TruNeo: an integrated pipeline improves personalized true tumor neoantigen identification

Yunxia Tang,Yu Wang,Zhibo Gao,Linmin Peng,Guochao Wei,Yixing Zhang,Miao Li,Jin Li,Jiaqian Wang

doi:10.1186/s12859-020-03869-9

Abstract

BackgroundNeoantigen-based personal vaccines and adoptive T cell immunotherapy have shown high efficacy as a cancer treatment in clinical trials. Algorithms for the accurate prediction of neoantigens have played a pivotal role in such studies. Some existing bioinformatics methods, such as MHCflurry and NetMHCpan, identify neoantigens mainly through the prediction of peptide-MHC binding affinity. However, the predictive accuracy of immunogenicity of these methods has been shown to be low. Thus, a ranking algorithm to select highly immunogenic neoantigens of patients is needed urgently in research and clinical practice.ResultsWe develop TruNeo, an integrated computational pipeline to identify and select highly immunogenic neoantigens based on multiple biological processes. The performance of TruNeo and other algorithms were compared based on data from published literature as well as raw data from a lung cancer patient. Recall rate of immunogenic ones among the top 10-ranked neoantigens were compared based on the published combined data set. Recall rate of TruNeo was 52.63%, which was 2.5 times higher than that predicted by MHCflurry (21.05%), and 2 times higher than NetMHCpan 4 (26.32%). Furthermore, the positive rate of top 10-ranked neoantigens for the lung cancer patient were compared, showing a 50% positive rate identified by TruNeo, which was 2.5 times higher than that predicted by MHCflurry (20%).ConclusionsTruNeo, which considers multiple biological processes rather than peptide-MHC binding affinity prediction only, provides prioritization of candidate neoantigens with high immunogenicity for neoantigen-targeting personalized immunotherapies.

Highlights

Neoantigen-based personal vaccines and adoptive T cell immunotherapy have shown high efficacy as a cancer treatment in clinical trials
All possible 8–11-mer amino acid fragments were derived from single nucleotide variants (SNVs), insertions and deletions (InDels) and fusions, MHC binding affinity was predicted using NetMHCpan
Recent studies have shown that neoantigen peptides predicted by current bioinformatic tools such as NetMHCpan or MHCflurry were found on the surface of cells was lower than 5% [34, 35], likely because the training data capture information about only one of multiple steps in the human leukocyte antigen (HLA) class I processing pathway [25]

Summary

Introduction

Neoantigen-based personal vaccines and adoptive T cell immunotherapy have shown high efficacy as a cancer treatment in clinical trials. Some existing bioinformatics methods, such as MHCflurry and NetMHCpan, identify neoantigens mainly through the prediction of peptide-MHC binding affinity. Neoantigens are tumor-specific antigens formed by somatic mutations and are ideal targets for immunotherapy. They are highly immunogenic because they are not expressed in normal tissues and bypass central thymic tolerance. We have developed an integrated pipeline called TruNeo to predict neoantigens by considering the following biological factors: peptide-MHC class I binding affinity, proteasomal C terminal cleavage, transporter associated with antigen processing (TAP) transport efficiency, expression abundance, tumor heterogeneity, clonality and HLA LOH (loss of heterozygosity)

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