LENS

LENS identifies and prioritizes tumor-specific and tumor-associated peptide epitopes from DNA and RNA sequencing data to predict T cell recognition for cancer immunotherapy research.

Key Features:

• Input data types: Processes DNA- and RNA-sequencing data as input for neoantigen discovery.
• Broad genomic source analysis: Analyzes single nucleotide variants (SNVs), insertions and deletions (indels), fusion events, splice variants, cancer-testis antigens, overexpressed self-antigens, viruses, and endogenous retroviruses as potential antigen sources.
• Peptide epitope prediction: Predicts peptide epitopes that can be recognized by T cells and generates immunogenicity predictions.
• Expression harmonization: Harmonizes relative expression levels across multiple genomic sources when evaluating candidate neoantigens.
• Modularity and extensibility: Uses a modular architecture that enables integration of additional genomic data sources and analysis components.
• Workflow management: Implements analyses as a Nextflow DSL2 workflow.

Scientific Applications:

• Neoantigen discovery in low-mutation tumors: Identifies alternative sources of tumor-specific epitopes for cancers characterized by few somatic variants.
• Candidate epitope prioritization for immunotherapy: Combines expression and immunogenicity evaluations to prioritize peptide targets for T cell–based interventions.
• Cohort-level analysis: Applied to an analysis of 115 acute myeloid leukemia samples to identify candidate neoantigens.

Methodology:

Implemented as a Nextflow DSL2 workflow that processes DNA and RNA sequencing from multiple genomic sources, predicts peptide epitopes and immunogenicity, and harmonizes relative expression levels across sources.