Elimination of cancer cells by T cells is a critical mechanism of antitumor immunity and cancer immunotherapy response. T cells recognize cancer cells via engagement of T cell receptors with peptide epitopes presented by major histocompatibility complex (MHC) molecules on the cancer cell surface. Peptide epitopes can be derived from antigen proteins coded for by multiple genomic sources. Bioinformatics tools used to identify tumor-specific epitopes via analysis of DNA and RNA sequencing data have largely focused on epitopes derived from somatic variants, though a smaller number have evaluated potential antigens from other genomic sources. We report here an open-source workflow utilizing the Nextflow DSL2 workflow manager, Landscape of Effective Neoantigen Software (LENS), which predicts tumor-specific and tumor-associated antigens from single nucleotide variants (SNVs), insertions and deletions (InDels), fusion events, splice variants, cancer testis antigens (CTAs), overexpressed self-antigens, viruses, and human endogenous retroviruses (hERVs). The main advantage of LENS is that it extends the breadth of genomic sources of tumor antigens that may be discovered using genomics data. Other advantages include modularity, extensibility, ease of use, incorporation of phasing and germline variant information in epitope identification, and harmonization of relative expression level and immunogenicity prediction across multiple genomic sources. Current limitations include lack of support for class II MHC epitope predictions and advanced visualization features. To demonstrate the utility of LENS, we present an analysis of predicted antigen landscape in 115 acute myeloid leukemia (AML) samples. We expect that LENS will be a valuable platform and resource for T cell epitope discovery bioinformatics, especially in cancers with few somatic variants and increased importance of tumor-specific epitopes from alternative genomic sources.
LENS - Landscape of Effective Neoantigens Software