Prof. Michal Bassani-Sternberg - From Discovery to Therapy: Overcoming Challenges in Neoantigen-Driven Cancer Immunotherapy
Prof. Michal Bassani-Sternberg - From Discovery to Therapy: Overcoming Challenges in Neoantigen-Driven Cancer Immunotherapy
Michal Bassani-Sternberg
Immunopeptidomics lab, University of Lausanne
From Discovery to Therapy: Overcoming Challenges in Neoantigen-Driven Cancer Immunotherapy
The precise identification and prioritization of antigenic peptides presented by HLA-I and HLA-II molecules and recognized by autologous T cells is critical for advancing personalized cancer immunotherapies. However, existing clinical neoantigen prediction pipelines do not support the direct integration of mass spectrometry–based immunopeptidomics data, limiting the discovery of antigens from diverse canonical and non-canonical sources. To address this, we developed NeoDisc, a rapid, modular, end-to-end clinical proteogenomic pipeline that integrates genomics, transcriptomics, and immunopeptidomics with advanced in silico tools to identify and prioritize tumor-specific and immunogenic antigens, including neoantigens, viral antigens, and noncanonical tumor-specific peptides. Furthermore, by integrating multi-omics data, NeoDisc can uncover defects in the cellular antigen presentation machinery, providing insights into tumor antigen heterogeneity and mechanisms that shape the antigenic landscape. We demonstrate the superiority of NeoDisc in prioritizing immunogenic neoantigens compared to recent pipelines and highlight its flexibility for machine-learning–driven antigen discovery and vaccine design. Importantly, NeoDisc is currently being applied in clinical Phase I trials, supporting real-time, clinically actionable neoantigen identification for personalized cancer immunotherapy.
Selected references
- Huber F et al. (2025) A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy. Nature Biotechnology, [10.1038/s41587-024-02420-y]
- Müller M et al. (2023) Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction. Immunity [10.1016/j.immuni.2023.09.002]
- Chong C et al. (2020) Integrated proteogenomic deep sequencing and analytics accurately identify non-canonical peptides in tumor immunopeptidomes. Nature Communications [10.1038/s41467-020-14968-9]
This event is supported by the B2S Federation.
b2s
Prof. Michal Bassani-Sternberg - From Discovery to Therapy: Overcoming Challenges in Neoantigen-Driven Cancer Immunotherapy
Prof. Michal Bassani-Sternberg - From Discovery to Therapy: Overcoming Challenges in Neoantigen-Driven Cancer Immunotherapy
Michal Bassani-Sternberg
Immunopeptidomics lab, University of Lausanne
From Discovery to Therapy: Overcoming Challenges in Neoantigen-Driven Cancer Immunotherapy
The precise identification and prioritization of antigenic peptides presented by HLA-I and HLA-II molecules and recognized by autologous T cells is critical for advancing personalized cancer immunotherapies. However, existing clinical neoantigen prediction pipelines do not support the direct integration of mass spectrometry–based immunopeptidomics data, limiting the discovery of antigens from diverse canonical and non-canonical sources. To address this, we developed NeoDisc, a rapid, modular, end-to-end clinical proteogenomic pipeline that integrates genomics, transcriptomics, and immunopeptidomics with advanced in silico tools to identify and prioritize tumor-specific and immunogenic antigens, including neoantigens, viral antigens, and noncanonical tumor-specific peptides. Furthermore, by integrating multi-omics data, NeoDisc can uncover defects in the cellular antigen presentation machinery, providing insights into tumor antigen heterogeneity and mechanisms that shape the antigenic landscape. We demonstrate the superiority of NeoDisc in prioritizing immunogenic neoantigens compared to recent pipelines and highlight its flexibility for machine-learning–driven antigen discovery and vaccine design. Importantly, NeoDisc is currently being applied in clinical Phase I trials, supporting real-time, clinically actionable neoantigen identification for personalized cancer immunotherapy.
Selected references
- Huber F et al. (2025) A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy. Nature Biotechnology, [10.1038/s41587-024-02420-y]
- Müller M et al. (2023) Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction. Immunity [10.1016/j.immuni.2023.09.002]
- Chong C et al. (2020) Integrated proteogenomic deep sequencing and analytics accurately identify non-canonical peptides in tumor immunopeptidomes. Nature Communications [10.1038/s41467-020-14968-9]
This event is supported by the B2S Federation.
b2s
