Affiliations of authors: Department of Otolaryngology-Head and Neck Surgery, Head and Neck Cancer Research Division, The Johns Hopkins University School of Medicine, Baltimore, MD (DS, JAC, NB); Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore (RI, XL); Department of Thoracic/Head and Neck Medical Oncology, University of Texas M. D. Anderson Cancer Center, Houston (HR, LM).
Correspondence to: David Sidransky, MD, Head and Neck Cancer Research, The Johns Hopkins University School of Medicine, 818 Ross Research Bldg., 720 Rutland Ave., Baltimore, MD 21205-2196 (e-mail: dsidrans{at}jhmi.edi) or Rafael Irizarry, Department of Biostatistics, The Johns Hopkins University, Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205-2179 (e-mail: rafa{at}jhu.edu)
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ABSTRACT |
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INTRODUCTION |
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The use of tobacco products, particularly cigarette smoking, causes not only lung cancer but also head and neck cancer. Head and neck cancer has a high incidence of debilitating morbidity and high mortality in patients with advanced disease (8). In the United States, approximately 50 000 cases of head and neck cancer are diagnosed annually, the majority (90%) of which are SCCs (9). Although the most effective treatment for patients with head and neck cancer is surgical resection, more than 50% of all patients have advanced disease at the time of diagnosis. Consequently, a large percentage of patients who undergo surgical resection ultimately die of local or regional recurrence, suggesting that occult residual local or metastatic disease is often present at the time of diagnosis. Thus, early detection of head and neck cancer would potentially lead to more effective management of the disease.
Standard diagnostic techniques for both lung and head and neck cancer rely on direct or augmented visualization (11) and on imaging techniques such as spiral computed tomography or positron emission tomography (12-15). However, these cannot be used to detect tumors smaller than 0.51.0 cm2 (representing approximately 109 cells). This limitation has spurred continued interest in identifying reliable tumor markers that can be detected in serum or blood. Although various serum tumor markers for lung and head and neck cancer have been identified, none have been integrated into general clinical practice because, as a rule, these markers have lacked adequate sensitivity and specificity for the clinic (16). Therefore, it is important to develop new methods that provide sensitive and reliable diagnostic markers for both lung and head and neck cancer (17).
Recently, there has been great interest in trying to identify quantitative or qualitative differences in serum protein components between cancer patients and control subjects. The existence of such differences is postulated on the basis that, when cancer cell products, including tumor-specific proteins, enter the circulation, they change the profile of circulating serum and/or plasma proteins. Serum profiling (i.e., the characterization of proteins, peptides, and macromolecules from serum) by surface-enhanced laser desorption/ionization (SELDI) mass spectroscopy (19) coupled with statistical algorithms has been used to distinguish patients with cancer from control subjects and patients with benign conditions (20-23). Whether serum profiling will prove to be sensitive and reliable in the diagnosis of all cancers is unknown.
Here, we hypothesized that the protein spectra from patients with head and neck cancer would be different qualitatively and quantitatively from the protein spectra of control subjects. To test this hypothesis, we used matrix-assisted laser desorption and ionization (MALDI) mass spectroscopy to determine the serum protein profiles from patients with head and neck cancer or lung cancer and control subjects at risk for the development of these cancers. We analyzed the spectra using a simple classification procedure based on a t test feature-selection procedure and linear discriminant analysis (LDA). We then assessed whether the model could be used to distinguish lung cancer patients from the same control subjects.
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SUBJECTS AND METHODS |
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A total of 341 serum samples were obtained from archived aliquots stored in the head and neck cancer tissue and human materials sample bank at The Johns Hopkins University. Samples were stored and maintained in the sample bank under approval by The Johns Hopkins University Institutional Review Board.
This study included sera from 191 cancer patients (99 patients with SCC of the head and neck and 92 patients with NSCLC), seven patients with pulmonary disease but free of cancer (confounding patients), and 143 control subjects. All sera from case patients were obtained at or after diagnosis but before treatment. The control subjects were chosen randomly from a community cancer-screening program that targeted individuals at increased risk of lung and head and neck cancers. Demographic information was collected by using an on-site questionnaire. This community cancer-screening program includes a higher percentage of individuals with a smoking and drinking history than the percentage found among the general population. We reasoned that these control subjects would be appropriate to define markers used in high-risk populations for eventual serum-screening approaches. All of the control subjects were believed to be free of cancer on the basis of clinical history and physical examination. However, no additional imaging approaches or routine marker assays were performed on the control subjects. Sera from control subjects and case patients were collected and stored in an identical fashion in the sample bank as aliquots at 80 °C. Patient demographic, tumor staging, and pathology information was collected from an institutional clinical database that was linked to the sample bank. All study participants provided informed consent.
Serum Preparation and Mass Spectrum Acquisition
Before the analysis, an aliquot of each serum was thawed at room temperature and mixed briefly by vortexing. To prepare the samples for MALDI mass spectroscopy, each serum sample was diluted 1 : 100 by first adding 5 µL of serum to 45 µL of a 1% solution of n-octyl-D-glucopyranoside (a detergent that allows proteins to be isolated in their native state) (Sigma Aldrich, St. Louis, MO) and then adding 5 µL of this solution to 45 µL of distilled water. The final sample solution contained 1% serum in 0.1% n-octyl
-D-glucopyranoside. A 50% saturated solution of sinapinic acid (Sigma Aldrich), to be used as the MALDI matrix, was prepared in 30% acetonitrile0.1% trifluoroacetic acid. Equal volumes of a diluted serum sample and the matrix solution (0.5 µL each) were mixed and added to a stainless steel sample plate (specifically designed for the MALDI mass spectrometer) that contained defined areas for individual samples. The plate was shielded from strong light, and the samples were air-dried.
Mass spectroscopy was performed in a blinded fashion (i.e., with no knowledge of whether a sample was from a case patient or control subject) by using a Kratos AXIMA CFR (Shimadzu Biotech, Chestnut Ridge, NY) mass spectrometer operated in a linear mode. The following parameters were set for the data acquisition: mass range from 0 to approximately 180 000 d; laser power at 90; profile at 300; and five shots per spot. The instrument was calibrated using m/z ratios for the standards bovine serum albumin, aldolase, apomyoglobin, and cytochrome c. m/z ratios were determined for all data points.
Statistical Analysis
We treated patient status as a binary outcome (i.e., no cancer or cancer) denoted Y. For each individual, the mass spectrum contained 284 027 data points. Each data point was dissected from the mass spectrometer signal. We then simplified the data by considering only every 100th value in the individual spectra to considerably reduce the volume of data and the length of computing time. From some preliminary evaluations of the data, this reduction in the volume of data did not affect the final results (data not shown). For spectral data, observations with high mean values tended to have larger variances than observations with low mean values. Thus, the spectral values were log-transformed to reduce the meanvariance dependence. Because we wanted to predict outcomes using the mass spectra, the log-transformed spectra were designated as predictors or covariates and denoted as X = X1,...,X2840.
We used LDA with the spectral masses as predictors of outcome (24). To reduce the number of predictors, i.e., to make the calculations simpler, we used a simple feature-selection procedure that is based on the ratio of the across-group variance to the within-group variance (equivalent to the t test) comparing the values in control subjects with those in patients with head and neck cancer. We ranked all spectral masses by their absolute value of the t test and chose only the highest P (P = 45 top predictors, see below) to include in the LDA. To assess the predictive ability of our procedure, we used a cross-validation protocol in which we randomly chose two-thirds of the data as a training set and the other one-third as a test set to determine how well we could predict a cancer case. The 45 predictors were recalculated with each training set (n = 200).
We considered false-positive and true-positive results only in the test set. We created 200 cross-validation data sets by considering 200 randomly chosen groupings of the subjects. The average false-positive and true-positive rates, which were generated from the cross-validation data sets, were considered measures of the predictive ability of our procedure. To compute the expected false-positive and true-positive rates under the null hypothesis that the spectra lack predictive ability, we repeated this procedure after randomly permuting the outcomes (Y).
The specificity (false-positive) and sensitivity (true-positive) rates derived from the LDA can be altered by using a simple stochastic model. We assumed that the predictors X followed a multivariate normal distribution that was conditional on the binary outcome (Y). To predict Y for a particular value of X, we found the value of Y that maximized the posterior probability, which is the probability of being a case or a control given the observed spectra. We assigned a prior probability to each value of Y. These prior probabilities were used to control sensitivity and specificity. For example, if the prior probability of being a case patient was assumed to be 0, then the false-positive and true-positive rates would be 0%. If the prior probability was assumed to be 1, then the false-positive rate is maximized and the true-positive rate would be 100%. We used the training data to estimate the parameters (mean and covariance matrix) associated with each of the conditional distributions (i.e., the probability of observing the spectra for a case patient or a control subject). As already noted, with LDA it is possible to set a tuning parameter that directly affects the balance between sensitivity and specificity (25). Therefore, we used the cross-validation results for a range of tuning parameters to construct receiver operating characteristic (ROC) curves. Because theoretical P values are intractable (due to the complicated nature of our procedure), we estimated a P value based on the 200 simulations.
To obtain the mean false-positive and true-positive rates per subject, we considered the number of times that correct and incorrect calls were made over the 200 simulations. We then compared these false-positive and true-positive rates across different groups of subjects stratified by the covariates sex, age, stage of disease, smoking history (pack-years), and history of alcohol use using the general linear methods function in R (26).
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RESULTS |
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We created a summary of the average spectra for head and neck cancer case patients and control subjects (Fig. 4). Sera from head and neck cancer case patients generally contained more total protein than sera from control subjects (Fig. 4, upper portion). The lower portion of Fig. 4 is a histogram distribution of individual points, demonstrating the number of times the points emerged as features during the 200 random divisions of the data. The most frequently appearing points correspond to positions where peaks appeared or disappeared in the head and neck cancer samples. One particular peak, at 111 kd, was different between sera from case patients and control subjects in all 200 simulations. Many peaks represent proteins of less than 70 kd (i.e., 5, 10, 12, 15, 20, 45, 47, 54, and 64 kd) and may represent interesting molecules and candidate serum cancer markers that merit further identification (28-30).
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DISCUSSION |
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Recently, at least two other studies (21) have sought to identify differences in sera protein spectra between case patients and control subjects. Both studies used SELDI, a separation approach that preselects proteins in a sample by fractionation based on prebinding to different surfaces or chemical coatings, and analyzed only a subset of proteins over a small range of m/z ratios. In one study (20), sinapinic acid was used to fractionate serum proteins, but only proteins between 2 and 40 kd were examined. In the other study (21), proteins were ionized with -cyano-4-hydroxycinnamic acid, and only proteins less than 20 kd were examined. By contrast, we used MALDI technology and studied proteins that ranged up to 180 kd in size. MALDI is more straightforward technically than SELDI because it requires no washing procedures, which simplifies the sample processing and may also reduce the possibility of introducing artifacts. We identified many protein peaks at higher molecular masses (e.g., 111 kd) in sera from cancer patients that were usually absent in sera from control subjects. We believe that these higher molecular mass peaks may represent intact circulating proteins and not fragments or nonprotein macromolecules, which may contribute to some of the lower molecular mass peaks. Moreover, because MALDI does not require modification of the proteins, we will be able to fractionate, digest, and identify the protein peaks to identify key molecules likely to play a role in lung and head and neck cancer tumorigenesis (28-30).
In general, control populations are limited by a low prevalence of pertinent risk factors for the disease to be tested. Our control patient population was selected to include patients at risk for aerodigestive tract cancers and, thus, our study provides information on the practical use of cancer detection by using MALDI. The cancer patients in our study represent typical patients who undergo attempts at curative treatment in an academic medical institution. Our lung cancer patient population also consisted of individuals with different histologic diagnoses, which is characteristic of NSCLC. One advantage of this diverse population was that it allowed us to optimize the LDA for SCC (or any specific histologic type) and to detect other histologic types of NSCLC (Table 2 and data not shown). These results suggest that a pattern of serum protein markers can ultimately be identified that will enable the detection of most major histologic types of lung cancer.
Our control population included individuals that were closely matched with the case patients and fit the demographic profile for those at increased risk of both head and neck and lung cancer. Approximately 33% of our control subjects were smokers and 15% were heavy drinkers, and thus the control subjects represent a reasonable target population for eventual cancer screening. When the heavy drinkers (more than one drink/day) were included in the models, the rate of false-positive results increased slightly (i.e., the models overpredicted). One explanation for this result is that the overfitting of the data could be associated with factors expressed in the sera of heavy alcohol drinkers (and perhaps smokers) that will continue to confound prediction algorithms in exposed populations. An alternative explanation is that patients at high risk for aerodigestive tract cancers may already harbor premalignant lesions or small occult cancers that will be discovered with time. It was reassuring to find that a small number of individuals with no history of drinking alcohol but with confounding conditions could be identified as free of cancer when they were included among the lung cancer population. A larger number of control subjects with clear risk factors and comorbid or confounding conditions will help validate serum protein patterns and ascertain the ultimate value of such tests. In addition, peak differences remain to be tested prospectively in an entirely independent population of case patients and control subjects.
Statistical analyses for protein spectral data continue to be developed. Currently, two common methods are cluster analysis and decision tree classification analysis. A recent example of cluster analysis for protein spectral data can be found in a study on ovarian cancer (21). The analysis was based on genetic algorithms first described by Holland (31) and on a cluster analysis attributed to Kohonen (32,33). In phase I of the data analysis (i.e., the training phase), the algorithm first identified a small subset of key values across the x-axis using an iterative searching process. A subset of values was judged important because the y amplitude patterns at the specific m/z values segregated case patients from control subjects. In phase II of the analysis (i.e., the test phase), only the key subset of m/z values identified in phase I was used to classify the unknown samples. Each unknown was then classified as a case patient, a control subject, or a new cluster (http://clinicalproteomics.steem.com). A recent example of a decision tree classification analysis for protein spectral data can be found from the same group in a study on prostate cancer (20). A decision tree classification analysis was used to split the dataset into two bins by using one rule or question at a time. This splitting of the dataset continued until terminal nodes or leaves were produced and further splitting provided no additional gain. The classification of terminal nodes was determined by the class of samples (case patients, control subjects, or subjects with benign disease) representing the majority of samples in that node. Protein peaks selected by this process to form splitting rules were the ones that achieved the maximum reduction of cost (or complexity) in the two descendant nodes. The area under the curve was then computed to identify the m/z peaks with the highest potential to discriminate between the major groups or class of samples. A Bayesian approach was used to calculate the expected probabilities of each class in each terminal node. Cluster algorithms such as these do not necessarily take advantage of thoroughly training the data.
To analyze our data, we searched for a procedure that could reliably predict the outcome Y given the protein spectra X. For this problem, we were more interested in correct predictions than in interpretation of model parameters, which made the typical logistic regression model impractical. We selected a procedure that could reliably predict outcomes between case patients and control subjects regardless of the interpretability of the individual parameters. Thus, we did not preselect key features that would likely raise the predictability but lower the reproducibility in different populations. The sensitivity of our procedure could be raised by various simple approaches, including better alignment of peaks from run to run and further refinement of key predictors (34). Moreover, we used only one-tenth of the data; more input data could result in further refinements of the model.
Our study thus used a standard statistical analysis to test the hypothesis that serum mass spectra contain sufficient information to separate case patients from control subjects (24). Unlike other studies (19-23), which reported the best-case analysis, our study was a fundamental test of the hypothesis, the results of which gave a reliable estimate of what can be routinely achieved with such an experimental approach. Indeed, inspection of the ROC curves shows that they approach high sensitivity and specificity similar to those sensitivities and specificities reported in some earlier studies (20-21). Clearly, a number of statistical approaches can generate the relevant m/z peaks for group classification. Studies (19-23) on serum mass spectra published so far have all shown that several peaks are needed to separate case patients from control subjects. No single peak, and thus no single protein, is able to distinguish case patients from control subjects reliably. Serum-profiling approaches are a substantial advance over previous attempts to isolate and characterize a single protein marker to detect any of a variety of cancers. Indeed, investigators using a variety of molecularly based approaches in serum DNA have come to the same conclusionthe need to use more than one molecular target in cancer detection (35).
Although single serum biomarkers for cancer such as the CA 125 antigen for ovarian cancer or prostate-specific antigen for prostate cancer have been identified, they have several limitations including limited sensitivity and recurring false-positive results. Studies have yet to identify protein biomarkers for lung or head and neck cancers that are suitable for evaluation in screening trials. We have now identified m/z peaks that may yield such markers in these diseases. Our study suggests that a number of informative patterns and key protein markers may be identified in the near future for different cancers using similar serum-based approaches. One benefit of such an approach is that MALDI and SELDI assays are known to be reliable and highly reproducible. Moreover, after the key proteins are identified, antibodies can be used to develop high-throughput, low-cost immunoassays. These assays can then be incorporated into prospective longitudinal trials to assess the true predictive values of these proteins in cancer detection (36).
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NOTES |
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Cangen provided partial funding for the research described in this article. Under a licensing agreement between Cangen and The Johns Hopkins University, Dr. Sidransky is entitled to a share of royalties received by the University on sales of products described in this study. Dr. Sidransky and the University own Cangen stock, which is subject to certain restrictions under University policy. The terms of this arrangement are being managed by The Johns Hopkins University in accordance with its conflict of interest policies.
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Manuscript received April 3, 2003; revised September 4, 2003; accepted September 22, 2003.
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