Search

Lung cancer is the leading cause of cancer mortality in the United States with non-small cell lung cancer (NSCLC) adenocarcinoma being the most common histological type. Early perturbations in cellular metabolism are a hallmark of cancer, but the extent of these changes in early stage lung adenocarcinoma remains largely unknown. In the current study, an integrated metabolomics and proteomics approach was utilized to characterize the biochemical and molecular alterations between malignant and matched control tissue from 27 subjects diagnosed with early stage lung adenocarcinoma. Differential analysis identified 71 metabolites and 1102 proteins that delineated tumor from control tissue. Integrated results indicated four major metabolic changes in early stage adenocarcinoma: (1) increased glycosylation and glutaminolysis; (2) elevated Nrf2 activation; (3) increase in nicotinic and nicotinamide salvaging pathways; and (4) elevated polyamine biosynthesis linked to differential regulation of the SAM/nicotinamide methyl-donor pathway. Genomic data from publicly available databases were included to strengthen proteomic findings. Our findings provide insight into the biochemical and molecular biological reprogramming that may accompanies early stage lung tumorigenesis and highlight potential therapeutic targets.

BackgroundRecent computed tomography (CT) screening trials showed that it is effective for early detection of lung cancer, but were plagued by high false positive rates. Additional blood biomarker tests designed to complement CT screening and reduce false positive rates are highly desirable.ObjectiveIdentify blood-based metabolite biomarkers for diagnosing lung cancer.MehtodsSerum samples from subjects participating in a CT screening trial were analyzed using untargeted GC-TOFMS and HILIC-qTOFMS-based metabolomics. Samples were acquired prior to diagnosis (pre-diagnostic, n= 17), at-diagnosis (n= 25) and post-diagnosis (n= 19) of lung cancer and from subjects with benign nodules (n= 29).ResultsUnivariate analysis identified 40, 102 and 30 features which were significantly different between subjects with malignant (pre-, at- and post-diagnosis) solitary pulmonary nodules (SPNs) and benign SPNs, respectively. Ten metabolites were consistently different between subjects presenting malignant (pre- and at-diagnosis) or benign SPNs. Three of these 10 metabolites were phosphatidylethanolamines (PE) suggesting alterations in lipid metabolism. Accuracies of 77%, 83% and 78% in the pre-diagnosis group and 69%, 71% and 67% in the at-diagnosis group were determined for PE(34:2), PE(36:2) and PE(38:4), respectively.ConclusionsThis study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant from benign SPNs. Further studies in larger pools of samples are warranted.

To decrease the mortality of lung cancer, better screening and diagnostic tools as well as treatment options are needed. Protein glycosylation is one of the major post-translational modifications that is altered in cancer, but it is not exactly clear which glycan structures are affected. A better understanding of the glycan structures that are differentially regulated in lung tumor tissue is highly desirable and will allow us to gain greater insight into the underlying biological mechanisms of aberrant glycosylation in lung cancer. Here, we assess differential glycosylation patterns of lung tumor tissue and nonmalignant tissue at the level of individual glycan structures using nLC-chip-TOF-MS. Using tissue samples from 42 lung adenocarcinoma patients, 29 differentially expressed (FDR < 0.05) glycan structures were identified. The levels of several oligomannose type glycans were upregulated in tumor tissue. Furthermore, levels of fully galactosylated glycans, some of which were of the hybrid type and mostly without fucose, were decreased in cancerous tissue, whereas levels of non- or low-galactosylated glycans mostly with fucose were increased. To further assess the regulation of the altered glycosylation, the glycomics data was compared to publicly available gene expression data from lung adenocarcinoma tissue compared to nonmalignant lung tissue. The results are consistent with the possibility that the observed N-glycan changes have their origin in differentially expressed glycosyltransferases. These results will be used as a starting point for the further development of clinical glycan applications in the fields of imaging, drug targeting, and biomarkers for lung cancer.

Adenocarcinoma, a type of non-small cell lung cancer, is the most frequently diagnosed lung cancer and the leading cause of lung cancer mortality in the United States. It is well documented that biochemical changes occur early in the transition from normal to cancer cells, but the extent to which these alterations affect tumorigenesis in adenocarcinoma remains largely unknown. Herein, we describe the application of mass spectrometry and multivariate statistical analysis in one of the largest biomarker research studies to date aimed at distinguishing metabolic differences between malignant and nonmalignant lung tissue. Gas chromatography time-of-flight mass spectrometry was used to measure 462 metabolites in 39 malignant and nonmalignant lung tissue pairs from current or former smokers with early stage (stage IA-IB) adenocarcinoma. Statistical mixed effects models, orthogonal partial least squares discriminant analysis and network integration, were used to identify key cancer-associated metabolic perturbations in adenocarcinoma compared with nonmalignant tissue. Cancer-associated biochemical alterations were characterized by (i) decreased glucose levels, consistent with the Warburg effect, (ii) changes in cellular redox status highlighted by elevations in cysteine and antioxidants, alpha- and gamma-tocopherol, (iii) elevations in nucleotide metabolites 5,6-dihydrouracil and xanthine suggestive of increased dihydropyrimidine dehydrogenase and xanthine oxidoreductase activity, (iv) increased 5'-deoxy-5'-methylthioadenosine levels indicative of reduced purine salvage and increased de novo purine synthesis, and (v) coordinated elevations in glutamate and UDP-N-acetylglucosamine suggesting increased protein glycosylation. The present study revealed distinct metabolic perturbations associated with early stage lung adenocarcinoma, which may provide candidate molecular targets for personalizing therapeutic interventions and treatment efficacy monitoring.

Background: Emphysema in asymptomatic heavy smokers can be detected during CT-scan screening for lung cancer. Metalloproteinases (MMPs) have been found to play a role in the pathogenesis of chronic obstructive pulmonary disease and to possibly serve as biomarkers for emphysema.Methods: The NYU Lung Cancer Biomarker Center enrolled study subjects over 50 years of age with lung cancer risk factors from January 1, 2010, to December 31, 2015. These subjects received chest multi-detector computed tomography, spirometry, and provided serum for immunoassays for metalloproteinases (MMP) -1, -2, -7, -9, -10 and tissue inhibitor of metalloproteinases (TIMP) -1 and -2.Results: Three hundred sixteen study subjects were enrolled. Of the 222 patients who met the inclusion criteria, 46% had emphysema. Smokers with emphysema had increased pack-years of smoking compared to smokers without emphysema (51 ± 24 pack-years (mean ± sd) versus 37 ± 20; p < 0.0001). Smokers with emphysema also had lower FEV1/FVC percent compared to smokers without emphysema (68 ± 11 (mean ± sd) versus 75 ± 8; p < 0.0001). Increased age and pack-years of smoking were associated with increased odds of emphysema. None of the metalloproteinases or tissue inhibitors of metalloproteinases were useful to predict the presence of emphysema in smokers.Conclusion: Emphysema was detected by CT in almost half of heavy urban smokers. Serum MMP levels provided minimal additional information to improve the detection of mild emphysema among smokers given their clinical characteristics (age, pack-years, and FEV1/FVC ratio). [ABSTRACT FROM AUTHOR]

Background: Support for early detection of lung cancer has emerged from the National Lung Screening Trial (NLST), in which low-dose computed tomography (LDCT) screening reduced lung cancer mortality by 20 % relative to chest x-ray. The US Preventive Services Task Force (USPSTF) recently recommended annual screening for the high-risk population, concluding that the benefits (life years gained) outweighed harms (false positive findings, abortive biopsy/surgery, radiation exposure). In making their recommendation, the USPSTF noted that the moderate net benefit of screening was dependent on the resolution of most false-positive results without invasive procedures. Circulating biomarkers may serve as a valuable adjunctive tool to imaging. Results: We developed a broad-based proteomics discovery program, integrating liquid chromatography/mass spectrometry (LC/MS) analyses of freshly resected lung tumor specimens (n = 13), lung cancer cell lines (n = 17), and conditioned media collected from tumor cell lines (n = 7). To enrich for biomarkers likely to be found at elevated levels in the peripheral circulation of lung cancer patients, proteins were prioritized based on predicted subcellular localization (secreted, cell-membrane associated) and differential expression in disease samples. 179 candidate biomarkers were identified. Several markers selected for further validation showed elevated levels in serum collected from subjects with stage I NSCLC (n = 94), relative to healthy smoker controls (n = 189). An 8-marker model was developed (TFPI, MDK, OPN, MMP2, TIMP1, CEA, CYFRA 21-1, SCC) which accurately distinguished subjects with lung cancer (n = 50) from high risk smokers (n = 50) in an independent validation study (AUC = 0.775). Conclusions: Integrating biomarker discovery from multiple sample types (fresh tissue, cell lines and conditioned medium) has resulted in a diverse repertoire of candidate biomarkers. This unique collection of biomarkers may have clinical utility in lung cancer detection and diagnoses. [ABSTRACT FROM AUTHOR]

Lung cancer is the leading cause of cancer deaths worldwide largely owing to diagnosis of the disease at an advanced stage. Recent advances in blood-based biomarker research have the potential to reduce mortality by providing a means for detecting lung cancer at an earlier stage. Since the publication of the National Lung Cancer Screening Trial demonstrating reduction in mortality with computed tomography (CT) scan screening, the U.S. Preventive Services Task Force has released a draft statement recommending annual low-dose CT scan screening for high-risk patients. However, CT screening has a high false-positive rate leading to the need for additional imaging and invasive procedures. In this article, we review recent literature on blood-based lung cancer biomarkers that we believe will have a significant role in enhancing screening efficacy in the near future. [ABSTRACT FROM AUTHOR]

Decreasing the risk of lung cancer, or preventing its development in high-risk individuals, would have a huge impact on public health. The most effective means to decrease lung cancer incidence is to eliminate exposure to carcinogens. However, with recent advances in the understanding of pulmonary carcinogenesis and the identification of intermediate biomarkers, the prospects for the field of chemoprevention research have improved dramatically. Here we review the most recent research in lung cancer chemoprevention—focusing on those agents that have been investigated in human clinical trials. These agents fall into three major categories. First, oxidative stress plays an important role in pulmonary carcinogenesis; and therefore, antioxidants (including vitamins, selenium, green tea extracts, and isothiocyanates) may be particularly effective in preventing the development of lung cancer. Second, inflammation is increasingly accepted as a crucial factor in carcinogenesis, and many investigators have focused on anti-inflammatory agents, such as glucocorticoids, NSAIDs, statins, and PPARγ agonists. Finally, the PI3K/AKT/mTOR pathway is recognized to play a central role in tobacco-induced carcinogenesis, and inhibitors of this pathway, including myoinositol and metformin, are promising agents for lung cancer prevention. Successful chemoprevention will likely require targeting of multiple pathways to carcinogenesis—both to minimize toxicity and maximize efficacy. [ABSTRACT FROM AUTHOR]

Background: Low-dose computed tomography (CT) for lung cancer screening can reduce lung cancer mortality. The National Lung Screening Trial reported a 20% reduction in lung cancer mortality in high-risk smokers. However, CT scanning is extremely sensitive and detects non-calcified nodules (NCNs) in 24-50% of subjects, suggesting an unacceptably high false-positive rate. We hypothesized that by reviewing demographic, clinical and nodule characteristics, we could identify risk factors associated with the presence of nodules on screening CT, and with the probability that a NCN was malignant. Methods: We performed a longitudinal lung cancer biomarker discovery trial (NYU LCBC) that included low-dose CTscreening of high-risk individuals over 50 years of age, with more than 20 pack-year smoking histories, living in an urban setting, and with a potential for asbestos exposure. We used case-control studies to identify risk factors associated with the presence of nodules (n = 625) versus no nodules (n = 557), and lung cancer patients (n = 30) versus benign nodules (n = 128). Results: The NYU LCBC followed 1182 study subjects prospectively over a 10-year period. We found 52% to have NCNs .4 mm on their baseline screen. Most of the nodules were stable, and 9.7% of solid and 26.2% of sub-solid nodules resolved. We diagnosed 30 lung cancers, 26 stage I. Three patients had synchronous primary lung cancers or multifocal disease. Thus, there were 33 lung cancers: 10 incident, and 23 prevalent. A sub-group of the prevalent group were stable for a prolonged period prior to diagnosis. These were all stage I at diagnosis and 12/13 were adenocarcinomas. Conclusions: NCNs are common among CT-screened high-risk subjects and can often be managed conservatively. Risk factors for malignancy included increasing age, size and number of nodules, reduced FEV1 and FVC, and increased packyears smoking. A sub-group of screen-detected cancers are slow-growing and may contribute to over-diagnosis and leadtime biases. [ABSTRACT FROM AUTHOR]

The development of lung cancer in humans can be divided into three steps: initiation, promotion and progression. This process is driven by alterations in related signal transduction pathways. These pathways signal the aberrant activation of NF-kappaB, a transcription factor that regulates the expression of genes important for lung tumorigenesis. Our current knowledge about the role of the NF-kappaB signaling pathway in the development of lung cancer has been bolstered by animal models demonstrating the connection between K-ras and tobacco induced lung transformation with NF-kappaB. Activation of downstream genes leads to cell proliferation, inhibition of apoptosis, angiogenesis, inflammation, invasion, and metastasis. [ABSTRACT FROM AUTHOR]

Lung cancer is the leading cause of cancer deaths, with an overall survival of 15% at five years. Biomarkers that can sensitively and specifically detect lung cancer at early stage are crucial for improving this poor survival rate. Sputum has been the target for the discovery of non-invasive biomarkers for lung cancer because it contains airway epithelial cells, and molecular alterations identified in sputum are most likely to reflect tumor-associated changes or field cancerization caused by smoking in the lung. Sputum-based molecular biomarkers include morphology, allelic imbalance, promoter hypermethylation, gene mutations and, recently, differential miRNA expression. To improve the sensitivity and reproducibility of sputum-based biomarkers, we recommend standardization of processing protocols, bronchial epithelial cell enrichment, and identification of field cancerization biomarkers. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is the leading cause of cancer deaths worldwide. New diagnostics are needed to detect early stage lung cancer because it may be cured with surgery. However, most cases are diagnosed too late for curative surgery. Here we present a comprehensive clinical biomarker study of lung cancer and the first large-scale clinical application of a new aptamer-based proteomic technology to discover blood protein biomarkers in disease. Methodology/Principal Findings: We conducted a multi-center case-control study in archived serum samples from 1,326 subjects from four independent studies of non-small cell lung cancer (NSCLC) in long-term tobacco-exposed populations. Sera were collected and processed under uniform protocols. Case sera were collected from 291 patients within 8 weeks of the first biopsy-proven lung cancer and prior to tumor removal by surgery. Control sera were collected from 1,035 asymptomatic study participants with ≥10 pack-years of cigarette smoking. We measured 813 proteins in each sample with a new aptamer-based proteomic technology, identified 44 candidate biomarkers, and developed a 12-protein panel (cadherin-1, CD30 ligand, endostatin, HSP90α, LRIG3, MIP-4, pleiotrophin, PRKCI, RGM-C, SCF-sR, sL-selectin, and YES) that discriminates NSCLC from controls with 91% sensitivity and 84% specificity in cross-validated training and 89% sensitivity and 83% specificity in a separate verification set, with similar performance for early and late stage NSCLC. Conclusions/Significance: This study is a significant advance in clinical proteomics in an area of high unmet clinical need. Our analysis exceeds the breadth and dynamic range of proteome interrogated of previously published clinical studies of broad serum proteome profiling platforms including mass spectrometry, antibody arrays, and autoantibody arrays. The sensitivity and specificity of our 12-biomarker panel improves upon published protein and gene expression panels. Separate verification of classifier performance provides evidence against over-fitting and is encouraging for the next development phase, independent validation. This careful study provides a solid foundation to develop tests sorely needed to identify early stage lung cancer. [ABSTRACT FROM AUTHOR]

Background: Sera from lung cancer patients contain autoantibodies that react with tumor associated antigens (TAAs) that reflect genetic over-expression, mutation, or other anomalies of cell cycle, growth, signaling, and metabolism pathways. Methods: We performed immunoassays to detect autoantibodies to ten tumor associated antigens (TAAs) selected on the basis of previous studies showing that they had preferential specificity for certain cancers. Sera examined were from lung cancer patients (22); smokers with ground-glass opacities (GGOs) (46), benign solid nodules (55), or normal CTs (35); and normal non-smokers (36). Logistic regression models based on the antibody biomarker levels among the high risk and lung cancer groups were developed to identify the combinations of biomarkers that predict lung cancer in these cohorts. Results: Statistically significant differences in the distributions of each of the biomarkers were identified among all five groups. Using Receiver Operating Characteristic (ROC) curves based on age, c-myc, Cyclin A, Cyclin B1, Cyclin D1, CDK2, and survivin, we obtained a sensitivity = 81% and specificity = 97% for the classification of cancer vs smokers(no nodules, solid nodules, or GGO) and correctly predicted 31/36 healthy controls as noncancer. Conclusion: A pattern of autoantibody reactivity to TAAs may distinguish patients with lung cancer versus smokers with normal CTs, stable solid nodules, ground glass opacities, or normal healthy never smokers. [ABSTRACT FROM AUTHOR]

Lung cancer causes over one million deaths per year worldwide and cigarette smoking, the proximate cause, results in a field cancerization of the respiratory track. Lung cancer cells or premalignant cells may be susceptible to apoptosis or necrosis-inducing agents. Statins inhibit the acetyl coenzyme A pathway reducing L-mevalonate that is a precursor to isoprenoids necessary for post-translational processing, resulting in apoptosis. Lovastatin was added to four lung cancer cell lines and normal human bronchial epithelial cells followed by Western blots to evaluate proteins in the cell cycle, oxidant, and apoptotic pathways. Flow cytometry revealed significant increases in three of four lung cancer cell lines in apoptosis and necrosis after lovastatin treatment at 10 μM for 72 h. Lovastatin adversely affected lung cancer cell survival with increases in cell-cycle check-point inhibitors p21WAF and/or p27KIP and a decrease in cyclin D1. All four lung cancer cell lines had a decrease in glutathione after lovastatin treatment consistent with reduced protection against reactive oxidant species. Three of four lung cancer cell lines had increased cytochrome c release with reduced pro-caspase-3 and increases in activated caspase-3. Lovastatin induces apoptosis and necrosis in lung cancer cell lines by causing alterations in the cell cycle, reducing glutathione, and activating p53, Bax protein, and caspases while increasing cytochrome c in apoptosis pathways. Targeting HMG-CoA reductase may represent an approach to lung cancer chemotherapy, e.g., reversing ground glass opacities detected on CT scans or resolving airway preneoplasias detected by bronchoscopy before they progress to malignant transformation. [ABSTRACT FROM AUTHOR]

The article discusses the study of S-Adenosylmethionine (AdoMet) as a biomaker for the early detection of lung cancer. Accordingly, AdoMet is a major methyl donor for transmethylation reactions and propylamine donor for the biosynthesis of polyamines in biological systems. The study uses a high-performance liquid chromatography to analyze plasma AdoMet levels in triplicate samples from 68 patients.

Background: Normal metabolism generates several volatile organic compounds (VOCs) that are excreted in the breath (e.g. alkanes). In patients with lung cancer, induction of high-risk cytochrome p450 genotypes may accelerate catabolism of these VOCs, so that their altered abundance in breath may provide biomarkers of lung cancer. Methods: VOCs in 1.0 L alveolar breath were analyzed in 193 subjects with primary lung cancer and 211 controls with a negative chest CT. Subjects were randomly assigned to a training set or to a prediction set in a 2:1 split. A fuzzy logic model of breath biomarkers of lung cancer was constructed in the training set and then tested in subjects in the prediction set by generating their typicality scores for lung cancer. Results: Mean typicality scores employing a 16 VOC model were significantly higher in lung cancer patients than in the control group (p<0.0001 in all TNM stages). The model predicted primary lung cancer with 84.6% sensitivity, 80.0% specificity, and 0.88 area under curve (AUC) of the receiver operating characteristic (ROC) curve. Predictive accuracy was similar in TNM stages 1 through 4, and was not affected by current or former tobacco smoking. The predictive model achieved near-maximal performance with six breath VOCs, and was progressively degraded by random classifiers. Predictions with fuzzy logic were consistently superior to multilinear analysis. If applied to a population with 2% prevalence of lung cancer, a screening breath test would have a negative predictive value of 0.985 and a positive predictive value of 0.163 (true positive rate =0.277, false positive rate =0.029). [ABSTRACT FROM AUTHOR]

Background: Cancer serum protein profiling by mass spectrometry has uncovered mass profiles that are potentially diagnostic for several common types of cancer. However, direct mass spectrometric profiling has a limited dynamic range and difficulties in providing the identification of the distinctive proteins. We hypothesized that distinctive profiles may result from the differential expression of relatively abundant serum proteins associated with the host response. Methods: Eighty-four antibodies, targeting a wide range of serum proteins, were spotted onto nitrocellulose-coated microscope slides. The abundances of the corresponding proteins were measured in 80 serum samples, from 24 newly diagnosed subjects with lung cancer, 24 healthy controls, and 32 subjects with chronic obstructive pulmonary disease (COPD). Two-color rollingcircle amplification was used to measure protein abundance. Results: Seven of the 84 antibodies gave a significant difference (p < 0.01) for the lung cancer patients as compared to healthy controls, as well as compared to COPD patients. Proteins that exhibited higher abundances in the lung cancer samples relative to the control samples included Creactive protein (CRP; a 13.3 fold increase), serum amyloid A (SAA; a 2.0 fold increase), mucin 1 and a-1-antitrypsin (1.4 fold increases). The increased expression levels of CRP and SAA were validated by Western blot analysis. Leave-one-out cross-validation was used to construct Diagonal Linear Discriminant Analysis (DLDA) classifiers. At a cutoff where all 56 of the non-tumor samples were correctly classified, 15/24 lung tumor patient sera were correctly classified. Conclusion: Our results suggest that a distinctive serum protein profile involving abundant proteins may be observed in lung cancer patients relative to healthy subjects or patients with chronic disease and may have utility as part of strategies for detecting lung cancer. [ABSTRACT FROM AUTHOR]

Study objectives: To evaluate volatile organic compounds (VOCs) in the breath as tumor markers in lung cancer. Alkanes and monomethylated alkanes are oxidative stress products that are excreted in the breath, the catabolism of which may be accelerated by polymorphic cytochrome p450-mixed oxidase enzymes that are induced in patients with lung cancer. Design: Combined case-control and cross-sectional study. Setting: Five academic pulmonary medicine services in the United States and the United Kingdom. Patients and participants: One hundred seventy-eight bronchoscopy patients and 41 healthy volunteers. Intervention: Breath samples were analyzed by gas chromatography and mass spectroscopy to determine alveolar gradients (ie, the abundance in breath minus the abundance in room air) of CA-C20 alkanes and monomethylated alkanes. Measurements: Patients with primary lung cancer (PLC) were compared to healthy volunteers, and a predictive model was constructed using forward stepwise discriminant analysis of the alveolar gradients. This model was cross-validated with a leave-one-out jackknife technique and was tested in two additional groups of patients who had not been used to develop the model (ie, bronchoscopy patients in whom cancer was not detected, and patients with metastatic lung cancer [MLC]). Results: Eighty-seven of 178 patients had lung cancer (PLC, 67 patients; MLC, 15 patients; undetermined, 5 patients). A predictive model employing nine VOCs identified PLC with a sensitivity of 89.6% (60 of 67 patients) and a specificity of 82.9% (34 of 41 patients). On cross-validation, the sensitivity was 85.1% (57 of 67 patients) and the specificity was 80.5% (33 of 41 patients). The stratification of patients by tobacco smoking status, histologic type of cancer, and TNM stage of cancer revealed no marked effects. In the two additional tests, the model predicted MLC with a sensitivity of 66.7% (10 of 15 patients), and it classified the cancer-negative bronchoscopy... [ABSTRACT FROM AUTHOR]

Identification of biomarkers for early detection of lung cancer (LC) is important, in turn leading to more effective treatment and reduction of mortality. Serological proteome analysis (SERPA) was used to identify proteins around 34 kD as ECH1 and HNRNPA2B1, which had been recognized by serum autoantibody from 25 LC patients. In the validation study, including 90 sera from LC patients and 89 sera from normal individuals, autoantibody to ECH1 achieved an area under the curve (AUC) of 0.799 with sensitivity of 62.2% and specificity of 95.5% in discriminating LC from normal individuals, and showed negative correlation with tumor size (rs= −0.256,p= 0.023). Autoantibody to HNRNPA2B1 performed an AUC of 0.874 with sensitivity of 72.2% and specificity of 95.5%, and showed negative correlation with lymph node metastasis (rs= −0.279,p= 0.012). By using longitudinal preclinical samples, autoantibody to ECH1 showed an AUC of 0.763 with sensitivity of 60.0% and specificity of 89.3% in distinguishing early stage LC from matched normal controls, and elevated autoantibody levels could be detected greater than 2 y before LC diagnosis. ECH1 and HNRNPA2B1 are autoantigens that elicit autoimmune responses in LC and their autoantibody can be the potential biomarkers for the early detection of LC. [ABSTRACT FROM PUBLISHER]

The article presents a study which identifies a variant form of Ciz1, a biomarker and a novel DNA-binding coactivator of the estrogen receptor, that can be used in recognizing early-stage lung cancer. It describes the identification and evaluation of Ciz1 in two independent patient cohorts. Results show that expression of Ciz1 is restricted to tumor cells.

Objectives Autoantibodies against tumor-associated antigens (TAAs) identified in patients with advanced lung cancer may be detected in subjects with early lung cancer or even predate the diagnosis. The purpose of this study is to address the temporal relationship between lung cancer development and serum autoantibody response. Materials and methods Two cohorts of patients with newly diagnosed lung cancer were included. The first cohort included 90 sera from patients with lung cancer (Stages I–III) and 89 normal control sera. In the second cohort, 93 serial serum samples from 25 patients with CT-scan screen-detected stage I lung cancer were collected before the diagnosis of lung cancer (average 32 months) and 56 controls were matched on age, gender, and smoking. Autoantibody levels were measured by immunoassay. Results Measurement of autoantibodies against seven TAAs (14-3-3ζ, c-Myc, MDM2, NPM1, p16, p53 and cyclin B1) individually could discriminate lung cancer patients from normal individuals in the first cohort and the area under curve (AUC) was 0.863 based on a panel of seven autoantibodies, with sensitivity of 68.9% and specificity of 79.5%. Autoantibodies in serial pre-diagnostic serum samples against the same panel of seven TAAs were detected prior to lung cancer diagnosis with sensitivity of 76.0% and specificity of 73.2% (AUC) (95%CI): 0.885 (0.797–0.973)). Elevated autoantibody levels could be detected greater than four years prior to lung cancer diagnosis. Conclusion A panel of seven TAAs may enhance the early detection of lung cancer, consistent with a humoral immune response to TAAs that can be detected months to years prior to the diagnosis. [ABSTRACT FROM AUTHOR]

Abstract: Background: A combination of biomarkers in a multivariate model may predict disease with greater accuracy than a single biomarker employed alone. We developed a non-linear method of multivariate analysis, weighted digital analysis (WDA), and evaluated its ability to predict lung cancer employing volatile biomarkers in the breath. Methods: WDA generates a discriminant function to predict membership in disease vs no disease groups by determining weight, a cutoff value, and a sign for each predictor variable employed in the model. The weight of each predictor variable was the area under the curve (AUC) of the receiver operating characteristic (ROC) curve minus a fixed offset of 0.55, where the AUC was obtained by employing that predictor variable alone, as the sole marker of disease. The sign (±) was used to invert the predictor variable if a lower value indicated a higher probability of disease. When employed to predict the presence of a disease in a particular patient, the discriminant function was determined as the sum of the weights of all predictor variables that exceeded their cutoff values. The algorithm that generates the discriminant function is deterministic because parameters are calculated from each individual predictor variable without any optimization or adjustment. We employed WDA to re-evaluate data from a recent study of breath biomarkers of lung cancer, comprising the volatile organic compounds (VOCs) in the alveolar breath of 193 subjects with primary lung cancer and 211 controls with a negative chest CT. Results: The WDA discriminant function accurately identified patients with lung cancer in a model employing 30 breath VOCs (ROC curve AUC=0.90; sensitivity=84.5%, specificity=81.0%). These results were superior to multilinear regression analysis of the same data set (AUC=0.74, sensitivity=68.4, specificity=73.5%). WDA test accuracy did not vary appreciably with TNM (tumor, node, metastasis) stage of disease, and results were not affected by tobacco smoking (ROC curve AUC=0.92 in current smokers, 0.90 in former smokers). WDA was a robust predictor of lung cancer: random removal of 1/3 of the VOCs did not reduce the AUC of the ROC curve by >10% (99.7% CI). Conclusions: A test employing WDA of breath VOCs predicted lung cancer with accuracy similar to chest computed tomography. The algorithm identified dependencies that were not apparent with traditional linear methods. WDA appears to provide a useful new technique for non-linear multivariate analysis of data. [Copyright &y& Elsevier]

The success of replicating adenoviruses in cancer therapy is limited by inefficient delivery. The suboptimal transduction of cancer cells is compounded by poor distribution of the virus within the tumor mass. The hypothesis that is addressed in this study is that matrix within the tumor hinders the free cell-to-cell spread of replicating adenoviral vectors. Collagen I, collagen IV, fibronectin and laminin matrix components were obtained commercially pre-coated on membrane inserts. The membrane inserts were placed into the chambers of 24-well plates that had been pre-seeded with 293 cells growing as a monolayer on the base. Adβ-gal virus was added to the upper chamber, and in order to infect the 293 cells plated on the base of the wells, the virus had to spread through the matrix pre-coated insert. The collagen I insert blocked the spread of Adβ-gal virus so that only a few 293 cells stained blue after the addition of X-gal. In contrast, control, collagen IV, fibronectin and laminin inserts allowed the passage of virus with subsequent intense blue staining of the 293 indicator cells. A significant reduction of viral passage through the Collagen 1 matrix was confirmed with assays of β-gal acivity. Based on reports of the effective collagen I-degrading activity of MMP-8, we chose to construct an non-replicating adenovirus to express the MMP-8 transgene. Established human A549 cell lung cancer xenografts were injected with a wild-type replicating adenovirus Adwt (5 × 108 PFU) together with the non-replicating AdMMP-8 virus (5 × 108 PFU). Histochemical analysis demonstrated reduced amounts of collagen within necrotic areas of Adwt plus AdMMP-8 injected tumors compared to controls. The addition of AdMMP-8 to the wild type virus also significantly reduced the growth of tumors compared with control tumors injected with the wild type virus in combination with the non-replicating control virus. AdMMP-8 injection alone did not affect the growth of the lung A549 tumor xenografts. These results suggest that intratumoral AdMMP-8 and collagen disruption is a possible strategy for improving viral spread and improving the oncolytic activity of replicating adenovirus.Molecular Therapy (2006) 13, S248–S248; doi: 10.1016/j.ymthe.2006.08.719 [ABSTRACT FROM AUTHOR]