Abstract To facilitate identification of therapeutic strategies targeting pediatric brain tumors, the Children’s Hospital of Philadelphia Center for Data Driven Discovery in Biomedicine (D3b) and Children’s Brain Tumor Network (CBTN), in partnership with foundations, academic, and governmental institutions, are leaders in developing open science models for specimen collection, data generation, and resource sharing. Here we highlight initiatives to generate multimodal proteomics and metabolomics data characterizing over 1000 events from 11 pediatric, adolescent, and young adult brain tumor histologies, along with additional derived preclinical model cell lines. The data comprise combinations of whole cell proteomics, phosphoproteomics, ubiquitin proteomics, glycosylation, acetylation, and metabolomics to complement existing WGS, RNA-Seq, methylation, miRNA-Seq, snRNA-Seq, clinical, and imaging data. Integration of proteomics with genomics and transcriptomics modalities provides evidence for functional or phenotypic manifestations of genomic events, thereby identifying potential therapeutic targets and/or biomarkers. Proteomics and metabolomics data also provide mechanistic insights by measuring changes in protein abundance or function, characterizing signaling networks, and quantifying metabolic activity impacting oncogenesis in tumor cells. We are developing architectures for processing, integrating, and disseminating these data. We are addressing technical challenges associated with harmonizing data from diverse proteomics experimental strategies and mass spectrometry methods. We will develop standards for proteomics data products and harmonization with other modalities. Finally, we will establish pipelines for upstream analysis/re-analysis of proteomics data using custom parameters. The pipelines, raw, and processed data will be deposited within D3b Cavatica, OpenPedCan, and Kids First infrastructures, and be indexed and accessible across platforms. Our efforts will enable open use and interpretation of these unique data modalities, and thus promote engagement within the wider community to advance basic and translational pediatric brain tumor research. One exciting application of our infrastructure will be to enable tumor-specific proteome identification, which has the potential to inform precision-based, personalized medicine approaches.
Dybas et al. (Fri,) studied this question.
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