Credentialing Individual Samples for Proteogenomic Analysis.

Area of Science:

• Proteogenomics
• Molecular Biology
• Bioinformatics

Background:

• Integrated analysis of DNA, RNA, and protein (proteogenomics) enhances understanding of physiology and disease.
• Lack of systematic sample credentialing in proteogenomics introduces noise, hindering biological signal identification.
• A previous study found 26% of samples unsatisfactory, increasing costs and information loss.

Purpose of the Study:

• To propose a robust metric for credentialing individual samples in integrated proteogenomic studies.
• To address the challenge of sample quality control in large-scale proteogenomic analyses.

Main Methods:

• Analysis of large-scale RNA sequencing (RNA-seq) and proteomic data (RPPA and mass spectrometry).
• Development and application of a protein-mRNA correlation-based (PMC) score.
• Evaluation of sample credentialing based on PMC scores.

Main Results:

• The proposed protein-mRNA correlation-based (PMC) score effectively credentials single samples for proteogenomic analysis.
• Samples with high PMC scores exhibit significantly higher protein-mRNA correlation.
• High PMC scores correlate with increased total protein content and tumor purity.

Conclusions:

• The PMC score is a robust metric for ensuring sample quality in proteogenomic studies.
• Credentialing individual samples using the PMC score is crucial before proteogenomic analysis.
• This approach minimizes noise and maximizes the identification of important biological signals.