Proteomic analysis of resectable non-small cell lung cancer: post-resection serum samples may be useful in identifying potential markers (original) (raw)
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Türkiye klinikleri tıp bilimleri dergisi, 2012
Although proteomic profiles detected by using SELDI-TOF-MS method distinguish lung cancer patients from healthy individuals, there are no studies concerning proteomic patterns of tumor subsets in lung cancer. The goal of this study was to establish proteomic patterns of tumor subsets in lung cancer patients with surface enhanced laser desabsorbtion ionization time of flight mass spectrometry (SELDI-TOF-MS) method. M Ma at te er ri ia al l a an nd d M Me et th ho od ds s: : A total of 169 patients diagnosed histopathologically including 142 non-small cell lung cancer (NSCLC) and 27 small cell lung cancer (SCLC) patients were included in this study. In NSCLC group, there were 60 squamous cell carcinoma, 38 non-squamous cell carcinoma and 44 unclassified type NSCLC patients. Venous blood samples were obtained from all cases. All of the serum samples were analyzed by SELDI-TOF-MS method for proteomics investigation. R Re es su ul lt ts s: : Three peaks (9065 m/z, 9175 m/z, 9394 m/z) were found to be discriminatory in serum SELDI profiles between NSCLC and SCLC groups (p<0.05). All of these three peaks showed higher intensity in patients with SCLC. The analysis between non-squamous and squamous cancer groups of NSCLC revealed eight discriminatory proteomic features. Among these peaks, only two (5815 m/z, 5906 m/z) showed higher intensity in patients in the non-squamous group (p<0.05). C Co on nc cl lu us si io on n: : Proteomic patterns could provide some valuable clues on the carcinogenetic mechanism of different types of lung cancer and may help us to discover some potential subtype-specific biomarkers of lung cancer by SELDI-TOF-MS method. K Ke ey y W Wo or rd ds s: : Lung neoplasms; proteomics; spectrometry, mass, matrix-assisted laser desorption-ionization Ö ÖZ ZE ET T A Am ma aç ç: : SELDI-TOF-MS yöntemi ile tespit edilen proteomik pikler akciğer kanserini sağlıklı kişilerden ayırt edebilmesine rağmen akciğer kanserinin alt tiplerinin proteomik paternleri hakkında çalışma yoktur. Çalışmanın amacı; akciğer kanserli olguların serum örneklerinde SELDI-TOF-MS yöntemi ile serum protein profillerinin analizlerini yaparak akciğer kanserli olgularda histolojik tip ile serum protein profilleri arasındaki ilişkiyi değerlendirmektir. G Ge er re eç ç v ve e Y Yö ön nt te em ml le er r: : Histopatolojik olarak akciğer kanseri tanısı almış142 küçük hücreli dışı akciğer kanserli (KHDAK) hasta, 27 küçük hücreli akciğer kanserli (KHAK) hasta olmak üzere toplam 169 hasta çalışmaya alındı. KHDAK'li grup içerisinde 60 skuamöz hücreli, 38 non-skuamöz hücreli ve 44 tip ayrımı yapılamayan hasta mevcuttu. Tüm olgulardan venöz kan örnekleri alındı. Alınan bu örneklerin proteomik analizleri SELDI-TOF-MS yöntemi kullanılarak yapıldı. B Bu ul lg gu ul la ar r: : SELDI-TOF analizi ile KHDAK'li hastalar ile KHAK'li hastalar arasında ayırt edici protein pikleri 9065 m/z, 9175 m/z ve 9394 m/z olarak bulundu (p<0,05). Söz konusu üç proteinin KHAK'li olgularda yüksek yoğunlukta bulunduğu görüldü. Ayrıca KHDAK'li grup içerisinde yer alan non-skuamöz ve skuamöz gruplar arasında sekiz farklı proteomik pik tespit edildi. Bu pikler arasında yer alan 5815 m/z ve 5906 m/z pikleri non-skuamöz grupta yüksek yoğunlukta bulundu (p<0,05). S So on nu uç ç: : Proteomik paternler, akciğer kanserinin farklı histolojik tiplerinde görülen karsinogenetik mekanizmalar hakkında bazı değerli bilgiler sağlayabilir. Ayrıca SELDI-TOF-MS analizi ile tespit edilen protein pikleri akciğer kanserinin alt histolojik tiplerine özgü bazı potansiyel biyo-belirteçlerin keşfedilmesine yardımcı olabilir. A An na ah ht ta ar r K Ke el li im me el le er r: : Akciğer tümörleri; proteomiks; spektrometri, kütle, matriks-yardımlı lazer salmalı-iyonizasyon T Tu ur rk ki iy ye e K Kl li in ni ik kl le er ri i J J M Me ed d S Sc ci i 2 20 01 12 2; ;3 32 2((4 4)):
Confounding Effects of Benign Lung Diseases on Non-Small Cell Lung Cancer Serum Biomarker Discovery
Clinical Proteomics, 2009
Surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF-MS) can be used to analyse peptides and proteins in clinical samples. A prospective study was undertaken on patients undergoing curative resection for non-small cell lung cancer (NSCLC): we used SELDI-TOF-MS to compare the proteomic profiles of serum from these patients both before surgical resection and after resection (disease-free) to identify potential biomarkers. Student t-tests were used, and a P-value of <0.01 was considered significant. Twenty-five patients with NSCLC [76% male, mean age 69 (range 53-81) years] were analysed. There were 13 squamous cell carcinomas, 10 adenocarcinomas and 2 large cell carcinomas with a stage distribution of four stage IA, 11 stage IB, five stage IIB, three stage IIIA, one stage IIIB and one stage IV. SELDI spectra generated with immobilised metal affinity chromatography arrays produced 170 peaks. Of these, 35 showed significant differences in their intensities between the preoperative and post-resection states (P<0.01). Postoperative samples in the disease-free state may represent good controls to identify biomarkers in NSCLC, avoiding the difficulties associated with cross-sectional studies. These pilot data need to be validated with larger numbers of patients.
Mass Spectrometry-based Proteomic Profiling of Lung Cancer
Annals of the American Thoracic Society, 2009
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.
Diagnostic Accuracy of MALDI Mass Spectrometric Analysis of Unfractionated Serum in Lung Cancer
Journal of Thoracic Oncology, 2007
Purpose: There is a critical need for improvements in the noninvasive diagnosis of lung cancer. We hypothesized that matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) analysis of the most abundant peptides in the serum may distinguish lung cancer cases from matched controls. Patients and Methods: We used MALDI MS to analyze unfractionated serum from a total of 288 cases and matched controls split into training (n ϭ 182) and test sets (n ϭ 106). We used a training-testing paradigm with application of the model profile defined in a training set to a blinded test cohort. Results: Reproducibility and lack of analytical bias was confirmed in quality-control studies. A serum proteomic signature of seven features in the training set reached an overall accuracy of 78%, a sensitivity of 67.4%, and a specificity of 88.9%. In the blinded test set, this signature reached an overall accuracy of 72.6 %, a sensitivity of 58%, and a specificity of 85.7%. The serum signature was associated with the diagnosis of lung cancer independently of gender, smoking status, smoking pack-years, and C-reactive protein levels. From this signature, we identified three discriminatory features as members of a cluster of truncated forms of serum amyloid A. Conclusions: We found a serum proteomic profile that discriminates lung cancer from matched controls. Proteomic analysis of unfractionated serum may have a role in the noninvasive diagnosis of lung cancer and will require methodological refinements and prospective validation to achieve clinical utility.
Plasma proteomic profiling: Search for lung cancer diagnostic and early detection markers
Oncology Reports, 2006
Environmental and occupational exposure to asbestos is among the established risk factors for lung cancer, the leading cause of cancer-related deaths in the United States. This link between exposure to asbestos and the excessive death rate from lung cancer was evident in a study of former workers of an asbestos pipe insulation manufacturing plant in Tyler, TX. We performed comparative proteomic profiling of plasma samples that were collected from nine patients within 12 months before death and their age-, raceand exposure-matched disease-free controls on strong anion exchange chips using surface-enhanced laser desorption ionization time-of-flight mass spectrometry. A distancedependent K-nearest neighbor (KNN) classification algorithm identified spectral features of m/z values 7558.9 and 15103.0 that were able to distinguish lung cancer patients from disease-free individuals with high sensitivity and specificity. The high correlation between the intensities of these two peaks (r=0.987) strongly suggests that they are the doubly and singly charged ions of the same protein product. Examination of these proteomic markers in the plasma samples of subjects from >5 years before death from lung cancer suggested that they are related to the early development of lung cancer. Validation of these biomarkers would have significant implications for the early detection of lung cancer and better management of high-risk patients.
Improving Detection Accuracy of Lung Cancer Serum Proteomic Profiling via Two-Stage Training Process
Proteome Science, 2011
Background Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant inflammation. This study aimed to increase detection accuracy using a two-stage analysis process. Methods Sera from 118 lung cancer patients, 72 healthy individuals, and 31 patients with inflammatory disease were randomly divided into training and testing groups by 3:2 ratio. In the training group, the traditional method of using SELDI profile analysis to directly distinguish lung cancer patients from sera was used. The two-stage analysis of distinguishing the healthy people and non-healthy patients (1st-stage) and then differentiating cancer patients from inflammatory disease patients (2nd-stage) to minimize the influence of inflammation was validated in the test group. Results In the test group, the one-stage method had 87.2% sensitivity,...
Lung Cancer, 2003
Objectives: Early lung cancer detection and treatment remain a challenge. The efficacy of surface-enhanced laser desorption/ ionization (SELDI) technology in lung cancer detection, has not been defined. This study identifies specific protein peak patterns in malignant lung tumors, and in pre-malignant airways epithelium showing neoplastic transformation. Methods: Lung tumor specimens taken from patients participating in a lung cancer screening study (H. Lee Moffitt Cancer Center, Tampa, FL) were laser capture microdissected to obtain pure cell populations from frozen sections of normal lung, atypical adenomatous hyperplasia (AAH) and malignant tumors. SELDI mass spectrometry was used to generate protein profiles in each epithelial cell type. Results: SELDI mass spectroscopy is highly reproducible in detecting lung tumor-specific protein profiles. Three peaks at 17 Á/23 kDa mass range from tumor cells showed markedly increased compared with normal cells. The peak at 17 250 Da was not detected in any of the normal cells. This peak appeared to be present at low levels in the atypical cell samples. Conclusions: This study demonstrates the feasibility of detecting ''malignant'' protein signatures from lung tumor and pre-malignant pulmonary epithelium using SELDI mass spectrometry. Although additional study is necessary to validate these patterns as unique diagnostic tools, these ''malignant'' protein signatures lend themselves to identification of populations at high-risk for lung cancer and for monitoring response to lung cancer chemopreventive agents.
Journal of Cellular Physiology, 2019
Non‐small‐cell lung carcinomas (NSCLC) is the most common type of lung cancer and it has a poor prognosis, because overall survival after 5 years is 20–25% for all stages. Thus, it is extremely important to increase the survival rate in the early stages NSCLC by focusing on novel screening tests of cancer identifying specific biomarkers expression associated with a more accurate tumor staging and patient prognosis. In this study, we focused our attention on quantitative proteomics of three heavily glycosylated serum proteins: AMBP, α2 macroglobulin, and SERPINA1. In particular, we analyzed serum samples from 20 NSCLC lung adenocarcinoma cancer patients in early and advanced stages, and 10 healthy donors to obtain a relative quantification through the MRM analysis of these proteins that have shown to be markers of cancer development and progression. AMBP, α2 macroglobulin, and SERPINA1 were chosen because all of them possess endopeptidase inhibitor activity and play key roles in canc...
Enriched sera protein profiling for detection of non-small cell lung cancer biomarkers
Proteome …, 2011
Background: Non Small Cell Lung Cancer (NSCLC) is the major cause of cancer related-death. Many patients receive diagnosis at advanced stage leading to a poor prognosis. At present, no satisfactory screening tests are available in clinical practice and the discovery and validation of new biomarkers is mandatory. Surface Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-ToF-MS) is a recent high-throughput technique used to detect new tumour markers. In this study we performed SELDI-ToF-MS analysis on serum samples treated with the ProteoMiner™ kit, a combinatorial library of hexapeptide ligands coupled to beads, to reduce the wide dynamic range of protein concentration in the sample. Serum from 44 NSCLC patients and 19 healthy controls were analyzed with IMAC30-Cu and H50 ProteinChip Arrays. Results: Comparing SELDI-ToF-MS protein profiles of NSCLC patients and healthy controls, 28 protein peaks were found significantly different (p < 0.05), and were used as predictors to build decision classification trees. This statistical analysis selected 10 protein peaks in the low-mass range (2-24 kDa) and 6 in the high-mass range (40-80 kDa). The classification models for the low-mass range had a sensitivity and specificity of 70.45% (31/44) and 68.42% (13/19) for IMAC30-Cu, and 72.73% (32/44) and 73.68% (14/19) for H50 ProteinChip Arrays. Conclusions: These preliminary results suggest that SELDI-ToF-MS protein profiling of serum samples pretreated with ProteoMiner™ can improve the discovery of protein peaks differentially expressed between NSCLC patients and healthy subjects, useful to build classification algorithms with high sensitivity and specificity. However, identification of the significantly different protein peaks needs further study in order to provide a better understanding of the biological nature of these potential biomarkers and their role in the underlying disease process.
Lung Cancer Diagnosis from Proteomic Analysis of Preinvasive Lesions
Cancer Research, 2011
Early detection may help improve survival from lung cancer. In this study, our goal was to derive and validate a signature from the proteomic analysis of bronchial lesions that could predict the diagnosis of lung cancer. Using previously published studies of bronchial tissues, we selected a signature of nine matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) mass-to-charge ratio features to build a prediction model diagnostic of lung cancer. The model was based on MALDI MS signal intensity (MALDI score) from bronchial tissue specimens from our 2005 published cohort of 51 patients. The performance of the prediction model in identifying lung cancer was tested in an independent cohort of bronchial specimens from 60 patients. The probability of having lung cancer based on the proteomic analysis of the bronchial specimens was characterized by an area under the receiver operating characteristic curve of 0.77 (95% CI 0.66-0.88) in this validation cohort. Eight of the nine features were identified and validated by Western blotting and immunohistochemistry. These results show that proteomic analysis of endobronchial lesions may facilitate the diagnosis of lung cancer and the monitoring of high-risk individuals for lung cancer in surveillance and chemoprevention trials. Cancer Res; 71(8); 3009-17. Ó2011 AACR.