Proteogenomic discovery of immunotherapeutic targets in cancer: focus on rhabdomyosarcoma

Rawan Shraim

doi:10.17918/00010451

Cancer is a genetic disease characterized by abnormal cell growth and metastases and remains a leading cause of mortality globally. Chemotherapy and other traditional treatments often result in adverse effects, necessitating the development of targeted and less toxic approaches, such as immunotherapies. Immunotherapies harness the immune system to fight diseases. A challenge in developing targeted immunotherapies such as adoptive cell therapies and monoclonal antibodies is the identification of cancer specific surface expressed proteins or antigens. Rhabdomyosarcoma (RMS) is the most common soft tissue cancer diagnosed in children, adolescents, and young adults, comprising 3-4% of all childhood cancers. Treatment is commonly multi-modal involving surgery, radiation, and chemotherapy and has not substantively changed over the last four decades. Approximately 90% of patients with localized low-risk disease are cured with multi-modal therapy; however, the survival rate for patients with metastatic disease remains mired at 30% or less and relapsed disease is typically lethal. There are currently no approved immunotherapies or targeted therapies for RMS and there have been no significant improvements in survival outcomes for RMS patients despite multiple randomized controlled trials of various chemotherapy combination. To address the challenge of identifying and systematically prioritizing immunotherapeutic targets computationally, we developed IMMUNOTAR. IMMUNOTAR integrates user-provided expression data with features extracted from multiple publicly available databases and quantitively prioritizes potential surface protein targets based on predefined optimal immunotherapeutic target criteria. Both adult-derived and pediatric-specific databases are included within IMMUNOTAR. To address the challenge of identifying immunotherapies specifically in RMS, we generate a surface mass-spectrometry dataset to profile surface proteins within RMS subtypes and applied IMMUNOTAR to identify potential immunotherapeutic targets within this cohort. We hypothesize that a computational approach that integrates various public databases that tackle the ideal immunotherapeutic criteria can aid in identifying target candidates and that applying this methodology to surface-protein specific data generated from RMS will identify and prioritize surface proteins preferentially expressed in RMS, or RMS subtypes, to undergo further functional validation as a candidate immunotherapeutic target. Through our work, we were able to validate IMMUNOTAR's applicability across various cancer phenotypes by replicating results obtained by other groups who used more manual scoring algorithms to identify and validate immunotherapeutic targets within Ewing's sarcoma and multiple myeloma. Additionally using our RMS data, we were able to identify glypican 2 (GPC2) as potential immunotherapeutic target. Preclinical testing of GPC2 targeted immunotherapy showed that it was efficacious in eradicating RMS tumors.

Proteogenomic discovery of immunotherapeutic targets in cancer: focus on rhabdomyosarcoma

Files and links (1)

Abstract

Metrics

Details

Proteogenomic discovery of immunotherapeutic targets in cancer: focus on rhabdomyosarcoma

Files and links (1)

Abstract

Metrics

Details

Drexel University Social media