Proteomics Signature Profiling (PSP): A Novel Contextualization Approach for Cancer Proteomics

Abstract

Traditional proteomics analysis is plagued by the use of arbitrary thresholds resulting in large loss of information. We propose here a novel method in proteomics that utilizes all detected proteins. We demonstrate its efficacy in a proteomics screen of 5 and 7 liver cancer patients in the moderate and late stage, respectively. Utilizing biological complexes as a cluster vector, and augmenting it with submodules obtained from partitioning an integrated and cleaned protein–protein interaction network, we calculate a Proteomics Signature Profile (PSP) for each patient based on the hit rates of their reported proteins, in the absence of fold change thresholds, against the cluster vector. Using this, we demonstrated that moderate- and late-stage patients segregate with high confidence. We also discovered a moderate-stage patient who displayed a proteomics profile similar to other poor-stage patients. We identified significant clusters using a modified version of the SNet approach. Comparing our results against the Proteomics Expansion Pipeline (PEP) on which the same patient data was analyzed, we found good correlation. Building on this finding, we report significantly more clusters (176 clusters here compared to 70 in PEP), demonstrating the sensitivity of this approach. Gene Ontology (GO) terms analysis also reveals that the significant clusters are functionally congruent with the liver cancer phenotype. PSP is a powerful and sensitive method for analyzing proteomics profiles even when sample sizes are small. It does not rely on the ratio scores but, rather, whether a protein is detected or not. Although consistency of individual proteins between patients is low, we found the reported proteins tend to hit clusters in a meaningful and informative manner. By extracting this information in the form of a Proteomics Signature Profile, we confirm that this information is conserved and can be used for (1) clustering of patient samples, (2) identification of significant clusters based on real biological complexes, and (3) overcoming consistency and coverage issues prevalent in proteomics data sets.

Interpreting Personal Transcriptomes: Personalized Mechanism-Scale Profiling of Rna-Seq Data

“…Co-expression modules derived from large scale studies of multiple disease conditions have provided insight in new biology and could be utilized as non-curated gene-sets. Protein complexes, that worked well in mass spectrometry [17], could also be utilized as gene-sets for pathway discovery in RNA-seq.…”

“…These studies [10–14] transcend those using conventional gene enrichment or gene set enrichment analyses (GSEA) that cannot provide individual measurements of mechanisms on a single sample and require comparison between multiple samples groups (in distinct categorical phenotypes) to infer gene-set-level predictions. Recently, related work in mass spectrometry protein complexes (derived from interaction networks) were shown to be more accurate for designing classifiers than single proteins [17]. However, to our knowledge, no mechanism-level methodology has yet been designed specifically for interpreting individual RNA-sequencing samples.…”

Enhancing the utility of Proteomics Signature Profiling (PSP) with Pathway Derived Subnets (PDSs), performance analysis and specialised ontologies

“…This idea was tested in a published study of Hepatocellular Carcinoma (HCC) patients (5 in moderate and 7 in poor stage) [2]. Utilising real and predicted protein complexes, it is possible to strongly recover patient subclasses in agreement with histopathology.…”

Benchmarking Human Protein Complexes to Investigate Drug-Related Systems and Evaluate Predicted Protein Complexes

“…For example, Yang et al [8] integrated data of protein-protein interactions (PPI) and CORUM protein complexes to predict human disease genes. Goh et al [9] also utilized CUROM complexes to calculate Proteomics Signature Profiles (PSP) for cancer patients. PSP profiles can be used for effective clustering of patient samples and is a powerful tool for cancer proteomics.…”

“…Even records within the same database (e.g., CORUM) are highly redundant [9]. In this paper, we will process those redundant protein complexes to build a non-redundant and comprehensive catalogue called CHPC2012.…”