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Ann Thorac Surg 2009;88:1118-1123. doi:10.1016/j.athoracsur.2009.05.032
© 2009 The Society of Thoracic Surgeons
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Original Articles: General Thoracic

Serum Protein Profiles in Myasthenia Gravis

Chao Cheng, MD, PhDa, Guoyong Wu, MDa, Sai-Ching J. Yeung, MD, PhDb, Rong Li, MD, PhDc, Amos Ela Bella, MSa, Jinzhuo Pang, MDa, Fo-tian Zhong, MDa, Honghe Luo, MDa, Yanli Jin, MSd, Jingxuan Pan, MD, PhDd,*

a Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
c Lung Cancer Research Institute of Guangdong Provincial People's Hospital, Guangzhou, People's Republic of China
d Department of Pathophysiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
b Department of General Internal Medicine, Ambulatory Treatment and Emergency Care, The University of Texas M. D. Anderson Cancer Center, Houston, Texas

Accepted for publication May 13, 2009.

* Address correspondence to Dr Pan, Department of Pathophysiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510089, People's Republic of China (Email: jingx_pan{at}yahoo.com.cn).


   Abstract
Top
 Abstract
Introduction
 Material and Methods
 Results
 Comment
 Acknowledgments
 References
 
Background: The diagnosis of myasthenia gravis (MG) remains challenging. We performed a proteome-wide search for potential serum protein diagnostic markers for MG using surface-enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry (TOFMS).

Methods: Proteomic spectra from 80 MG patients and 80 healthy individuals were generated by SELDI. Samples from 56 MG patients and 56 healthy individuals in the training set were analyzed to set up the decision tree. Samples from 24 MG patients and 24 healthy individuals were used for cross-validation testing.

Results: The SELDI TOFMS analysis generated 101 peaks, representing differentially expressed proteins between 1000 and 20000 Da. Among them, 9 peaks were down-regulated and 30 others were up-regulated in the MG sera compared with the controls. The decision tree used the peak at M4168.94 Da and M1122.57 Da as splitters in the classification process. In the training set, 112 samples were classified as MG or control group, with a sensitivity of 100% and specificity of 89.3%; the 10-fold cross-validated analysis identified the optimal decision tree with the lowest relative cross-validated cost of 0.080. In the test set, the decision tree generated was able to identify 20 of 24 MG patients and 21 of 24 healthy individuals with a sensitivity of 83.3% and a specificity of 87.5%.

Conclusions: SELDI TOFMS is a useful tool for the detection and identification of potential serum biomarkers that can diagnose MG with high sensitivity and specificity.


   Introduction
Top
 Abstract
 Introduction
Material and Methods
 Results
 Comment
 Acknowledgments
 References
 
Myasthenia gravis (MG) is an autoimmune neuromuscular junction disorder that causes muscle weakness. The incidence rate for MG has increased over time [1], and this phenomenon is likely due to improvement in diagnosis. The diagnosis for MG remains challenging because of its fluctuating clinical course and signs and symptoms similar to other neuromuscular disorders such as Lambert-Eaton syndrome, botulism, congenital myasthenic syndromes, and tick paralysis [1]. At present, there are still no widely accepted diagnostic criteria for MG [2].

Surface-enhanced laser desorption/ionization (SELDI) time-of flight mass spectrometry (TOFMS) technology combines protein chip array with TOFMS and offers not only the advantages of speed, simplicity, sensitivity and suitability for a comparative study but also the advantage of using a very small amount of sample for the test. In recent years, this technique has been successfully used to study biomarkers of certain cancers [3–5]. In this study, we report the application of SELDI TOFMS in the diagnosis of MG using serum samples.


   Material and Methods
Top
 Abstract
 Introduction
 Material and Methods
Results
 Comment
 Acknowledgments
 References
 
This study was approved by the SunYat-sen University Institutional Review Board. All study participants provided informed consent according to institutional guidelines.

Patients
Peripheral blood samples were obtained before thymectomy from 80 patients (34 males and 46 females) aged 4 to 48 years old who were diagnosed with MG during June 2007 to June 2008 in The First Affiliated Hospital of Sun Yat-sen University. The MG patients underwent extended thymectomy consisting of complete en bloc extirpation of thymic and adjacent tissues, including fatty tissue through median sternotomy, followed by pathologic examination.

All patients met all of the five diagnostic criteria for MG [1]:

1 pharmacologic testing with edrophonium chloride that elicits unequivocal improvement in strength;
2 electrophysiologic testing with repetitive nerve stimulation studies or single-fiber electromyography (SFEMG), or both, demonstrating a primary postsynaptic neuromuscular junctional disorder;
3 typical clinical manifestation demonstrating oculomotor weakness with asymmetric ptosis and binocular diplopia or asymmetrical weakness of multiple extraocular muscles that cannot be attributed to neuropathy involving a single cranial nerve;
4 therapeutic benefits from anticholinesterase drugs or corticosteroids, or both; and
5 pathologic findings of hyperplasia of the thymus.

According to the Osserman classification [6], 41 of 80 patients were in class I (ie, ocular MG); 16 were class IIa; 18 were class IIb; and 5 were class III. The peripheral blood samples of 80 healthy individuals (36 males and 44 females) aged 13 to 40 years were used as the control.

Preparation of Specimens
All blood samples were allowed to clot at 4°C and centrifuged at 3000 rpm for 10 minutes. The serum was collected and frozen in aliquots for storage at –80°C.

SELDI Processing Of Serum Samples
Serum samples were processed robotically on a Ciphergen Biosystems (Freemont, CA) liquid handling system in an 8-well format for SELDI analysis. A 10-µL aliquot of serum was pretreated with 9M urea, 2% 3-[(3-cholamidopropyl)-dimethylammonio]-1-propane sulfonate (CHAPS), and 50mM Tris, and was vortexed for 30 minutes at 4°C. Further dilution was made in 50mM sodium acetate binding buffer. Each sample position was spotted onto WCX-2 chips. Protein chips were used for SELDI TOFMS analysis with the aid of a bioprocessor. The pretreated samples were added to each well of chips and incubated for 30 minutes. After washing three times with 200 µL binding buffer (50mM sodium acetate, pH 7.4) and deionized water, the chips were removed from the bioprocessor and air-dried for 20 minutes.

A saturated solution of sinapinic acid (0.5 µL) in 50% acetonitrile and 0.5% trifluoroacetic acid was applied to each spot on the chip surfaces. The chips were read on a protein biologic system II (PBS-II) and a MS reader (Ciphergen Biosystems Inc). The molecular mass per charge (m/z) and relative intensity of each protein adhered to the chip were measured with TOFMS. The mass spectra were obtained by averaging 140 laser shots with an intensity of 200, a detector sensitivity of 9, a peak mass of 30,000 Da, and an optimized range of 1000 to 20,000 Da. The mass accuracy was calibrated to less than 0.1% using the All-in-one Peptide Molecular Mass Standard (Ciphergen Biosystems Inc). Peaks were autodetected when occurring in at least 30% of spectra and with first- and second-pass signal-to-noise (S/N) of 5 and 2, respectively, in a 0.3% cluster mass window.

SELDI Data Analysis
Biomarker Wizard (BMW) software (Ciphergen Biosystems) was used for peak clustering. Pattern recognition and sample classification were performed with the Biomarker Pattern Software (Ciphergen Biosystems), in which multiple decision trees were initially generated with the use of all the peaks as variables. A learning set of 56 samples of MG patients and 56 healthy controls were used to set up the decision tree. The 10-fold cross-validated analysis was used to identify the optimal decision tree with the overall lowest relative cross-validated cost. Under 10-fold cross validation analysis, all the computational work of generating trees is repeated at least 10 times, which can assess the validation of the performance of the decision tree.

Next, the decision tree was subjected to external cross-validation (a blind test). A blinded validation set of 24 samples from MG patients and 24 samples from healthy controls were categorized using the decision tree and compared with their true MG status to evaluate the validity and diagnostic performance of the decision tree. The peaks that formed the main splitters of the tree with the highest prediction rates in the training set was selected and used to lay out a final decision tree with the greatest possible predictive power.

Sensitivity is defined as the percentage of MG patients in the validation set correctly detected by SELDI (true positive/total number of MG). Specificity is defined as the percentage of normal controls in the validation set correctly identified by SELDI (true negative/total number of normal controls). The positive likelihood ratio is defined as the ratio of true positive rate/negative positive rate. The negative likelihood ratio is defined as the ratio of false negative rate/true negative rate. The positive predictive value (PPV) is the proportion of patients with positive test results who are correctly diagnosed. The negative predictive value (NPV) is the proportion of patients with negative test results who are correctly diagnosed.


Formula



Formula

With Sen, sensitivity; Spe, specificity; and P0, prevalence in the population.


   Results
Top
 Abstract
 Introduction
 Material and Methods
 Results
Comment
 Acknowledgments
 References
 
In the training set, sera samples from 56 MG patients vs the 56 healthy individuals were analyzed by affinity Protein Chips. The resultant SELDI TOFMS data were analyzed with Biomarker Wizard and generated 101 peaks representing differentially expressed proteins ranged from 1000 to 20,000 Da. Of these, 39 protein peaks had significant differences (p < 005) between the MG and control groups. Among them, 30 peaks were increased in quantities (Table 1) and 9 peaks were decreased in quantities (Table 2) in the MG sera compared with the control sera. Figure 1 illustrates representative SELDI spectra graphs with the differentially expressed peak intensities between the MG group and the control group. The expression of proteins at M3951.23, M3969.56, M4168.94, and M4276.40 positions in MG sera were reduced in comparison with those in normal control sera.


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  		Table 1 Comparison of Protein Peaks That Were Significantly Different With Increased Intensities
 

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  		Table 2 Comparison of Protein Peaks That Were Significantly Different With Decreased Intensities
 

Figure 1
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  		Fig 1. Representative surface-enhanced laser desorption/ionization spectra comparison of serum between the myasthenia gravis (MG) patients and healthy control groups. In the MG sera, the proteins at M3951.23, M3969.56, M4168.94, and M4276.40 were decreased in comparison with the normal control sera. The x axis shows the molecular weight for each spectrum (mass/charge ratio values), and the y axis shows the relative intensity.

 
The peak intensity values of the 101 differentially expressed peaks were collectively applied to the Biomarker patterns algorithm program to generate an optimal decision and classification tree. Under 10-fold cross validation analysis, the output showed a tree sequence summary, with 3 terminal nodes identified as optimal (Fig 2). This tree had the overall lowest relative cross-validated cost of 0.080, and the cost was considerably less than that of the next smallest tree. The optimal decision tree used the peak at M4168.94 Da as a primary splitter in the classification process (Fig 2). Node 1 was split at M4168.94, and a case went left if M4168.94 was 7.233 or less: 50 cases went left as terminal node 1 and 62 cases right as node 2. Node 2 was split at M1122.57, and a case went left if M1122.57 was 1.368 or less: 54 cases were divided to the left side as terminal node 2 and 8 cases to the right side as node 3. In the training data set of 112 samples (MG and control), the sensitivity and specificity of this algorithm were 100% and 89.3%, respectively.


Figure 2
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  		Fig 2. In the training data set, the Biomarker Pattern Software built the classification tree to classify myasthenia gravis patients or healthy controls. All the sera were categorized for a sensitivity and specificity of 100% and 89.3%, respectively, using peak at M4168.94 Da, M1122.57 Da as splitters.

 
Furthermore, using a blinded validation set of sera samples (test set, 24 MG patients and 24 healthy controls), the cross-validation analysis showed that 20 MG patients and 21 healthy controls were correctly identified following the decision classification tree for an 85.4% accuracy rate. For the validation set, the algorithm had a sensitivity of 83.3% and a specificity of 87.5%. In the training and test groups, the positive likelihood ratios were 9.35 and 6.66, the negative likelihood ratios were 0 and 0.191, positive predictive values (PPV) were 1/1071 and 1/1501, and negative predictive values (NPV) were 100% and 99.99%, respectively.


   Comment
Top
 Abstract
 Introduction
 Material and Methods
 Results
 Comment
Acknowledgments
 References
 
Acquired MG is a rare disorder with 200 to 400 cases/million as reported [7]. This prevalence is almost similar in China and Western countries [8]. Several approaches have been taken to facilitate accurate and early diagnosis of MG. Pharmacologic testing with intravenous edrophonium is plagued with the problem of both false-negative and false-positive results. The repetitive nerve stimulation test is relatively insensitive in ocular and in mild generalized MG. In addition, nearly half of patients with ocular MG are seronegative by acetylcholine receptor (AchR) or muscle-specific tyrosine kinase (MuSK) antibodies tests. Given the shortcomings and disadvantages of these approaches, there is a dire need for a new diagnostic method to diagnose MG early and accurately because early and accurate diagnosis is important to obtain the best clinical benefit from thymectomy during the early course of MG [9, 10].

In this study, we analyzed the protein spectra in MG patients by SELDI TOFMS. Our results suggest that this technology may offer a useful tool for the diagnosis of MG using serum samples and holds promise to identify novel biomarkers of MG that can provide insights in the pathophysiology of MG.

SELDI TOFMS has several potential advantages as a clinical assay. This technology provides reliable reproducible data from readily accessible serum samples, has good throughput, poses minimal risk to patients, and is relatively inexpensive [11–13]. Moreover, this technology has been successfully used in the diagnosis of many diseases by protein profiling of complex biologic mixtures. Because protein chip arrays can selectively bind to proteins based on the physical or chemical modifications of their surface, the protein contents of the samples are thus effectively simplified, and the contaminants such as buffer salts or detergents can also be easily washed away before analysis. Compared with 2-dimensional protein gel electrophoresis (2D gel) and MS, SELDI technology is much faster with a high throughput capability and is advantageous in its ability to effectively resolve low-mass proteins (2000 to 20,000 Da), hydrophobic proteins, and very basic or acidic proteins [14]. Not only are the mass/charge values of proteins available but also some physical and chemical properties of proteins, including hydrophobicity/hydrophilicity, phosphorylation and metal-binding.

A particular advantage is that this technique needs only a very small amount of sample to analyze biologic mixtures directly or after simple pretreatment. Furthermore, the cross-validation analysis in the training and test set performed by the Biomarker Pattern Software can enhance the validation of the performance of the decision tree and in particular, to evaluate the sensitivity and the specificity.

Because 41 of the 80 patients in this study were in the ocular MG class, our data suggest that SELDI-based serum analysis may be beneficial to the diagnosis for this subset of patients. In addition, Chinese patients with generalized MG have a much lower rate of 32% to 63% [15] of positive serum AchR antibodies compared with 80% to 90% in white patients [16]. This implies that SELDI-based serum analysis may be of importance for Chinese patients with generalized MG.

With SELDI TOFMS, we identified 39 protein peaks, which showed a statistical difference between the MG group and the healthy controls. Only 2 peaks (M4168.94 and M1122.57 Da) were used in the classification tree algorithms. The study of Wadsworth and colleagues [17] suggested that the classification and regression tree approach examined all the possible protein peak combinations from the input spectral data and made the best classification tree. The classification tree in our study has shown that the 112 samples in the training set were classified as either the MG or control group with only two nodes, with an encouraging outcome of 100% sensitivity and 89.4% specificity. In the subsequent cross validation analysis, the sensitivity was 83.3% and specificity was 87.5%.

The high sensitivity and specificity obtained by the serum protein profiling approach presented in this study demonstrate that SELDI protein chip MS can be an intelligent classification algorithm to facilitate the discovery of serum biomarkers for MG and provide an innovative clinical diagnostic platform that has the potential for early detection and differential diagnosis of MG.

In the 4 nonidentified patients in the MG group, 2 patients had accompanying hyperthyroidism. Whether hyperthyroidism affects protein peaks is not clear at the present time. However, no major clinical and biologic differences were found in the 3 nonidentified individuals in the control group.

A limitation of this study is that the peaks generated have not been identified. In the decision tree, the protein peaks used as the splitters were identified with an average mass of M4168.94 and M1122.57 Da. MG is an antibody-mediated autoimmune disease, and some special serum antibodies, including AChRs and MuSK, are often detected in generalized MG [18, 19]. Recently, Leite and colleagues [20] reported immunoglobulin G1 antibodies to AChRs in seronegative MG patients whose test results for AChR and MuSK antibodies were negative. Therefore, the identities of the proteins at M4168.94 and M1122.57 Da remains to be further clarified.

Another limitation is the lack of gold standard for the diagnosis of MG. To minimize diagnostic mistakes, we strictly set the inclusion criteria for the MG patients in this study to meet the 5 diagnostic criteria mentioned above. The detection of AChR and MuSK antibodies was not standard routine clinical practice in China when this study was conducted, but the serology status of the MG patients will be included in future studies.

Our results suggested that certain proteins might potentially be identified to differentiate MG disease by SELDI TOFMS analysis. However, a large prospective clinical trial is still needed to validate the reliability and diagnostic performance of the decision tree model. Because MG is markedly influenced by genetic factors [21], validation in Western countries is also needed.

Furthermore, extended transsternal thymectomies for MG can obtain complete remission in less than 50% of patients after long-term follow-up [22]. That means many patients will not benefit from thymectomy. Therefore, it is important to use this technology to identify the subpopulation of MG patients who would not benefit from thymectomy to avoid unnecessary operations. Most of the MG patients in our study are being monitored to see if they derive benefit from thymectomy. When the follow-up data are available, the stored sera of these patients will be analyzed to identify the serum markers that can predict the therapeutic response to thymectomy.

In conclusion, our results demonstrate that serum protein profiling using the new technology of SELDI TOFMS can discriminate patients with MG from healthy individuals. There is a potential of combining the serum protein profiles and clinical features for the early diagnosis of MG with a high sensitivity and specificity.


   Acknowledgments
Top
 Abstract
 Introduction
 Material and Methods
 Results
 Comment
 Acknowledgments
References
 
We thank Dr Yilong Wu (Lung Cancer Research Institute of Guangdong Provincial people's Hospital) for providing the SELDI TOFMS service. This study was supported by grants from the National Natural Science Fund of China (30801136 to C.C., 90713036 to J. Pan), National High Technology Research and Development Program of China (863 Program 2008AA02Z420 to J. Pan), and National Basic Research Program of China (973 Program 2009CB825506 to J. Pan).


   References
Top
 Abstract
 Introduction
 Material and Methods
 Results
 Comment
 Acknowledgments
 References
 

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  9. Monden Y, Nakahara K, Kagotani K, et al. Effects of preoperative duration of symptoms on patients with myasthenia gravis Ann Thorac Surg 1984;38:287-291.[Abstract/Free Full Text]
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  11. Xiao Z, Adam BL, Cazares LH, et al. Quantitation of serum prostate-specific membrane antigen by a novel protein biochip immunoassay discriminates benign from malignant prostate disease Cancer Res 2001;61:6029-6033.[Abstract/Free Full Text]
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