Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.

Multidimensional LC-MS/MS has been used for the analysis of biological samples labeled with isobaric mass tags for relative and absolute quantitation (iTRAQ) to identify proteins that are differentially expressed in human headand-neck squamous cell carcinomas (HNSCCs) in relation to non-cancerous head-and-neck tissues (controls) for cancer biomarker discovery. Fifteen individual samples (cancer and non-cancerous tissues) were compared against a pooled non-cancerous control (prepared by pooling equal amounts of proteins from six non-cancerous tissues) in five sets by on-line and off-line separation. We identified 811 non-redundant proteins in HNSCCs, including structural proteins, signaling components, enzymes, receptors, transcription factors, and chaperones. A panel of proteins showing consistent differential expression in HNSCC relative to the non-cancerous controls was discovered. Some of the proteins include stratifin (14-3-3); YWHAZ (14-3-3); three calcium-binding proteins of the S100 family, S100-A2, S100-A7 (psoriasin), and S100-A11 (calgizarrin); prothymosin ␣ (PTHA); L-lactate dehydrogenase A chain; glutathione S-transferase Pi; APC-binding protein EB1; and fascin. Peroxiredoxin2, carbonic anhydrase I, flavin reductase, histone H3, and polybromo-1D (BAF180) were underexpressed in HNSCCs. A panel of the three best performing biomarkers, YWHAZ, stratifin, and S100-A7, achieved a sensitivity of 0.92 and a specificity of 0.91 in discriminating cancerous from non-cancerous head-and-neck tissues. Verification of differential expression of YWHAZ, stratifin, and S100-A7 proteins in clinical samples of HNSCCs and paired and nonpaired non-cancerous tissues by immunohistochemistry, immunoblotting, and RT-PCR confirmed their overexpression in head-and-neck cancer. Verification of YWHAZ, stratifin, and S100-A7 in an independent set of HNSCCs achieved a sensitivity of 0.92 and a specificity of 0.87 in discriminating cancerous from non-cancerous head-andneck tissues, thereby confirming their overexpressions and utility as credible cancer biomarkers.

. (2005) Search for cancer markers from endometrial tissues using differentially labeled tags iTRAQ and cleavable ICAT with multidimensional liquid chromatography and tandem mass spectrometry. J. Proteome Res. 4, 377-386) to discriminate malignant and benign endometrial tissue samples was verified in a 40-sample iTRAQ (isobaric tags for relative and absolute quantitation) labeling study involving normal proliferative and secretory samples and Types I and II endometrial cancer samples. None of these proteins had the sensitivity and specificity to be used individually to discriminate between normal and cancer samples. However, a panel of pyruvate kinase, chaperonin 10, and ␣ 1 -antitrypsin achieved the best results with a sensitivity, specificity, predictive value, and positive predictive value of 0.95 each in a logistic regression analysis. In addition, three new potential markers were discovered, whereas two other proteins showed promising trends but were not detected in sufficient numbers of samples to permit statistical validation. Differential expressions of some of these candidate biomarkers were independently verified using immunohistochemistry. Molecular & Cellular Proteomics 6:1170 -1182, 2007.Differential tagging with isotopic reagents, such as ICAT (1) or the more recent variation that uses isobaric tagging reagents, iTRAQ 1 (Applied Biosystems, Foster City, CA), followed by multidimensional LC and MS/MS analysis is quickly being recognized as one of the more powerful methodologies in the search for biomarkers for various disease states. Our recent studies using both ICAT and iTRAQ reagents as means to facilitate the identification and relative quantification of proteins from endometrial tissue homogenates have resulted in some interesting potential cancer markers (PCMs) (2, 3). Those studies, however, were performed on small sample sets. This study describes the results of a more detailed investigation using a larger cohort of 40 samples and the iTRAQ technology and was aimed at validating the earlier results as well as expanding the panel of biomarkers.Endometrial carcinoma (EmCa), a cancer of the lining of the uterus, is the fourth most common cancer in Canadian women.2 Current methods of diagnosis rely on invasive techniques (biopsy and curettage), and no screening is available. A panel of biomarkers that helps in early diagnosis would, therefore, be useful especially for high risk groups, e.g. women who are on tamoxifen treatment or have hereditary nonpolyposis colorectal cancer syndrome. Although the eventual diagnostic testing for such biomarkers would be most facile from bodily fluids, such as blood or urine, the iTRAQ experiments performed thus far have been on resected From the Departments of ‡Chemistry, §Biology, and ʈMathematics

Chronic exposure of the oral mucosa to carcinogens in tobacco is linked to inflammation and development of oral premalignant lesions (OPLs) with high risk of progression to cancer; there is currently no clinical methodology to identify high-risk lesions. We hypothesized that identification of differentially expressed proteins in OPLs in relation to normal oral tissues using proteomic approach will reveal changes in multiple cellular pathways and aid in biomarker discovery. Isobaric mass tags (iTRAQ)-labeled oral dysplasias and normal tissues were compared against pooled normal control by online liquid chromatography and tandem mass spectrometry. Verification of biomarkers was carried out in an independent set of samples by immunohistochemistry, immunoblotting, and RT-PCR. We identified 459 nonredundant proteins in OPLs, including structural proteins, signaling components, enzymes, receptors, transcription factors, and chaperones. A panel of three best-performing biomarkers identified by iTRAQ analysis and verified by immunohistochemistrystratifin (SFN), YWHAZ, and hnRNPKachieved a sensitivity of 0.83, 0.91, specificity of 0.74, 0.95, and predictive value of 0.87 and 0.96, respectively, in discriminating dysplasias from normal tissues, thereby confirming their utility as potential OPL biomarkers. Pathway analysis revealed direct interactions between all the three biomarkers and their involvement in two major networks involved in inflammation, signaling, proliferation, regulation of gene expression, and cancer. In conclusion, our work on determining the OPL proteome unraveled novel networks linking inflammation and development of epithelial dysplasia and their key regulatory proteins may serve as novel chemopreventive/therapeutic targets for early intervention. Additionally, we identified and verified a panel of OPL biomarkers that hold promise for large-scale validation for ultimate clinical use.