Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Use of a Single-Chain Antibody Library for Ovarian Cancer Biomarker Discovery

The discovery of novel early detection biomarkers of disease could offer one of the best approaches to decrease the morbidity and mortality of ovarian and other cancers. We report on the use of a single-chain variable fragment antibody library for screening ovarian serum to find novel biomarkers for the detection of cancer. We alternately panned the library with ovarian cancer and disease-free control sera to make a sublibrary of antibodies that bind proteins differentially expressed in cancer. This sublibrary was printed on antibody microarrays that were incubated with labeled serum from multiple sets of cancer patients and controls. The antibodies that performed best at discriminating disease status were selected, and their cognate antigens were identified using a functional protein microarray. Overexpression of some of these antigens was observed in cancer serum, tumor proximal fluid, and cancer tissue via dot blot and immunohistochemical staining. Thus, our use of recombinant antibody microarrays for unbiased discovery found targets for ovarian cancer detection in multiple sample sets, supporting their further study for disease diagnosis.Despite many advances in the treatment of cancer, early detection and tumor removal remains the best prospect for overcoming disease. Ovarian cancer is an excellent example of the potential prognostic value of early detection because diagnosis at a localized stage has a 5-year survival rate of 93%. However, only 19% of cases are diagnosed at this stage, and by the time the disease has evolved to an advanced stage, the 5-year survival rate drops to 31% (1).Much effort has been expended to find early detection markers of ovarian cancer, and some success has been achieved. Most notable is CA125, the only approved marker for the detection of recurrence of ovarian cancer (2). Other leading targets are mesothelin and HE4, which have been examined by several groups for their efficacy as early detection markers (3–8). Nevertheless, several conditions necessitate the discovery of more specific and sensitive ovarian cancer markers: the heterogeneity of this disease, the ambiguity of its symptoms, its low incidence in the general population, and the low sensitivity and specificity of currently available markers.One of the difficulties in finding markers in blood is the complexity of the plasma/serum proteome, estimated in the tens to hundreds of thousands of proteins, as well as its large range in constituent protein concentrations, which can span 12 orders of magnitude (9). However, along with its easy accessibility, the fact that blood is in contact with virtually every tissue and contains tissue- and tumor-derived proteins makes it a preferred source for disease biomarker discovery.Our previous results (10, 11) and those of others (12–14) using high density, full-length IgG antibody microarrays to validate and discover cancer serum biomarkers demonstrated that this platform is valuable for simultaneously comparing the levels of hundreds of proteins on dozens of serum samples from cancer patients and healthy controls. We confirmed overexpression of CA125, mesothelin, and HE4 in ovarian cancer samples using this high density microarray platform, validating our array methodology for measurement of cancer serum biomarkers and yielding new putative biomarkers for this disease (10, 11).Previously reported approaches are typically limited to a few hundred antibodies. The methodology reported here allows us to exploit the specific advantages of antibodies as high affinity capture reagents to detect differential expression of thousands of tumor biomarkers using a diverse (2 × 10⁸ binding agents) single-chain variable fragment antibody (scFv)¹ library for detection of ovarian cancer markers in serum, tumor cyst fluid, and ascites fluid. Our results build on previous reports of phage display library microarrays to discover autoantibody (15–18) and other protein (12, 19, 20) cancer biomarkers. Our scFv are high affinity capture reagents consisting of the variable regions of human antibody heavy and light chains joined by a flexible linker peptide. These recombinant antibodies are able to recognize a wide variety of antigens, including many previously thought difficult, such as self-antigens and proteins that are not normally immunogenic in animals (21–24). Using a highly diverse recombinant antibody library, one has the ability to overcome the complexity of the serum proteome. It has been calculated that for an immune repertoire to be complete (at least one antibody in the repertoire has reasonable affinity for every epitope possible in nature) it requires a diversity of at least 10⁶ antibodies (25). The reported diversity of our scFv library exceeds this value by 100-fold (21).To enrich for antibodies that differentiate disease status, we performed a selection or panning of the naïve library for proteins that are differentially expressed in cyst fluid, ascites fluid, or serum of cancer patients with respect to healthy serum. We printed this sublibrary on activated hydrogel slides that were queried with three different sets of labeled case and control sera to further select those that discriminate cancer status in a statistically significant manner. Next, we identified some of the targets that bind to the individual scFv using high density nucleic acid programmable protein arrays (NAPPAs) expressing a total of over 7000 proteins. Finally, we validated the effectiveness of the selection process by confirming overexpression of these targets in cancer serum, cyst fluid, and ascites fluid as well as in tumor sections.     

Identification of Tumor-associated Autoantigens for the Diagnosis of Colorectal Cancer in Serum Using High Density Protein Microarrays

There is a mounting evidence of the existence of autoantibodies associated to cancer progression. Antibodies are the target of choice for serum screening because of their stability and suitability for sensitive immunoassays. By using commercial protein microarrays containing 8000 human proteins, we examined 20 sera from colorectal cancer (CRC) patients and healthy subjects to identify autoantibody patterns and associated antigens. Forty-three proteins were differentially recognized by tumoral and reference sera (p value <0.04) in the protein microarrays. Five immunoreactive antigens, PIM1, MAPKAPK3, STK4, SRC, and FGFR4, showed the highest prevalence in cancer samples, whereas ACVR2B was more abundant in normal sera. Three of them, PIM1, MAPKAPK3, and ACVR2B, were used for further validation. A significant increase in the expression level of these antigens on CRC cell lines and colonic mucosa was confirmed by immunoblotting and immunohistochemistry on tissue microarrays. A diagnostic ELISA based on the combination of MAPKAPK3 and ACVR2B proteins yielded specificity and sensitivity values of 73.9 and 83.3% (area under the curve, 0.85), respectively, for CRC discrimination after using an independent sample set containing 94 sera representative of different stages of progression and control subjects. In summary, these studies confirmed the presence of specific autoantibodies for CRC and revealed new individual markers of disease (PIM1, MAPKAPK3, and ACVR2B) with the potential to diagnose CRC with higher specificity and sensitivity than previously reported serum biomarkers.Colorectal cancer (CRC)¹ is the second most prevalent cancer in the western world. The development of this disease takes decades and involves multiple genetic events. CRC remains a major cause of mortality in developed countries because most of the patients are diagnosed at advanced stages because of the reluctance to use highly invasive diagnostic tools like colonoscopy. Actually only a few proteins have been described as biomarkers in CRC (carcinoembryonic antigen (CEA), CA19.9, and CA125 (1–3)), although none of them is recommended for clinical screening (4). Proteomics analysis is actively used for the identification of new biomarkers. In previous studies, the use of two-dimensional DIGE and antibody microarrays allowed the identification of differentially expressed proteins in CRC tissue, including isoforms and post-translational modifications responsible for modifications in signaling pathways (5–8). Both approaches resulted in the identification of a collection of potential tumoral tissue biomarkers that is currently being investigated.However, the implementation of simpler, non-invasive methods for the early detection of CRC should be based on the identification of proteins or antibodies in serum or plasma (9–13). There is ample evidence of the existence of an immune response to cancer in humans as demonstrated by the presence of autoantibodies in cancer sera. Self-proteins (autoantigens) altered before or during tumor formation can elicit an immune response (13–19). These tumor-specific autoantibodies can be detected at early cancer stages and prior to cancer diagnosis revealing a great potential as biomarkers (14, 15, 20). Tumor proteins can be affected by specific point mutations, misfolding, overexpression, aberrant glycosylation, truncation, or aberrant degradation (e.g. p53, HER2, NY-ESO1, or MUC1 (16, 21–25)). In fact, a number of tumor-associated autoantigens (TAAs) were identified previously in different studies involving autoantibody screening in CRC (26–28).Several approaches have been used to identify TAAs in cancer, including natural protein arrays prepared with fractions obtained from two-dimensional LC separations of tumoral samples (29, 30) or protein extracts from cancer cells or tissue (9, 31) probed by Western blot with patient sera, cancer tissue peptide libraries expressed as cDNA expression libraries for serological screening (serological analysis of recombinant cDNA expression libraries (SEREX)) (22, 32), or peptides expressed on the surface of phages in combination with microarrays (17, 18, 33, 34). However, these approaches suffer from several drawbacks. In some cases TAAs have to be isolated and identified from the reactive protein lysate by LC-MS techniques, or in the phage display approach, the reactive TAA could be a mimotope without a corresponding linear amino acid sequence. Moreover, cDNA libraries might not be representative of the protein expression levels in tumors as there is a poor correspondence between mRNA and protein levels.Protein arrays provide a novel platform for the identification of both autoantibodies and their respective TAAs for diagnostic purposes in cancer serum patients. They present some advantages. Proteins printed on the microarray are known “a priori,” avoiding the need for later identifications and the discovery of mimotopes. There is no bias in protein selection as the proteins are printed at a similar concentration. This should result in a higher sensitivity for biomarker identification (13, 35, 36).In this study, we used commercially available high density protein microarrays for the identification of autoantibody signatures and tumor-associated antigens in colorectal cancer. We screened 20 CRC patient and control sera with protein microarrays containing 8000 human proteins to identify the CRC-associated autoantibody repertoire and the corresponding TAAs. Autoantibody profiles that discriminated the different types of CRC metastasis were identified. Moreover a set of TAAs showing increased or decreased expression in cancer cell lines and paired tumoral tissues was found. Finally an ELISA was set up to test the ability of the most immunoreactive proteins to detect colorectal adenocarcinoma. On the basis of the antibody response, combinations of three antigens, PIM1, MAPKAPK3, and ACVR2B, showed a great potential for diagnosis.     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Quantitative Proteomics with siRNA Screening Identifies Novel Mechanisms of Trastuzumab Resistance in HER2 Amplified Breast Cancers

HER2 is a receptor tyrosine kinase that is overexpressed in 20% to 30% of human breast cancers and which affects patient prognosis and survival. Treatment of HER2-positive breast cancer with the monoclonal antibody trastuzumab (Herceptin) has improved patient survival, but the development of trastuzumab resistance is a major medical problem. Many of the known mechanisms of trastuzumab resistance cause changes in protein phosphorylation patterns, and therefore quantitative proteomics was used to examine phosphotyrosine signaling networks in trastuzumab-resistant cells. The model system used in this study was two pairs of trastuzumab-sensitive and -resistant breast cancer cell lines. Using stable isotope labeling, phosphotyrosine immunoprecipitations, and online TiO₂ chromatography utilizing a dual trap configuration, ∼1700 proteins were quantified. Comparing quantified proteins between the two cell line pairs showed only a small number of common protein ratio changes, demonstrating heterogeneity in phosphotyrosine signaling networks across different trastuzumab-resistant cancers. Proteins showing significant increases in resistant versus sensitive cells were subjected to a focused siRNA screen to evaluate their functional relevance to trastuzumab resistance. The screen revealed proteins related to the Src kinase pathway, such as CDCP1/Trask, embryonal Fyn substrate, and Paxillin. We also identify several novel proteins that increased trastuzumab sensitivity in resistant cells when targeted by siRNAs, including FAM83A and MAPK1. These proteins may present targets for the development of clinical diagnostics or therapeutic strategies to guide the treatment of HER2+ breast cancer patients who develop trastuzumab resistance.HER2 is a member of the epidermal growth factor receptor (EGFR)/ErbB family of receptor tyrosine kinases. Under normal physiologic conditions, HER2 tyrosine kinase signaling is tightly regulated spatially and temporally by the requirement for it to heterodimerize with a ligand bound family member, such as EGFR, HER3/ErbB3, or HER4/ErbB4 (1). However, in 20% to 30% of human breast cancer cases, HER2 gene amplification is present, resulting in a high level of HER2 protein overexpression and unregulated, constitutive HER2 tyrosine kinase signaling (2, 3). HER2 gene amplified breast cancer, also termed HER2-positive breast cancer, carries a poor prognosis, but the development of the HER2 targeted monoclonal antibody trastuzumab (Herceptin) has significantly improved patient survival (2). Despite the clinical effectiveness of trastuzumab, the development of drug resistance significantly increases the risk of patient death. This poses a major medical problem, as most metastatic HER2-positive breast cancer patients develop trastuzumab resistance over the course of their cancer treatment (4). The treatment approach for HER2+ breast cancer patients after trastuzumab resistance develops is mostly a trial-and-error process that subjects the patient to increased toxicity. Therefore, there is a substantial medical need for strategies to overcome trastuzumab resistance.Multiple trastuzumab-resistance mechanisms have been identified, and they alter signaling networks and protein phosphorylation patterns in either a direct or an indirect manner. These mechanisms can be grouped into three categories. The first category is the activation of a parallel signaling network by other tyrosine kinases. These kinases include the receptor tyrosine kinases, EGFR, IGF1R, Her3, Met, EphA2, and Axl, as well as the erythropoietin-receptor-mediated activation of the cytoplasmic tyrosine kinases Jak2 and Src (5–11). The second category is the activation of downstream signaling proteins. Multiple studies have demonstrated activation of the phosphatidylinositol-3-kinase (PI3K)/AKT pathway in trastuzumab resistance, which occurs either via deletion of the PTEN lipid phosphatase or mutation of the PI3K genes (12, 13). Activation of Src family kinases or overexpression of cyclin E, which increases the cyclin E–cyclin-dependent kinase 2 signaling pathway, has also been reported (14). The third category includes mechanisms that maintain HER2 signaling even in the presence of trastuzumab. The production of a truncated isoform of HER2, p95HER2, which lacks the trastuzumab binding site, causes constitutive HER2 signaling (15, 16). Overexpression of the MUC4 sialomucin complex inhibits trastuzumab binding to HER2 and thereby maintains HER2 signaling (17, 18).Given that multiple trastuzumab-resistance mechanisms alter signaling networks and protein phosphorylation patterns, we reasoned that mapping phosphotyrosine signaling networks using quantitative proteomics would be a highly useful strategy for analyzing known mechanisms and identifying novel mechanisms of trastuzumab resistance. Quantitative proteomics and phosphotyrosine enrichment approaches have been extensively used to study the EGFR signal networks (19–23). We and others have used these approaches to map the HER2 signaling network (22, 24, 25). Multiple other tyrosine kinase signaling networks were analyzed using quantitative proteomics, including Ephrin receptor, EphB2 (26–28), platelet-derived growth factor receptor (PDGFR) (21), insulin receptor (29, 30), and the receptor for hepatocyte growth factor, c-MET (31).The goal of this study is to identify, quantify, and functionally screen proteins that might be involved in trastuzumab resistance. We used two pairs of HER2 gene amplified trastuzumab-sensitive (parental, SkBr3 and BT474) and -resistant (SkBr3^R and BT474^R) human breast cancer cell lines as models for trastuzumab resistance. These cell lines and their trastuzumab-resistant derivatives have been extensively characterized and highly cited in the breast cancer literature (32, 33). Using stable isotope labeling of amino acids in cell culture (SILAC),¹ phosphotyrosine immunoprecipitations, and online TiO₂ chromatography with dual trap configuration, we quantified the changes in phosphotyrosine containing proteins and interactors between trastuzumab-sensitive and -resistant cells. Several of the known trastuzumab-resistance mechanisms were identified, which serves as a positive control and validation of our approach, and large protein ratio changes were measured in proteins that had not been previously connected with trastuzumab resistance. We then performed a focused siRNA screen targeting the proteins with significantly increased protein ratios. This screen functionally tested the role of the identified proteins and identifies which proteins might have the largest effect on reversing trastuzumab resistance.     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Proteomic Analysis of Temporally Stimulated Ovarian Cancer Cells for Biomarker Discovery

While ovarian cancer remains the most lethal gynecological malignancy in the United States, there are no biomarkers available that are able to predict therapeutic responses to ovarian malignancies. One major hurdle in the identification of useful biomarkers has been the ability to obtain enough ovarian cancer cells from primary tissues diagnosed in the early stages of serous carcinomas, the most deadly subtype of ovarian tumor. In order to detect ovarian cancer in a state of hyperproliferation, we analyzed the implications of molecular signaling cascades in the ovarian cancer cell line OVCAR3 in a temporal manner, using a mass-spectrometry-based proteomics approach. OVCAR3 cells were treated with EGF¹, and the time course of cell progression was monitored based on Akt phosphorylation and growth dynamics. EGF-stimulated Akt phosphorylation was detected at 12 h post-treatment, but an effect on proliferation was not observed until 48 h post-exposure. Growth-stimulated cellular lysates were analyzed for protein profiles between treatment groups and across time points using iTRAQ labeling and mass spectrometry. The protein response to EGF treatment was identified via iTRAQ analysis in EGF-stimulated lysates relative to vehicle-treated specimens across the treatment time course. Validation studies were performed on one of the differentially regulated proteins, lysosomal-associated membrane protein 1 (LAMP-1), in human tissue lysates and ovarian tumor tissue sections. Further, tissue microarray analysis was performed to demarcate LAMP-1 expression across different stages of epithelial ovarian cancers. These data support the use of this approach for the efficient identification of tissue-based markers in tumor development related to specific signaling pathways. LAMP-1 is a promising biomarker for studies of the progression of EGF-stimulated ovarian cancers and might be useful in predicting treatment responses involving tyrosine kinase inhibitors or EGF receptor monoclonal antibodies.Ovarian cancer is the leading cause of death from gynecologic malignancy in the United States, and the fifth leading cause of cancer-related deaths in women (1). Epithelial ovarian cancers are extensively heterogeneous; histological sub-classification by cell type includes serous, endometrioid, clear-cell, mucinous, transitional, squamous, and undifferentiated (2). Serous epithelial cancers are the most commonly diagnosed epithelial ovarian cancer subtype and are associated with the majority of ovarian-cancer-related deaths (1).From a molecular perspective, the basic characteristic of any cancerous cell is its ability to grow uncontrollably. As a cell proliferates, a cascade of molecular and morphological changes occurs, including the activation of signaling cascades that modulate cytoskeletal dynamics, cell cycle progression, and angiogenesis (3–5). In addition to the unrestrained aberrant proliferation of cancer cells, other processes are required for disease progression, including changes in cellular adhesion to endothelial cells and in the extracellular microenvironment (6). It is important to note, however, that cancer cell progression is not an instantaneous event, and the demarcation between non-cancer and cancer is not static. It is postulated that epithelial cancer cells transition to a highly motile and invasive mesenchymal cell type, and this epithelial-to-mesenchymal transition is a critical molecular mechanism in tumor progression and metastasis (6). Several important signaling cascades have been implicated in this transition, including those mediated by EGF, PDGF, and TGFβ and those involving PI3K/Akt activation (7, 8). Thus, biomarkers of cancer progression can serve as indicators of disease etiology and potential staging, as well as predictive markers of therapeutic regimen responses. The identification of differentially expressed proteins during cancer metastasis has the potential to be utilized both prognostically with regard to metastatic development and predictively, through the implementation of pathway-specific therapies.Molecular analyses indicate the oncogenic role of the epidermal growth factor receptor (EGFR) in several human cancers, including lung cancers and Her2-amplified breast cancers (9). However, less is known regarding the implications of aberrant EGFR expression in ovarian cancer progression, particularly in terms of increased activation of downstream signaling cascades and efficacious therapeutic regimens. Studies illustrate overamplification of the EGFR gene in between 4% and 22% of ovarian cancers, with aberrant protein expression in up to 60% of ovarian malignancies (10–12). Aberrant EGFR expression has been associated with high tumor grade, increased cancerous cell proliferation, and poorer patient outcomes (12–15). Gene amplification and the overexpression of other EGFR family members such as Her2 and ErbB3 have also been reported in epithelial ovarian cancers (15). Further, studies performed in vitro illustrate the ability of EGF to induce DNA synthesis and stimulate cell growth in OVCAR3 cells (16).Although EGFR and downstream EGF-regulated signaling cascades have been implicated in ovarian malignancies, the treatment of ovarian tumors with anti-EGFR agents has induced minimal response. Targeted EGFR therapies fall into two categories: monoclonal antibodies that target the receptor extracellular domain to prevent ligand binding, and tyrosine kinase inhibitors (TKIs), which aim to prevent the activation of downstream signaling cascades. Although EGFR inhibitors exhibit modest success in vitro, no agents have been approved by the U.S. Food and Drug Administration for the treatment of malignant ovarian tumors (17). Among other therapeutic approaches, studies have looked at the potential efficacy of the TKIs erlotinib and gefitinib in the treatment of ovarian cancers; unfortunately, neither drug was effective in eliciting a significant response in ovarian tumor treatment (12, 15, 18, 19). However, the identification of markers of pathway-stimulated processes might help to stratify disease and select patients with EGF signaling activation. The identified markers might facilitate the prediction of treatment responses.MS-based proteomic studies have been heavily implemented in the identification of candidate biomarkers in a variety of specimen sources ranging from epithelial ovarian cancer tissue to immortalized cell lines and cultured media (20–22). The human adenocarcinoma OVCAR3 cell line is derived from an epithelial ovarian cancer with a high grade serous cell type and exhibits many of the molecular and morphological aspects of serous epithelial cancers (23, 24). This cell line can be stimulated to promote or inhibit cellular proliferation using various molecular agonists and antagonists (23–25). Because of the molecular and morphological similarities between the OVCAR3 cell line and ovarian adenocarcinoma cells, it serves as an appropriate high-throughput surrogate for candidate biomarker identification. Further, the analysis of a single cell line allows for the identification of temporal protein regulation within a single homogeneous cell population using an orthogonal approach.In the present study, the OVCAR3 cell line was treated with the hyperproliferative molecule EGF or the PI3K/Akt inhibitor {"type":"entrez-nucleotide","attrs":{"text":"LY294002","term_id":"1257998346","term_text":"LY294002"}}LY294002 over a 48-h time course. Three time points were analyzed for biochemical and molecular changes, including Akt phosphorylation status and increased proliferation. Additionally, growth-stimulated and growth-inhibited cellular lysates were analyzed using quantitative proteomics with iTRAQ and MS/MS, and these analyses illustrated comparable global protein profiles between treatment groups and across time points. Differentially expressed proteins were identified in growth-stimulated cells as opposed to control (vehicle-treated) cells. One of the differentially regulated proteins, lysosomal-associated membrane protein 1 (LAMP-1, also known as CD107a), was further verified via immunoblotting and immunohistochemical analyses in normal and ovarian cancer tissues, in addition to tissue microarray analysis. This study demonstrates that through the use of a growth-stimulated cell culture model using EGF, the rapid identification of differentially regulated proteins as proliferation progresses may be achieved via large-scale proteomic analyses. The identification of regulated proteins along the pathway of increased cellular growth and proliferation might serve a predictive role in treatment outcomes.