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

Goh WW, Lee YH, Ramdzan ZM, Sergot MJ, Chung M, Wong L - J. Proteome Res. (2012)

Bottom Line: 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.Although consistency of individual proteins between patients is low, we found the reported proteins tend to hit clusters in a meaningful and informative manner.

View Article: PubMed Central - PubMed

Affiliation: Department of Computing, Imperial College London , London, United Kingdom.

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.

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Proteomicssignature profiling (PSP) pipeline. The pipeline consistsof incorporating data from complex, PPI and GO. Protein lists fromindividual patients are converted into a proteomics signature profile(PSP) based on a vector of complexes generated from CORUM and graphlet-derivedclusters. The PSP can then be used for performing sample clusteringfor assessing the patient samples and determining significant clusters.GO terms are used to evaluate functional significance and coherence.(Abbreviations: GDV, Graphlet degree vector; GDS, Graphlet DegreeSimilarity Scores). For detailed explanations, refer to Results.
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fig1: Proteomicssignature profiling (PSP) pipeline. The pipeline consistsof incorporating data from complex, PPI and GO. Protein lists fromindividual patients are converted into a proteomics signature profile(PSP) based on a vector of complexes generated from CORUM and graphlet-derivedclusters. The PSP can then be used for performing sample clusteringfor assessing the patient samples and determining significant clusters.GO terms are used to evaluate functional significance and coherence.(Abbreviations: GDV, Graphlet degree vector; GDS, Graphlet DegreeSimilarity Scores). For detailed explanations, refer to Results.

Mentions: Protein complexes canbe regarded as units of biological functionand is suitable for contextualizing proteomics data. A given set ofcomplexes can be represented as an unranked “cluster vector”against which we can measure the hit rate of a patient’s reportedproteins. For each patient and each cluster, the hit rate = max(Np/N), where Np is the number of proteins in that specific patient foundin that cluster, and N is the total number of proteinsfound in that cluster (Figure 1). The patient’sProteomics Signature Profile, or PSP, is therefore simply a vectorof hit rates checked against the cluster vector. Since a patient’sPSP is a vector of fixed length m, a set of n PSPs can be represented as a matrix of dimensions (n × m) on which statistical and mathematicalanalysis can be performed.


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

Goh WW, Lee YH, Ramdzan ZM, Sergot MJ, Chung M, Wong L - J. Proteome Res. (2012)

Proteomicssignature profiling (PSP) pipeline. The pipeline consistsof incorporating data from complex, PPI and GO. Protein lists fromindividual patients are converted into a proteomics signature profile(PSP) based on a vector of complexes generated from CORUM and graphlet-derivedclusters. The PSP can then be used for performing sample clusteringfor assessing the patient samples and determining significant clusters.GO terms are used to evaluate functional significance and coherence.(Abbreviations: GDV, Graphlet degree vector; GDS, Graphlet DegreeSimilarity Scores). For detailed explanations, refer to Results.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472506&req=5

fig1: Proteomicssignature profiling (PSP) pipeline. The pipeline consistsof incorporating data from complex, PPI and GO. Protein lists fromindividual patients are converted into a proteomics signature profile(PSP) based on a vector of complexes generated from CORUM and graphlet-derivedclusters. The PSP can then be used for performing sample clusteringfor assessing the patient samples and determining significant clusters.GO terms are used to evaluate functional significance and coherence.(Abbreviations: GDV, Graphlet degree vector; GDS, Graphlet DegreeSimilarity Scores). For detailed explanations, refer to Results.
Mentions: Protein complexes canbe regarded as units of biological functionand is suitable for contextualizing proteomics data. A given set ofcomplexes can be represented as an unranked “cluster vector”against which we can measure the hit rate of a patient’s reportedproteins. For each patient and each cluster, the hit rate = max(Np/N), where Np is the number of proteins in that specific patient foundin that cluster, and N is the total number of proteinsfound in that cluster (Figure 1). The patient’sProteomics Signature Profile, or PSP, is therefore simply a vectorof hit rates checked against the cluster vector. Since a patient’sPSP is a vector of fixed length m, a set of n PSPs can be represented as a matrix of dimensions (n × m) on which statistical and mathematicalanalysis can be performed.

Bottom Line: 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.Although consistency of individual proteins between patients is low, we found the reported proteins tend to hit clusters in a meaningful and informative manner.

View Article: PubMed Central - PubMed

Affiliation: Department of Computing, Imperial College London , London, United Kingdom.

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.

Show MeSH
Related in: MedlinePlus