Credentialing Individual Samples for Proteogenomic Analysis

Abstract

An integrated analysis of DNA, RNA and protein, so called proteogenomic studies, has the potential to greatly increase our understanding of both normal physiology and disease development. However, such studies are challenged by a lack of a systematic approach to credential individual samples resulting in the introduction of noise into the system that limits the ability to identify important biological signals. Indeed, a recent proteogenomic CPTAC study identified 26% of samples as unsatisfactory, resulting in a marked increase in cost and loss of information content. Based on a large-scale analysis of RNA-seq and proteomic data generated by reverse phase protein arrays (RPPA) and by mass spectrometry, we propose a rotein-RNA orrelation-based (PMC) score as a robust metric to credential single samples for integrated proteogenomic studies. Samples with high PMC scores have significantly higher protein-mRNA correlation, total protein content and tumor purity. Our results highlight the importance of credentialing individual samples prior to proteogenomic analysis.

Proteo-genomic characterization of virus-associated liver cancers reveals potential subtypes and therapeutic targets

“…Among 222 mRNA-protein pairs, 30 pairs (14%) had negative correlation coefficients. There are multiple potential reasons for the low correlation with these proteins including low-expression levels, low dynamic range, tissue heterogeneity, and post-transcriptional regulation 13 . As expected, there was a much weaker correlation between phosphoprotein abundance and corresponding total protein abundance emphasizing the utility of the RPPA assay (Fig.…”

Using Reverse Phase Protein Array (RPPA) to Identify and Target Adaptive Resistance

“…However, sample collection approaches such as needle aspirates, core biopsies and surgical excision as well as warm ischemia during surgery can markedly affect phosphoprotein levels. Perhaps more critically in terms of longitudinal analysis of patient samples, heterogeneity between lesions samples and in particular changes in tumor and stroma content are critical confounding factors (Labrie et al 2019;Zhao et al 2018).…”

“…To further increase the power of the approach, adaptive responses can be assessed as pathways rather than as individual molecules (Labrie et al 2019;Lu et al 2016). Indeed, in a number of systems, RPPA has proven to have marked predictive value for response to therapeutic agents, evaluation of signaling networks, to predict mechanisms of resistance and patient outcomes, in some cases, exceeding that of transcriptional profiling (Cancer Genome Atlas 2015; Hill et al 2016;Kwong et al 2015;Yuan et al 2014;Zhao et al 2018). However, the information content that can be obtained is enhanced by integration of data from multiple levels.…”

“…An ability to select effective combinations for individual patients will require that we move beyond the current focus on DNA mutations, fusions and copy number aberrations (CNA) as the dominant method for personalized therapy selection. We have demonstrated that changes at the DNA and RNA level are poorly reflected at the protein level which represents the functional unit in the cell and the most common therapeutic target (Akbani et al 2014;Johansson et al 2019;Lu et al 2016;Zhao et al 2018). As DNA and RNA changes poorly reflect post translational modifications and the location and function of protein complexes, proteomic data includes important information content not available by nucleotide analysis.…”