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.

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.

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.

A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.

Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities to adult glioma, it is increasingly recognized as a molecularly distinct, yet highly heterogeneous disease. In this study, we longitudinally profiled a molecularly diverse cohort of 16 pHGG patients before and after standard therapy through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of the tumor microenvironment during progression following treatment. We found that the canonical neoplastic cell phenotypes of adult glioblastoma are insufficient to capture the range of tumor cell states in a pediatric cohort and observed differential tumor-myeloid interactions between malignant cell states. We identified key transcriptional regulators of pHGG cell states and did not observe the marked proneural to mesenchymal shift characteristic of adult glioblastoma. We showed that essential neuromodulators and the interferon response are upregulated post-therapy along with an increase in non-neoplastic oligodendrocytes. Through in vitro pharmacological perturbation, we demonstrated novel malignant cell-intrinsic targets. This multiomic atlas of longitudinal pHGG captures the key features of therapy response that support distinction from its adult counterpart and suggests therapeutic strategies which are targeted to pediatric gliomas.

University of Washington Quality of Life subdomain outcomes after treatment of sinonasal malignancy: A prospective, multicenter study.

PURPOSE: Sinonasal malignancies (SNMs) adversely impact patients' quality of life (QOL) and are frequently identified at an advanced stage. Because these tumors are rare, there are few studies that examine the specific QOL areas that are impacted. This knowledge would help improve the care of these patients. METHODS: In this prospective, multi-institutional study, 273 patients with SNMs who underwent definitive treatment with curative intent were evaluated. We used the University of Washington Quality of Life (UWQOL) instrument over 5 years from diagnosis to identify demographic, treatment, and disease-related factors that influence each of the 12 UWQOL subdomains from baseline to 5 -years post-treatment. RESULTS: Multivariate models found endoscopic resection predicted improved pain (vs. nonsurgical treatment CI 2.4, 19.4, p = 0.01) and appearance versus open (CI 27.0, 35.0, p < 0.001) or combined (CI 10.4, 17.1, p < 0.001). Pterygopalatine fossa involvement predicted worse swallow (CI -10.8, -2.4, p = 0.01) and pain (CI -17.0, -4.0, p < 0.001). Neck dissection predicted worse swallow (CI -14.8, -2.8, p < 0.001), taste (CI -31.7, -1.5, p = 0.02), and salivary symptoms (CI -28.4, -8.6, p < 0.001). Maxillary involvement predicted worse chewing (CI 9.8, 33.2; p < 0.001) and speech (CI -21.8, -5.4, p < 0.001) relative to other sites. Advanced T stage predicted worse anxiety (CI -13.0, -2.0, p = 0.03). CONCLUSIONS: Surgical approach, management of cervical disease, tumor extent, and site of involvement impacted variable UWQOL symptom areas. Endoscopic resection predicted better pain, appearance, and chewing compared with open. These results may aid in counseling patients regarding potential QOL expectations in their SNM treatment and recovery course.

The splicing modulator CLK1 is a candidate oncogenic dependency in pediatric high-grade gliomas.

Pediatric brain cancer is the leading cause of disease-related mortality in children, and many aggressive tumors still lack effective treatment strategies. Despite extensive studies characterizing these tumor genomes, alternative transcriptional splicing patterns remain underexplored. Here, we systematically characterized aberrant alternative splicing across pediatric brain tumors, identifying pediatric high-grade gliomas (HGGs) among the most heterogeneous. Through integration with UniProt Knowledgebase annotations, we identified 12,145 splice events in 5,424 genes, leading to functional changes in protein activation, folding, and localization. We discovered that the master splicing factor and cell-cycle modulator, CDC-like kinase 1 ( CLK1 ), is aberrantly spliced in HGGs to include exon 4, resulting in a gain of two phosphorylation sites and subsequent activation of CLK1. Inhibition of CLK1 with Cirtuvivint in the pediatric HGG KNS-42 cell line significantly decreased both cell viability and proliferation in a dose-dependent manner. Morpholino-mediated depletion of CLK1 exon 4 splicing reduced RNA expression, protein abundance, and cell viability. Notably, KNS-42 cells treated with the CLK1 exon 4 morpholino demonstrated differential expression impacting 78 genes and differential splicing with loss or gain of a functional site in 193 genes annotated as oncogene or tumor suppressor genes (TSGs). These genes were enriched for cancer-associated pathways, with 15 identified as significant gene dependencies in pediatric HGGs. Our findings highlight a dependency of pediatric HGGs on CLK1 and its roles contributing to tumor splicing heterogeneity through transcriptional dysregulation of splicing factors and transcriptional modulation of oncogenes. Overall, aberrant splicing in HGGs and other pediatric brain tumors represents a potentially targetable oncogenic pathway contributing to tumor growth and maintenance.

SNOT-22 subdomain outcomes following treatment for sinonasal malignancy: A prospective, multicenter study.

BACKGROUND: Patients with sinonasal malignancy (SNM) present with significant sinonasal quality of life (QOL) impairment. Global sinonasal QOL as measured by the 22-item Sinonasal Outcomes Test (SNOT-22) has been shown to improve with treatment. This study aims to characterize SNOT-22 subdomain outcomes in SNM. METHODS: Patients diagnosed with SNM were prospectively enrolled in a multi-center patient registry. SNOT-22 scores were collected at the time of diagnosis and through the post-treatment period for up to 5 years. Multivariable regression analysis was used to identify drivers of variation in SNOT-22 subdomains. RESULTS: Note that 234 patients were reviewed, with a mean follow-up of 22 months (3 months-64 months). Rhinologic, psychological, and sleep subdomains significantly improved versus baseline (all p < 0.05). Subanalysis of 40 patients with follow-up at all timepoints showed statistically significant improvement in rhinologic, extra-nasal, psychological, and sleep subdomains, with minimal clinically important difference met between 2 and 5 years in sleep and psychological subdomains. Adjuvant chemoradiation was associated with worse outcomes in rhinologic (adjusted odds ratio (5.22 [1.69-8.66])), extra-nasal (2.21 [0.22-4.17]) and ear/facial (5.53 [2.10-8.91]) subdomains. Pterygopalatine fossa involvement was associated with worse outcomes in rhinologic (3.22 [0.54-5.93]) and ear/facial (2.97 [0.32-5.65]) subdomains. Positive margins (5.74 [2.17-9.29]) and surgical approach-combined versus endoscopic (3.41 [0.78-6.05])-were associated with worse psychological outcomes. Adjuvant radiation (2.28 [0.18-4.40]) was associated with worse sleep outcomes. CONCLUSIONS: Sinonasal QOL improvements associated with treatment of SNM are driven by rhinologic, extra-nasal, psychological, and sleep subdomains.

The Open Pediatric Cancer Project.

Background: In 2019, the Open Pediatric Brain Tumor Atlas (OpenPBTA) was created as a global, collaborative open-science initiative to genomically characterize 1,074 pediatric brain tumors and 22 patient-derived cell lines. Here, we extend the OpenPBTA to create the Open Pediatric Cancer (OpenPedCan) Project, a harmonized open-source multi-omic dataset from 6,112 pediatric cancer patients with 7,096 tumor events across more than 100 histologies. Combined with RNA-Seq from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA), OpenPedCan contains nearly 48,000 total biospecimens (24,002 tumor and 23,893 normal specimens). Findings: We utilized Gabriella Miller Kids First (GMKF) workflows to harmonize WGS, WXS, RNA-seq, and Targeted Sequencing datasets to include somatic SNVs, InDels, CNVs, SVs, RNA expression, fusions, and splice variants. We integrated summarized CPTAC whole cell proteomics and phospho-proteomics data, miRNA-Seq data, and have developed a methylation array harmonization workflow to include m-values, beta-vales, and copy number calls. OpenPedCan contains reproducible, dockerized workflows in GitHub, CAVATICA, and Amazon Web Services (AWS) to deliver harmonized and processed data from over 60 scalable modules which can be leveraged both locally and on AWS. The processed data are released in a versioned manner and accessible through CAVATICA or AWS S3 download (from GitHub), and queryable through PedcBioPortal and the NCI's pediatric Molecular Targets Platform. Notably, we have expanded PBTA molecular subtyping to include methylation information to align with the WHO 2021 Central Nervous System Tumor classifications, allowing us to create research- grade integrated diagnoses for these tumors. Conclusions: OpenPedCan data and its reproducible analysis module framework are openly available and can be utilized and/or adapted by researchers to accelerate discovery, validation, and clinical translation.

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.