Weighted Flexible Compound Covariate Method for Classifying Microarray Data

Shyr, Yu; Kim, KyungMann

doi:10.1007/0-306-47815-3_10

Cited by 17 publications

(18 citation statements)

References 8 publications

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“…This processing results in 100 to 300 m/z peaks per spectrum on average, using conservative parameters. Statistical analyses of these data for biomarkers focus on the selection of MS features and differential expression levels between the study groups and on building class prediction models based on the selected features (68,69,(100)(101)(102). The misclassification rate is typically estimated using the leave-one-out cross-validation.…”

Section: Discussionmentioning

confidence: 99%

“…Protein expression maps (or images) can be reconstructed for every m/z value detected by integrating the corresponding signal intensities and plotting these as a function of sampling coordinates. a class prediction model using established methods (69). We identified protein signals that allowed the classification of lung tumors by histology, the distinction of primary tumors from metastases, and the identification of nodal involvement with 75% accuracy.…”

Section: Applications To Lung Cancer-related Biospecimens Lung Tissuesmentioning

confidence: 99%

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Mass Spectrometry-based Proteomic Profiling of Lung Cancer

Ocak¹,

Chaurand²,

Massion³

2009

Proceedings of the American Thoracic Society

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In an effort to further our understanding of lung cancer biology and to identify new candidate biomarkers to be used in the management of lung cancer, we need to probe these tissues and biological fluids with tools that address the biology of lung cancer directly at the protein level. Proteins are responsible of the function and phenotype of cells. Cancer cells express proteins that distinguish them from normal cells. Proteomics is defined as the study of the proteome, the complete set of proteins produced by a species, using the technologies of large-scale protein separation and identification. As a result, new technologies are being developed to allow the rapid and systematic analysis of thousands of proteins. The analytical advantages of mass spectrometry (MS), including sensitivity and highthroughput, promise to make it a mainstay of novel biomarker discovery to differentiate cancer from normal cells and to predict individuals likely to develop or recur with lung cancer. In this review, we summarize the progress made in clinical proteomics as it applies to the management of lung cancer. We will focus our discussion on how MS approaches may advance the areas of early detection, response to therapy, and prognostic evaluation.Keywords: proteome; translational research; biomarkers Lung cancer is the leading cause of cancer-related death worldwide among both males and females, with more than 1 million deaths annually (1). Non-small cell lung cancer (NSCLC) accounts for about 80% of all lung cancers. Although advances have been made in diagnosis and treatment strategies in the last decade, the prognosis of NSCLC patients is poor, with a 5-year overall survival of 15 to 20% (2). This is mainly due to a lack of early diagnosis tools, with more than 60% of the patients diagnosed with advanced or metastatic disease (3) and therefore not eligible for a curative surgical resection. Lung cancer is often suspected on the basis of abnormal chest imaging and/or nonspecific symptoms. Bronchoscopy, with cytopathologic examination of bronchoalveolar lavage, endobronchial brushings and biopsies obtained from the suspect area, is in general used as an initial diagnostic tool. However, while this procedure is 100% specific for lung cancer, the sensitivity is low and ranges from 30% for small peripheral lesions to 80% for central endobronchial tumors (4). More invasive and expensive diagnostic tests are often required, delaying the diagnosis and the subsequent treatment initiation. Surgical resection offers the best chance for cure. For patients undergoing surgery, the longterm prognosis remains disappointing, with a 5-year overall survival of 50% only (5). Recent studies showed that survival of surgically resected patients with NSCLC might be improved by systemic platinum-based adjuvant chemotherapy (6, 7), but which patients might benefit from this treatment cannot be determined accurately.To improve lung cancer management and survival, there is a great need to develop screening and early diagnosis strategies that are sensitive,...

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Section: Discussionmentioning

confidence: 99%

Section: Applications To Lung Cancer-related Biospecimens Lung Tissuesmentioning

confidence: 99%

Mass Spectrometry-based Proteomic Profiling of Lung Cancer

Ocak¹,

Chaurand²,

Massion³

2009

Proceedings of the American Thoracic Society

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“…The statistical class-prediction model based on the selected features was applied to determine whether the proteomic patterns could be used to classify normal from preinvasive and invasive tissue samples. The weighted flexible compound covariate method (WFCCM) (11,12,28,29) was used in the classprediction model based on the selected features to determine whether the proteomic patterns could be used to classify tissue samples among groups. We estimated the misclassification rate using the leave-one-out cross-validation class-prediction method based on the WFCCM.…”

Section: Discussionmentioning

confidence: 99%

“…Using biostatistical methods to select differentially expressed peaks (MS signals) and after the development of a class prediction model (12), 82 discriminatory signals were found to classify normal lung from lung cancer tissue samples in a derivation and validation study design with excellent accuracy. Other recent studies indicated the importance of using protein expression profiles as a diagnostic or prognostic biomarker for patients with early-stage lung cancer using two-dimensional polyacrylamide gel electrophoresis analysis (13,14), protein arrays (15), or surface-enhanced laser desorption/ionization MS (16).…”

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confidence: 99%

Proteomic Patterns of Preinvasive Bronchial Lesions

Rahman

Shyr

Yıldız

et al. 2005

Am J Respir Crit Care Med

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Purpose: A proteomics approach is warranted to further elucidate the molecular steps involved in lung tumor development. We asked whether we could classify preinvasive lesions of airway epithelium according to their proteomic profile. Experimental Design: We obtained matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry profiles from 10-m sections of fresh-frozen tissue samples: 25 normal lung, 29 normal bronchial epithelium, and 20 preinvasive and 36 invasive lung tumor tissue samples from 53 patients. Proteomic profiles were calibrated, binned, and normalized before analysis. We performed class comparison, class prediction, and supervised hierarchic cluster analysis. We tested a set of discriminatory features obtained in a previously published dataset to classify this independent set of normal, preinvasive, and invasive lung tissues. Results: We found a specific proteomic profile that allows an overall predictive accuracy of over 90% of normal, preinvasive, and invasive lung tissues. The proteomic profiles of these tissues were distinct from each other within a disease continuum. We trained our prediction model in a previously published dataset and tested it in a new blinded test set to reach an overall 74% accuracy in classifying tumors from normal tissues. Conclusions: We found specific patterns of protein expression of the airway epithelium that accurately classify bronchial and alveolar tissue with normal histology from preinvasive bronchial lesions and from invasive lung cancer. Although further study is needed to validate this approach and to identify biomarkers of tumor development, this is a first step toward a new proteomic characterization of the human model of lung cancer tumorigenesis. Keywords: early detection; mass spectrometry; preneoplasia; profilingThe discovery of preinvasive lesions in the high-risk population places patients at increased risk of developing lung cancer (1-3). Although the natural history of these preinvasive lesions is poorly understood, increasing evidence suggests that, in the absence of treatment, high-grade preinvasive lesions, most commonly found in patients with a prior history or with concomitant cancer, will develop into invasive carcinoma in 30 to 50% of the cases, whereas the vast majority of low-grade preinvasive lesions remain stable or regress on follow-up (4-7). In addition, nonstep- wise progression of preinvasive lesions makes prediction of lung cancer progression based on histologic grade unreliable. Therefore, the need for molecular biomarkers predictive of tumor progression is becoming more evident (8-10).In a recent report, we used conventional matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI MS) to generate protein profiles that accurately classify and predict histologic subgroups of lung cancer (11). Using biostatistical methods to select differentially expressed peaks (MS signals) and after the development of a class prediction model (12), 82 discriminatory signals were found to classify normal...

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“…Protein markers that passed at least two of these three selection criteria were finally selected. The weighted flexible compound covariate method (WFCCM) (26) was used in the class prediction model based on the selected proteins to verify whether the proteomic patterns could be used to classify tissue samples into different groups. The WFCCM reduced the dimensionality of the problem by using a new covariate obtained as a weighted sum of the most important predictors and combining the most significant proteins associated with the biological status from each analysis method.…”

Section: Methodsmentioning

confidence: 99%

Identification of Early Intestinal Neoplasia Protein Biomarkers Using Laser Capture Microdissection and MALDI MS

Xu¹,

Beauchamp

et al. 2009

Molecular & Cellular Proteomics

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Obtaining protein profiles from a homogeneous cell population in tissues can significantly improve our capability in protein biomarker discovery. In this study, unique protein profiles from the top and bottom sections of mouse crypts and Apc Min؉/؊ adenomas were obtained using laser capture microdissection (LCM) combined with MALDI MS. Statistically significant protein peaks with differential expression were selected, and a set of novel protein biomarkers were identified. Immunohistochemistry was performed to confirm the differentially expressed protein biomarkers found by LCM combined with MALDI MS. To validate the relevance of the findings in human colorectal cancer (CRC), S100A8 was further confirmed in human CRC using immunohistochemistry. In addition, S100A8 was found to have an increased expression at different human CRC stages (Duke's A-D) compared with controls at both protein (n ‫؍‬ 168 cases) and RNA (n ‫؍‬ 215 cases) levels. Overall LCM combined with MALDI MS is a promising method to identify intestinal protein biomarkers from minute amounts of tissue. The novel protein biomarkers identified from the top and bottom crypts will increase our knowledge of the specific protein changes taking place during cell migration from the crypt bottom to top. In addition, the identified cancer protein biomarkers will aid in the exploration of colorectal tumorigenesis mechanisms as well as in the advancement of molecularly based diagnosis of colorectal cancer. Molecular & Cellular Proteomics 8:936 -945, 2009.

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Weighted Flexible Compound Covariate Method for Classifying Microarray Data

Cited by 17 publications

References 8 publications

Mass Spectrometry-based Proteomic Profiling of Lung Cancer

Mass Spectrometry-based Proteomic Profiling of Lung Cancer

Proteomic Patterns of Preinvasive Bronchial Lesions

Identification of Early Intestinal Neoplasia Protein Biomarkers Using Laser Capture Microdissection and MALDI MS

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