Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

Abstract

Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens.

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Cited by papers we hold 4

• MSBooster: improving peptide identification rates using deep learning-based features · 2023
  No notice
• Protein evidence of unannotated ORFs in Drosophila reveals diversity in the evolution and properties of young proteins · 2022
  Not checked
• AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics · 2022
  No notice
• Deep Learning in Proteomics · 2020
  No notice

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