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. 2019 May 22;20(1):264.
doi: 10.1186/s12859-019-2876-4.

NeoPredPipe: high-throughput neoantigen prediction and recognition potential pipeline

Ryan O Schenck  1   2 , Eszter Lakatos  3 , Chandler Gatenbee  4 , Trevor A Graham  3 , Alexander R A Anderson  4
Affiliations

NeoPredPipe: high-throughput neoantigen prediction and recognition potential pipeline

Ryan O Schenck et al. BMC Bioinformatics. .

Abstract

Background: Next 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.

Results: Here 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.

Conclusions: Through 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 .

Keywords: Cancer; Evolution; Heterogeneity; Neoantigens; Next-generation sequencing.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
NeoPredPipe workflow differentiating between user steps (green) and execution processes (purple). NeoPredPipe provides low level details and high level summary statistics as output for downstream analysis (red)
Fig. 2
Fig. 2
Analysis of neoantigens in two colorectal tumors using NeoPredPipe. a Venn diagram of all neoantigens in the five regions of Adenoma 3. b Number of neoantigens in the two samples that are clonal (present in all regions, shown in blue), shared (present in at least two regions, in yellow) or subclonal (present in a single region, red). Separate counts of weak and strong MHC-binding neoantigens (WB and SB, respectively) are also shown. c Distribution of recognition potential values of neoantigens present in Adenoma 3 (green) and Carcinoma 7 (red). The boxplots represent the median and upper and lower 25 percentile. Only neoantigens with recognition potential higher than zero are shown. d Phylogenetic tree reconstructed from all exonic mutations for Adenoma 3 (left) and Carcinoma 7 (right). Pie-charts and the bar-charts represent the number of weak (orange) and strong (red) binder neoantigens assigned to each branch. The size of each circle is proportional to the percentage of total neoantigens on that branch
See this image and copyright information in PMC

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