nextNEOpi

nextNEOpi predicts neoantigens from raw DNA and RNA sequencing data by identifying tumor-specific somatic mutations and gene fusions and assessing their potential immunogenicity.

Key Features:

• Input data: Processes raw DNA and RNA sequencing data as the basis for analysis.
• Variant detection: Identifies tumor-specific somatic mutations from sequencing data.
• Gene fusion detection: Detects gene fusions that can generate neoantigenic peptides.
• Peptide derivation: Derives peptides (neoepitopes) resulting from somatic alterations and fusions.
• HLA typing: Infers patients' Human Leukocyte Antigen (HLA) types from sequencing data.
• Binding prediction: Predicts binding affinity of neoepitopes to HLA molecules.
• Immunogenicity evaluation: Evaluates features associated with neoepitope immunogenicity and their potential impact on immunotherapy response.
• Quantification: Quantifies neoepitope- and patient-specific characteristics relevant to tumor immunogenicity.
• Automation: Implements an automated pipeline to integrate the computational steps for neoantigen prediction.

Scientific Applications:

• Neoantigen discovery: Computational prediction of neoantigens arising from somatic mutations and gene fusions.
• Tumor immunogenicity assessment: Characterization and quantification of features that reflect tumor immunogenicity.
• Immunotherapy response analysis: Evaluation of neoepitope features that may impact patient response to immunotherapy.
• Personalized cancer treatment: Informing strategies for personalized cancer immunotherapy based on predicted neoantigens.

Methodology:

Processes raw DNA and RNA sequencing data to identify somatic mutations and gene fusions, derive resulting peptides (neoepitopes), infer patients' HLA types, predict neoepitope–HLA binding affinity, evaluate immunogenicity-associated features, and quantify neoepitope- and patient-specific characteristics.