Proteogenomic Discovery Of Immunotherapeutic Targets in Cancer: Focus on Rhabdomyosarcoma

Wednesday, June 5, 2024

2:30 PM-4:30 PM

BIOMED PhD Thesis Defense

Title:
Proteogenomic Discovery of Immunotherapeutic Targets in Cancer: Focus on Rhabdomyosarcoma

Speaker:
Rawan Shraim, PhD Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Advisors:
Sharon J. Diskin, PhD
Assistant Professor of Pediatrics
Perelman School of Medicine
University of Pennsylvania
Department of Biomedical and Health Informatics
Children's Hospital of Philadelphia (CHOP)

John M. Maris, MD
Professor of Pediatrics
Perelman School of Medicine
University of Pennsylvania
Pediatric Oncologist
Children's Hospital of Philadelphia (CHOP)

Ahmet Sacan, PhD
Teaching Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University

Details:
The identification of surface targets for the development of immunotherapies lacks a systematic prioritization method, with traditional methods proving to be labor-intensive and inconsistent. Identifying these targets is crucial for advancing immunotherapies such as adoptive cell therapies and monoclonal antibodies. To address this critical gap, IMMUNOTAR was developed as a computational tool to enhance the efficiency and accuracy of target prioritization. IMMUNOTAR integrates user-provided RNA-sequencing or proteomics data with features extracted from multiple publicly available databases, which survey ideal immunotherapeutic target criteria, to quantitatively rank potential candidate targets. IMMUNOTAR was validated using several datasets, including proteomics datasets surveying Ewing sarcoma and multiple myeloma. It successfully identified known validated targets and potential novel targets within each phenotype that warrant further validation.

In the specific context of rhabdomyosarcoma (RMS), a surface-proteomics dataset was generated, profiling a total of 24 fusion-positive and fusion-negative RMS patient-derived xenograft models. IMMUNOTAR was utilized to analyze the surface mass-spectrometry dataset, identifying glypican 2 (GPC2) as a promising immunotherapeutic target within this phenotype. GPC2 has been studied in neuroblastoma and other neural cancers, showing efficacious targeting in those phenotypes. Preclinical testing demonstrated that GPC2-targeted chimeric antigen receptor (CAR) T-cell therapy effectively eradicated RMS tumors, offering a potential new treatment avenue for a cancer type that has seen little improvement in survival outcomes over the past three decades. This work underscores the potential of computational tools in advancing cancer treatment by enabling the systematic identification and validation of novel therapeutic targets.

Contact Information

Natalia Broz
njb33@drexel.edu