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Updated: Jun 12, 2026
Ultra-Fast Amplicon-Based Next-Generation Sequencing in Non-Squamous Non-Small Cell Lung Cancer
Published on: September 8, 2023
Immunopeptidomics-guided identification of functional neoantigens in non-small cell lung cancer
Ben Nicholas1,2, Alistair Bailey3,4, Katy J McCann5
1Centre for Proteomic Research, Biological Sciences and Institute for Life Sciences, University of Southampton, Southampton, UK. bln1@soton.ac.uk.
Abstract:
Non-small cell lung cancer (NSCLC) has poor survival even with modern checkpoint inhibitor therapies. Personalised vaccines based on short peptide neoantigens containing tumour mutations are an attractive precision medicine strategy, but identifying therapeutically relevant neoantigens remains challenging, with existing methods yielding positive responses in only 6% of candidates tested. We developed an immunopeptidomics approach to improve neoantigen identification in 24 NSCLC patients (15 adenocarcinoma, 9 squamous cell carcinoma). We directly identified one neoantigen and using whole exome sequencing, transcriptomics and mass spectrometry-based immunopeptidomics, we filtered predicted neoantigens based on observed cohort HLA peptide presentation. This approach achieved positive functional responses in 5 of 6 patients tested (83% success rate) with 13% of putative neoantigens (9 out of 70) eliciting strong responses. Bayesian modelling of our initial rules-based neoantigen selection further revealed patient specific peptide presentation patterns and propensities. Our findings demonstrate that incorporating donor-specific HLA peptide presentation data substantially improves neoantigen identification success rates and immune response specificity, advancing personalised cancer vaccine development.
Insights
Identifying effective neoantigens for personalized cancer vaccines is crucial for non-small cell lung cancer (NSCLC) treatment. Our immunopeptidomics approach significantly improves neoantigen identification and response rates in patients.
Area of Science:
- Oncology
- Immunology
- Genomics
Background:
- Non-small cell lung cancer (NSCLC) exhibits poor survival rates despite current immunotherapies.
- Personalized vaccines targeting tumor-specific neoantigens offer a precision medicine approach.
- Current methods for neoantigen identification have limited success rates.
Purpose of the Study:
- To develop and validate an immunopeptidomics approach for improved neoantigen identification in NSCLC patients.
- To enhance the success rate of personalized cancer vaccine development by refining neoantigen selection.
Main Methods:
- Employed whole exome sequencing, transcriptomics, and mass spectrometry-based immunopeptidomics.
- Filtered predicted neoantigens based on patient-specific HLA peptide presentation data.
- Utilized Bayesian modeling to analyze peptide presentation patterns and propensities.
Main Results:
- Achieved an 83% success rate (5 out of 6 patients) in eliciting positive functional immune responses.
- Identified specific neoantigens, with 13% (9 out of 70) showing strong immunogenicity.
- Demonstrated that incorporating HLA peptide presentation data improves neoantigen identification and response specificity.
Conclusions:
- The developed immunopeptidomics strategy significantly enhances neoantigen identification for personalized cancer vaccines.
- This approach advances the development of effective and specific personalized cancer vaccines for NSCLC.
- Patient-specific HLA peptide presentation data is critical for improving neoantigen-guided vaccine efficacy.
