NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #16

serial: '16'
sid: '145067'
uuid: 907d8148-5eaa-4be6-9161-2148c6e36169
uri: /nci/ods-data-jamboree/abstractsubmissions
created: '1750429021'
completed: '1750429021'
changed: '1750429921'
in_draft: '0'
current_page: ''
remote_addr: 10.208.24.91
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: 'Development or refinement of analysis pipelines or AI/ML algorithms'
  degree_s_: Ph.D.
  email: Liang.Liu@advocatehealth.org
  first_name: Liang
  keywords_abstracts: ''
  last_name: Liu
  middle_initial: ''
  organization: 'Wake Forest University Health Sciences'
  organization_address:
    address: ''
    address_2: ''
    city: Winston-Salem
    country: ''
    postal_code: ''
    state_province: ''
  other_please_specify_: ''
  summary: |-
    Alternative splicing (AS) is a key post-transcriptional mechanism that contributes to transcriptomic diversity and plays a critical role in pediatric brain tumor biology. This project aims to develop advanced machine learning models to systematically characterize AS events and their functional and immunological consequences in pediatric brain tumors.
    We propose a two-tiered computational framework. First, we will develop a graph-transformer deep learning model to predict the functional impact of tumor-specific splice variants. This model will integrate multimodal biological features—including splice site strength, isoform usage, protein domain disruption, and gene network context—into a graph structure where nodes represent AS events and edges encode known gene-gene and pathway interactions. The model’s self-attention mechanism will be adapted to prioritize biologically meaningful relationships, enabling accurate prediction of oncogenic potential and splicing dysregulation.
    Second, we will construct a graph neural network (GNN) to link AS events to the tumor immune microenvironment. This model will identify immune-associated splice variants and predict immunotherapy responsiveness by correlating AS profiles with immune cell infiltration and neoantigen load. An AS-Immune score will be derived to quantify the immunogenic potential of splicing alterations and validated across multiple pediatric brain tumor cohorts.
    The modeling framework will be trained using transcriptomic data from the Childhood Cancer Data Initiative (CCDI), aligning with national efforts to accelerate pediatric cancer research. Validation will be performed using independent datasets from the Children’s Brain Tumor Network (CBTN), Pediatric Brain Tumor Portal (PBTP), and Beat Childhood Cancer (BCC) consortium. These models are designed to be extensible to other pediatric and adolescent and young adult (AYA) cancers.
    Our multidisciplinary team includes Liang Liu, Ph.D., Wei Zhang, Ph.D., Anderson Cox, M.S., and Deha Ay, M.S. from Wake Forest University Health Sciences, and Giselle Sholler, M.D., Jeremy Hengst, Ph.D., and Abhinav Nagulapally, M.S. from Penn State Health Children’s Hospital.
  title: ''
  ttile: 'Deep Learning Frameworks for Functional and Immune Profiling of Splice Variants in Pediatric Brain Tumors'
  upload_abstract: ''