Abstract C047: Comprehensive curation of label-free proteomics data for 186 patient-derived xenograft (PDX) models in HuBase™
Authors: Liu Hengyuan , Chen Xiaobo , Guo Sheng , Mao Binchen , Pal Rekha 
Published: December 2023

Introduction. HuBase™, a Crown Bioscience Inc. database, hosts pharmacological and genomic data from over 2500 patient-derived xenograft (PDX) models. It is widely used in pharmacology for model selection and biomarker analysis. Recognizing the pivotal role of proteomics data in unraveling intricate molecular mechanisms underlying biological processes and diseases, we acknowledge the challenge posed by the significant sequence similarity between mouse and human homologs in PDX samples, which hampers the accurate characterization of proteomics data using traditional run-then-separate protocols. In this study, building upon our previously published separate-then-run protocol1, we established a label-free proteomics quantification pipeline to accurately determine the protein expression level of 186 PDX models in a systematic manner. The resulting proteomics data have been seamlessly integrated into HuBase™, constituting the first comprehensive collection of PDX proteomics data derived from mouse cell-depleted samples.

Methods. Mouse stromal cells were removed from the 186 PDX models with Mouse Cell Depletion Kit. The mouse depleted samples were analyzed using the 4D Data-Independent Acquisition (4D-DIA) technology. Quality control (QC) samples were measured at every 15 MS runs to assess technical variability. Raw MS data was processed with Spectronaut® 17 to generate label-free quantification (LFQ), which were log-transformed for subsequent analysis. To address batch effects, the Combat method in the sva R package was used on 2 different batches. Additionally, a subset of 10 models without mouse cell removal, underwent the same proteomics quantification pipeline for comparative analysis with their mouse depleted counterparts.

Results. We quantified the expression levels of over 12,000 proteins per sample, with a significant number of proteins showing positive correlations with mRNA expression in all PDX samples. After the batch effect correction, we observed no systematic discrepancies between the two batches, and the median intensity of QC samples remained comparable. Through a comparative analysis of 10 pairs of PDX models with and without mouse stromal cell removal, we found differentially expressed proteins were enriched in the extracellular matrix for non-mouse depleted samples, which could be attributed to the upregulation of these proteins in mouse stromal cells. The significant presence of some mouse proteins underscores the importance of removing mouse stromal cells prior to proteomic characterization to avoid adversely confounding subsequent analyses leading to misinterpretations.

Conclusion. Powered by state-of-the-art 4D-DIA technology, we have curated the first comprehensive PDX proteomics dataset derived from mouse-depleted samples, offering additional value for PDX model selection and exploration of potential biomarkers.

References. 1. Shi, Z., Mao, B., Chen, X., Hao, P. & Guo, S. Mouse Stromal Cells Confound Proteomic Characterization and Quantification of Xenograft Models. Cancer Research Communications 3, 202–214 (2023).

Citation Format: Hengyuan Liu, Xiaobo Chen, Sheng Guo, Binchen Mao, Rekha Pal. Comprehensive curation of label-free proteomics data for 186 patient-derived xenograft (PDX) models in HuBase™ [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C047.