Deciphering Phenotypes from Protein Biomarkers for Translational Research with PIPER

Area of Science:

• Biochemistry
• Proteomics
• Clinical Diagnostics

Background:

• Liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM) is crucial for clinical assays, including disease detection and drug monitoring.
• LC-MRM quantifies peptides as surrogates for protein biomarkers, essential for cancer research and understanding biological pathways.
• Translating research-grade LC-MRM panels to clinical applications requires robust data visualization and interpretation tools.

Purpose of the Study:

• To develop a web-based tool for targeted proteomics data visualization and interpretation.
• To empower translational researchers in moving protein biomarker panels from discovery to clinical use.
• To facilitate pathway-level evaluations of biological drivers and phenotypic signatures.

Main Methods:

• Development of a web-based application for targeted proteomics data analysis.
• Integration of pathway-level evaluations, signature scores (phenotypes), and drug target quantification.
• Framework for integrating summary information, decision algorithms, and risk scores.

Main Results:

• A novel web tool facilitates the interpretation of targeted proteomics data.
• The tool enables pathway-level analysis of key biological drivers (e.g., EGFR signaling).
• It provides signature scores for phenotypes (e.g., EMT) and quantifies drug targets across cohorts.

Conclusions:

• The developed tool supports Physician-Interpretable Phenotypic Evaluation in R (PIPER).
• This framework can be reused or repurposed for communicating and interpreting biomarker panels.
• It bridges the gap between proteomics research and clinical application, enhancing diagnostic capabilities.