From the Departments of a Etiology and Carcinogenesis, d Pathology, and e Chest Surgery, Cancer Institute (Hospital), Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100021, China, c Department of Genomics and Proteomics, Beijing Institute of Radiation Medicine and f Center of Computational Biology, Beijing Institute of Basic Medical Sciences, 27 Taiping Road, Beijing 100850, China
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ABSTRACT |
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Scientists have been seeking tumor-specific biomarkers for decades. Neuron-specific enolase, carcinoembryonic antigens, cytokeratin 19 fragments, and some other proteins are the most commonly used lung cancer markers (3). However, few of these markers are useful in a routine clinical setting, thus underscoring the need for new clinically relevant sources (4). Liotta et al. (5) state "it is time to rethink our approach to tumor biomarker discovery. The blood contains a treasure trove of previously unstudied biomarkers that could reflect the ongoing physiological state of all tissues" (5) including tumor tissues. Protein expression level meta-analysis of patients blood serum holds promise for diagnostic tumor signature detection.
Each year millions of blood samples are collected for medical diagnosis, forming a vast clinical data resource. However, blood plasma is difficult to analyze because it comprises large amounts of albumin and a wide dynamic range of other heterogenic proteins (6). The human plasma proteome provides a tremendous opportunity for detecting, therapeutic monitoring, and deciphering basic cancer mechanisms. Mass spectrometry is a powerful high throughput analytical approach to cancer identification that yields comprehensive peptide and protein serum profiles. Mass spectrometry is efficient because it does not require large samples or arduous protein separations (7, 8). However, its clinical effectiveness has garnered doubt from the scientific community (8, 9). Proteome complexity reduction is key for proteomic analysis of human blood. Recent advances in mass spectrometry may accelerate disease protein marker identification by proteome complexity reduction.
Our study objective was to provide pilot data from a novel tumor cell protein detection method and to identify lung cancer-related protein blood profiles. Initially in this study we analyzed the proteins released into serum-free conditioned medium (CM)1 from the tumor microenvironment with short time-cultured lung cancer and adjacent normal bronchial epithelial cells, one-dimensional (1-D) PAGE, and liquid chromatography-tandem mass spectrometry. We then analyzed select proteins from this dataset with ELISA in patient plasma. Using this system allowed us to investigate the diagnostic aspects of the in vivo tumor-host microenvironment. Moreover this method should be applicable for blood detection of other human solid tumors in organs such as liver, prostate, pancreas, and breast.
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EXPERIMENTAL PROCEDURES |
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We used 628 plasma samples for ELISA, including 419 lung cancer patients between the ages of 15 and 87 (median, 61) years old, 75 non-malignant lung disease patients between the ages of 28 and 74 (median, 53) years old, and 134 non-cancer control patients between the ages of 19 and 79 (median, 44) years old. The malignant and non-malignant lung cancer characteristics used for categorizing the different protein plasma levels are described in Supplemental Tables 1 and 2.
Cell Culture and CM Preparation
We purchased all reagents for cell culture from Sigma unless otherwise stated. Supplemental Table 3 describes the primary cell and organ culture sample characteristics.
For the primary lung cell cultures, we harvested lung cancer tissues and the adjacent normal bronchi from six lung cancer patients into washing buffer (L 15 medium containing 1% antibiotic) immediately after surgery and then transferred them to the laboratory within 30 min. We cultured the primary cells according to the procedures of Lu et al. (10). Briefly we washed and minced the tissues in washing buffer immediately after harvesting and digested them in MCDB 151 medium containing DNase I, collagenase type I, and hyaluronidase. We then seeded the cells in 100-mm culture plates in serum-free MCDB 151 medium with growth factors and pituitary extract maintained at 36.5 °C in a humidified atmosphere containing 4% CO2.
We conducted the lung cancer and bronchial tissue organ cultures according to our previously published method (11). Briefly we chopped the fresh harvested tissues into small (23-mm3) pieces and washed them in washing buffer. We transferred the tissue explants into 100-mm culture dishes and established them in the same serum-free medium as mentioned above. We placed the culture dishes in a culture box containing 50%O2, 45%N2, and 5%CO2 at 36.5 °C on a shaker and agitated the dishes three to four times per minute to expose the explants continuously to the gas and medium.
Conditioned Medium Collection
In this study, we collected the CM after 3 x 106 cell growth or within 1 week on each 100-mm dish for cell culture or organ culture, respectively. After removing the spent medium, we rinsed the cultures three times with Hanks balanced salt solution and then incubated them in the pituitary- and serum-free MCDB 151 CM for 1 h at 36.5 °C. We washed the cultures two more times and then added 3 ml of fresh CM/dish. After 48 h of incubation, we collected and centrifuged the CM for 5 min at 4,000 x g at 4 °C to remove cell debris.
SDS-PAGE Separation and Liquid Chromatography-Tandem Mass Spectrometry Analysis
We dialyzed the CM supernatant (molecular mass cutoff, 3500 Da; Serva, Heidelberg, Germany) against distilled water for 24 h at 4 °C. We then lyophilized the dialyzed CM using a vacuum centrifuge (Savant). We dissolved the lyophilized proteins in sampling buffer and separated them with 10% SDS-PAGE. After separating, imaging, and documenting the protein separation gel (Fig. 1), we sliced it into 34 slivers manually from the loading well to the bottom. After in-gel digestion, we diluted the peptides directly into a quadrupole time-of-flight mass spectrometer (Micromass, Manchester, UK). Following the MS/MS analysis, we generated the peak list files with MassLynx3.5 (Micromass) and used these files to search against the UniProt (www.ebi.ac.uk) protein database through a local Mascot server.
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ELISA Analysis
We collected venous blood preoperatively in sodium heparin-coated plastic tubes for preparation of plasma samples. We then centrifuged the tubes at 1,600 x g for 20 min, removed supernatants, and divided and stored them in aliquots at 80 °C until analysis. We measured the matrix metalloproteinase 1 (MMP1) and tissue inhibitor of metalloproteinases 1 (TIMP1) concentrations in human plasma with a commercially available ELISA kit (Oncogene, San Diego, CA) according to the manufacturers recommendations.
We detected the plasma levels of fascin, CD98, ezrin, polymeric immunoglobulin receptor/secretory component (PIGR/SC), annexin A4, laminin -2 (LAMC2), 14-3-3
, 14-3-3 ß, and 14-3-3
by double antibody sandwich ELISA according to the published method (1, 20). The antibodies used for ELISA analysis are listed in Supplemental Table 4. Two independent researchers from our group performed all analyses blind and in duplicate.
Statistical Analysis
We used SPSS software, Version 11.5 (SPSS Inc., Chicago, IL) to calculate all statistical comparisons. All comparisons were two-tailed, and p values of <0.05 were considered significant. We used the independent sample t test to analyze protein plasma differences among the various groups.
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RESULTS |
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Among these 231 proteins, 42 (18.18%) are extracellular or secreted proteins (such as 1-antichymotrypsin, metalloproteinase inhibitor 1, serotransferrin, and plasminogen activator inhibitor-1), 56 (24.24%) are cytoplasmic and/or others (such as fibronectin, heat shock protein 90-
, L-lactate dehydrogenase B chain, and
enolase), and 27 (11.69%) are membrane-associated proteins (such as low density lipoprotein receptor, integrin
V, and CD98 antigen). Only 3.03% of the proteins are nuclear or mitochondrial (Fig. 2). The CM proteins span a broad range of molecular weights, and only a few proteins are very basic or acidic, whereas most are hydrophilic (data not shown) according to the isoelectric point distribution.
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We analyzed two pairs of CM lung cancer and bronchus organ culture samples with the same proteomic technique and detected 117 proteins (Supplemental Table 6). Of these proteins, we found 68 to be unique to the organ culture samples (that is, we did not find them among the primary cell culture CM protein population). These potential organ tissue biomarkers include the lung and nasal epithelium carcinoma-associated protein 1 precursor, 1,4-ß-N-acetylmuramidase C, antileukoproteinase 1, spermatogenesis-associated protein 7, and many hypothetical proteins or proteins of unknown function.
Measurement of MMP1 and TIMP1 Levels in Plasma of Patients and Healthy Controls
The plasma levels of MMP1 were significantly elevated in patients with lung cancer (n = 157; median = 1.82 ng/ml; range, from 0.01 to 44.05 ng/ml) compared with those in healthy controls (n = 30; median = 1.08 ng/ml; range, from 0.71 to 7.14 ng/ml; p = 0.001). The plasma level of MMP1 in the late stage (III and IV) patients (n = 80; median = 2.26 ng/ml; range, from 0.05 to 44.05 ng/ml) was significantly higher than that in the early stage (I and II) patients (n = 76; median = 1.60 ng/ml; range, from 0.01 to 7.42 ng/ml; p = 0.005). However, we found no significant difference between MMP1 levels in lung cancer patients and non-malignant lung disease patients (n = 58; median = 1.90 ng/ml; range, from undetectable to 44.05 ng/ml; p = 0.992; Fig. 3a).
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Measurement of Human Plasma Levels of CD98, Ezrin, Fascin, PIGR/SC, Annexin A4, LAMC2, 14-3-3 , 14-3-3 ß, and 14-3-3
We used the ELISA absorbance at 450/570 nm to analyze semiquantitatively the plasma protein levels. We observed a significantly reduced CD98 plasma level among the lung cancer patients (n = 115; median = 0.562; range, from undetectable to 1.068) compared with that in the non-malignant lung disease patients (n = 24; median = 0.597; range, from 0.320 to 1.216; p = 0.035). Lung cancer levels were not statistically different from those of healthy controls (n = 51; median = 0.462; range, from 0.234 to 1.192; p = 0.517; Fig. 3b). Plasma CD98 levels in small cell lung cancer patients (n = 24; median = 0.452; range, from 0.280 to 0.844) were significantly lower than those in non-small cell lung cancer patients (n = 91; median = 0.568; range, from undetectable to 1.068; p = 0.011). The fascin plasma level was significantly reduced in the lung cancer patients (n = 115; median = 0.154; range, from undetectable to 0.650) compared with the healthy controls (n = 51; median = 0.230; range, from 0.040 to 0.800; p = 0.008; Fig. 3c) but not compared with the non-malignant lung disease patients (n = 24; median = 0.197; range, from 0.080 to 1.620; p = 0.075). The PIGR/SC plasma level was significantly higher in the lung cancer patients (n = 107; median = 0.935; range, from 0.147 to 2.122) than in the non-malignant lung disease patients (n = 38; median = 0.779; range, from 0.516 to 1.989; p = 0.013). The mean PIGR/SC plasma levels were elevated among the lung cancer patients compared with the healthy controls (n = 42; median = 0.873; range, from 0.668 to 1.360) although not significantly (p = 0.174, Fig. 3d). LAMC2 plasma levels were significantly lower among the lung cancer patients (n = 123; median = 0.059; range, from 0.017 to 0.641) than they were among the non-malignant lung disease patients (n = 32; median = 0.119; range, from 0.044 to 0.494; p = 0.008) and the healthy controls (n = 30; median = 0.147; range, from 0.054 to 1.453; p = 0.019; Fig. 3e). We did not detect significant ezrin and annexin A4 plasma level differences among the lung cancer patients, non-malignant lung disease patients, or the healthy control patients (p > 0.05; data not shown).
The 14-3-3 plasma levels in the lung cancer patients (n = 197; median = 0.029; range, from undetectable to 1.648) were significantly lower than those in non-malignant lung disease patients (n = 70; median = 0.069; range, from undetectable to 0.618; p < 0.001) and those in the healthy controls (n = 69; median = 0.061; range, from undetectable to 3.447; p < 0.001; Fig. 3f). The 14-3-3 ß plasma levels in the lung cancer patients (n = 215; median = 0.557; range, from undetectable to 2.140) were reduced significantly compared with those in the healthy controls (n = 78; median = 0.857; range, from undetectable to 3.063; p < 0.001). The median 14-3-3 ß plasma levels in the lung cancer patients were also lower than those in the non-malignant lung disease patients (n = 60; median = 0.602; range, from undetectable to 2.586), although this difference was not statistically significant (p = 0.187; Fig. 3g). The 14-3-3
plasma levels in the lung cancer patients (n = 219; median = 0.560; range, from undetectable to 2.343) were significantly reduced compared with those in the non-malignant lung disease patients (n = 59; median = 0.760; range, from 0.060 to 3.000; p = 0.001) and in the healthy controls (n = 77; median = 0.790; range, from 0.184 to 3.183; p < 0.001; Fig. 3h). We did not find any significant plasma protein differences due to patient age among the lung cancer patients, non-malignant lung disease patients, or healthy control patients (p > 0.05; data not shown).
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DISCUSSION |
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It was essential in our study to determine whether we could detect these proteins in the plasma of lung cancer patients. Thus, we selected 13 proteins from our CM list and analyzed the plasma of both lung cancer patients and controls using ELISA. Of these 13 proteins, we detected 11 in the plasma of lung cancer and control subjects. Only nm23-H1 and hnRNP A2/B1 were not detected possibly because they were below the present sensitivity threshold. An extensive literature search revealed that 82 proteins were previously detected and reported in human plasma (Supplemental Table 5). For example, the serum levels of cathepsin B, 1-antitrypsin and urokinase-type plasminogen activator are associated with tumor progression and prognosis (2123). To confirm these findings, we need to identify and validate at least 200 additional human plasma proteins. We believe that this detection method will allow us to explore the expression patterns of many more proteins and other biological molecules in the highly complex human blood proteome.
Zhu et al. (24) observed MMP1 overexpression in tumor tissue and associated it with tumor invasion, metastasis, and poor prognosis. In our studies, the MMP1 plasma levels in the lung cancer patients were elevated significantly above that in the healthy controls. Furthermore the MMP1 levels in late stage patients were significantly higher than those in the early stage patients. However, neither MMP1 nor TIMP1 plasma levels in the lung cancer patients were significantly different from those in non-malignant lung disease patients. These findings suggest that the MMP1 overexpression and disproportionate MMP1/TIMP1 ratios may be late lung carcinogenesis events. Thus, MMP1 and TIMP1 may not be useful biomarkers for early diagnosis of lung cancer because of their inability to distinguish cancer from pneumonia or tuberculosis.
Fascin, a 55-kDa actin-bundling protein, plays a primary role in membrane protrusion development (25), which is associated with tumor cell motility. Recent data by Pelosi et al. (26, 27) demonstrate that fascin is overexpressed in invasive and more aggressive non-small cell lung cancer and may affect lung tumor cell differentiation. However, our data are contradictory. The ELISA analysis in our study revealed that the fascin plasma levels in the lung cancer patients are significantly lower than those in healthy subjects. We observed a similar phenomenon for LAMC2, which is considered a potential malignant and metastatic cancer invasion marker (28, 29). In our experiments the LAMC2 plasma levels in the lung cancer patients were significantly lower than in the non-malignant lung disease patients and in the healthy controls. One interpretation to reconcile these conflicting results is that the complex tumor microenvironment mechanism tethers fascin or LAMC2, preventing their release into the blood. The biological roles of these two proteins warrant further investigation.
We measured significantly higher protein plasma levels of PIGR/SC in the lung cancer patients than in the non-malignant lung disease patients. This finding may indicate that overexpression of PIGR/SC plays an important role in lung tumorigenesis. PIGR/SC is involved in the secretory immune system of the human embryo and fetus and is widely distributed in 4-week-old embryos. This protein is detectable later in the mucosa and glands of the digestive, respiratory, and urogenital tracts (30).
In the present study, we found that 14-3-3 , ß, and
plasma levels in lung cancer patients were significantly lower than those in either the non-malignant lung disease patients or healthy subjects. The 14-3-3 family comprises seven highly conserved proteins (
, ß,
,
,
,
, and
) that are broadly expressed in all eukaryotic cells and are associated with regulating cell proliferation, differentiation, apoptosis, maintaining cell cycle checkpoints, and aiding in cell adhesion and motility (31). For example, 14-3-3
may act as a G2 checkpoint sentinel by preventing mitotic catastrophe after DNA damage (32). Chan et al. (32) determined a decrease in 14-3-3
protein expression using immunohistochemical analysis. Hypermethylation of 14-3-3
in the promoter region can result in decreased 14-3-3
gene expression in various human epithelial cancers of the lung and breast (33, 34). However, the biological functions of 14-3-3 ß and
remain unclear. Our results point to the 14-3-3 protein family as a harbinger and potential propagator of human lung carcinogenesis. Furthermore to our knowledge, ours is the first observation of fascin, ezrin, CD98, annexin A4, 14-3-3
, 14-3-3 ß, and 14-3-3
in human plasma by ELISA.
Cancer is produced by the tumor-host microenvironment (5). Tumor growth and progression is not only dependent on the malignant potential of the tumor cells but also on the multidirectional interactions of local factors produced by the various cell types present in the local microenvironment (4). To elucidate the role of the tumor microenvironment on carcinogenesis, we analyzed short time (<1 week) organ-cultured lung cancer tissues and adjacent normal bronchus CM proteins. From these tissues we identified 117 proteins in the organ-cultured CM. Of these, 68 are distinct from the CM proteins of primary cultured cells, including lung and nasal epithelium carcinoma-associated protein 1 precursor and 1,4-ß-N-acetylmuramidase C. Fresh cancer tissue organ cultures may provide further insight into the in vivo tumor microenvironment.
Proteome constituents of body fluids such as serum (plasma) and urine are measured routinely and extensively to identify potential protein markers for early disease diagnostics. Adkins et al. (13) and Pieper et al. (14, 35) identified 891 proteins while characterizing the serum proteome with numerous sample preparation methods and technique platforms. Anderson et al. (12) constructed a blood proteome database for these and other proteins including 433 "non-proteomic" serum or plasma proteins identified through literature searches. Nielsen et al. (17) characterized 150 non-redundant proteins in the urinary proteome. We cross-referenced the sequences and homologue searches from the CM proteins identified in the present study with the four published body fluid proteome protein databases. Only 12 of these CM proteins appear in all four datasets (Fig. 4).
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The cancer biomarker detection system detailed herein should be applicable to any human solid tumor type and may provide a springboard for discovering other tumor-specific proteins. As new human blood proteins are identified, the tumor-related profile will become more robust to staging the various carcinomas.
Currently we and our colleagues are identifying human plasma proteins in the liver and gastric systems of cancer patients. We are building a new database to store and analyze cancer-related proteins and biological molecules of human solid tumors. The next challenge we face is raising antibodies in-house against the plethora of new proteins. With these new antibodies, we will design antibody chips for rapid analysis of protein levels in human blood samples and establish tumor marker profiles for cancer patients. We will also perform studies to determine the functionalities of the new proteins identified in this model.
In conclusion, we report pilot data and a method for detecting proteins released into the circulatory system by the tumor microenvironment. This method detects novel protein markers in blood and provides an approach to establish tumor biomarker profiles for early human cancer detection and therapy.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Published, MCP Papers in Press, June 20, 2005, DOI 10.1074/mcp.M500055-MCP200
1 The abbreviations used are: CM, conditioned medium; PIGR/SC, polymeric immunoglobulin receptor/secretory component; LAMC2, laminin -2; MMP, matrix metalloproteinase; TIMP, tissue inhibitor of metalloproteinases; hnRNP, heterogeneous nuclear ribonucleoprotein; 1-D, one dimensional.
* This work was supported by grants from Chinese State Key Projects for Basic Research (2004CB518707 and 2001CB510201), State High Technology R&D Plan (2002BA711A11), the National Natural Science Foundation of China for Creative Research Groups (30321003 and 30270582), Beijing Municipal Key Project (H030230280190), and the Specialized Research Fund for the Doctoral Program of Higher Education (20030023005). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
S The on-line version of this article (available at http://www.mcponline.org) contains supplemental material.
b These authors contributed equally to this work.
g To whom correspondence may be addressed. E-mail: zhangkt{at}vip.sina.com
h To whom correspondence may be addressed. E-mail: yngao{at}pubem.cicams.ac.cn
i To whom correspondence may be addressed. Tel.: 86-10-68279585; Fax: 86-10-68279585; E-mail: qianxh{at}nic.bmi.ac.cn
j To whom correspondence may be addressed: P O. Box 2258, Beijing 100021, China. Tel.: 86-10-6776-6703; Fax: 86-10-6776-7548; E-mail: chengshj{at}263.net.cn
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REFERENCES |
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