Title
SPA: A Quantitation Strategy for MS Data in Patient-derived Xenograft Models
Abstract
With the development of mass spectrometry (MS)-based proteomics technologies, patient-derived xenograft (PDX), which is generated from the primary tumor of a patient, is widely used for the proteome-wide analysis of cancer mechanism and biomarker identification of a drug. However, the proteomics data interpretation is still challenging due to complex data deconvolution from the PDX sample that is a cross-species mixture of human cancerous tissues and immunodeficient mouse tissues. In this study, by using the lab-assembled mixture of human and mouse cells with different mixing ratios as a benchmark, we developed and evaluated a new method, SPA (shared peptide allocation), for protein quantitation by considering the unique and shared peptides of both species. The results showed that SPA could provide more convenient and accurate protein quantitation in human-mouse mixed samples. Further validation on a pair of gastric PDX samples (one bearing FGFR2 amplification while the other one not) showed that our new method not only significantly improved the overall protein identification, but also detected the differential phosphorylation of FGFR2 and its downstream mediators (such as RAS and ERK) exclusively. The tool pdxSPA is freely available at https://github.com/LiLab-Proteomics/pdxSPA.
Year
DOI
Venue
2021
10.1016/j.gpb.2019.11.016
GENOMICS PROTEOMICS & BIOINFORMATICS
Keywords
DocType
Volume
Patient-derived xenograft model, Label-free, Shared peptide, FGFR2 amplification, Biomarker
Journal
19
Issue
ISSN
Citations 
4
1672-0229
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Xi Cheng100.34
Lili Qian200.34
Bo Wang300.34
Minjia Tan400.34
Jing Li5343.76