bioRxivCancer Biology
LENS - Landscape of Effective Neoantigens Software
S. P. Vensko, K. Olsen, D. S. Bortone, C. C. Smith, S. Chai, W. Beckabir, M. Fini, O. Jadi, A. Rubinsteyn, B. Vincent
Abstract
MotivationElimination 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. ResultsWe 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. AvailabilityMore information about LENS, including workflow documentation and instructions, can be found at https://gitlab.com/landscape-of-effective-neoantigens-software Contactsteven_vensko@med.unc.edu, benjamin_vincent@med.unc.edu Supplementary informationSupplementary data are available at Bioinformatics online.
- organism: human
- assay: whole-exome sequencing
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
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