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QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in
Harsha P Gunawardena1, Jonathon O'Brien2, John A Wrobel1
1From the ‡Department of Biochemistry and Biophysics, §Lineberger Comprehensive Cancer Center, and.
Molecular & Cellular Proteomics : MCP
|November 25, 2015
Summary
QuantFusion integrates label-free and label-based proteomics to enhance protein quantification accuracy and coverage. This novel method improves differential protein expression analysis in complex samples like breast cancer xenografts.
Area of Science:
- Proteomics
- Quantitative Mass Spectrometry
- Cancer Biology
Background:
- Existing single quantitative platforms (label-based and label-free quantitation) have limitations in accuracy, precision, and protein coverage.
- Maximizing quantitative precision and the number of quantifiable proteins is crucial for comprehensive proteome analysis.
- Patient-derived tumor xenografts (PDXs) are valuable models for studying cancer subtypes like basal and luminal breast cancer.
Purpose of the Study:
- To develop and demonstrate a unified approach, QuantFusion, for combining label-free and label-based quantitative proteomics data.
- To improve the accuracy, precision, and coverage of quantifiable proteins in tissue proteomes.
- To identify differentially expressed proteins between basal and luminal breast cancer subtypes using PDX models.
Main Methods:
- Developed QuantFusion, a method that integrates quantitative peptide ratios from both label-free quantitation (LFQ) and label-based methodologies (amino acid-coded tagging, AACT/SILAC).
- Utilized Ratio-of-Ratio estimates for label-free peptides paired with AACT peptides in PDX tumors.
- Employed mixed model statistical analysis to combine complementary peptide ratios from LFQ and Ratio-of-Ratios for differential expression analysis.
Main Results:
- QuantFusion increased the number of distinct peptide ratios by 65% in PDX tumor proteome analysis.
- The method improved quantifiable coverage, increasing measurable protein fold-changes by 8% and average quantitative precision by 181%.
- QuantFusion successfully rescued missing data and identified differentially expressed proteins between breast cancer subtypes.
Conclusions:
- QuantFusion offers a unique strategy for enhancing quantitative precision and coverage of tissue proteomes by integrating multiple quantitative approaches.
- The method effectively addresses data limitations inherent in individual LFQ and label-based quantitation techniques.
- QuantFusion provides a robust platform for identifying differentially expressed proteins, crucial for understanding complex diseases like breast cancer.
