Integrated Proteomic Analysis of Human Cancer Cells and Plasma from Tumor Bearing Mice for Ovarian Cancer Biomarker Discovery

Background

The complexity of the human plasma proteome represents a substantial challenge for biomarker discovery. Proteomic analysis of genetically engineered mouse models of cancer and isolated cancer cells and cell lines provide alternative methods for identification of potential cancer markers that would be detectable in human blood using sensitive assays. The goal of this work is to evaluate the utility of an integrative strategy using these two approaches for biomarker discovery.

Methodology/Principal Findings

We investigated a strategy that combined quantitative plasma proteomics of an ovarian cancer mouse model with analysis of proteins secreted or shed by human ovarian cancer cells. Of 106 plasma proteins identified with increased levels in tumor bearing mice, 58 were also secreted or shed from ovarian cancer cells. The remainder consisted primarily of host-response proteins. Of 25 proteins identified in the study that were assayed, 8 mostly secreted proteins common to mouse plasma and human cancer cells were significantly upregulated in a set of plasmas from ovarian cancer patients. Five of the eight proteins were confirmed to be upregulated in a second independent set of ovarian cancer plasmas, including in early stage disease.

Conclusions/Significance

Integrated proteomic analysis of cancer mouse models and human cancer cell populations provides an effective approach to identify potential circulating protein biomarkers.     

Protein Array-based Approaches for Biomarker Discovery in Cancer

Biomarkers are deemed to be potential tools in early diagnosis, therapeutic monitoring, and prognosis evaluation for cancer, with simplicity as well as economic advantages compared with computed tomography and biopsy. However, most of the current cancer biomarkers present insufficient sensitivity as well as specificity. Therefore, there is urgent requirement for the discovery of biomarkers for cancer. As one of the most exciting emerging technologies, protein array provides a versatile and robust platform in cancer proteomics research because it shows tremendous advantages of miniaturized features, high throughput, and sensitive detections in last decades. Here, we will present a relatively complete picture on the characteristics and advance of different types of protein arrays in application for biomarker discovery in cancer, and give the future perspectives in this area of research.     

Probing the O-Glycoproteome of Gastric Cancer Cell Lines for Biomarker Discovery

Circulating O-glycoproteins shed from cancer cells represent important serum biomarkers for diagnostic and prognostic purposes. We have recently shown that selective detection of cancer-associated aberrant glycoforms of circulating O-glycoprotein biomarkers can increase specificity of cancer biomarker assays. However, the current knowledge of secreted and circulating O-glycoproteins is limited. Here, we used the COSMC KO “SimpleCell” (SC) strategy to characterize the O-glycoproteome of two gastric cancer SimpleCell lines (AGS, MKN45) as well as a gastric cell line (KATO III) which naturally expresses at least partially truncated O-glycans. Overall, we identified 499 O-glycoproteins and 1236 O-glycosites in gastric cancer SimpleCells, and a total 47 O-glycoproteins and 73 O-glycosites in the KATO III cell line. We next modified the glycoproteomic strategy to apply it to pools of sera from gastric cancer and healthy individuals to identify circulating O-glycoproteins with the STn glycoform. We identified 37 O-glycoproteins in the pool of cancer sera, and only nine of these were also found in sera from healthy individuals. Two identified candidate O-glycoprotein biomarkers (CD44 and GalNAc-T5) circulating with the STn glycoform were further validated as being expressed in gastric cancer tissue. A proximity ligation assay was used to show that CD44 was expressed with the STn glycoform in gastric cancer tissues. The study provides a discovery strategy for aberrantly glycosylated O-glycoproteins and a set of O-glycoprotein candidates with biomarker potential in gastric cancer.Most broad proteomic studies for discovery of cancer biomarkers in serum have been designed to interrogate the proteome and not taking into account that cancer cells often produce aberrant glycoforms (1). Many cancer biomarkers currently used in the clinic are based on circulating O-glycoproteins that are detected in established serological assays (CA125, CA15–3, CEA, and CA19.9) (2). In addition to being overexpressed in cancer, these proteins also carry aberrant glycans, which open for the opportunity to selectively detect aberrant glycoforms. An inherent problem with most cancer biomarker assays is that they often have poor specificity because the detected glycoprotein is found in elevated levels in nonmalignant conditions (2, 3). We recently found that the specificity of the widely used CA125 biomarker assay can be increased by selectively detecting aberrant O-glycoforms of the MUC16 mucin probed in the CA125 assay (4). Thus, the truncated O-glycan STn (NeuAcα2–6GalNAcα1-O-Ser/Thr)¹ (Fig. 1) was particularly suited for discrimination of MUC16 circulating in cancer patients in contrast to MUC16 circulating in benign conditions (4).Open in a separate windowFig. 1.Schematic depiction of the initial biosynthetic pathways of O-linked protein glycosylation. Overview of the O-linked protein glycosylation. O-GalNAc glycosylation is initiated by up to 20 different GalNAc-transferases. The addition of GalNAc to serines or threonines (or tyrosines) forms the Tn structure that can be sialylated by ST6GalNAc-I or further elongated to form up to four core structures. The core structures can be further elongated.One of the most characteristic phenotypes of cancer cells is the expression of truncated O-glycans, and the structures T (Galβ1–3GalNAcα1-O-Ser/Thr), STn, and Tn (GalNAcα1-O-Ser/Thr) (Fig. 1) are considered pancarcinoma antigens (2, 5). These truncated O-glycans are essentially not produced in normal and benign cells, which suggests that circulating O-glycoproteins in normal and benign conditions should have more mature O-glycans, whereas O-glycoproteins shed from cancer cells are expected to display truncated glycan structures. Cancer cells produce, secrete, and shed many different O-glycoproteins with truncated O-glycans, and provided these glycoproteins reach the circulation they may be detectable in serum. However, it is also known that nonsialylated glycoproteins are cleared from circulation through innate immune lectin receptors (6). In fact, we were previously unable to detect circulating T and Tn glycoforms of MUC1 and MUC16, while the sialylated ST (NeuAcα2–3Galβ1–3[NeuAcα2–6]_±GalNAcα1-O-Ser/Thr) and STn glycoforms were readily detectable (4, 7). Furthermore, two classical serological biomarker assays, CA19–9 (8) and CA72.4 (9–11), are based on the detection of sialylated O-glycans, and especially the latter that detects STn shows that proteins expressing the STn glycoform circulate in serum of cancer patients. Interestingly, although CA72.4 has been used for decades, it is still largely unknown which O-glycoproteins carry STn and are detected by the CA72.4 assay (9, 10).The truncated STn O-glycan has attracted much attention because it is highly expressed in most gastric (12), colorectal (13), ovarian (14), breast (15), pancreatic (16), and bladder (17) carcinomas, whereas expression of STn on normal tissues is highly restricted (11, 18). In addition, STn expression is associated with carcinoma aggressiveness and poor prognosis (15, 19). We have recently described the presence of a few STn bearing glycoproteins in serum from individuals with gastric cancer and gastric cancer precursor lesions (20). The biosynthetic and genetic mechanisms underlying the expression of this truncated O-glycan in cancer have remained poorly understood, and a number of mechanisms have been proposed that may not be mutually exclusive. One mechanism is the altered expression of the sialyltransferase ST6GalNAc-I, which is believed to be the main STn synthase (21, 22) (Fig. 1), and in fact overexpression of this enzyme in cell lines appears to override the normal O-glycan elongation machinery and result in expression of STn (22, 23). Another mechanism may be reduced core1 elongation that leads to accumulation of Tn, which serves as substrate for ST6GalNAc-I (22). The core1 synthase C1GALT1 is dependent on a private chaperone Cosmc, and several studies have reported that somatic mutations in COSMC gene (24), or hypermethylation of COSMC gene in cancer (25) lead to increased expression of Tn and STn. We have further shown that knockout (KO) of COSMC in a number of human cancer cell lines produce cells that express different levels of Tn and STn truncated O-glycans ranging from exclusive Tn to exclusive STn (26). A third potential mechanism offered recently may be related to cancer-associated relocation of the polypeptide GalNAc-transferases (GalNAc-Ts) that initiate O-glycosylation (Fig. 1) from Golgi to ER, which appear to induce expression of the Tn truncated O-glycans, although expression of STn has not been explored yet (27).In the present study, we applied a glycoproteomics strategy to explore potential biomarker O-glycoproteins with the STn glycoform in gastric cancer. We first characterized the O-glycoproteome and including the secretome of two gastric cancer cell lines, AGS (intestinal type gastric carcinoma) and MKN45 (diffuse type gastric carcinoma), using our SimpleCell (SC) discovery platform where we identified a total of 499 O-glycoproteins (1236 O-glycosites). This strategy involves genetic engineering of cell lines to produce homogenous truncated O-glycans (Tn and/or STn) by KO of COSMC, followed by Vicia Villosa lectin (VVA) enrichment of Tn glycoproteins and/or glycopeptides for sensitive identification of O-glycoproteins and O-glycosites by mass spectrometry (26, 28) (Fig. 1). We applied the same glycoproteomics workflow to a wild type (wt) gastric cancer cell line, KATO III (diffuse type gastric carcinoma), which naturally expresses Tn and STn O-glycans in a mixture with more complex structures, and identified a significantly smaller O-glycoproteome (total of 47 O-glycoproteins) compared with SimpleCells (total of 499 O-glycoproteins). We next modified the strategy to enrich for STn O-glycoproteins in pools of serum from cancer patients and normal controls using pretreatment with neuraminidase to remove sialic acid and expose Tn for VVA capture. This approach enabled us to isolate and identify 37 O-glycoproteins (49 O-glycosites) in gastric cancer serum. Finally, we confirmed that two of the identified serum O-glycoproteins (CD44 and GalNAc-T5) were expressed in gastric cancer tumors by immunohistology, and further used proximity ligation assay (PLA) to show that STn glycoforms of CD44 was expressed in cancer tissue. This study clearly shows that cancer patients have a variety of circulating O-glycoproteins with the STn glycoform, and supports the hypothesis that these glycoproteins originate from the cancer tissue. The identified secreted and circulating aberrant O-glycoproteins serve as a discovery set for biomarkers of gastric cancer.     

Cell Stiffness Is a Biomarker of the Metastatic Potential of Ovarian Cancer Cells

The metastatic potential of cells is an important parameter in the design of optimal strategies for the personalized treatment of cancer. Using atomic force microscopy (AFM), we show, consistent with previous studies conducted in other types of epithelial cancer, that ovarian cancer cells are generally softer and display lower intrinsic variability in cell stiffness than non-malignant ovarian epithelial cells. A detailed examination of highly invasive ovarian cancer cells (HEY A8) relative to their less invasive parental cells (HEY), demonstrates that deformability is also an accurate biomarker of metastatic potential. Comparative gene expression analyses indicate that the reduced stiffness of highly metastatic HEY A8 cells is associated with actin cytoskeleton remodeling and microscopic examination of actin fiber structure in these cell lines is consistent with this prediction. Our results indicate that cell stiffness may be a useful biomarker to evaluate the relative metastatic potential of ovarian and perhaps other types of cancer cells.     

Optimizing a Proteomics Platform for Urine Biomarker Discovery

Biomarker discovery approaches in urine have been hindered by concerns for reproducibility and inadequate standardization of proteomics protocols. In this study, we describe an optimized quantitative proteomics strategy for urine biomarker discovery, which is applicable to fresh or long frozen samples. We used urine from healthy controls to standardize iTRAQ (isobaric tags for relative and absolute quantitation) for variation induced by protease inhibitors, starting protein and iTRAQ label quantities, protein extraction methods, and depletion of albumin and immunoglobulin G (IgG). We observed the following: (a) Absence of protease inhibitors did not affect the number or identity of the high confidence proteins. (b) Use of less than 20 μg of protein per sample led to a significant drop in the number of identified proteins. (c) Use of as little as a quarter unit of an iTRAQ label did not affect the number or identity of the identified proteins. (d) Protein extraction by methanol precipitation led to the highest protein yields and the most reproducible spectra. (e) Depletion of albumin and IgG did not increase the number of identified proteins or deepen the proteome coverage. Applying this optimized protocol to four pairs of long frozen urine samples from diabetic Pima Indians with or without nephropathy, we observed patterns suggesting segregation of cases and controls by iTRAQ spectra. We also identified several previously reported candidate biomarkers that showed trends toward differential expression, albeit not reaching statistical significance in this small sample set.With ongoing advances in mass spectrometry (MS) and proteomics technology, proteomics analysis is progressively occupying a central position in biomarker discovery platforms. Biofluids such as urine and blood are the preferred media for proteomics analysis because of their ease of collection and extensive history of use in clinical laboratory practice. Urine, in particular, is an information-rich fluid that can be collected non-invasively and in large quantities. Many urine proteins are produced or shed in the kidney and urogenital tract (1), making urine a promising proximal source of biomarkers for diseases affecting these structures.However, proteomics-based biomarker discovery in urine faces multiple challenges. Urine proteomics is complicated by low urine protein concentration, variations in pH, and high concentrations of salts and urea or other urine components that interfere with sample processing. The urine proteome can also change with individual variables such as hydration, diurnal change, diet, and physical activity as well as variation in sample collection, processing, and storage. In addition, urine proteomics shares the usual challenges of biomarker discovery in other biofluids such as throughput, cost, and the need for a reproducible and quantitative work flow.Isotopic or isobaric labeling methods to reduce variation, increase throughput, and enable quantitative analysis have been developed to address some of these challenges. One such method, isobaric tags for relative and absolute quantitation (iTRAQ)¹ (2), combines relative and absolute peptide quantification with multiplexing ability to enable an increased throughput as well as simultaneous comparison of up to eight samples within one experimental run. Variations induced by urine sample processing have been systematically evaluated for proteomics analyses using two-dimensional gel electrophoresis (3–6), differential gel electrophoresis (7), and liquid chromatography-coupled mass spectrometry (LC-MS) (5, 8, 9). However, no systematic analyses of urine sample collection and processing have been reported for iTRAQ.Before utilizing iTRAQ-based quantitative proteomics for urine biomarker discovery, we evaluated the impact of variation in several processing steps (addition of protease inhibitors, the starting protein quantities, quantity of the iTRAQ label, protein extraction methods, and depletion of abundant proteins) on iTRAQ protein identification and quantitation. Applying this optimized biomarker discovery protocol to small quantities of long frozen urine samples from the Pima longitudinal study of diabetic nephropathy, we observed patterns suggestive of segregation of cases and controls by iTRAQ spectra. We also observed trends toward differential expression in several proteins that had been identified as putative biomarkers in previous studies. However, given the small sample size, none of these proteins retained statistical significance after multiple testing correction.     

Bladder Cancer Biomarker Discovery Using Global Metabolomic Profiling of Urine

Bladder cancer (BCa) is a common malignancy worldwide and has a high probability of recurrence after initial diagnosis and treatment. As a result, recurrent surveillance, primarily involving repeated cystoscopies, is a critical component of post diagnosis patient management. Since cystoscopy is invasive, expensive and a possible deterrent to patient compliance with regular follow-up screening, new non-invasive technologies to aid in the detection of recurrent and/or primary bladder cancer are strongly needed. In this study, mass spectrometry based metabolomics was employed to identify biochemical signatures in human urine that differentiate bladder cancer from non-cancer controls. Over 1000 distinct compounds were measured including 587 named compounds of known chemical identity. Initial biomarker identification was conducted using a 332 subject sample set of retrospective urine samples (cohort 1), which included 66 BCa positive samples. A set of 25 candidate biomarkers was selected based on statistical significance, fold difference and metabolic pathway coverage. The 25 candidate biomarkers were tested against an independent urine sample set (cohort 2) using random forest analysis, with palmitoyl sphingomyelin, lactate, adenosine and succinate providing the strongest predictive power for differentiating cohort 2 cancer from non-cancer urines. Cohort 2 metabolite profiling revealed additional metabolites, including arachidonate, that were higher in cohort 2 cancer vs. non-cancer controls, but were below quantitation limits in the cohort 1 profiling. Metabolites related to lipid metabolism may be especially interesting biomarkers. The results suggest that urine metabolites may provide a much needed non-invasive adjunct diagnostic to cystoscopy for detection of bladder cancer and recurrent disease management.     

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.     

DPHL:A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.     

Cancer Subtype Discovery and Biomarker Identification via a New Robust Network Clustering Algorithm

In cancer biology, it is very important to understand the phenotypic changes of the patients and discover new cancer subtypes. Recently, microarray-based technologies have shed light on this problem based on gene expression profiles which may contain outliers due to either chemical or electrical reasons. These undiscovered subtypes may be heterogeneous with respect to underlying networks or pathways, and are related with only a few of interdependent biomarkers. This motivates a need for the robust gene expression-based methods capable of discovering such subtypes, elucidating the corresponding network structures and identifying cancer related biomarkers. This study proposes a penalized model-based Student’s t clustering with unconstrained covariance (PMT-UC) to discover cancer subtypes with cluster-specific networks, taking gene dependencies into account and having robustness against outliers. Meanwhile, biomarker identification and network reconstruction are achieved by imposing an adaptive penalty on the means and the inverse scale matrices. The model is fitted via the expectation maximization algorithm utilizing the graphical lasso. Here, a network-based gene selection criterion that identifies biomarkers not as individual genes but as subnetworks is applied. This allows us to implicate low discriminative biomarkers which play a central role in the subnetwork by interconnecting many differentially expressed genes, or have cluster-specific underlying network structures. Experiment results on simulated datasets and one available cancer dataset attest to the effectiveness, robustness of PMT-UC in cancer subtype discovering. Moveover, PMT-UC has the ability to select cancer related biomarkers which have been verified in biochemical or biomedical research and learn the biological significant correlation among genes.     

Validation of LRG1 as a Potential Biomarker for Detection of Epithelial Ovarian Cancer by a Blinded Study

Background

Leucine-rich alpha-2-glycoprotein (LRG1) was found to be differentially expressed in sera from patients with Epithelial Ovarian Cancer (EOC). The aim of this study is to investigate the performance of LRG1 for detection of EOC, including early stage EOC, and to evaluate if LRG1 can complement CA125 in order to improve EOC detection using two independent blinded sample sets.

Methods and Results

Serum LRG1 and CA125 were measured by immunoassays. All assays were performed blinded to clinical data. Using the two independent sample sets (156 participants for sample set 1, and 233 for sample set 2), LRG1 was differentially expressed in EOC cases as compared to healthy, surgical, and benign controls, and its performance was not affected by the conditions of blood collection. The areas under the ROC curve (AUC) for LRG1 in differentiating EOC cases from non-cases were 0.797 and 0.786 for sample set 1 and 2. For differentiating EOC cases from healthy controls, the AUC values for LRG1 were 0.792 and 0.794. At a fixed specificity of 95%, LRG1 detects 52%, and 53.5% of EOC cases from healthy controls for sample set 1 and 2. When combining LRG1 and CA125, the AUC value increased to 0.927, which was improved compared to CA125 (AUC=0.916) (p=0.008) alone in distinguishing EOC cases from non-cases. More importantly, LRG1 also showed potential performance in differentiating early stage EOC from non-cases with an AUC of 0.715 for sample set 1, and 0.690 for sample set 2. The combination of LRG1 and CA125 resulted in an AUC of 0.838, which outperforms CA125 (AUC=0.785) (p=0.018) in detecting early stage EOC cases from non-cases using the larger sample set.

Conclusions

LRG1 could be a useful biomarker alone or in combination with CA125 for the diagnosis of ovarian cancer.     

Development of a Streptavidin-Conjugated Single-Chain Antibody That Binds Bacillus cereus Spores

Control of microorganisms such as Bacillus cereus spores is critical to ensure the safety and a long shelf life of foods. A bifunctional single chain antibody has been developed for detection and binding of B. cereus T spores. The genes that encode B. cereus T spore single-chain antibody and streptavidin were connected for use in immunoassays and immobilization of the recombinant antibodies. A truncated streptavidin, which is smaller than but has biotin binding ability similar to that of streptavidin, was used as the affinity domain because of its high and specific affinity with biotin. The fusion protein gene was expressed in Escherichia coli BL21 (DE3) with the T7 RNA polymerase-T7 promoter expression system. Immunoblotting revealed an antigen specificity similar to that of its parent native monoclonal antibody. The single-chain antibody-streptavidin fusion protein can be used in an immunoassay of B. cereus spores by applying a biotinylated enzyme detection system. The recombinant antibodies were immobilized on biotinylated magnetic beads by taking advantage of the strong biotin-streptavidin affinity. Various liquids were artificially contaminated with 5 × 10⁴ B. cereus spores per ml. Greater than 90% of the B. cereus spores in phosphate buffer or 37% of the spores in whole milk were tightly bound and removed from the liquid phase by the immunomagnetic beads.     

Cancer Biomarker Discovery via Targeted Profiling of Multiclass Tumor Tissue-Derived Proteomes

Introduction

Tumor-derived proteins and naturally occurring peptides represent a rich source of potential cancer markers for multiclass cancer distinction.

Materials and Methods

In this study, proteomes/peptidomes derived from primary colon cancer, kidney cancer, liver cancer, and glioblastoma were analyzed by liquid chromatography coupled with mass spectrometry to identify multiclass cancer discriminative protein and peptide candidates. Spectral counting and peptidomic analyses found two biomarker panels, one with 12 proteins and the other with 53 peptides, both capable of multiclass cancer detection and classification.

Results and Discussion

Shed from tumor tissues through apoptosis/necrosis, cell secretion, or tumor-specific degradation of extracellular matrix proteins, these proteins/peptides are likely to enter into circulation and, therefore, have the potential to be configured into practical serological diagnostic and prognostic utilities.     

应用噬菌体抗体库技术制备特异抗人纤维蛋白鼠单链抗体

应用Pharmacia公司的重组噬菌体抗体系统,从经过人交联纤维蛋白特异抗原D二聚体(DD)免疫过的鼠脾细胞mRNA中构建出组合单链抗体(ScFv)cDNA文库。文库cDNA克隆到噬菌粒载体pCANTAB5E,转化大肠杆菌TG1,得到2.5×10~5个氨苄抗性菌落。通过噬菌体表面呈现,用DD对表达的重组噬菌体单链抗体文库进行三轮亲和富集获得一株特异抗DD的噬菌体单链抗体(ScFvA11)。经Phage-ELISA鉴定,呈现在噬菌体表面的ScFvA11与DD结合的ELISA阳性滴度小于10~7tfu/ml,而与人纤维蛋白原结合的ELISA滴度大于10~(10)tfu/ml,两者相差1000倍以上。表明ScFvA11具有较好的DD结合特异性。经序列分析,ScFvA11cDNA全长729bp,其中Vh基因354bp,编码118个氨基酸;Vl基因327bp,编码108个氨基酸;Vh与Vl之间为(Gly_4Ser)_315个氨基酸连接肽。     

Tumor Stress-Induced Phosphoprotein1 (STIP1) as a Prognostic Biomarker in Ovarian Cancer

Stress-induced phosphoprotein 1 (STIP1) has been recently identified as a released biomarker in human ovarian cancer. In addition, STIP1 secreted by human ovarian cancer cells has been shown to promote tumor cell proliferation by binding to ALK2 (activin A receptor, type II-like kinase 2) and activating the SMAD-ID3 signaling pathways. In this study, a total of 330 ovarian cancer tumor samples were evaluated for STIP1 expression by immunohistochemistry and analyzed for a possible correlation with patient characteristics and survival. The quantification of immunoreactivity was accomplished by applying an immunohistochemical scoring system (histoscore). Patients with high-level STIP1 expression (histoscore ≥169) had a significantly worse survival (high STIP1, mean survival time = 76 months; low STIP1, mean survival time = 112 months; P<0.0001). Moreover, STIP1 histoscores were significantly higher in high-grade tumors (grade 3) than in low-grade (grade 1–2) malignancies (P<0.0001), suggesting that STIP1 may be a proxy for tumor aggressiveness. The results of multivariable analysis revealed that high STIP1 histoscores, advanced stages, histologic types, and the presence of residual disease (≥2 cm) were independent predictors of poor prognosis. The addition of STIP1 histoscores improved the prediction of overall and progression-free survival rates in the multivariable Cox proportional hazard model. The treatment of ovarian cancer cells with recombinant STIP1 stimulated cell proliferation and migration, but co-treatment with anti-STIP1 antibodies abrogated this effect. Our findings suggest that STIP1 expression may be related to prognosis and that the STIP1 pathway may represent a novel therapeutic target for human ovarian cancer.