garnish_antigens: List top peptides using TESLA criteria for recognition...

In andrewrech/antigen.garnish: Tumor neoantigen prediction

List top peptides using TESLA criteria for recognition features of immunogenic peptides.

Description

The TESLA consortium identified recognition features of immunogenic peptides. This function filters peptides meeting any of these criteria.

Usage

garnish_antigens(
  dt,
  affinity_threshold = 34,
  differential_agretopcity_threshold = 10,
  dissimilarity_threshold = 0,
  foreignness_threshold = 1e-15
)

Arguments

dt	An output data table from garnish_affinity, either a data table object or path to a file.
affinity_threshold	Numeric. Neoantigen affinity threshold, nanomolar (nM) scale.
differential_agretopcity_threshold	Numeric. Neoantigen differential agretopcity threshold. Differential agretopicty is the proteome-wide ratio of MHC binding afinity between mutant and closest normal peptide, with higher values indicating greater relative binding of the mutant peptide.
dissimilarity_threshold	Numeric. Neoantigen dissimilarity threshold. Value of 0 to 1 indicating alignment to the self-proteome, calculated in an analogous manner to neoanigen foreignness, with 1 indicating greater dissimilarity.
foreignness_threshold	Numeric. Neoantigen foreignness threshold. Value of 0 to 1 indicating the TCR recognition probability, calculated by summing alignments in IEDB immunogenic peptides, with 1 indicating greater homology to immunogenic peptides.

Value

A data table with ranked and annotated peptides.

References

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.

See Also

garnish_variants

garnish_affinity

andrewrech/antigen.garnish documentation built on July 8, 2022, 5:19 p.m.