Within ovarian cancer research, patient-derived xenograft (PDX) models recapitulate histologic features and genomic aberrations found in original tumors. However, conflicting data from published studies have demonstrated significant transcriptional differences between PDXs and original tumors, challenging the fidelity of these models. We employed a quantitative mass spectrometry-based proteomic approach coupled with generation of patient-specific databases using RNA-seq data to investigate the proteogenomic landscape of serially-passaged PDX models established from two patients with distinct subtypes of ovarian cancer. We demonstrate that the utilization of patient-specific databases guided by transcriptional profiles increases the depth of human protein identification in PDX models. Our data show that human proteomes of serially passaged PDXs differ significantly from their patient-derived tumor of origin. Analysis of differentially abundant proteins revealed enrichment of distinct biological pathways with major downregulated processes including extracellular matrix organization and the immune system. Finally, we investigated the relative abundances of ovarian cancer-related proteins identified from the Cancer Gene Census across serially passaged PDXs, and found their protein levels to be unstable across PDX models. Our findings highlight features of distinct and dynamic proteomes of serially-passaged PDX models of ovarian cancer.