Human and mouse ensemble tumor neoantigen prediction from SNVs and complex variants. Immunogenicity filtering based on the Tumor Neoantigen Selection Alliance (TESLA).
Richman LP, Vonderheide RH, and Rech AJ. Neoantigen dissimilarity to the self-proteome predicts immunogenicity and response to immune checkpoint blockade. Cell Systems. 2019.
Duan, F., Duitama, J., Seesi, S.A., Ayres, C.M., Corcelli, S.A., Pawashe, A.P., Blanchard, T., McMahon, D., Sidney, J., Sette, A., et al. Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity. J Exp Med. 2014.
Luksza, M, Riaz, N, Makarov, V, Balachandran VP, et al. A neoepitope fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature. 2017.
Rech AJ, Balli D, Mantero A, Ishwaran H, Nathanson KL, Stanger BZ, Vonderheide RH. Tumor immunity and survival as a function of alternative neopeptides in human cancer. Clinical Cancer Research, 2018.
Wells DK, van Buuren MM, Dang KK, Hubbard-Lucey VM, Sheehan KCF, Campbell KM, Lamb A, Ward JP, Sidney J, Blazquez AB, Rech AJ, Zaretsky JM, Comin-Anduix B, Ng AHC, Chour W, Yu TV, Rizvi1 H, Chen JM, Manning P, Steiner GM, Doan XC, The TESLA Consortium, Merghoub T, Guinney J, Kolom A, Selinsky C, Ribas A, Hellmann MD, Hacohen N, Sette A, Heath JR, Bhardwaj N, Ramsdell F, Schreiber RD, Schumacher TN, Kvistborg P, Defranoux N. Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction. Cell. 2020.
Two methods exist to run antigen.garnish:
- Docker
- Linux
docker pull andrewrech/antigen.garnish:2.3.1
cID=$(docker run -it -d andrewrech/antigen.garnish:2.3.1 /bin/bash)Download netMHC binaries (academic license): NetMHC 4.0, NetMHCpan 4.1b, NetMHCII 2.3, NetMHCIIpan 4.0.
Copy netMHC tar.gz files to the container and run the installation script:
docker cp netMHC-4.0a.Linux.tar.gz $cID:/netMHC-4.0a.Linux.tar.gz
docker cp netMHCII-2.3.Linux.tar.gz $cID:/netMHCII-2.3.Linux.tar.gz
docker cp netMHCpan-4.1b.Linux.tar.gz $cID:netMHCpan-4.1b.Linux.tar.gz
docker cp netMHCIIpan-4.0.Linux.tar.gz $cID:netMHCIIpan-4.0.Linux.tar.gz
docker exec $cID config_netMHC.sh- R ≥ 3.5.0
- Bioconductor Biostrings package
- GNU Parallel
- mhcflurry
- netMHC tools (see below)
- tcsh (required for netMHC, install via your package manager)
Install the dependencies listed above. Then, download and extract antigen.garnish data:
ANTIGEN_GARNISH_DIR="~/antigen.garnish"
cd ~
curl -fsSL "https://s3.amazonaws.com/get.rech.io/antigen.garnish-2.3.0.tar.gz" | tar -xvz
chmod 700 -R "$ANTIGEN_GARNISH_DIR"Install antigen.garnish:
# install.packages("remotes")
remotes::install_github("andrewrech/antigen.garnish")Next, download netMHC binaries (academic license): NetMHC 4.0, NetMHCpan 4.1b, NetMHCII 2.3, NetMHCIIpan 4.0.
Move the binaries into the antigen.garnish data directory, first setting the NET_MHC_DIR and ANTIGEN_GARNISH_DIR environment variables:
NET_MHC_DIR=/path/to/folder/containing/netMHC/downloads
cd "$NET_MHC_DIR"
mkdir -p "$ANTIGEN_GARNISH_DIR/netMHC"
tar xvzf netMHC-4.0a.Linux.tar.gz -C "$ANTIGEN_GARNISH_DIR/netMHC"
tar xvzf netMHCII-2.3.Linux.tar.gz -C "$ANTIGEN_GARNISH_DIR/netMHC"
tar xvzf netMHCpan-4.1b.Linux.tar.gz -C "$ANTIGEN_GARNISH_DIR/netMHC"
tar xvzf netMHCIIpan-4.0.Linux.tar.gz -C "$ANTIGEN_GARNISH_DIR/netMHC"
chown "$USER" "$ANTIGEN_GARNISH_DIR/netMHC"
chmod 700 -R "$ANTIGEN_GARNISH_DIR/netMHC"See the reference manual.
Copy GRCh38-annotated VCF files and/or metadata including HLA alleles onto the running container using the docker cp command. The container ID is still saved as $cID from the installation above. You will also need to use this command and container ID to copy saved output files from the docker container after you complete your analysis.
Copy any needed files onto the running container, for example:
docker cp myfile.txt $cID:/myfilecopy.txt
Now launch the interactive virtual machine with the container you started:
docker exec -it $cID bash
R
library(antigen.garnish)
Follow the instructions in the next section titled Linux to complete your interactive R analysis. When you complete your analysis, copy any desired output files off the container to your local machine with the docker cp command. Shut down and clean up your container like this:
docker cp $cID:/myoutput.txt ~/myagdockeroutput.txt
docker stop $cID
docker rm $cID
Parallel cores used can be set via environment variable AG_THREADS (default: all available).
library(magrittr)
library(data.table)
library(antigen.garnish)
# load an example VCF
dir <- system.file(package = "antigen.garnish") %>%
file.path(., "extdata/testdata")
file <- file.path(dir, "TUMOR.vcf")
# extract variants
dt <- garnish_variants(file)
# add space separated MHC types
# see list_mhc() for nomenclature of supported alleles
# MHC may also be set to "all_human" or "all_mouse" to use all supported alleles
dt[, MHC := c("HLA-A*01:47 HLA-A*02:01 HLA-DRB1*14:67")]
# predict neoantigens
result <- dt %>% garnish_affinity(.)
result %>% strTranscript ID level input table format:
# sample_id ensembl_transcript_id cDNA_change MHC
# sample_1 ENST00000311936 c.718T>A HLA-A*02:01 HLA-A*03:01
# sample_1 ENST00000311936 c.718T>A H-2-Kb H-2-Kb
Protein level input (with optional WT paired input) table format:
# sample_id pep_mut pep_wt mutant_index MHC
# sample_1 MTEYKLVVVDADGVGK MTEYKLVVVDAGGVGK 12 HLA-A*02:01
# sample_1 MTEYKLVVVDDDGVGK MTEYKLVVVDAGGVGK 12 13 HLA-A*02:01
# sample_1 MTEYKLVVVDAGGAAA MTEYKLVVVDAGGVGK 14 15 16 HLA-A*02:01
# sample_1 SIINFEKLMILKATFI MTEYKLVVVDAGGVGK all HLA-A*02:01
library(magrittr)
library(data.table)
library(antigen.garnish)
library(rio) # package to import Excel and other tables
# load an example table
dir <- system.file(package = "antigen.garnish") %>%
file.path(., "extdata/testdata")
file <- file.path(dir, "antigen.garnish_example_peptide_with_WT_input.txt")
# read in excel or other format file with rio::import and convert to data table
# or substitute the path to your file here
mytable <- rio::import(file) %>% data.table::as.data.table()
# only use first two rows of table for example
mytable <- mytable[1:2]
# predict neoantigens from data table object
result <- garnish_affinity(mytable)
result %>% strlibrary(magrittr)
library(data.table)
library(antigen.garnish)
# generate our character vector of sequences
v <- c("SIINFEKL", "ILAKFLHWL", "GILGFVFTL")
# calculate foreignness score
v %>% foreignness_score(db = "human") %>% print
# calculate dissimilarity
v %>% dissimilarity_score(db = "human") %>% printlibrary(magrittr)
library(data.table)
library(antigen.garnish)
data.table::data.table(
pep_base = "Y___*___THIS_IS_________*___A_PEPTIDE_TEST!______*__X",
mutant_index = c(5, 25, 47, 50),
pep_type = "test",
var_uuid = c(
"front_truncate",
"middle",
"back_truncate",
"end")) %>%
make_nmers %>% printWe thank the follow individuals for contributions and helpful discussion:
Please see LICENSE.
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