Candidate Serological Biomarkers for Cancer Identified from the Secretomes of 23 Cancer Cell Lines and the Human Protein Atlas

Although cancer cell secretome profiling is a promising strategy used to identify potential body fluid-accessible cancer biomarkers, questions remain regarding the depth to which the cancer cell secretome can be mined and the efficiency with which researchers can select useful candidates from the growing list of identified proteins. Therefore, we analyzed the secretomes of 23 human cancer cell lines derived from 11 cancer types using one-dimensional SDS-PAGE and nano-LC-MS/MS performed on an LTQ-Orbitrap mass spectrometer to generate a more comprehensive cancer cell secretome. A total of 31,180 proteins was detected, accounting for 4,584 non-redundant proteins, with an average of 1,300 proteins identified per cell line. Using protein secretion-predictive algorithms, 55.8% of the proteins appeared to be released or shed from cells. The identified proteins were selected as potential marker candidates according to three strategies: (i) proteins apparently secreted by one cancer type but not by others (cancer type-specific marker candidates), (ii) proteins released by most cancer cell lines (pan-cancer marker candidates), and (iii) proteins putatively linked to cancer-relevant pathways. We then examined protein expression profiles in the Human Protein Atlas to identify biomarker candidates that were simultaneously detected in the secretomes and highly expressed in cancer tissues. This analysis yielded 6–137 marker candidates selective for each tumor type and 94 potential pan-cancer markers. Among these, we selectively validated monocyte differentiation antigen CD14 (for liver cancer), stromal cell-derived factor 1 (for lung cancer), and cathepsin L1 and interferon-induced 17-kDa protein (for nasopharyngeal carcinoma) as potential serological cancer markers. In summary, the proteins identified from the secretomes of 23 cancer cell lines and the Human Protein Atlas represent a focused reservoir of potential cancer biomarkers.Cancer is a major cause of mortality worldwide, accounting for 10 million new cases and more than 6 million deaths per year. In developing countries, cancer is the second most common cause of death, accounting for 23–25% of the overall mortality rate (1). Notwithstanding improvements in diagnostic imaging technologies and medical treatments, the long term survival of most cancer patients is poor. Cancer therapy is often challenging because the majority of cancers are initially diagnosed in their advanced stages. For example, the 5-year survival rate for patients with HNC¹ is less than 50%. More than 50% of all HNC patients have advanced disease at the time of diagnosis (2, 3). Enormous effort has been devoted to screening and characterizing cancer markers for the early detection of cancer. Thus far, these markers include carcinoembryonic antigen, prostate-specific antigen, α-fetoprotein, CA 125, CA 15-3, and CA 19-9. Unfortunately, most biomarkers have limited specificity, sensitivity, or both (4). Thus, there is a growing consensus that marker panels, which are more sensitive and specific than individual markers, would increase the efficacy and accuracy of early stage cancer detection (4–8). The development of novel and useful biomarker panels is therefore an urgent need in the field of cancer management.Proteomics technology platforms are promising tools for the discovery of new cancer biomarkers (9). Over the past decade, serum and plasma have been the major targets of proteomics studies aimed at identifying potential cancer biomarkers (10–13). However, the progress of these studies has been hampered by the complex nature of serum/plasma samples and the large dynamic range between the concentrations of different proteins (14). As cancer biomarkers are likely to be present in low amounts in blood samples, the direct isolation of these markers from plasma and serum samples requires a labor-intensive process involving the depletion of abundant proteins and extensive protein fractionation prior to mass spectrometric analysis (15–18). Alternatively, the secretome, or group of proteins secreted by cancer cells (19), can be analyzed to identify circulating molecules present at elevated levels in serum or plasma samples from cancer patients. These proteins have the potential to act as cancer-derived marker candidates, which are distinct from host-responsive marker candidates. We, along with other groups, have demonstrated the efficacy of secretome-based strategies in a variety of cancer types, including NPC (20), breast cancer (21, 22), lung cancer (23, 24), CRC (25, 26), oral cancer (27), prostate cancer (28, 29), ovarian cancer (30), and Hodgkin lymphoma (31). In these studies, proteins secreted from cancer cells into serum-free media were resolved by one- or two-dimensional gels followed by in-gel tryptic digestion and analysis via MALDI-TOF MS or LC-MS/MS. Alternatively, the proteins were trypsin-digested in solution and analyzed by LC-MS/MS. In general, more proteins were detected in the secretome using the LC-MS/MS method than the MALDI-TOF MS method. Advanced protein separation and identification technologies have made it possible to detect more proteins in the secretomes of cancer cells, thereby facilitating the discovery of cancer biomarkers.Although the cancer cell secretomes of various tumor types have been individually analyzed by different groups using distinct protocols, few studies have used the same protocol to compare cancer cell secretomes derived from different tumor types. We previously assessed the secretomes of 21 cancer cell lines derived from 12 cancer types (i.e. consisting of 795 protein identities and 325 non-redundant proteins) by one-dimensional gel and MALDI-TOF MS (25). Our preliminary findings revealed that different cell lines have distinct secreted protein profiles and that several putative biomarkers, such as Mac-2BP (20, 26, 27, 29) and cathepsin D (21, 23, 32), present in the secretome of a given cancer cell type are commonly shared among different cancers. These observations suggest that an in-depth comparison of secretomes derived from different tumor types may identify marker candidates common to most cancers as well as markers for specific cancer types. As an increasing number of proteins are identified in the secretomes of various cancer cell lines, scientists are faced with the challenge of quickly and efficiently narrowing down the list to candidates with higher chances of success during validation testing with precious clinical specimens.In the present study, we applied one-dimensional SDS-PAGE in conjunction with nano-LC-MS/MS (GeLC-MS/MS) (33, 34) to analyze the conditioned media of 23 cancer cell lines derived from 11 cancer types, including NPC, breast cancer, bladder cancer, cervical cancer, CRC, epidermoid carcinoma, liver cancer, lung cancer, T cell lymphoma, oral cancer, and pancreatic cancer. Within this data set, 4,584 non-redundant proteins were identified from a total of 23 cell lines, yielding an average of ∼1,300 proteins per cell line. Potential marker candidates were identified via the comparative analysis of different cell line secretomes and by putative linkages to cancer-relevant pathways. The selected proteins were further compared with the HPA (35) to generate a focused data set of proteins that are secreted or released, cancer type-specific, and highly expressed in human cancer tissues. Finally, we selectively validated four proteins as potential serological cancer markers using blood samples from cancer patients.     

Proteomics of Microparticles with SILAC Quantification (PROMIS-Quan): A Novel Proteomic Method for Plasma Biomarker Quantification

Unbiased proteomic analysis of plasma samples holds the promise to reveal clinically invaluable disease biomarkers. However, the tremendous dynamic range of the plasma proteome has so far hampered the identification of such low abundant markers. To overcome this challenge we analyzed the plasma microparticle proteome, and reached an unprecedented depth of over 3000 plasma proteins in single runs. To add a quantitative dimension, we developed PROMIS-Quan—PROteomics of MIcroparticles with Super-Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) Quantification, a novel mass spectrometry-based technology for plasma microparticle proteome quantification. PROMIS-Quan enables a two-step relative and absolute SILAC quantification. First, plasma microparticle proteomes are quantified relative to a super-SILAC mix composed of cell lines from distinct origins. Next, the absolute amounts of selected proteins of interest are quantified relative to the super-SILAC mix. We applied PROMIS-Quan to prostate cancer and compared plasma microparticle samples of healthy individuals and prostate cancer patients. We identified in total 5374 plasma-microparticle proteins, and revealed a predictive signature of three proteins that were elevated in the patient-derived plasma microparticles. Finally, PROMIS-Quan enabled determination of the absolute quantitative changes in prostate specific antigen (PSA) upon treatment. We propose PROMIS-Quan as an innovative platform for biomarker discovery, validation, and quantification in both the biomedical research and in the clinical worlds.Biomarker discovery in plasma is one of the holy grails of the proteomic field toward the development of noninvasive diagnostic/prognostic tests (1). To achieve this goal, proteomics necessitates a comprehensive view of the plasma proteome, accurate proteome quantification, combined with relatively short analytical times to enable multiple sample comparisons. However, MS-based biomarker discovery is limited by the vast dynamic range of the plasma, over 11 orders of magnitude (2, 3), which leads to the masking of “tissue leakage” proteins that comprise of potential biomarkers by the core plasma proteins. Two main complementary strategies have been employed to reach identification of low abundance proteins: (i) Targeted proteomics, in which the MS identifies and quantifies only predetermined peptides, thereby circumventing the system''s inherent tendency to preferentially detect abundant proteins. This approach is utilized for validation of preselected candidate markers (4–6). (ii) Plasma fractionation, which biochemically reduces the complexity of the proteomes, and enables discovery of novel biomarkers (7, 8).Targeted MS analysis is dominated by the selected reaction monitoring approach, often in combination with antibody-based enrichment of proteins or peptides and stable isotope labeled standards for quantification (9). This approach benefits from the sensitivity and quantitative capabilities of the triple-quadrupole instruments. Its major limitation is that it relies on prior discovery of candidates within the plasma samples using extensive tissue/cell-line-based analysis and prediction of potential biomarkers. The fractionation strategy reduces both the complexity and the dynamic range of the plasma through depletion of the most abundant plasma proteins, and/or through extensive biochemical separation of proteins and peptides. Although these fractionation approaches enabled identification of thousands of plasma proteins (7), they dramatically reduce the throughput of the method, and thus, the applicability to clinical studies.A distinct fractionation approach involves the isolation of plasma microparticles and exosomes. Microparticles are large vesicles (100 nm–1 μm), which protrude directly from the plasma membrane, whereas exosomes are smaller (40–100 nm) and originate from endocytic compartments known as the multivesicular endosomes. These microvesicles are constitutively shed from all cell types into the blood, carrying a proteomic signature of their cells of origin (10). Microparticles mediate local and systemic communication in various conditions, in particularly in cancer, where they can promote metastasis, immune evasion of cancer cells and angiogenesis (10–13), but also in other conditions including autoimmune diseases (14) and cardiovascular disorders (15). Therefore, circulating plasma microparticle proteomics can reveal biomarkers of various diseases as the basis for further diagnostic test development.The profiling of plasma microparticle proteomes initiated by Jin et al. in 2005, with the analysis of 16 samples using two-dimensional (2D)-gels followed by matrix assisted laser desorption ionization- time of flight (MALDI-TOF) MS analysis, which resulted in the identification of 83 proteins (16). In the following years, low resolution MS analysis of plasma microparticles reached up to 229 plasma microparticle proteins and high resolution MS analysis reached 458 proteins (all without false discovery rate (FDR)¹ correction)(17, 18). The latest and most comprehensive study of plasma microparticles proteome profiling was published in 2012 by Ostergaard et al., who analyzed 12 samples on the LTQ Orbitrap XL mass spectrometer and identified 536 proteins in total, after 1% FDR correction (19). Other studies have profiled the proteomes of microparticles and exosomes derived from various body fluids other than plasma, including urine (20), saliva (21), cerebral spinal fluid (22), breast milk (23), amniotic fluid (24), seminal fluid (25), and more. However, despite the dramatic reduction of the dynamic range of the analytes, so far it has not yet provided sufficient depth for biomarker discovery. Nevertheless, it has a good prospective for discovering biomarkers. For example, biochemical analysis of breast cancer patient leukocytes-derived microparticles correlated between increased tumor size and increased levels of carcinoembryonic antigen (CEA) and cancer antigen 15-3 (CA15-3), two well-known prognostic markers for colon and breast cancer, respectively (26).Combining all of the plasma proteomics approaches mentioned above, several prominent surveys of the human plasma proteome have been reported. The first large-scale collaborative study was conducted by the Human Proteome Organization (HuPO) group, which collectively identified 3020 proteins (7). These were later condensed to a list of 889 nonredundant proteins, after taking into account multiple hypotheses control with at least 95% confidence in protein identification (27). The Peptide Atlas team initially combined 91 studies, including the one conducted by HuPO, and altogether produced a list of 1929 proteins (28). Recently this team has elaborated their survey by assembling 127 studies (29) and reached the largest high-confidence list published so far of overall 3677 plasma proteins.In the current work we applied state of the art proteomics to study the microparticle proteome and developed the PROteomics of MIcroparticles with Super-SILAC Quantification (PROMIS-Quan) method, which combines deep plasma microparticle coverage of more than 3200 proteins in a single run, with dual-mode relative and absolute Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) quantification. We demonstrated its utilization on samples of prostate cancer patients, and calculated the absolute amount of PSA, a well-known prostate cancer biomarker.     

Analysis of Proteins That Rapidly Change Upon Mechanistic/Mammalian Target of Rapamycin Complex 1 (mTORC1) Repression Identifies Parkinson Protein 7 (PARK7) as a Novel Protein Aberrantly Expressed in Tuberous Sclerosis Complex (TSC)

Many biological processes involve the mechanistic/mammalian target of rapamycin complex 1 (mTORC1). Thus, the challenge of deciphering mTORC1-mediated functions during normal and pathological states in the central nervous system is challenging. Because mTORC1 is at the core of translation, we have investigated mTORC1 function in global and regional protein expression. Activation of mTORC1 has been generally regarded to promote translation. Few but recent works have shown that suppression of mTORC1 can also promote local protein synthesis. Moreover, excessive mTORC1 activation during diseased states represses basal and activity-induced protein synthesis. To determine the role of mTORC1 activation in protein expression, we have used an unbiased, large-scale proteomic approach. We provide evidence that a brief repression of mTORC1 activity in vivo by rapamycin has little effect globally, yet leads to a significant remodeling of synaptic proteins, in particular those proteins that reside in the postsynaptic density. We have also found that curtailing the activity of mTORC1 bidirectionally alters the expression of proteins associated with epilepsy, Alzheimer''s disease, and autism spectrum disorder—neurological disorders that exhibit elevated mTORC1 activity. Through a protein–protein interaction network analysis, we have identified common proteins shared among these mTORC1-related diseases. One such protein is Parkinson protein 7, which has been implicated in Parkinson''s disease, yet not associated with epilepsy, Alzheimers disease, or autism spectrum disorder. To verify our finding, we provide evidence that the protein expression of Parkinson protein 7, including new protein synthesis, is sensitive to mTORC1 inhibition. Using a mouse model of tuberous sclerosis complex, a disease that displays both epilepsy and autism spectrum disorder phenotypes and has overactive mTORC1 signaling, we show that Parkinson protein 7 protein is elevated in the dendrites and colocalizes with the postsynaptic marker postsynaptic density-95. Our work offers a comprehensive view of mTORC1 and its role in regulating regional protein expression in normal and diseased states.The mechanistic/mammalian target of rapamycin complex 1 (mTORC1)¹ is a serine/threonine protein kinase that is highly expressed in many cell types (1). In the brain, mTORC1 tightly coordinates different synaptic plasticities — long-term potentiation (LTP) and long-term depression (LTD) — the molecular correlates of learning and memory (2–5). Because mTORC1 is at the core of many synaptic signaling pathways downstream of glutamate and neurotrophin receptors, many hypothesize that dysregulated mTORC1 signaling underlies cognitive deficits observed in several neurodegenerative diseases (3, 6–17). For example, mTORC1 and its downstream targets are hyperactive in human brains diagnosed with Alzheimer''s disease (AD) (18–20). Additionally in animal models of autism spectrum disorder (ASD), altered mTORC1 signaling contributes to the observed synaptic dysfunction and aberrant network connectivity (13, 15, 21–27). Furthermore, epilepsy, which is common in AD and ASD, has enhanced mTORC1 activity (28–32).Phosphorylation of mTORC1, considered the active form, is generally regarded to promote protein synthesis (33). Thus, many theorize that diseases with overactive mTORC1 arise from excessive protein synthesis (14). Emerging data, however, show that suppressing mTORC1 activation can trigger local translation in neurons (34, 35). Pharmacological antagonism of N-methyl-d-aspartate (NMDA) receptors, a subtype of glutamate receptors that lies upstream of mTOR activation, promotes the synthesis of the voltage-gated potassium channel, Kv1.1, in dendrites (34, 35). Consistent with these results, in models of temporal lobe epilepsy there is a reduction in the expression of voltage-gated ion channels including Kv1.1 (30, 31, 36). Interestingly in a model of focal neocortical epilepsy, overexpression of Kv1.1 blocked seizure activity (37). Because both active and inactive mTORC1 permit protein synthesis, we sought to determine the proteins whose expression is altered when mTORC1 phosphorylation is reduced in vivo.Rapamycin is an FDA-approved, immunosuppressive drug that inhibits mTORC1 activity (38). We capitalized on the ability of rapamycin to reduce mTORC1 activity in vivo and the unbiased approach of mass spectrometry to identify changes in protein expression. Herein, we provide evidence that mTORC1 activation bidirectionally regulates protein expression, especially in the PSD where roughly an equal distribution of proteins dynamically appear and disappear. Remarkably, using protein–protein interaction networks facilitated the novel discovery that PARK7, a protein thus far only implicated in Parkinson''s disease, (1) is up-regulated by increased mTORC1 activity, (2) resides in the PSD only when mTORC1 is active, and (3) is aberrantly expressed in a rodent model of TSC, an mTORC1-related disease that has symptoms of epilepsy and autism. Collectively, these data provide the first comprehensive list of proteins whose abundance or subcellular distributions are altered with acute changes in mTORC1 activity in vivo.     

Evaluation of Protein Profiles From Treated Xenograft Tumor Models Identifies an Antibody Panel for Formalin-fixed and Paraffin-embedded (FFPE) Tissue Analysis by Reverse Phase Protein Arrays (RPPA)

Reverse phase protein arrays (RPPA) are an established tool for measuring the expression and activation status of multiple proteins in parallel using only very small amounts of tissue. Several studies have demonstrated the value of this technique for signaling pathway analysis using proteins extracted from fresh frozen (FF) tissue in line with validated antibodies for this tissue type; however, formalin fixation and paraffin embedding (FFPE) is the standard method for tissue preservation in the clinical setting. Hence, we performed RPPA to measure profiles for a set of 300 protein markers using matched FF and FFPE tissue specimens to identify which markers performed similarly using the RPPA technique in fixed and unfixed tissues. Protein lysates were prepared from matched FF and FFPE tissue specimens of individual tumors taken from three different xenograft models of human cancer. Materials from both untreated mice and mice treated with either anti-HER3 or bispecific anti-IGF-1R/EGFR monoclonal antibodies were analyzed. Correlations between signals from FF and FFPE tissue samples were investigated. Overall, 60 markers were identified that produced comparable profiles between FF and FFPE tissues, demonstrating significant correlation between the two sample types. The top 25 markers also showed significance after correction for multiple testing. The panel of markers covered several clinically relevant tumor signaling pathways and both phosphorylated and nonphosphorylated proteins were represented. Biologically relevant changes in marker expression were noted when RPPA profiles from treated and untreated xenografts were compared. These data demonstrate that, using appropriately selected antibodies, RPPA analysis from FFPE tissue is well feasible and generates biologically meaningful information. The identified panel of markers that generate similar profiles in matched fixed and unfixed tissue samples may be clinically useful for pharmacodynamic studies of drug effect using FFPE tissues.Many human diseases are characterized by abnormalities in complex signaling pathways (1). The expression and activation status of proteins from these deregulated pathways has traditionally been analyzed using single marker techniques such as immunohistochemistry and Western blotting. Although these techniques have provided valuable information on the molecular abnormalities underlying human disease, they are labor intensive, have a low throughput, and often require high sample volume. Furthermore, techniques such as Western blotting are not applicable in the routine clinical setting. Miniaturized parallel immunoassay techniques have been developed in recent years and have played a pivotal role in biomarker discovery (2). Antibody arrays enable multiple potential disease markers to be investigated in a single sample in parallel (3). Beyond this, Reverse Phase Protein Arrays (RPPA)¹ are sensitive high throughput tools that can quantify protein expression levels and activation status (posttranslational modifications such as phosphorylation) in multiple experimental samples simultaneously. The technique requires only minute amounts of samples, printed as lysate arrays onto slides, and hundreds of markers of interest can be investigated, array by array, in a miniaturized dot blot manner. Numerous reports have demonstrated that RPPA can be applied to various sources of cells and tissues to analyze protein profiles, signaling pathway networks, and for the identification of biomarkers (4–13). A recently published workshop report reviews the full potential and advances of RPPA for use in clinical, translational, and basic research (11).In oncology, the parallel profiling of multiple protein markers is particularly desirable to study tumor initiation and progression, to classify tumor disease states on the molecular level, and to discover and monitor biomarkers that can predict therapeutic response or tumor recurrence (14–16). The study of signaling response and analysis of pharmacodynamic (PD) markers upon treatment using in vitro and in vivo test systems (e.g. cell line or patient derived xenograft tumor models) is an established component of preclinical and early clinical drug development. These techniques can provide evidence of target pathway modulation for new therapeutic lead candidate compounds and provide valuable information on the drug mode of action (17), especially in the translational phase. Multiplex analyses of PD biomarkers by RPPA have been performed in vitro using cancer cell lines (18, 19) as well as in patient-derived tumor tissue and blood samples (20, 21) to assess response to treatment and target inhibition. A combination of RPPA signaling pathway mapping and functional PET imaging has recently been successfully evaluated in xenograft models as an early response PD marker for anti-cancer drug efficacy (13).Translating miniaturized multiple protein analysis platforms-such as RPPA - from preclinical to clinical applicability is highly desirable; however, issues such as the limited amount of available clinical samples and tumor heterogeneity must first be addressed. Furthermore, most studies of RPPA in tumor tissue to date have been conducted using proteins extracted from fresh-frozen (FF) tissue specimens; whereas, formalin fixation and paraffin embedding (FFPE) is the standard method for tissue preservation used in clinical pathology laboratories. FFPE yields excellent tissue architecture for histological assessment and enables analysis of individual proteins in situ by techniques such as immunohistochemistry. However, formalin fixation leads to extensive protein–protein and protein–nucleic acid cross-linking (22), which can hamper protein extraction and reduce both the overall yield of extracted protein and the profile of proteins detectable by proteomic techniques (23, 24). Furthermore, formalin-induced cross-linking induces conformational changes in protein structure that can alter the immunoreactivity of some proteins in situ by hiding or altering peptide epitopes (25, 26). Such artifacts are absent from snap-frozen tissue; therefore, protein profiles obtained from FF tissue are likely to reflect the in vivo biology of the tumor more closely. However, FF tumor tissue is not widely available because it is costly to collect and maintain in the clinical setting. FFPE tissue samples are routinely archived by nearly every hospital and offer a unique opportunity to study thousands of samples retrospectively with extensive clinical records and follow-up information.Several groups have now established protocols for retrieving cross-linked proteins from fixed tissues (27–33). These methods are mainly based on the use of concentrated ionic detergents and high temperature protocols closely related to the antigen retrieval methods developed for immunohistochemistry. These studies show that obtaining nondegraded, full-length proteins from FFPE tissues for multiplex analyses is feasible (27–33). More recently, protein extraction techniques optimized for fixed samples have been used to successfully conduct RPPA using FFPE tissue biopsies from different cancer types (34–40). Guo et al. systematically investigated several protein extraction methods and demonstrated that RPPA of FFPE materials is feasible, reproducible and can generate biologically relevant protein profiles (41). Other studies have confirmed the validity of this approach and shown that data generated from RPPA analyses of FFPE tissue demonstrate good concordance with traditional immunohistochemistry markers such as HER2 protein in breast cancer (34, 40). However, to date, analyses have been performed only for a limited set of protein markers.To evaluate whether analysis of a broader panel of protein markers is feasible and generates meaningful data from FFPE tumor tissue sections, we conducted RPPA on matched samples of FF and FFPE tissues using a set of 300 markers, the largest panel reported to date. Our aim was to identify markers that performed similarly when comparing the protein profiles measured in protein extracts from matched FF and FFPE tissue, using RPPA assays established for use in frozen tissues. Correlating selected markers and assays in such a way should qualify RPPA for further use with FFPE tissues of clinical relevance, e.g. in PD marker studies. In this paper, we have specifically focused on the technical issues relevant for using the RPPA platform in a clinical setting, and did not address the biology of the test systems used in detail. However, the models used have been pre-characterized to identify key signaling parameters in context of targeted drug treatment (42). We conducted a systematic comparison of RPPA protein profiles in matched FF and FFPE tumor tissues resected from three different xenograft models of human cancer, each treated with targeted therapeutic antibodies that have previously been shown to achieve tumor growth inhibition. Furthermore, we investigated the effect of targeted drug treatment on protein expression and activation status, and the concordance of matched FF and FFPE tissue RPPA profiles. Finally, with one of the applied tumor models, we compared a set of protein profiles measured with two different multiple assay platforms - the RPPA and the Luminex Bio-Plex system, and discuss their relevance with respect to the analysis of FFPE tissue.     

Matricellular Protein CCN3 (NOV) Regulates Actin Cytoskeleton Reorganization

CCN3 (NOV), a putative ligand for integrin receptors, is tightly associated with the extracellular matrix and mediates diverse cellular functions, including cell adhesion and proliferation. CCN3 has been shown to negatively regulate growth although it promotes migration in a cell type-specific manner. In this study, overexpression of CCN3 reduces growth and increases intercellular adhesion of breast cancer cells. Interestingly, CCN3 overexpression also led to the formation of multiple pseudopodia that are enriched in actin, CCN3, and vinculin. Breast cancer cells preincubated with exogenous CCN3 protein also induced the same phenotype, indicating that secreted CCN3 is sufficient to induce changes in cell morphology. Surprisingly, extracellular CCN3 is internalized to the early endosomes but not to the membrane protrusions, suggesting pseudopodia-enriched CCN3 may derive from a different source. The presence of an intracellular variant of CCN3 will be consistent with our finding that the cytoplasmic tail of the gap junction protein connexin43 (Cx43) associates with CCN3. Cx43 is a channel protein permitting intercellular communication to occur. However, neither the channel properties nor the protein levels of Cx43 are affected by the CCN3 protein. In contrast, CCN3 proteins are down-regulated in the absence of Cx43. Finally, we showed that overexpression of CCN3 increases the activity of the small GTPase Rac1, thereby revealing a pathway that links Cx43 directly to actin reorganization.The CCN (CYR61/Connective Tissue Growth Factor/Nephroblastoma Overexpressed) family of multimodular proteins mediates diverse cellular functions, including cell adhesion, migration, and proliferation (1–3). Overexpression of CCN3, one of the founding members of the family, inhibits proliferation in most types of tumors such as glioblastoma and Ewing sarcoma (4, 5). Similarly, down-regulation of CCN3 has been suggested to promote melanoma progression (6). On the other hand, CCN3 can also promote migration in sarcoma and glioblastoma (4, 7), although a separate study shows that it decreases the invasion of melanoma (6). Therefore, in contrast to its role in growth suppression, the role of CCN3 signaling in cell motility is less clear.Most evidence suggests CCN3 mediates its effects by binding to the integrin proteins, such as the αVβ3 receptors (8, 9), and that CCN3 alters cell adhesion in an integrin-dependent fashion (4, 10). In melanocytes, the discoidin domain receptor 1 mediates CCN3-dependent adhesion (11). CCN3 has also been observed to associate with Notch1 (12), fibulin 1C (13), S100A4 (14), and the gap junction protein Cx43³ (15, 16), suggesting that CCN3 may also modulate cell growth via non-integrin signaling pathways.Gap junction proteins are best known for forming channels between cells, contributing to intercellular communication by allowing the exchange of small ions and molecules (17, 18). Consequently, attenuated intercellular communication has been implicated in promoting carcinogenesis (19, 20). Recent evidence has indicated that connexins can mediate channel-independent growth control through interaction of their C-terminal cytoplasmic tail with various intracellular signaling molecules (21–23). In addition, many Cx43-interacting proteins, including ZO-1 (zonula occludens-1) (24), Drebrin (25), and N-cadherin (26) associate with F-actin, thus placing Cx43 in close proximity to the actin cytoskeleton.In this study, we show for the first time that CCN3 reorganizes the actin cytoskeleton of the breast cancer cells MDA-MB-231 with the formation of multiple cell protrusions, possibly by activating the small GTPase Rac1. Our results also suggest an alternative route by which Cx43 may be functionally linked to actin cytoskeletal signaling via CCN3.     

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]     

Targeted Proteomics to Assess the Response to Anti-Angiogenic Treatment in Human Glioblastoma (GBM)

Glioblastoma (GBM) is a highly aggressive primary brain tumor with dismal outcome for affected patients. Because of the significant neo-angiogenesis exhibited by GBMs, anti-angiogenic therapies have been intensively evaluated during the past years. Recent clinical studies were however disappointing, although a subpopulation of patients may benefit from such treatment. We have previously shown that anti-angiogenic targeting in GBM increases hypoxia and leads to a metabolic adaptation toward glycolysis, suggesting that combination treatments also targeting the glycolytic phenotype may be effective in GBM patients. The aim of this study was to identify marker proteins that are altered by treatment and may serve as a short term readout of anti-angiogenic therapy. Ultimately such proteins could be tested as markers of efficacy able to identify patient subpopulations responsive to the treatment. We applied a proteomics approach based on selected reaction monitoring (SRM) to precisely quantify targeted protein candidates, selected from pathways related to metabolism, apoptosis and angiogenesis. The workflow was developed in the context of patient-derived intracranial GBM xenografts developed in rodents and ensured the specific identification of human tumor versus rodent stroma-derived proteins. Quality control experiments were applied to assess sample heterogeneity and reproducibility of SRM assays at different levels. The data demonstrate that tumor specific proteins can be precisely quantified within complex biological samples, reliably identifying small concentration differences induced by the treatment. In line with previous work, we identified decreased levels of TCA cycle enzymes, including isocitrate dehydrogenase, whereas malectin, calnexin, and lactate dehydrogenase A were augmented after treatment. We propose the most responsive proteins of our subset as potential novel biomarkers to assess treatment response after anti-angiogenic therapy that warrant future analysis in clinical GBM samples.In the context of glioblastoma (GBM)¹, the quest for effective biomarkers is vital given that GBM is the most aggressive primary brain tumor in adults and no curative treatment is currently available (1). GBM is characterized by extensive invasion into the brain parenchyma, a high proliferation rate, neo-angiogenesis and significant cellular and molecular heterogeneity. Current treatment involves neurosurgery, radiotherapy and chemotherapy, yet the median life expectancy of affected patients is less than fifteen months. Recent efforts have focused on targeting the vascular endothelial growth factor (VEGF) system which is critical for tumor angiogenesis, however GBM quickly develop escape mechanisms leading to tumor progression (2, 3). Previous work from our group demonstrated that GBMs adapt to anti-VEGF treatment via a metabolic switch in tumor cells toward increased glycolysis (4, 5). This was accompanied by increased hypoxia and tumor cell invasion, with little or no effect on tumor growth (4). In agreement with these preclinical studies, two large scale clinical trials addressing the impact of bevacizumab, a VEGF targeting antibody, in newly diagnosed GBM patients reported disappointing results: although progression free survival appeared to be improved, no effect on overall survival was observed (6, 7). The evaluation of such studies are complicated by the fact that anti-angiogenic agents affect blood vessel permeability thereby directly modulating neuroimaging parameters used to determine treatment effects (8, 9). Thus there is a need for molecular biomarkers to adequately determine treatment response to anti-angiogenic agents.MS-based proteomics (10, 11) is widely used in the field of cancer research in particular in the context of biomarker development including discovery and verification. The application of the selected reaction monitoring (SRM) approach to proteomics reinforced the importance of MS in biomarker development (12–14). Indeed, SRM is a targeted proteomics approach that allows a precise and absolute quantification of previously selected marker candidates (15, 16). Moreover it can be applied in a supervised discovery phase for potential biomarkers (17, 18), i.e. the precise quantification of a wider range of selected biomarkers of interest by the use of stable isotope labeled (SIL) peptides in crude quality. Because of its high selectivity, sensitivity and accuracy, SRM, also named multiple reaction monitoring (MRM), is currently the reference method in targeted proteomics (14, 19).The aim of this study was to identify proteins that are altered by anti-angiogenic treatment, thereby providing biomolecular signatures of tumor response in GBM. Ultimately such protein markers could be evaluated for their utility as markers of efficacy that allow to discriminate responders from nonresponders. The study was focused on target proteins that may exhibit significant differences in protein expression reflecting the metabolic switch exhibited during anti-angiogenic therapy. An SRM workflow designed on a triple quadrupole platform (20), was developed and optimized in the context of GBM xenografts treated with bevacizumab in order to perform, in a supervised manner, a precise relative quantification of target proteins. We have previously shown that patient derived GBM xenografts developed in rodents faithfully reflect human pathology and allow a detailed analysis of the tumor and stromal compartments (4, 21–24). Furthermore xenograft models facilitate the access to control samples as well as the possibility of controlled interventional studies (25). The results presented herein demonstrate the feasibility of SRM to precisely quantify small changes in protein concentration after treatment. We highlight the importance of peptide selection, data normalization and consideration of the variability of target proteins within complex biological samples before assessing their concentration changes in subsequent comparative studies. From an initial set of 100 candidates, we screened 74 proteins and identified 32 responsive to anti-angiogenic treatment. We propose malectin, calnexin, lactate dehydrogenase A (LDHA), and isocitrate dehydrogenase (IDH) as novel response markers to anti-angiogenic therapy.     

Secretomic Analysis Identifies Alpha-1 Antitrypsin (A1AT) as a Required Protein in Cancer Cell Migration,Invasion, and Pericellular Fibronectin Assembly for Facilitating Lung Colonization of Lung Adenocarcinoma Cells

Metastasis is a major obstacle that must be overcome for the successful treatment of lung cancer. Proteins secreted by cancer cells may facilitate the progression of metastasis, particularly within the phases of migration and invasion. To discover metastasis-promoting secretory proteins within cancer cells, we used the label-free quantitative proteomics approach and compared the secretomes from the lung adenocarcinoma cell lines CL1-0 and CL1-5, which exhibit low and high metastatic properties, respectively. By employing quantitative analyses, we identified 660 proteins, 68 of which were considered to be expressed at different levels between the two cell lines. High levels of A1AT were secreted by CL1-5, and the roles of A1AT in the influence of lung adenocarcinoma metastasis were investigated. Molecular and pathological confirmation demonstrated that altered expression of A1AT correlates with the metastatic potential of lung adenocarcinoma. The migration and invasion properties of CL1-5 cells were significantly diminished by reducing the expression and secretion of their A1AT proteins. Conversely, the migration and invasion properties of CL1-0 cells were significantly increased through the overexpression and secretion of A1AT proteins. Furthermore, the assembly levels of the metastasis-promoting pericellular fibronectin (FN1), which facilitates colonization of lung capillary endothelia by adhering to the cell surface receptor dipeptidyl peptidase IV (DPP IV), were higher on the surfaces of suspended CL1-5 cells than on those of the CL1-0 cells. This discovery reflects previous findings in breast cancer. In line with this finding, FN1 assembly and the lung colonization of suspended CL1-5 cells were inhibited when endogenous A1AT protein was knocked down using siRNA. The major thrust of this study is to demonstrate the effects of coupling the label-free proteomics strategy with the secretomes of cancer cells that differentially exhibit invasive and metastatic properties. This provides a new opportunity for the effective identification of metastasis-associated proteins that are secreted by cancer cells and promote experimental metastasis.Lung cancer is the leading cause of cancer death, and ∼90% of all lung cancer deaths are attributed to metastases (1). Approximately 95% of lung cancer patients are not diagnosed until they develop symptoms, and 85% of the newly diagnosed lung cancer patients are already in the advanced stages of the disease (2, 3). Once the tumor cells have metastasized and spread throughout the lungs, the cancer is considerably more difficult to treat. Invasiveness and metastasis are major threats to successful treatment. Cancer metastasis is an intricate, multi-step process in which the tumor cells must gain both migratory and invasive properties (4). In metastasis research, there are two common in vivo models, spontaneous and experimental metastasis (5–7). In brief, spontaneous metastasis refers to primary tumor cells that are able to dissociate from the primary tumor and metastasize to the secondary organ via the circulatory system. In contrast, experimental metastasis refers to the injection of tumor cells directly into the systemic circulation. Many researchers have attempted to determine the molecular basis of these transitions in hopes of developing target-specific drugs or biomarkers for the prevention and diagnosis of metastasis. Although there have been many discoveries regarding a particular protein''s influence on metastasis, the contribution of many protein targets to the metastatic process remains poorly defined.The term “secretome” was originally coined to refer to the secretory proteins from the entire genome of Bacillus subtilis (8). The word secretome has developed a broader meaning and now refers to the proteins released by a cell, tissue, or organism through various mechanisms, which include classical secretion, nonclassical secretion, membrane protein shedding, and secretion via exosomes (9–11). Each step involved in tumor metastasis, including migration and invasion, requires specific molecular interactions by both the tumor cells and the surrounding extracellular matrix (12). Some interactions are mediated by secretory factors that function as catalytic agents or by specific recognitions. For example, cathepsins, a family of lysosomal cysteine and aspartic proteases, plays a role in breaking down the connective barriers in the extracellular matrix and basement membranes, effectively enhancing the metastasis of tumor cells (13). These unique functions correlate with invasive activity and are otherwise known as the promigratory and pro-invasive effects on cells (14, 15). With respect to cancer progression, chronic changes or abnormal secretions of certain proteins may indicate a pathologic condition and, therefore, provide suitable targets for therapeutic and biomarker discoveries (16).Proteomic tools have been proposed as a new platform for studying complex biological functions, which entail large numbers and networks of proteins (17). Moving beyond the imposing burden of providing lists of proteins identified in certain samples, the field of quantitative proteomics yields information that specifically recognizes the differences between samples and has emerged as a very important area of research in the study of cancer. These proteomics approaches have been extensively applied to cell secretome analyses for the elucidation of disease mechanisms, diagnoses, and new drug developments (16, 18–23). To comprehensively understand the roles of the secretion-related regulations in metastatic progression, the label-free quantitative proteomics approach was used to identify metastatic-associated proteins secreted by lung adenocarcinoma cells. Comparative secretome analysis was conducted in lung adenocarcinoma cells with differing levels of migration and invasiveness (CL1-0 versus CL1-5) (24). The CL1 cell lines have been used in previous metastasis research as novel protein targets associated with lung cancer metastasis discovery (25–28). The characterized protein, A1AT, was validated for its association and functions involved with lung adenocarcinoma metastasis by subjecting the cells to experimental metastasis assays in vitro and in vivo.     

Enhanced Interferon Signaling Pathway in Oral Cancer Revealed by Quantitative Proteome Analysis of Microdissected Specimens Using 16O/18O Labeling and Integrated Two-dimensional LC-ESI-MALDI Tandem MS

Oral squamous cell carcinoma (OSCC) remains one of the most common cancers worldwide, and the mortality rate of this disease has increased in recent years. No molecular markers are available to assist with the early detection and therapeutic evaluation of OSCC; thus, identification of differentially expressed proteins may assist with the detection of potential disease markers and shed light on the molecular mechanisms of OSCC pathogenesis. We performed a multidimensional ¹⁶O/¹⁸O proteomics analysis using an integrated ESI-ion trap and MALDI-TOF/TOF MS system and a computational data analysis pipeline to identify proteins that are differentially expressed in microdissected OSCC tumor cells relative to adjacent non-tumor epithelia. We identified 1233 unique proteins in microdissected oral squamous epithelia obtained from three pairs of OSCC specimens with a false discovery rate of <3%. Among these, 977 proteins were quantified between tumor and non-tumor cells. Our data revealed 80 dysregulated proteins (53 up-regulated and 27 down-regulated) when a 2.5-fold change was used as the threshold. Immunohistochemical staining and Western blot analyses were performed to confirm the overexpression of 12 up-regulated proteins in OSCC tissues. When the biological roles of 80 differentially expressed proteins were assessed via MetaCore™ analysis, the interferon (IFN) signaling pathway emerged as one of the most significantly altered pathways in OSCC. As many as 20% (10 of 53) of the up-regulated proteins belonged to the IFN-stimulated gene (ISG) family, including ubiquitin cross-reactive protein (UCRP)/ISG15. Using head-and-neck cancer tissue microarrays, we determined that UCRP is overexpressed in the majority of cheek and tongue cancers and in several cases of larynx cancer. In addition, we found that IFN-β stimulates UCRP expression in oral cancer cells and enhances their motility in vitro. Our findings shed new light on OSCC pathogenesis and provide a basis for the future development of novel biomarkers.Oral cancer is one of the most common cancers worldwide. In Taiwan, it remains the sixth most prevalent cancer overall and the fourth most common cancer to afflict males. Over the past 2 decades, the overall incidence and morbidity rates of patients with oral cancer have increased continuously. Epidemiological studies show that ∼50–70% of patients who undergo surgery for oral cancer die within 5 years (1–6). This poor prognosis predominantly reflects late stage presentation, secondary cancer occurrence, local recurrence, and metastasis (7) as well as the lack of suitable markers for cancer detection. Therefore, there is an urgent need to identify proteins that are dysregulated in patients with oral cancer. Such proteins would serve as a valuable resource to find markers for the early diagnosis and disease monitoring of patients with oral cancer.Oral cancer, a subtype of head-and-neck squamous cell carcinoma (HNSCC),¹ can form at various locations within the oral cavity, including the lips, tongue, buccal surfaces, gingiva, palate, floor of mouth, and oropharynx. Tongue and buccal cancers are the most common and most serious types of oral squamous cell carcinoma (OSCC) especially in southeast Asia (2, 8). Alcohol abuse, smoking, and betel nut chewing are the main risk factors for OSCC. Genome-wide approaches have revealed many epigenetic and genetic alterations in patients with OSCC, including several biochemical pathways (9–11). However, these studies have provided little information regarding alterations in the protein profiles of patients with OSCC. Recently state-of-the-art proteomics technologies have revealed alterations in protein abundance, posttranslational modification and turnover, and spatial and temporal distribution within tumor specimens. Using proteomics approaches, aberrantly expressed proteins have been identified in body fluids (12–14), frozen or paraffin-embedded tissues (15–18), and cultured cell lines (19–22). The fold changes in protein expression in samples from healthy and cancerous states as well as the roles of each protein in disease progression must be determined to identify potential candidates for biomarkers and therapeutic targets.Blood samples are often used in clinical studies because they are less invasive and more convenient than other types of bodily samples and can be analyzed using automatic and high throughput techniques. Unfortunately the extremely dynamic range of protein concentrations in serum and plasma impedes the direct discovery of potential biomarkers (23, 24). Proteins can be released into the blood from diseased tissues during cell death or via secretory pathways. To counteract this problem, serum and plasma biomarkers are sometimes identified by analyzing differential protein expression in tumors and adjacent normal tissues (25).Like many other types of solid tumors, OSCCs often contain heterogeneous cell populations. Laser capture microdissection (LCM) is a common technique used to dissect a particular tumor cell type from heterogeneous cell populations, thereby reducing the tissue complexity and facilitating the discovery of tumor-associated molecules in small samples (9, 26–28). Several laboratories have studied differential protein expression in microdissected tissue specimens from patients with head-and-neck cancer in efforts to discover novel tumor markers (15, 17, 29–31). However, the semiquantitative approaches used in these studies may have limited the number of potential markers identified as well as the reliability of the protein quantification. To minimize technical variations and improve the reliability of protein quantification, a variety of sophisticated stable isotope labeling techniques have been developed for MS-based proteomics analysis, including chemical (32, 33), metabolic (34, 35), and enzymatic (36–38) labeling techniques. Improvements in the quality and accuracy of quantitative proteomics analysis via such stable isotope labeling strategies have facilitated the discovery of potential tumor markers in malignancies such as OSCC/HNSCC (16, 39, 40).Here we describe a strategy consisting of LCM, ¹⁸O labeling, two-dimensional (2D) LC separation and an integrated ESI-MS/MS and MALDI-TOF/TOF MS (ESI-MALDI tandem MS) system. This strategy was used to identify differentially expressed proteins in OSCC cells microdissected from oral cancer tissue biopsies. A computational data analysis pipeline was also developed to calculate the relative abundances of ¹⁶O- and ¹⁸O-labeled peptides (similar to that described in a previous report (26)) and to assist with multidimensional protein identification and quantification. Using three pairs of OSCC specimens, we identified 1233 unique proteins with a false discovery rate less than 3%. Of these, we quantified 977 non-redundant proteins in which 80 proteins displayed ≥2.5-fold changes in expression in microdissected tumor cells versus non-tumor cells. We validated these results in 12 selected targets via immunohistochemical staining and Western blot analysis of OSCC tissues. Our findings reveal that the interferon (IFN) signaling pathway is significantly altered in OSCC lesions.     

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.     

Proteomics Analysis of Cancer Exosomes Using a Novel Modified Aptamer-based Array (SOMAscanTM) Platform

We have used a novel affinity-based proteomics technology to examine the protein signature of small secreted extracellular vesicles called exosomes. The technology uses a new class of protein binding reagents called SOMAmers® (slow off-rate modified aptamers) and allows the simultaneous precise measurement of over 1000 proteins. Exosomes were highly purified from the Du145 prostate cancer cell line, by pooling selected fractions from a continuous sucrose gradient (within the density range of 1.1 to 1.2 g/ml), and examined under standard conditions or with additional detergent treatment by the SOMAscan^TM array (version 3.0). Lysates of Du145 cells were also prepared, and the profiles were compared. Housekeeping proteins such as cyclophilin-A, LDH, and Hsp70 were present in exosomes, and we identified almost 100 proteins that were enriched in exosomes relative to cells. These included proteins of known association with cancer exosomes such as MFG-E8, integrins, and MET, and also those less widely reported as exosomally associated, such as ROR1 and ITIH4. Several proteins with no previously known exosomal association were confirmed as exosomally expressed in experiments using individual SOMAmer® reagents or antibodies in micro-plate assays. Western blotting confirmed the SOMAscan^TM-identified enrichment of exosomal NOTCH-3, L1CAM, RAC1, and ADAM9. In conclusion, we describe here over 300 proteins of hitherto unknown association with prostate cancer exosomes and suggest that the SOMAmer®-based assay technology is an effective proteomics platform for exosome-associated biomarker discovery in diverse clinical settings.Prostate carcinoma is the most frequent male cancer, with an estimated 240,000 newly diagnosed individuals and 28,000 deaths in the United States during 2012 (National Cancer Institute (NIH)). Methods for detecting this cancer are based on a combination of physical examination through digital rectal examination, clinical imaging, quantification of circulating levels of prostate specific antigen (PSA),¹ and transrectal ultrasound-guided biopsy. As a non-invasive test, PSA measurement is still widely used, but it remains insensitive, as around 15% of men with normal levels of PSA will have prostate cancer according to biopsy results (1), and 60% of men with elevated PSA levels may have other, noncancerous conditions but be subjected to further, unnecessary investigations and interventions (2). PSA may be of better utility in monitoring disease progression (2). An ability to diagnose the disease more specifically at an early stage is likely to save lives and alleviate the healthcare burden and morbidities arising from misdiagnosis. In addition, methods for monitoring the course of the disease in a non-invasive and perhaps predictive manner would offer increased patient benefit, enabling early detection of imminent relapse under hormone therapy, for example. Therefore there is a clinical need for improved molecular approaches for disease diagnosis and monitoring in these settings.Small vesicles termed exosomes are present in body fluids, including serum, plasma, urine, and seminal plasma (3–7), and their isolation and examiniation may prove useful as a minimally invasive means of obtaining a complex set of disease markers. Exosomes are secreted by most, if not all, cell types and are generally accepted as derived principally from multivesicular bodies of the late endocytic tract (8), although examples of plasma membrane budding nanovesicles of similar phenotype have also been described (9). Exosomes are particularly enriched in membrane proteins and in factors related to such endosomal compartments. They also contain proteins found in the cytosol, but they poorly represent components of organelles such as the mitochondria, nucleus, and endoplasmic reticulum (10). Exosomes also comprise an assortment of coding and noncoding RNA. There has been considerable global effort toward defining disease-related alterations in exosomal RNA. However, it is well established that aberrant alterations in cancer cells in response to metabolic, hypoxic, or other forms of stress are reflected in protein changes in the exosomes produced (11–13). Thus exosomes from diseased origins can be distinguished from those of a normal phenotype based on their protein profiles alone.Proteomics studies using mass spectrometry (MS) have previously been conducted on prostate cancer exosomes/microvesicles obtained from cell lines (14, 15), xenotransplantation models (16), or ex vivo biofluids (17). Hundreds of proteins with putative associations with exosomes/microvesicles have been identified. These studies highlight several interesting candidate markers of potential biomarker utility that are currently being explored. However, global proteomic approaches of this nature can have two major limitations. Although the most abundant proteins are more likely to be identified by MS, it is difficult to infer information about relative abundances of proteins in complex samples when using these methods. Secondly, given the often exacting, difficult-to-reproduce, and time-consuming workflows involved, such technologies are poorly suited for the analysis of a large number of samples. Multiplex protein array methodologies have the potential to overcome such issues and offer quantification and options for more rapid sample throughput. However, most platforms are based on antibodies, and these arrays are typically limited to <100 proteins, principally because the cross-reactivity of secondary antibodies can negatively affect assay specificity (18).A recently developed proteomics platform, termed SOMAscan^TM, provides a new generation of protein detection technologies. The platform is capable of the simultaneous quantitative analysis of 1129 proteins per sample in its current form. It is also an approach well suited to handling large numbers of specimens required for well-powered clinical studies (19). The key to this technology, which is described in detail by Gold et al. (20, 21), is the use of slow off-rate modified aptamers (SOMAmers) containing chemically modified nucleotides. This confers greater stability, expanded target range, and improved affinity for the target proteins. This multiplex platform has been applied successfully to small volumes (∼15 μl) of plasma specimens from chronic renal disease patients (20), serum specimens from mesothelioma (22) or lung cancer patients (19), tissue lysates (23), and cerebrospinal fluid (24). However, to date, the compatibility of this array technology with exosomes as the specimen has not been investigated.The purpose of the current study was to examine the utility of this evolving technology in profiling the protein repertoire of exosomes. Research was conducted using highly pure exosomes isolated from a prostate cancer cell line, and we compared this sample to the protein profile of the parent cells. By so doing, we obtained evidence of the compatibility of the platform with this difficult, membranous sample and identified several proteins of previously unknown association with exosomes. In summary, SOMAscan^TM is a versatile tool for probing the composition of exosomes and is a suitable platform to provide a high-throughput approach for exosome-based biomarker discovery in prostate cancer and other clinical settings.     

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]