Xenografts are essential models for studying cancer biology and developing oncology drugs, and are more informative with omics data. Most reported xenograft proteomics projects directly profiled tumors comprising human cancer cells and mouse stromal cells, followed by computational algorithms for assigning peptides to human and mouse proteins. We evaluated the performance of three main algorithms by carrying out benchmark studies on a series of human and mouse cell line mixtures and a set of liver patient-derived xenograft (PDX) models. Our study showed that approximately half of the characterized peptides are common between human and mouse proteins, and their allocations to human or mouse proteins cannot be satisfactorily achieved by any algorithm. As a result, many human proteins are erroneously labeled as differentially expressed proteins (DEP) between samples from the same human cell line mixed with different percentages of mouse cells, and the number of such false DEPs increases superquadratically with the mouse cell percentage. When mouse stromal cells are not removed from PDX tumors, about 30%-40% of DEPs from pairwise comparisons of PDX models are false positives, and about 20% of real DEPs cannot be identified irrespective of the threshold for calling differential expression. In conclusion, our study demonstrated that it is advisable to separate human and mouse cells in xenograft tumors before proteomic profiling to obtain more accurate measurement of species-specific protein expression.
Significance: This study advocates the separate-then-run over the run-then-separate approach as a better strategy for more reliable proteomic profiling of xenografts.
© 2023 The Authors; Published by the American Association for Cancer Research.