrophosphates (PP-InsPs) are unable to induce the ESR. We recently discovered a diphosphoinositol pentakisphosphate (PP-InsP5) phosphatase in Saccharomyces cerevisiae encoded by SIW14 Yeast strains deleted for SIW14 have increased levels of PP-InsPs. We hypothesized that strains with high inositol pyrophosphate levels will have an increased stress response. We examined the response of the siw14Δ mutant to heat shock, nutrient limitation, osmotic stress, and oxidative treatment using cell growth assays and found increased resistance to each. Transcriptional responses to oxidative and osmotic stresses were assessed using microarray and reverse transcriptase quantitative PCR. The ESR was partially induced in the siw14Δ mutant strain, consistent with the increased stress resistance, and the mutant strain further induced the ESR in response to oxidative and osmotic stresses. The levels of PP-InsPs increased in WT cells under oxidative stress but not under hyperosmotic stress, and they were high and unchanging in the mutant. Phosphatase activity of Siw14 was inhibited by oxidation that was reversible. To determine how altered PP-InsP levels affect the ESR, we performed epistasis experiments with mutations in rpd3 and msn2/4 combined with siw14Δ. We show that mutations in msn2Δ and msn4Δ, but not rpd3, are epistatic to siw14Δ by assessing growth under oxidative stress conditions and expression of CTT1 Msn2-GFP nuclear localization was increased in the siw14Δ. These data support a model in which the modulation of PP-InsPs influence the ESR through general stress response transcription factors Msn2/4.

Integrated molecular and clinical analysis of low-grade gliomas in children with neurofibromatosis type 1 (NF1).

Low-grade gliomas (LGGs) are the most common childhood brain tumor in the general population and in individuals with the Neurofibromatosis type 1 (NF1) cancer predisposition syndrome. Surgical biopsy is rarely performed prior to treatment in the setting of NF1, resulting in a paucity of tumor genomic information. To define the molecular landscape of NF1-associated LGGs (NF1-LGG), we integrated clinical data, histological diagnoses, and multi-level genetic/genomic analyses on 70 individuals from 25 centers worldwide. Whereas, most tumors harbored bi-allelic NF1 inactivation as the only genetic abnormality, 11% had additional mutations. Moreover, tumors classified as non-pilocytic astrocytoma based on DNA methylation analysis were significantly more likely to harbor these additional mutations. The most common secondary alteration was FGFR1 mutation, which conferred an additional growth advantage in multiple complementary experimental murine Nf1 models. Taken together, this comprehensive characterization has important implications for the management of children with NF1-LGG, distinct from their sporadic counterparts.

A transcriptome-based classifier to determine molecular subtypes in medulloblastoma.

Medulloblastoma is a highly heterogeneous pediatric brain tumor with five molecular subtypes, Sonic Hedgehog TP53-mutant, Sonic Hedgehog TP53-wildtype, WNT, Group 3, and Group 4, defined by the World Health Organization. The current mechanism for classification into these molecular subtypes is through the use of immunostaining, methylation, and/or genetics. We surveyed the literature and identified a number of RNA-Seq and microarray datasets in order to develop, train, test, and validate a robust classifier to identify medulloblastoma molecular subtypes through the use of transcriptomic profiling data. We have developed a GPL-3 licensed R package and a Shiny Application to enable users to quickly and robustly classify medulloblastoma samples using transcriptomic data. The classifier utilizes a large composite microarray dataset (15 individual datasets), an individual microarray study, and an RNA-Seq dataset, using gene ratios instead of gene expression measures as features for the model. Discriminating features were identified using the limma R package and samples were classified using an unweighted mean of normalized scores. We utilized two training datasets and applied the classifier in 15 separate datasets. We observed a minimum accuracy of 85.71% in the smallest dataset and a maximum of 100% accuracy in four datasets with an overall median accuracy of 97.8% across the 15 datasets, with the majority of misclassification occurring between the heterogeneous Group 3 and Group 4 subtypes. We anticipate this medulloblastoma transcriptomic subtype classifier will be broadly applicable to the cancer research and clinical communities.

BRAF fusions in pediatric histiocytic neoplasms define distinct therapeutic responsiveness to RAF paradox breakers.

Pediatric histiocytic neoplasms are hematopoietic disorders frequently driven by the BRAF-V600E mutation. Here, we identified two BRAF gene fusions (novel MTAP-BRAF and MS4A6A-BRAF) in two aggressive histiocytic neoplasms. In contrast to previously described BRAF fusions, MTAP-BRAF and MS4A6A-BRAF do not respond to the paradox breaker RAF inhibitor (RAFi) PLX8394 due to stable fusion dimerization mediated by the N-terminal fusion partners. This highlights a significant and clinically relevant shift from the current dogma that BRAF-fusions respond similarly to BRAF-inhibitors. As an alternative, we show suppression of fusion-driven oncogenic growth with the pan-RAFi LY3009120 and MEK inhibition.

annoFuse: an R Package to annotate, prioritize, and interactively explore putative oncogenic RNA fusions.

BACKGROUND: Gene fusion events are significant sources of somatic variation across adult and pediatric cancers and are some of the most clinically-effective therapeutic targets, yet low consensus of RNA-Seq fusion prediction algorithms makes therapeutic prioritization difficult. In addition, events such as polymerase read-throughs, mis-mapping due to gene homology, and fusions occurring in healthy normal tissue require informed filtering, making it difficult for researchers and clinicians to rapidly discern gene fusions that might be true underlying oncogenic drivers of a tumor and in some cases, appropriate targets for therapy. RESULTS: We developed annoFuse, an R package, and shinyFuse, a companion web application, to annotate, prioritize, and explore biologically-relevant expressed gene fusions, downstream of fusion calling. We validated annoFuse using a random cohort of TCGA RNA-Seq samples (N = 160) and achieved a 96% sensitivity for retention of high-confidence fusions (N = 603). annoFuse uses FusionAnnotator annotations to filter non-oncogenic and/or artifactual fusions. Then, fusions are prioritized if previously reported in TCGA and/or fusions containing gene partners that are known oncogenes, tumor suppressor genes, COSMIC genes, and/or transcription factors. We applied annoFuse to fusion calls from pediatric brain tumor RNA-Seq samples (N = 1028) provided as part of the Open Pediatric Brain Tumor Atlas (OpenPBTA) Project to determine recurrent fusions and recurrently-fused genes within different brain tumor histologies. annoFuse annotates protein domains using the PFAM database, assesses reciprocality, and annotates gene partners for kinase domain retention. As a standard function, reportFuse enables generation of a reproducible R Markdown report to summarize filtered fusions, visualize breakpoints and protein domains by transcript, and plot recurrent fusions within cohorts. Finally, we created shinyFuse for algorithm-agnostic interactive exploration and plotting of gene fusions. CONCLUSIONS: annoFuse provides standardized filtering and annotation for gene fusion calls from STAR-Fusion and Arriba by merging, filtering, and prioritizing putative oncogenic fusions across large cancer datasets, as demonstrated here with data from the OpenPBTA project. We are expanding the package to be widely-applicable to other fusion algorithms and expect annoFuse to provide researchers a method for rapidly evaluating, prioritizing, and translating fusion findings in patient tumors.

Ancestry and frequency of genetic variants in the general population are confounders in the characterization of germline variants linked to cancer.

BACKGROUND: Pediatric high-grade gliomas (pHGGs) are incurable malignant brain cancers. Clear somatic genetic drivers are difficult to identify in the majority of cases. We hypothesized that this may be due to the existence of germline variants that influence tumor etiology and/or progression and are filtered out using traditional pipelines for somatic mutation calling. METHODS: In this study, we analyzed whole-genome sequencing (WGS) datasets of matched germlines and tumor tissues to identify recurrent germline variants in pHGG patients. RESULTS: We identified two structural variants that were highly recurrent in a discovery cohort of 8 pHGG patients. One was a ~ 40 kb deletion immediately upstream of the NEGR1 locus and predicted to remove the promoter region of this gene. This copy number variant (CNV) was present in all patients in our discovery cohort (n = 8) and in 86.3% of patients in our validation cohort (n = 73 cases). We also identified a second recurrent deletion 55.7 kb in size affecting the BTNL3 and BTNL8 loci. This BTNL3-8 deletion was observed in 62.5% patients in our discovery cohort, and in 17.8% of the patients in the validation cohort. Our single-cell RNA sequencing (scRNA-seq) data showed that both deletions result in disruption of transcription of the affected genes. However, analysis of genomic information from multiple non-cancer cohorts showed that both the NEGR1 promoter deletion and the BTNL3-8 deletion were CNVs occurring at high frequencies in the general population. Intriguingly, the upstream NEGR1 CNV deletion was homozygous in ~ 40% of individuals in the non-cancer population. This finding was immediately relevant because the affected genes have important physiological functions, and our analyses showed that NEGR1 expression levels have prognostic value for pHGG patient survival. We also found that these deletions occurred at different frequencies among different ethnic groups. CONCLUSIONS: Our study highlights the need to integrate cancer genomic analyses and genomic data from large control populations. Failure to do so may lead to spurious association of genes with cancer etiology. Importantly, our results showcase the need for careful evaluation of differences in the frequency of genetic variants among different ethnic groups.

A pilot precision medicine trial for children with diffuse intrinsic pontine glioma-PNOC003: A report from the Pacific Pediatric Neuro-Oncology Consortium.

This clinical trial evaluated whether whole exome sequencing (WES) and RNA sequencing (RNAseq) of paired normal and tumor tissues could be incorporated into a personalized treatment plan for newly diagnosed patients (<25 years of age) with diffuse intrinsic pontine glioma (DIPG). Additionally, whole genome sequencing (WGS) was compared to WES to determine if WGS would further inform treatment decisions, and whether circulating tumor DNA (ctDNA) could detect the H3K27M mutation to allow assessment of therapy response. Patients were selected across three Pacific Pediatric Neuro-Oncology Consortium member institutions between September 2014 and January 2016. WES and RNAseq were performed at diagnosis and recurrence when possible in a CLIA-certified laboratory. Patient-derived cell line development was attempted for each subject. Collection of blood for ctDNA was done prior to treatment and with each MRI. A specialized tumor board generated a treatment recommendation including up to four FDA-approved agents based upon the genomic alterations detected. A treatment plan was successfully issued within 21 business days from tissue collection for all 15 subjects, with 14 of the 15 subjects fulfilling the feasibility criteria. WGS results did not significantly deviate from WES-based therapy recommendations; however, WGS data provided further insight into tumor evolution and fidelity of patient-derived cell models. Detection of the H3F3A or HIST1H3B K27M (H3K27M) mutation using ctDNA was successful in 92% of H3K27M mutant cases. A personalized treatment recommendation for DIPG can be rendered within a multicenter setting using comprehensive next-generation sequencing technology in a clinically relevant timeframe.

Inositol polyphosphates intersect with signaling and metabolic networks via two distinct mechanisms.

Inositol-based signaling molecules are central eukaryotic messengers and include the highly phosphorylated, diffusible inositol polyphosphates (InsPs) and inositol pyrophosphates (PP-InsPs). Despite the essential cellular regulatory functions of InsPs and PP-InsPs (including telomere maintenance, phosphate sensing, cell migration, and insulin secretion), the majority of their protein targets remain unknown. Here, the development of InsP and PP-InsP affinity reagents is described to comprehensively annotate the interactome of these messenger molecules. By using the reagents as bait, >150 putative protein targets were discovered from a eukaryotic cell lysate (Saccharomyces cerevisiae). Gene Ontology analysis of the binding partners revealed a significant overrepresentation of proteins involved in nucleotide metabolism, glucose metabolism, ribosome biogenesis, and phosphorylation-based signal transduction pathways. Notably, we isolated and characterized additional substrates of protein pyrophosphorylation, a unique posttranslational modification mediated by the PP-InsPs. Our findings not only demonstrate that the PP-InsPs provide a central line of communication between signaling and metabolic networks, but also highlight the unusual ability of these molecules to access two distinct modes of action.

A Novel Inositol Pyrophosphate Phosphatase in Saccharomyces cerevisiae: Siw14 PROTEIN SELECTIVELY CLEAVES THE ß-PHOSPHATE FROM 5-DIPHOSPHOINOSITOL PENTAKISPHOSPHATE (5PP-IP5).

Inositol pyrophosphates are high energy signaling molecules involved in cellular processes, such as energetic metabolism, telomere maintenance, stress responses, and vesicle trafficking, and can mediate protein phosphorylation. Although the inositol kinases underlying inositol pyrophosphate biosynthesis are well characterized, the phosphatases that selectively regulate their cellular pools are not fully described. The diphosphoinositol phosphate phosphohydrolase enzymes of the Nudix protein family have been demonstrated to dephosphorylate inositol pyrophosphates; however, theSaccharomyces cerevisiaehomolog Ddp1 prefers inorganic polyphosphate over inositol pyrophosphates. We identified a novel phosphatase of the recently discovered atypical dual specificity phosphatase family as a physiological inositol pyrophosphate phosphatase. Purified recombinant Siw14 hydrolyzes the ß-phosphate from 5-diphosphoinositol pentakisphosphate (5PP-IP5or IP7)in vitro. In vivo,siw14Δ yeast mutants possess increased IP7levels, whereas heterologousSIW14overexpression eliminates IP7from cells. IP7levels increased proportionately whensiw14Δ was combined withddp1Δ orvip1Δ, indicating independent activity by the enzymes encoded by these genes. We conclude that Siw14 is a physiological phosphatase that modulates inositol pyrophosphate metabolism by dephosphorylating the IP7isoform 5PP-IP5to IP6.

The biorepository portal toolkit: an honest brokered, modular service oriented software tool set for biospecimen-driven translational research.

BACKGROUND: High throughput molecular sequencing and increased biospecimen variety have introduced significant informatics challenges for research biorepository infrastructures. We applied a modular system integration approach to develop an operational biorepository management system. This method enables aggregation of the clinical, specimen and genomic data collected for biorepository resources. METHODS: We introduce an electronic Honest Broker (eHB) and Biorepository Portal (BRP) open source project that, in tandem, allow for data integration while protecting patient privacy. This modular approach allows data and specimens to be associated with a biorepository subject at any time point asynchronously. This lowers the bar to develop new research projects based on scientific merit without institutional review for a proposal. RESULTS: By facilitating the automated de-identification of specimen and associated clinical and genomic data we create a future proofed specimen set that can withstand new workflows and be connected to new associated information over time. Thus facilitating collaborative advanced genomic and tissue research. CONCLUSIONS: As of Janurary of 2016 there are 23 unique protocols/patient cohorts being managed in the Biorepository Portal (BRP). There are over 4000 unique subject records in the electronic honest broker (eHB), over 30,000 specimens accessioned and 8 institutions participating in various biobanking activities using this tool kit. We specifically set out to build rich annotation of biospecimens with longitudinal clinical data; BRP/REDCap integration for multi-institutional repositories; EMR integration; further annotated specimens with genomic data specific to a domain; build application hooks for experiments at the specimen level integrated with analytic software; while protecting privacy per the Office of Civil Rights (OCR) and HIPAA.

Paradoxical activation and RAF inhibitor resistance of BRAF protein kinase fusions characterizing pediatric astrocytomas.

Astrocytomas are the most common type of brain tumors in children. Activated BRAF protein kinase mutations are characteristic of pediatric astrocytomas with KIAA1549-BRAF fusion genes typifying low-grade astrocytomas and (V600E)BRAF alterations characterizing distinct or higher-grade tumors. Recently, BRAF-targeted therapies, such as vemurafenib, have shown great promise in treating V600E-dependent melanomas. Like (V600E)BRAF, BRAF fusion kinases activate MAPK signaling and are sufficient for malignant transformation; however, here we characterized the distinct mechanisms of action of KIAA1549-BRAF and its differential responsiveness to PLX4720, a first-generation BRAF inhibitor and research analog of vemurafenib. We found that in cells expressing KIAA1549-BRAF, the fusion kinase functions as a homodimer that is resistant to PLX4720 and accordingly is associated with CRAF-independent paradoxical activation of MAPK signaling. Mutagenesis studies demonstrated that KIAA1549-BRAF fusion-mediated signaling is diminished with disruption of the BRAF kinase dimer interface. In addition, the KIAA1549-BRAF fusion displays increased binding affinity to kinase suppressor of RAS (KSR), an RAF relative recently demonstrated to facilitate MEK phosphorylation by BRAF. Despite its resistance to PLX4720, the KIAA1549-BRAF fusion is responsive to a second-generation selective BRAF inhibitor that, unlike vemurafenib, does not induce activation of wild-type BRAF. Our data support the development of targeted treatment paradigms for BRAF-altered pediatric astrocytomas and also demonstrate that therapies must be tailored to the specific mutational context and distinct mechanisms of action of the mutant kinase.

Inositol polyphosphate multikinase is a physiologic PI3-kinase that activates Akt/PKB.

The second messenger phosphatidylinositol (3,4,5)-trisphosphate (PIP(3)), formed by the p110 family of PI3-kinases, promotes cellular growth, proliferation, and survival, in large part by activating the protein kinase Akt/PKB. We show that inositol polyphosphate multikinase (IPMK) physiologically generates PIP(3) as well as water soluble inositol phosphates. IPMK deletion reduces growth factor-elicited Akt signaling and cell proliferation caused uniquely by loss of its PI3-kinase activity. Inhibition of p110 PI3-kinases by wortmannin prevents IPMK phosphorylation and activation. Thus, growth factor stimulation of Akt signaling involves PIP(3) generation through the sequential activations of the p110 PI3-kinases and IPMK. As inositol phosphates inhibit Akt signaling, IPMK appears to act as a molecular switch, inhibiting or stimulating Akt via its inositol phosphate kinase or PI3-kinase activities, respectively. Drugs regulating IPMK may have therapeutic relevance in influencing cell proliferation.

Elucidating diphosphoinositol polyphosphate function with nonhydrolyzable analogues.

The diphosphoinositol polyphosphates (PP-IPs) represent a novel class of high-energy phosphate-containing messengers which control a wide variety of cellular processes. It is thought that PP-IPs exert their pleiotropic effects as allosteric regulators and through pyrophosphorylation of protein substrates. However, most details of PP-IP signaling have remained elusive because of a paucity of suitable tools. We describe the synthesis of PP-IP bisphosphonate analogues (PCP-IPs), which are resistant to chemical and biochemical degradation. While the two regioisomers 1PCP-IP5 and 5PCP-IP5 inhibited Akt phosphorylation with similar potencies, 1PCP-IP5 was much more effective at inhibiting its cognate phosphatase hDIPP1. Furthermore, the PCP analogues inhibit protein pyrophosphorylation because of their inability to transfer the ß-phosphoryl group, and thus enable the distinction between PP-IP signaling mechanisms. As such, the PCP analogues will find widespread applications for the structural and biochemical characterization of PP-IP signaling properties.

Multiomics2Targets identifies targets from cancer cohorts profiled with transcriptomics, proteomics, and phosphoproteomics.

The availability of data from profiling of cancer patients with multiomics is rapidly increasing. However, integrative analysis of such data for personalized target identification is not trivial. Multiomics2Targets is a platform that enables users to upload transcriptomics, proteomics, and phosphoproteomics data matrices collected from the same cohort of cancer patients. After uploading the data, Multiomics2Targets produces a report that resembles a research publication. The uploaded matrices are processed, analyzed, and visualized using the tools Enrichr, KEA3, ChEA3, Expression2Kinases, and TargetRanger to identify and prioritize proteins, genes, and transcripts as potential targets. Figures and tables, as well as descriptions of the methods and results, are automatically generated. Reports include an abstract, introduction, methods, results, discussion, conclusions, and references and are exportable as citable PDFs and Jupyter Notebooks. Multiomics2Targets is applied to analyze version 3 of the Clinical Proteomic Tumor Analysis Consortium (CPTAC3) pan-cancer cohort, identifying potential targets for each CPTAC3 cancer subtype. Multiomics2Targets is available from https://multiomics2targets.maayanlab.cloud/.

DNA methylation landscapes in DIPG reveal methylome variability that can be modified pharmacologically.

Background: Diffuse intrinsic pontine glioma (DIPG) is a uniformly lethal brainstem tumor of childhood, driven by histone H3 K27M mutation and resultant epigenetic dysregulation. Epigenomic analyses of DIPG have shown global loss of repressive chromatin marks accompanied by DNA hypomethylation. However, studies providing a static view of the epigenome do not adequately capture the regulatory underpinnings of DIPG cellular heterogeneity and plasticity. Methods: To address this, we performed whole-genome bisulfite sequencing on a large panel of primary DIPG specimens and applied a novel framework for analysis of DNA methylation variability, permitting the derivation of comprehensive genome-wide DNA methylation potential energy landscapes that capture intrinsic epigenetic variation. Results: We show that DIPG has a markedly disordered epigenome with increasingly stochastic DNA methylation at genes regulating pluripotency and developmental identity, potentially enabling cells to sample diverse transcriptional programs and differentiation states. The DIPG epigenetic landscape was responsive to treatment with the hypomethylating agent decitabine, which produced genome-wide demethylation and reduced the stochasticity of DNA methylation at active enhancers and bivalent promoters. Decitabine treatment elicited changes in gene expression, including upregulation of immune signaling such as the interferon response, STING, and MHC class I expression, and sensitized cells to the effects of histone deacetylase inhibition. Conclusions: This study provides a resource for understanding the epigenetic instability that underlies DIPG heterogeneity. It suggests the application of epigenetic therapies to constrain the range of epigenetic states available to DIPG cells, as well as the use of decitabine in priming for immune-based therapies.

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.

Everolimus for Children With Recurrent or Progressive Low-Grade Glioma: Results From the Phase II PNOC001 Trial.

PURPOSE: The PNOC001 phase II single-arm trial sought to estimate progression-free survival (PFS) associated with everolimus therapy for progressive/recurrent pediatric low-grade glioma (pLGG) on the basis of phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) pathway activation as measured by phosphorylated-ribosomal protein S6 and to identify prognostic and predictive biomarkers. PATIENTS AND METHODS: Patients, age 3-21 years, with progressive/recurrent pLGG received everolimus orally, 5 mg/m2 once daily. Frequency of driver gene alterations was compared among independent pLGG cohorts of newly diagnosed and progressive/recurrent patients. PFS at 6 months (primary end point) and median PFS (secondary end point) were estimated for association with everolimus therapy. RESULTS: Between 2012 and 2019, 65 subjects with progressive/recurrent pLGG (median age, 9.6 years; range, 3.0-19.9; 46% female) were enrolled, with a median follow-up of 57.5 months. The 6-month PFS was 67.4% (95% CI, 60.0 to 80.0) and median PFS was 11.1 months (95% CI, 7.6 to 19.8). Hypertriglyceridemia was the most common grade ≥3 adverse event. PI3K/AKT/mTOR pathway activation did not correlate with clinical outcomes (6-month PFS, active 68.4% v nonactive 63.3%; median PFS, active 11.2 months v nonactive 11.1 months; P = .80). Rare/novel KIAA1549::BRAF fusion breakpoints were most frequent in supratentorial midline pilocytic astrocytomas, in patients with progressive/recurrent disease, and correlated with poor clinical outcomes (median PFS, rare/novel KIAA1549::BRAF fusion breakpoints 6.1 months v common KIAA1549::BRAF fusion breakpoints 16.7 months; P < .05). Multivariate analysis confirmed their independent risk factor status for disease progression in PNOC001 and other, independent cohorts. Additionally, rare pathogenic germline variants in homologous recombination genes were identified in 6.8% of PNOC001 patients. CONCLUSION: Everolimus is a well-tolerated therapy for progressive/recurrent pLGGs. Rare/novel KIAA1549::BRAF fusion breakpoints may define biomarkers for progressive disease and should be assessed in future clinical trials.

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.