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LENS: Landscape of Effective Neoantigens Software
Steven P Vensko1, Kelly Olsen2, Dante Bortone1
1Lineberger Comprehensive Cancer Center, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States.
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 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 Neoantigens 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 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 And Implementation:
More information about LENS, including code, workflow documentation, and instructions, can be found at (https://gitlab.com/landscape-of-effective-neoantigens-software).
Insights
The Landscape of Effective Neoantigens Software (LENS) workflow identifies diverse tumor antigens for T cell epitope discovery. This bioinformatics tool expands antigen sources beyond somatic variants, aiding cancer immunotherapy research.
Area of Science:
- Bioinformatics
- Immunology
- Genomics
Background:
- T cell-mediated elimination of cancer cells is crucial for anti-tumor immunity and immunotherapy response.
- T cells recognize cancer cells via T cell receptors binding to peptide epitopes presented by major histocompatibility complex molecules.
- Current bioinformatics tools primarily focus on somatic variants for tumor-specific epitope identification, overlooking other genomic sources.
Purpose of the Study:
- To introduce an open-source workflow, Landscape of Effective Neoantigens Software (LENS), for predicting tumor-specific and tumor-associated antigens.
- To expand the range of genomic sources for neoantigen discovery beyond traditional methods.
- To provide a versatile and user-friendly platform for T cell epitope discovery.
Main Methods:
- Utilized Nextflow DSL2 workflow manager for an open-source bioinformatics workflow.
- Integrated analysis of diverse genomic sources including single nucleotide variants, insertions/deletions, fusion events, splice variants, cancer-testis antigens, overexpressed self-antigens, viruses, and endogenous retroviruses.
- Developed LENS to harmonize relative expression level and immunogenicity predictions across these sources.
Main Results:
- LENS successfully predicts tumor antigens from a broader spectrum of genomic sources.
- Demonstrated the utility of LENS through an analysis of 115 acute myeloid leukemia samples.
- Highlighted advantages including modularity, extensibility, and ease of use.
Conclusions:
- LENS significantly expands the breadth of discoverable tumor antigens by incorporating diverse genomic sources.
- The workflow is expected to be a valuable resource for T cell epitope discovery bioinformatics.
- LENS is particularly relevant for cancers with limited somatic variants, prioritizing alternative genomic sources for tumor-specific epitopes.
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