SPENCER

SPENCER catalogs small peptides encoded by noncoding RNAs (ncRNAs) from re-annotated mass spectrometry (MS) data to support analysis of differential expression and immunogenic potential of ncRNA-encoded peptides (ncPEPs) in cancer.

Key Features:

• Extensive Data Collection: Aggregates re-annotated MS data from 55 studies encompassing 1,007 tumor samples and 719 normal samples (over 1,700 patient samples) across diverse cancers.
• Identification of ncPEPs: Uses an MS-based proteomics analysis pipeline to identify 29,526 ncRNA-encoded small peptides across 15 cancer types, of which 22,060 have been experimentally validated in other studies.
• Expression Grouping: Classifies ncPEPs into tumor-specific, upregulated in cancer, downregulated in cancer, and others to support comparative expression analyses.
• Immunogenicity Prediction: Predicts immunogenicity by evaluating MHC-I binding affinity, stability, and TCR recognition probability, and highlights 4,497 ncPEPs predicted to be immunogenic.

Scientific Applications:

• Regulatory mechanism investigation: Enables study of the regulatory roles of ncRNA-encoded peptides in oncogenesis by providing identified ncPEPs and their expression patterns.
• Differential expression analysis: Supports comparative analyses between tumor and normal samples using aggregated MS-derived expression data.
• Neoantigen and immunotherapy research: Facilitates prioritization of ncPEPs with predicted MHC-I binding, stability, and TCR recognition for neoantigen-based cancer immunotherapy studies.
• Target prioritization for validation: Provides candidate ncPEPs for experimental validation and subsequent therapeutic development based on identification, expression grouping, and immunogenicity predictions.

Methodology:

Re-annotation of mass spectrometry (MS) data from over 1,700 patient samples across 55 studies; MS-based proteomics analysis pipeline for ncPEP identification; immunogenicity prediction based on MHC-I binding affinity, stability, and TCR recognition probability.