NeoPredPipe: high-throughput neoantigen prediction and recognition potential pipeline

Ryan O Schenck,Chandler Gatenbee,Alexander R.A Anderson,Trevor A Graham,Eszter Lakatos

doi:10.1186/s12859-019-2876-4

Abstract

BackgroundNext generation sequencing has yielded an unparalleled means of quickly determining the molecular make-up of patient tumors. In conjunction with emerging, effective immunotherapeutics for a number of cancers, this rapid data generation necessitates a paired high-throughput means of predicting and assessing neoantigens from tumor variants that may stimulate immune response.ResultsHere we offer NeoPredPipe (Neoantigen Prediction Pipeline) as a contiguous means of predicting putative neoantigens and their corresponding recognition potentials for both single and multi-region tumor samples. NeoPredPipe is able to quickly provide summary information for researchers, and clinicians alike, on predicted neoantigen burdens while providing high-level insights into tumor heterogeneity given somatic mutation calls and, optionally, patient HLA haplotypes. Given an example dataset we show how NeoPredPipe is able to rapidly provide insights into neoantigen heterogeneity, burden, and immune stimulation potential.ConclusionsThrough the integration of widely adopted tools for neoantigen discovery NeoPredPipe offers a contiguous means of processing single and multi-region sequence data. NeoPredPipe is user-friendly and adaptable for high-throughput performance. NeoPredPipe is freely available at https://github.com/MathOnco/NeoPredPipe.

Highlights

Generation sequencing has yielded an unparalleled means of quickly determining the molecular make-up of patient tumors
Neoantigens, epitopes derived from proteins translated from non-synonymous variants, are able to make their way to the cell surface in the hopes of stimulating an immune response after a number of cellular processing steps have occurred, primarily proteosomal cleavage and binding with major histocompatibility complexes (MHC) I or II
The bound neoantigen with its MHC-Class I complex makes its way to the cell surface where it may bind with cytotoxic Tcell receptors thereby eliciting infiltration of cytotoxic T-cells capable of detecting and eliminating cells carrying the neoantigen in the absence of immune evading tactics

Summary

Results

The output of the pipeline depends largely on the options set by the user, but at the very least, NeoPredPipe provides two tables of putative neoantigens and their predicted binding affinities, one for single nucleotide/amino acid, and one for indel(-type) variants. For rapid assessment, NeoPredPipe yields summary statistics on the neoantigen burden for each sample, a rapidly executed webbased visualization, as well as information to assess ITH by reporting neoantigen burdens for clonal, subclonal, and shared variants for multi-region samples. 31% and 23.5% of total exonic mutations are clonal in Carcinoma 7 and Adenoma 3, to the clonality of neoantigens shown in Panel B. The analysis can inform future experimental and bioinformatic investigations of samples allowing for new evolutionary and mechanistic insights into tumor development, evolution, and progression

Conclusions

Background