Overview

Welcome to the documentation of NeoFox!

About NeoFox

Neoantigens are tumor-specific antigens encoded by somatic mutations. Their break down products (neoepitopes) are presented by the major histocompatibility complex (MHC) on the surface of tumor cells enabling T cells to recognize these neoepitope sequences as foreign. This neoantigen-specific T-cell recognition may induce a potent anti-tumoral response which makes neoantigens highly interesting targets for cancer immunotherapy. Conventionally,
neoantigen candidates are predicted by mutation calling from tumor and normal genome sequencing, non-synonymous mutations are selected and translated amino acid sequences. For the final step, algorithms that predict the likelihood of a neoantigen candidate to be a true neoantigen are required.
Several neoantigen features that describe the ability of a neoantigen candidate to induce a T-cell response have been published in the last years.

NeoFox (NEOantigen Feature toolbOX) is a python package that annotates a given set of neoantigen candidate sequences with relevant neoantigen features. The annotation of neoepitope candidates is supported from NeoFox version 1.0.0. NeoFox supports annotation of neoantigen candidates derived from SNVs (single nucleotide variant) and alternative mutation classes such as INDELs or fusion genes. Furthermore, NeoFox supports both human and mouse derived neoantigen candidates.

NeoFox covers neoepitope prediction by MHC binding and ligand prediction, similarity/foreignness of a neoepitope candidate sequence, combinatorial features and machine learning approaches. A list of implemented features and their references are given in Table 1. Please note that some features are currently not available for mouse.

Table 1: Neoantigen features and prioritization algorithms (§ currently not supported for mouse)

Name	Reference	DOI
MHC I binding affinity/rank score (netMHCpan-v4.1)	Reynisson et al., 2020, Nucleic Acids Res.	https://doi.org/10.1093/nar/gkaa379
MHC II binding affinity/rank score (netMHCIIpan-v4.3)	Nilsson et al., 2023, Science Adv.	https://doi.org/10.1126/sciadv.adj6367
MixMHCpred score v2.2 §	Bassani-Sternberg et al., 2017, PLoS Comp Bio; Gfeller, 2018, J Immunol.	https://doi.org/10.1371/journal.pcbi.1005725 , https://doi.org/10.4049/jimmunol.1800914
MixMHC2pred score v2.0.2 §	Racle et al., 2019, Nat. Biotech. 2019	https://doi.org/10.1038/s41587-019-0289-6
Differential Agretopicity Index (DAI)	Duan et al., 2014, JEM; Ghorani et al., 2018, Ann Oncol.	https://doi.org/10.1084/jem.20141308
Self-Similarity	Bjerregaard et al., 2017, Front Immunol.	https://doi.org/10.3389/fimmu.2017.01566
IEDB immunogenicity	Calis et al., 2013, PLoS Comput Biol.	https://doi.org/10.1371/journal.pcbi.1003266
Neoantigen dissimilarity	Richman et al., 2019, Cell Systems	https://doi.org/10.1016/j.cels.2019.08.009
PHBR-I §	Marty et al., 2017, Cell	https://doi.org/10.1016/j.cell.2017.09.050
PHBR-II §	Marty Pyke et al., 2018, Cell	https://doi.org/10.1016/j.cell.2018.08.048
Generator rate	Rech et al., 2018, Cancer Immunology Research	https://doi.org/10.1158/2326-6066.CIR-17-0559
Recognition potential §	Łuksza et al., 2017, Nature; Balachandran et al, 2017, Nature	https://doi.org/10.1038/nature24473 , https://doi.org/10.1038/nature24462
Vaxrank	Rubinsteyn, 2017, Front Immunol	https://doi.org/10.3389/fimmu.2017.01807
Priority score	Bjerregaard et al., 2017, Cancer Immunol Immunother.	https://doi.org/10.1007/s00262-017-2001-3
PRIME v2.0 §	Schmidt et al., 2021, Cell Reports Medicine	https://doi.org/10.1016/j.xcrm.2021.100194
HEX §	Chiaro et al., 2021, Cancer Immunology Research	https://doi.org/10.1158/2326-6066.CIR-20-0814

Besides comprehensive annotation of neoantigen candidates, NeoFox creates biologically meaningful representations of neoantigens and related biological entities as programmatic models. For this purpose, Protocol buffers is employed to model Neofox’s input and output data: neoantigens, patients, MHC alleles and neoantigen feature annotations (Figure 1).

Figure 1

For detailed information about the required input data, output data and usage please refer to the User guide.

The data models are described in more detail here.

Happy annotation and modelling!

Contact information

For questions, please contact Franziska Lang (franziska.lang@tron-mainz.de) or Pablo Riesgo Ferreiro (pablo.riesgoferreiro@tron-mainz.de).

How to cite

Franziska Lang, Pablo Riesgo-Ferreiro, Martin Löwer, Ugur Sahin, Barbara Schrörs, NeoFox: annotating neoantigen candidates with neoantigen features, Bioinformatics, Volume 37, Issue 22, 15 November 2021, Pages 4246–4247, https://doi.org/10.1093/bioinformatics/btab344