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 for protein quantitation by considering both species unique and shared peptides. The results showed that the new algorithm could provide more convenient and accurate protein quantitation in human-mouse mixed samples. In the further validation on a pair of gastric PDX samples bearing FGFR2 gene amplification and the control, we found that not only the overall protein identification was improved significantly, but also the differential phosphorylation of FGFR2 and its downstream mediators (such as RAS and ERK) could be detected by our new method exclusively. The tool pdxSPA is freely available at https://github.com/Li-Lab-Proteomics/pdxSPA.