About this Research Topic
With the promise of curative potential in a number of solid and hematological malignancies, immunotherapy is becoming the next pillar of cancer treatment (next to surgery, radiation, chemotherapy, and targeted therapy). The remarkable clinical success of drugs that target immune checkpoint molecules advances in the development of cancer vaccines, and approval of engineered T cell products to treat cancer sparked tremendous research activities in basic, translational, and clinical medicine. At the core of each immunotherapy are neoantigens that drive effective T cell response, i.e. peptides that arise from the expression of somatically mutated genes. Hence, the characterization of neoantigens and identification of the immunogenic ones is of utmost importance for improving cancer immunotherapy and broadening the utility to a larger fraction of patients. However, despite recent advances in computational methods for predicting and experimental techniques for validating, the identification of neoantigens that elicit T-cell response remains challenging.
This Research Topic will focus on the computational methods for predicting neoantigens and the experimental methods for validating the immunogenicity of neoantigens, focusing on the considerable improvements of existing methods, development, and validation of new techniques, and gaining novel insights.
We welcome Original Research Articles, Reviews, Clinical Cases, Mini Reviews, and Perspectives. Topics of interest include, but are not limited to:
-Improved computational methods for predicting canonical neoantigens, i.e. antigens arising from point mutations
-Novel computational methods for predicting non-canonical neoantigens, i.e. antigens from fusion genes, alternative splice variants, non-coding regions, or post-translational modifications
-Experimental methods for ex vivo validation of immunogenic neoantigens
-Insights from preclinical and clinical studies on TCR recognition of antigen-MHC complexes
Application of AI-based methods including large language models and high-throughput experimental techniques are particularly encouraged.
Topic Editor Wouter Scheper acts as a paid consultant for BD Biosciences and Lumicks. The other Topic Editors declare no competing interests with regard to the Research Topic subject
This Research Topic will focus on the computational methods for predicting neoantigens and the experimental methods for validating the immunogenicity of neoantigens, focusing on the considerable improvements of existing methods, development, and validation of new techniques, and gaining novel insights.
We welcome Original Research Articles, Reviews, Clinical Cases, Mini Reviews, and Perspectives. Topics of interest include, but are not limited to:
-Improved computational methods for predicting canonical neoantigens, i.e. antigens arising from point mutations
-Novel computational methods for predicting non-canonical neoantigens, i.e. antigens from fusion genes, alternative splice variants, non-coding regions, or post-translational modifications
-Experimental methods for ex vivo validation of immunogenic neoantigens
-Insights from preclinical and clinical studies on TCR recognition of antigen-MHC complexes
Application of AI-based methods including large language models and high-throughput experimental techniques are particularly encouraged.
Topic Editor Wouter Scheper acts as a paid consultant for BD Biosciences and Lumicks. The other Topic Editors declare no competing interests with regard to the Research Topic subject
Keywords: Neoantigens, immunogenicity, T cell response, TCR-peptide-MHC complex, cancer vaccines, T- cell engineering
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
