Mouse Stromal Cells Confound Proteomic Characterization and Quantification of Xenograft Models

Area of Science:

• Proteomics
• Cancer Biology
• Xenograft Models

Background:

• Xenografts are crucial for cancer research and drug development, often analyzed with omics data.
• Proteomic profiling of xenografts typically involves analyzing mixed human and mouse cells, relying on algorithms for protein assignment.

Purpose of the Study:

• To evaluate the performance of algorithms used for assigning peptides to human and mouse proteins in xenograft samples.
• To determine the accuracy of differential protein expression analysis in xenografts when human and mouse cells are not separated.

Main Methods:

• Benchmark studies using mixtures of human and mouse cell lines.
• Proteomic profiling of liver patient-derived xenograft (PDX) models.
• Evaluation of three major algorithms for human/mouse protein assignment.

Main Results:

• Approximately 50% of peptides are common to both human and mouse proteins, challenging accurate algorithmic assignment.
• Significant numbers of false differentially expressed proteins (DEPs) were identified, particularly with increasing mouse cell percentages.
• 30-40% of DEPs in PDX models were false positives, and 20% of true DEPs were missed when mouse stromal cells were not removed.

Conclusions:

• Current algorithms struggle to accurately distinguish human from mouse proteins in xenografts due to shared peptides.
• Separating human and mouse cells prior to proteomic profiling is essential for reliable and accurate species-specific protein expression analysis in xenografts.
• The 'separate-then-run' approach is recommended over 'run-then-separate' for improved xenograft proteomic profiling.