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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.

SUBMITTER: Goh WW

PROVIDER: S-EPMC3472506 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Proteomics signature profiling (PSP): a novel contextualization approach for cancer proteomics.

Goh Wilson Wen Bin WW Lee Yie Hou YH Ramdzan Zubaidah M ZM Sergot Marek J MJ Chung Maxey M Wong Limsoon L

Journal of proteome research 20120209 3

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 net ...[more]

PMID: 22243476

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Project description:Proteomics Signature Profiling (PSP) is a novel hit-rate based method that proved useful in resolving consistency and coverage issues in proteomics. As a follow-up study, several points need to be addressed: 1/ PSP's generalisability to pathways, 2/ understanding the biological interplay between significant complexes and pathway subnets co-located on the same pathways on our liver cancer dataset, 3/ understanding PSP's false positive rate and 4/ demonstrating that PSP works on other suitable proteomics datasets as well as expanding PSP's analytical resolution via the use of specialised ontologies.1/ PSP performs well with Pathway-Derived Subnets (PDSs). Comparing the performance of PDSs derived from various pathway databases, we find that an integrative approach is best for optimising analytical resolution. Feature selection also confirms that significant PDSs are closely connected to the cancer phenotype.2/ In liver cancer, correlation studies of significant PSP complexes and PDSs co-localised on the same pathways revealed an interesting relationship between the purine metabolism pathway and two other complexes involved in DNA repair. Our work suggests progression to poor stage requires additional mutations that disrupt DNA repair enzymes.3/ False positive analysis reveals that PSP, applied on both complexes and PDSs, is powerful and precise.4/ Via an expert-curated lipid ontology, we uncovered several interesting lipid-associated complexes that could be associated with cancer progression. Of particular interest is the HMGB1-HMGB2-HSC70-ERP60-GAPDH complex which is also involved in DNA repair. We also demonstrated generalisability of PSP using a non-small-cell lung carcinoma data set.PSP is a powerful and precise technique, capable of identifying biologically coherent features. It works with biological complexes, network-predicted clusters as well as PDSs. Here, an instance of the interplay between significant PDSs and complexes, possibly significantly involved in liver cancer progression but not well understood as yet, is demonstrated. Also demonstrated is the enhancement of PSP's analytical resolution using specialised ontologies.

Project description:Visceral leishmaniasis (VL), caused by Leishmania infantum, is an oft-fatal neglected tropical disease. In the absence of an effective vaccine, the control of leishmaniasis relies exclusively on chemotherapy. Due to the lack of established molecular/genetic markers denoting parasite resistance, clinical treatment failure is often used as an indicator. Antimony-based drugs have been the standard antileishmanial treatment for more than seven decades, leading to major drug resistance in certain regions. Likewise, drug resistance to miltefosine and amphotericin B continues to spread at alarming rates. In consequence, innovative approaches are needed to accelerate the identification of antimicrobial drug targets and resistance mechanisms. To this end, we have implemented a novel approach based on thermal proteome profiling (TPP) to further characterize the mode of action of antileishmanials antimony, miltefosine and amphotericin B, as well as to better understand the mechanisms of drug resistance deployed by Leishmania. Proteins become more resistant to heat-induced denaturation when complexed with a ligand. In this way, we used multiplexed quantitative mass spectrometry-based proteomics to monitor the melting profile of thousands of expressed soluble proteins in WT, antimony-resistant, miltefosine-resistant, and amphotericin B-resistant L. infantum parasites, in the presence (or absence) of the above-mentioned drugs. Bioinformatics analyses were performed, including data normalization, melting profile fitting, and identification of proteins that underwent changes (fold change > 4) caused by complexation with a drug. With this unique approach, we were able to narrow down the regions of the L. infantum proteome that interact with antimony, miltefosine, and amphotericin B; validating previously-identified and unveiling novel drug targets. Moreover, analyses revealed candidate proteins potentially involved in drug resistance. Interestingly, we detected thermal proximity coaggregation for several proteins belonging to the same metabolic pathway (i.e., tryparedoxin peroxidase and aspartate aminotransferase in proteins exposed to antimony), highlighting the importance of these pathways. Collectively, our results could serve as a jumping-off point for the future development of innovative diagnostic tools for the detection and evaluation of antimicrobial-resistant Leishmania populations, as well as open the door for new on-target therapies.

Project description:A comprehensive proteomic profiling of nasal epithelium (NE) is described. This study relies on simple subcellular fractionation used to obtain soluble- and membrane-enriched fractions followed by 2-dimensional liquid chromatography (2D-LC) separation and tandem mass spectrometry (MS/MS). The cells were collected using a brushing technique applied on NE of clinically evaluated volunteers. Subsequently, the soluble- and the membrane-protein enriched fractions were prepared and analyzed in parallel using 2D-LC-MS/MS. In a set of 1482 identified proteins, 947 (63.9%) proteins were found to be associated to membrane fraction. Grand average hydropathy value index (GRAVY) analysis, the transmembrane protein mapping and annotations of primary location deposited in the Human Protein Reference Database (HPRD) confirmed an enrichment of hydrophobic proteins on this dataset. Ingenuity Pathway Analysis (IPA) of soluble fraction revealed an enrichment of molecular and cellular functions associated with cell death, protein folding and drug metabolism while in membrane fraction showed an enrichment of functions associated with molecular transport, protein trafficking and cell-to-cell signaling and interaction. The IPA showed similar enrichment of functions associated with cellular growth and proliferation in both soluble and membrane subproteomes. This finding was in agreement with protein content analysis using exponentially modified protein abundance index (emPAI). A comparison of our data with previously published studies focusing on respiratory tract epithelium revealed similarities related to identification of proteins associated with physical barrier function and immunological defence. In summary, we extended the NE molecular profile by identifying and characterizing proteins associated to pivotal functions of a respiratory epithelium, including the control of fluid volume and ionic composition at the airways' surface, physical barrier maintenance, detoxification and immunological defence. The extent of similarities supports the applicability of a less invasive analysis of NE to assess prognosis and treatment response of lung diseases such as asthma, cystic fibrosis and chronic obstructive pulmonary disease.

Project description:Motivation:Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Results:Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Availability and implementation:Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. Contact:mehreen.ali@helsinki.fi or tero.aittokallio@fimm.fi. Supplementary information:Supplementary data are available at Bioinformatics online.

Dataset Information

Proteomics signature profiling (PSP): a novel contextualization approach for cancer proteomics.

Publications

Proteomics signature profiling (PSP): a novel contextualization approach for cancer proteomics.

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