NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #20

serial: '20'
sid: '145109'
uuid: d3d0ad2f-09e2-4d6c-b7ec-0eec1f86d86c
uri: /nci/ods-data-jamboree/abstractsubmissions
created: '1750602681'
completed: '1750602707'
changed: '1750602707'
in_draft: '0'
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uid: '0'
langcode: en
webform_id: nci_office_of_data_sharing_abstr
entity_type: node
entity_id: '2107'
locked: '0'
sticky: '0'
notes: ''
metatag: meta
data:
  category: 'Proteogenomic data analysis'
  degree_s_: Ph.D.
  email: tlih1@jhmi.edu
  first_name: Tung-Shing
  keywords_abstracts: 'Ependymoma, proteogenomics, protein modifications, mass spectrometry'
  last_name: Lih
  middle_initial: M
  organization: 'Johns Hopkins University'
  organization_address:
    address: ''
    address_2: ''
    city: Baltimore
    country: ''
    postal_code: ''
    state_province: ''
  other_please_specify_: ''
  summary: 'Childhood ependymoma is a malignant brain tumor, which accounts for a significant portion of pediatric central nervous system tumors. Ependymoma poses a major clinical challenge due to its resistance to conventional therapies and high recurrence rate. Despite characterizations of childhood ependymoma by genomics, epigenomics, and transcriptomics, effective therapies for ependymoma remain limited, underscoring the need for deeper molecular understanding. To address this, we will perform proteogenomic analysis on the ependymoma cases from the Children’s Brain Tumor Network (CBTN) with genomic, epigenomic, transcriptomic, and proteomic data available on Gabriella Miller Kids First Portal, Open Pediatric Brain Tumor Atlas, and Proteomics Data Commons. These harmonized datasets capture molecular alterations across every regulatory tier, from genetic mutations and transcriptional dysregulation to protein expression, signaling pathway perturbations, and metabolic reprogramming. Using R- and Python-based bioinformatic/biostatistics pipelines, we will (1) integrate multi-omics data to identify molecular subtypes of ependymoma (2) Identify subtype-specific molecular target proteins, dysregulated signaling pathways and tumor microenvironment features (3) Utilize AI models to analyze the characteristics of molecular subtypes and leverage drug databases to identify actionable molecular targets and develop personalized therapeutic strategies. This integrated proteogenomic approach offers a comprehensive strategy to unravel the biological complexity of this challenging pediatric brain tumor. To ensure robustness and generalizability, we intend to validate the findings using other available datasets, either publicly available or control-access data, so that candidate targets reflect reproducible disease biology rather than dataset-specific effects. We have assembled a team of investigators with expertise in proteogenomics and computational biology. Overall, our goal is to identify mechanisms driving tumorigenesis and progression in childhood ependymoma and to identify potential therapeutic targets that can inform future preclinical studies and clinical trials.'
  title: 'Research Associate'
  ttile: 'Proteogenomic characterization of childhood ependymoma to identify new therapeutic targets'
  upload_abstract: '65580'