LENS: Landscape of Effective Neoantigens Software

Abstract

Motivation Elimination of cancer cells by T cells is a critical mechanism of anti-tumor immunity and cancer immunotherapy response. T cells recognize cancer cells by 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. Results 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, insertions and deletions, fusion events, splice variants, cancer testis antigens, overexpressed self-antigens, viruses, and endogenous retroviruses. The primary advantage of LENS is that it expands the breadth of genomic sources of discoverable tumor antigens using genomics data. Other advantages include modularity, extensibility, ease of use, and harmonization of relative expression level and immunogenicity prediction across multiple genomic sources. We present an analysis of 115 acute myeloid leukemia (AML) samples to demonstrate the utility of LENS. We expect LENS will be a valuable platform and resource for T cell epitope discovery bioinformatics, especially in cancers with few somatic variants where tumor-specific epitopes from alternative genomic sources are an elevated priority. Availability More information about LENS, including code, workflow documentation, and instructions, can be found at {https://gitlab.com/landscape-of-effective-neoantigens-software}. Supplementary information Supplementary data are available at Bioinformatics online.

Historical perspective and future directions: computational science in immuno-oncology

“… 178–180 Other groups have created pipelines that typically include a combination of mutation calling, HLA typing, MHC binding prediction, and neoantigen filtering or ranking ( figure 2B ). Workflows for predicting class I neoantigens include MuPeXI, 181 Neopepsee, 179 pTuneos, 182 NeoPredPipe, 183 and LENS, 184 and workflows that can predict both class I and II neoantigens include NeoFuse, 185 nextNEOpi, 186 and pVACtools 187 ( table 4 ). Several recent studies have begun experimentally validating the T cell reactivity of computationally predicted neoantigens.…”

Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens

“…The Landscape of Effective Neoantigens Software (LENS) offers predictions for tumorspecific and tumor-associated antigens, considering diverse genomic alterations such as single nucleotide variants, insertions and deletions, fusion events, splice variants, cancertestis antigens, overexpressed self-antigens, as well as viral and endogenous retroviral elements [18]. It is a pipeline of over two thousand separate tools for predicting the full suite of tumor antigens from genomics data.…”

INAEME: Integral Neoantigen Analysis with Entirety of Mutational Events

“…Protein sequences translated from positions around altered amino acids represent neoantigen candidates. For this purpose, a number of bioinformatic analyses (workflows) for neoantigen discovery using NGS data have been developed [9][10][11][12][13][14][15]. The differences between solutions mostly lie in bioinformatic tools being used and mutational events that are considered.…”