Find information on thousands of medical conditions and prescription drugs.

Carbenicillin

Carbenicillin is an antibiotic chemically similar to ampicillin. It is active against gram-negative germs and well soluble in water and acid-labile. Aqueous solutions are short-lived. Working concentration in the lab: up to 100 µg per ml.

It is a semi-synthetic analogue of the naturally occurring penicillin.

Home
Diseases
Medicines
A
B
C
Cabergoline
Caduet
Cafergot
Caffeine
Calan
Calciparine
Calcitonin
Calcitriol
Calcium folinate
Campath
Camptosar
Camptosar
Cancidas
Candesartan
Cannabinol
Capecitabine
Capoten
Captohexal
Captopril
Carbachol
Carbadox
Carbamazepine
Carbatrol
Carbenicillin
Carbidopa
Carbimazole
Carboplatin
Cardinorm
Cardiolite
Cardizem
Cardura
Carfentanil
Carisoprodol
Carnitine
Carvedilol
Casodex
Cataflam
Catapres
Cathine
Cathinone
Caverject
Ceclor
Cefacetrile
Cefaclor
Cefaclor
Cefadroxil
Cefazolin
Cefepime
Cefixime
Cefotan
Cefotaxime
Cefotetan
Cefpodoxime
Cefprozil
Ceftazidime
Ceftriaxone
Ceftriaxone
Cefuroxime
Cefuroxime
Cefzil
Celebrex
Celexa
Cellcept
Cephalexin
Cerebyx
Cerivastatin
Cerumenex
Cetirizine
Cetrimide
Chenodeoxycholic acid
Chloralose
Chlorambucil
Chloramphenicol
Chlordiazepoxide
Chlorhexidine
Chloropyramine
Chloroquine
Chloroxylenol
Chlorphenamine
Chlorpromazine
Chlorpropamide
Chlorprothixene
Chlortalidone
Chlortetracycline
Cholac
Cholybar
Choriogonadotropin alfa
Chorionic gonadotropin
Chymotrypsin
Cialis
Ciclopirox
Cicloral
Ciclosporin
Cidofovir
Ciglitazone
Cilastatin
Cilostazol
Cimehexal
Cimetidine
Cinchophen
Cinnarizine
Cipro
Ciprofloxacin
Cisapride
Cisplatin
Citalopram
Citicoline
Cladribine
Clamoxyquine
Clarinex
Clarithromycin
Claritin
Clavulanic acid
Clemastine
Clenbuterol
Climara
Clindamycin
Clioquinol
Clobazam
Clobetasol
Clofazimine
Clomhexal
Clomid
Clomifene
Clomipramine
Clonazepam
Clonidine
Clopidogrel
Clotrimazole
Cloxacillin
Clozapine
Clozaril
Cocarboxylase
Cogentin
Colistin
Colyte
Combivent
Commit
Compazine
Concerta
Copaxone
Cordarone
Coreg
Corgard
Corticotropin
Cortisone
Cotinine
Cotrim
Coumadin
Cozaar
Crestor
Crospovidone
Cuprimine
Cyanocobalamin
Cyclessa
Cyclizine
Cyclobenzaprine
Cyclopentolate
Cyclophosphamide
Cyclopropane
Cylert
Cyproterone
Cystagon
Cysteine
Cytarabine
Cytotec
Cytovene
Isotretinoin
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z

Read more at Wikipedia.org


[List your site here Free!]


Using Protein Microarray as a Diagnostic Assay for Non-Small Cell Lung Cancer
From American Journal of Respiratory and Critical Care Medicine, 11/15/05 by Zhong, Li

Rationale: Phenotypic and genotypic heterogeneity of lung cancer likely precludes the identification of a single predictive marker and suggests the importance of identifying and measuring multiple markers.

Objectives: We describe the use of a fluorescent protein microarray to identify and measure multiple non-small cell lung cancerassociated antibodies and show how simultaneous measurements can be combined into a single diagnostic assay.

Methods: T7 phage cDNA libraries of non-small cell lung cancer were first biopanned with plasma samples from normal subjects and patients with non-small cell lung cancer to enrich the component of tumor-associated proteins, and then applied to microarray slides. Two hundred twelve immunogenic phage-expressed proteins were identified from roughly 4,000 clones, using high-throughput screening with patient plasmas and assayed with 40 cancer and 41 normal plasma samples. Twenty patient and 21 normal plasma samples were randomly chosen and used for statistical determination of the predictive value of each putative marker. Statistical analysis identified antibody reactivity to seven unique phage-expressed proteins that were significantly different (p

Main Results: Measurements of the 5 most predictive phage proteins were combined in a logistic regression model that achieved 90% sensitivity and 95% specificity in prediction of patient samples, whereas leave-one-out statistical analysis achieved 88.9% diagnostic accuracy among all 81 samples.

Conclusion: Our data indicate that antibody profiling is a promising approach that could achieve high diagnostic accuracy for non-small cell lung cancer.

Keywords: biomarkers; fluorescent array; phage display; tumorassociated autoantibodies

Circulating tumor markers could impact non-small cell lung cancer (NSCLC) patient outcomes through improved screening, diagnosis, staging, and management (1-3). The search for biomarkers for the early detection and diagnosis of NSCLC has met with little success. Much emphasis has been placed on the discovery and characterization of unique tumor markers (4-11). Yet, no marker has been identified that has adequate sensitivity or specificity to be clinically useful, although a combination of multiple markers has been shown to increase diagnostic accuracy. Tumor-associated antibodies may expand the number of available markers (10-16). Our previous work clearly shows that some proteins from T7 phage display NSCLC libraries are recognized by antibodies in cancer patient plasma but not in normal plasma (17). Although additional investigation may identify a single highly predictive marker of NSCLC, heterogeneity of this disease will likely require a panel of markers to achieve the sensitivity and specificity required for clinical application.

In the context of developing a diagnostic assay for NSCLC, we have adapted fluorescent microarray technology to the task of identifying immunogenic phage-expressed proteins and assessing the presence of their corresponding antibodies. Robotic microarray spotters that allow grouping of thousands of proteins, in replicate, onto a single glass slide make this feasible, efficient, and reproducible. Automated spotting of the array also allows production of hundreds of identical chips, thus making this technology a logical tool for this application. To develop an assay for detecting NSCLC, we employed a high-throughput method of isolating immunogenic phage-expressed proteins from TV phage NSCLC tumor libraries, using antibodies in NSCLC patient plasmas. We then combined multiple phage-expressed proteins onto a single protein "diagnostic chip" and evaluated our ability to predict disease.

METHODS

Human Subjects

After informed consent was obtained, 50 plasma samples (10 used for selection and 40 for analysis) were obtained from individuals with histologically confirmed NSCLC (Table 1). Normal control subjects included 41 individuals with a minimum smoking history of 20 packyears, ages 50-75 years (considered "high-risk"), and 5 nonsmoking control subjects used for selection and analysis.

Phage Libraries and Biopanning

One T7 phage NSCLC cDNA library was purchased (Novagen, Madison. WI) and a second was constructed from the adenocarcinoma cell line NCI-1650, using Novagen OrientExpress cDNA synthesis and cloning systems (17). The libraries were biopanned with pooled plasma from five patients with NSCLC (stages 2-4; histology "NSCLC") and normal healthy donors, to enrich the population of phage-expressed proteins recognized by tumor-associated antibodies as previously described (17). Briefly, the phage-displayed library was affinity selected by incubation with protein G-agarose beads coated with antibodies from pooled normal sera (250 µl of pooled normal sera, diluted 1:20, at 4°C overnight) to remove non-tumor-specific proteins. Unbound phages were separated from phages bound to antibodies in normal plasma by centrifugation. The supernatant was then biopanned against protein G-agarose beads coated with pooled patient plasma (4°C overnight) and separated from unbound phages by centrifugation. The bound/reactive phages were eluted with 1% sodium dodecyl sulfate and centrifugation. The phages were amplified in Escherichia coli BLT5615 (GIBCO-BRL, Grand Island, NY) in the presence of 1 mM isopropyl-β-D-thiogalactopyranoside and carbenicillin (50 µg/ml) until lysis. Amplified phage-containing lysates were collected and subjected to three additional sequential rounds of biopan enrichment. Phage-containing lysates from the fourth biopan were amplified, and individual phage clones were isolated and then incorporated into protein arrays as described below.

Array Construction and High-throughput Screening

Phage lysates from the fourth round of biopanning were amplified and grown on LB-agar plates covered with 6% agarose for isolating individual phage. A colony-picking robot (QPixII: Genetix, New Milton, UK) was used to pick 4,000 individual colonies (2,000 per library). The picked phages were reamplified in 96-well plates and then 5-nl samples of clear lysate from each well were robotically spotted in duplicate on FAST slides (Schleicher & Schuell BioScience. Keene, NH). using an Affymetrix 417 Arrayer (Affymetrix, Santa Clara, CA).

Five individual NSCLC patient plasma samples not used in the biopan were used to identify immunogenic phage-displayed proteins from the screening slides. Rabbit anti-TV primary antibody (Jackson ImmunoResearch, West Grove, PA) was used to detect T7 capsid proteins as a control for phage amount. Both preabsorbed plasma (plasma:bacterial lysate, 1:30) samples and anti-T7 antibodies were diluted 1:3,000 with 1× Tris-buffered saline (TBS) plus 0.1% Tween 20 (TBST) and incubated with the screening slides for 1 hour at room temperature. Slides were washed and then probed with Cy5-labeled anti-human and Cy3-labeled anti-rabbit secondary antibodies (Jackson ImmunoResearch: each antibody diluted 1:4.000 in 1× TBST) together for 1 h at room temperature. Slides were washed again and then scanned with an Affymetrix 428 scanner. Images were analyzed with GenePix 5.0 software (Axon Instruments/Molecular Devices. Union City, CA). Phages bearing a Cy5:Cy3 signal ratio greater than 2 standard deviations from a linear regression were selected as candidates for use on a "diagnostic chip."

Diagnostic Chip Design and Antibody Measurement

Two hundred and twelve immunoreactive phages identified by highthroughput screening (described above), plus 120 "empty" T7 phages, were combined, reamplified, and spotted in duplicate onto FAST slides as single diagnostic chips. Replicate chips were used to assay 40 NSCLC plasma samples, according to the protocol described above for screening. The median Cy5 signal was normalized to the median Cy3 signal (Cy5:Cy3 signal ratio) as the measurement of human antibody against a unique phage-expressed protein. To compensate for chip-to-chip variability, measurements were further normalized by subtracting background reactivity of plasma against empty T7 phage proteins and dividing by the median of the T7 signal [(CY5:Cy3 of phage - Cy5:Cy3 of T7)/Cy5:Cy3 of T7].

Statistical Analysis

Normalized Cy5:Cy3 for each of the 212 phage-expressed proteins was independently analyzed for statistically significant differences between the patient group and normal group by t test, using JMP statistical software (SAS, Inc., Cary, NC). Candidate phage markers were chosen if p

Using a panel of nonredundant phage-expressed proteins, logistic regression analysis was performed to predict the probability that a sample was from a patient with NSCLC. All 81 samples were divided into two groups. The first group, consisting of 21 normal and 20 patient plasma samples, randomly chosen, was used as a training set to build up classifiers that were able to distinguish patient from normal samples on the basis of an individual marker or a combination of markers. The second group, consisting of 20 patient and 20 normal samples, was used to validate the prediction rate of classifiers derived with the training group. Receiver operating characteristic curves were generated to compare the predictive sensitivity and specificity with different markers, and the area under the curve. The classifiers were further examined by leave-one-out cross-validation. Smoking and stage of disease were also analyzed and compared.

Sequence Identification of Phage-expressed Proteins

When possible, phage identity was made on the basis of significant nucleotide and translated nucleotide matches (bit score, e value, and percent sequence match) with a single gene in the GenBank database, using BLASTN and BLASTX search engines.

RESULTS

High-throughput Screening

To assess the efficiency of high-throughput fluorescent array screening of phage-expressed proteins, using antibodies in plasma from patients with NSCLC. two T7 phage NSCLC libraries were biopanned with NSCLC patient and normal plasma to enrich the population of immunogenic clones, using an approach similar to that described in previous work (17). Four thousand phages from the fourth biopan were then spotted on membranecoated array slides and screened with five individual NSCLC patient plasma samples not used in the biopan to identify immunogenic phages. Linear regression of the Cy5:Cy3 signal revealed 212 individual phages with signal ratios greater than 2 standard deviations from the average, which were chosen as candidates for a "diagnostic chip." An example of linear Cy5:Cy3 regression used for phage selection from a screening chip is shown in Figure 1.

Data Processing and Choice of Candidate Markers

Forty NSCLC, 36 high-risk control, and 5 nonsmoking control samples not used in the biopan or in the high-throughput screening described above were assayed with a diagnostic chip containing 212 candidate phage-expressed proteins. Each specific signal (Cy5) was normalized for number of phage particles by the Cy3 (anti-T7 capsid) signal. Chip-to-chip variability, a suspected function of variable total IgG concentration in individual plasma samples, was normalized relative to the signal from empty T7 (Figure 2). A Student/test of normalized signal from 40 patients and 41 normal subjects afforded a statistical cutoff (p

Sequence Analysis of Phage-expressed Proteins

The 17 phages that were chosen for their putative predictive value, using the t test and p

Statistical Modeling and Assay Prediction Accuracy

To develop classifiers using the unique seven phage-expressed proteins for a better predictive rate, we divided the total 81 samples randomly into two groups: one was used for training purposes and the other for validation. Logistic regression was used to calculate the sensitivity and specificity for predictive accuracy, using individual phage-expressed proteins as well as a combination of multiple phage-expressed markers. Results show that five phage markers had significant ability to distinguish patient samples from normal control subjects in the training set. The area under the receiver operating characteristic curve for each individually ranged from 0.79 to 0.86. Combinations of these five markers achieved a promising prediction rate (area under the receiver operating characteristic curve, 0.98), with 95% sensitivity and 85% specificity (Table 2). Using this statistical model to test the validation group consisting of 20 control normal samples and 20 NSCLC samples achieved a sensitivity of 90% and a specificity of 95% (Table 2). It is worth noting that, because these five markers were chosen for their individual predictive value (p

To further examine the association of the classifiers with diagnostic sensitivity and specificity, we performed class prediction using leave-one-out cross-validation on all 81 chips. Using all 81 chips to estimate predictive ability results in upwardly biased estimates. The leave-one-out method removes the chips one at a time and uses the remaining 80 chips to classify the one left out. These classifications are used to compute sensitivity and specificity to correct for the fact that all 81 chips are being used. The results showed that sensitivity and specificity were 90 and 87%, respectively, with 81 samples, and the overall diagnostic accuracy was 89% (Table 3). Also, when using all 81 chips, the corresponding clone ID, gene name, and p value were as follows: 1864. GAGE7, p = 9.1 × 10^sup -9^; 1896, BAC clone RP11-499F19. p = 3.5 × 10^sup -8^; 1919, SEC15L2, p = 1.2 × 10^sup -6^: 1761, PMS2L15. p = 5.2 × 10^sup -7^; and 1747, EEF1A, p = 5.9 × 10^sup -7^. All five markers passed a Bonferroni correction of 0.001/262 = 3.8 × 10^sup -6^, making the probability of one or more of them being false positive less than 0.001.

We were unable to accurately distinguish stage or histologie type of tumor in this sample set, probably because of sample size.

DISCUSSION

Serum tumor markers have the potential of being incorporated into diagnostic and therapeutic practice to improve historically dismal outcomes in NSCLC (2). Potential uses include early detection or screening, differentiation of benign from malignant disease, differentiating histologies, defining stages and responses to therapy, identifying recurrences, and defining prognosis. These goals have generated considerable interest in identifying predictive tumor markers (1-3, 32-34). Although a number of NSCLC tumor markers are measurable and a combination of available NSCLC markers can enhance diagnostic value, limited sensitivity and specificity of these markers preclude their widespread clinical use (2, 3, 35-38). In context, tumor-associated antibodies may expand the range of available NSCLC markers (14-17). Consistent with the knowledge that an antibody response to a single protein is unlikely to be a universal marker (10, 11, 36), we have been exploring methodology for efficiently identifying and measuring multiple tumor-associated antibodies. This article describes a technique for enriching and screening phage-expressed tumor-associated proteins that are, in turn, used in array fashion to measure multiple antibodies simultaneously. We biopan-enriched the immunogenic phage content of two NSCLC T7 libraries, used fluorescent microarrays to screen 4,000 clones and sequentially refined a list of potential markers to generate a diagnostic array capable of distinguishing patients with NSCLC from normal control subjects.

High-throughput screening identified 212 candidate markers that were included in a diagnostic chip for further testing. Each phage clone was then individually analyzed for its potential to discriminate 40 patients with NSCLC from 41 normal subjects. Only the most promising markers, those that were identified at a high level and frequency in patients with cancer to make them significantly different from normal subjects by Student t test, made the final cut. The t test provided a stringent cutoff, although other methods could have been applied to increase the number of markers to be assessed. The list size was further reduced by finding that several of the 17 phages were redundant clones. Notably, the fact that four of seven unique markers identified by this high-throughput approach were also identified by a qualitative histochemical screening method previously described (17) not only validates the high-throughput screening methodology, but also provides some validation of the markers themselves, because different sets of patient samples were used for both biopanning and screening in the independent experiments.

The classifier using five combined phage markers has given us good predictive accuracy using both the training and validation method and the leave-one-out method. Data were assessed for the relative importance of smoking, stage, and histology, although numbers in each group were too small to draw definitive conclusions. For reasons of assay development, we tested predominantly advanced-stage disease as these individuals are theoretically more likely to express high levels and greater variety of antibodies. In this population, compared with high-risk control subjects, we obtained diagnostic accuracy of 88.9%. This exceeds the currently reported prediction values of the clinically available markers tissue polypeptide antigen, 80%; CA19-9, 62%; carcinoembryonic antigen, 73%; squamous cell carcinoma antigen, 62%; and neuron-specific enolase, 63% (38). Additional validation of these data should include samples from a broader range of normal control subjects who are carefully matched for age and smoking history, and also include individuals with a variety of benign lung diseases common in the high-risk population, including chronic obstructive pulmonary disease and granulomatous lung disease. We have also not assayed plasmas from other malignancies. Knowing that several of the tumor-associated antibodies we have identified in NSCLC plasma samples have been described in other malignancies and that several more of the tumor-associated proteins we describe in NSCLC are known to be expressed by other cancers (17, 39), NSCLC specificity will have to be independently evaluated.

From the standpoint of application, our most immediate interest is to develop a predictive assay that can assist in early diagnosis of lung cancer. This interest is driven by the fact that only 25% of new cases of lung cancer are diagnosed at an early stage, when curative surgical resection is still possible (1, 40). An appropriately sensitive and specific assay could help address some concerns surrounding the use of low-dose spiral chest computed tomography (CT) as a stand-alone test for lung cancer screening. Specifically, low-dose CT screening is costly, is characterized by high sensitivity but just 64% specificity, and screening criteria of age and smoking history have limited ability to refine the target population (40-42). Moreover, the routine identification of indeterminate pulmonary nodules during radiographi imaging frequently leads to expensive workup and often potentially harmful intervention, including major surgery (40-42). Antibody profiling could enhance screening efforts by defining a population with high probability of disease in which screening is most warranted, by assisting in the differentiation of benign from malignant nodules detected on screening CT scans and potentially by detecting occult lung cancer at a time before a nodule can be seen on CT scan. Testing with early-stage cancers and matched control subjects will be the immediate focus of subsequent studies. Investigations will then determine the ability of antibody profiling to distinguish benign from malignant disease in a population presenting with indeterminate radiographic pulmonary nodules. Linking biomarker analysis to radiographic screening trials will provide necessary validation for these two related applications and, in addition, will determine whether antibody profiling can detect lung cancer before it is radiographically apparent. Other applications such as staging and prognosis are also compelling and will similarly require independent sample analysis.

Relevant to the preceding discussion, the markers described in this article were selected for their predictive value, using the available patient and control blood samples. If on further validation we see a decrease in sensitivity or specificity, or if the defined marker set does not have adequate diagnostic accuracy for a specific application (e.g., early detection), inclusion of additional markers or selection of alternative marker sets may help achieve appropriate predictive accuracy. If necessary, additional patient samples can be used to screen available or newly constructed tumor libraries to expand the number and range of existing markers, although we did not find this necessary to generate a panel of markers capable of distinguishing our patient from control samples.

Importantly, we have not exhaustively screened these libraries for all possible markers and have likely not yet identified some significantly predictive circulating tumor-associated antibodies. Specifically, we have chosen markers using only two NSCLC tumor libraries and antibodies found in five advanced-stage NSCLC plasma samples that will not be representative of the full spectrum of disease. Further, phage-expressed proteins, derived from a cDNA tumor library expressed within a prokaryotic T7 bacteriophage system, would not be expected to include the wide variety of proteins with posttranslational modifications typical of a mammalian system and often anomalous in human tumors. As such, this approach is not going to be useful for generating a comprehensive proteomic profile, although the intrinsic ability of this approach to potentially identify a variety of aberrantly expressed tumor-associated proteins is attractive.

In summary, this article describes efficient methodology for screening, identifying, and measuring multiple circulating antibodies as markers of disease. Although we have not generated a comprehensive panel of antibodies and associated proteins we have obtained an inventory of markers that can distinguish NSCLC patient samples from control subject samples. These data support the rationale for further validation, development, and application of this approach for management of lung cancer.

Conflict of Interest Statement: None of the authors have a financial relationship with a commercial entity that has an interest in the subject of this manuscript.

Acknowledgment: The authors thank Dr. Xiaoju Wang for helpful discussion.

References

1. Hoffman PC, Mauer Am, Vokes EE. Lung cancer. Lancet 2000;5:479-485.

2. Niklinski J. Furman M. Clinical tumour markers in lung cancer. Eur J Cancer Prev 1995:4:129-138.

3. Ferrigno D, Buccheri G. Biggi A. Serum tumour markers in lung cancer: history, biology and clinical applications. Eur Respir J 1994:7:186-197.

4. Cure AO, Altorki NK, Stockert E, Scanlan MJ, Old LJ, Chen YT. Human lung cancer antigens recognized by autologous antibodies: definition of a novel cDNA derived from the tumor suppressor gene locus on chromosome 3p21.3. Cancer Res 1998:58:1034-1041.

5. Stockert E, Jager E, Chen YT, Scanlan MJ, Gout I, Karbach J, Arand M, Knuth A, Old LJ. A survey of the humoral immune response of cancer patients to a panel of human tumor antigens. J Exp Med 1998: 187:1349-1354.

6. Tureci O, Sahin U, Zwick C, Neumann F, Pfreundschuh M. Exploitation of the antibody repertoire of cancer patients for the identification of human tumor antigens. Hybridoma 1999:18:23-28.

7. Jager D, Stockert E, Cure AO, Scanlan MJ, Karbach J, Jager E, Knuth A, Old LJ, Chen YT. Identification of a tissue-specific putative transcription factor in breast tissue by serological screening of a breast cancer library. Cancer Res 2001:61:2055-2061.

8. Stevanovic S. Identification of tumour-associated T-cell epitopes for vaccine development. Nat Rev Cancer 2002:2:514.

9. Diesinger I, Bauer C, Brass N, Schaefers HJ, Comtesse N, Sybrecht G, Meese E. Toward a more complete recognition of immunoreactive antigens in squamous cell lung carcinoma. Int J Cancer 2002:102:372378.

10. Abu-Shakra M, Buskila D, Ehrenfeld M, Conrad K, Shoenfeld Y. Cancer and autoimmunity: autoimmune and rheumatic features in patients with malignancies. Ann Rheum Dis 2001:60:433-441.

11. Cheever MA, Disis ML, Bernhard H, Gralow JR, Hand SL, Huseby ES, Qin HL, Takahashi M, Chen W. Immunity to oncogenic proteins. Immunol Rev 1995:145:33-59.

12. Sioud M, Hansen MH. Profiling the immune response in patients with breast cancer by phage-displayed cDNA libraries. Eur J Immunol 2001:31:716-725.

13. Hanash S. Disease proteomics. Nature 2003:422:226-232.

14. Hanash S. Harnessing immunity for cancer marker discovery. Nat Biotechnol 2003:21:37-38.

15. Brichory FM, Misek DE, Yim AM, Krause MC, Giordano TJ, Beer DG, Hanash SM. An immune response manifested by the common occurrence of annexins I and II autoantibodies and high circulating levels of IL-6 in lung cancer. Proc Natl Acad Sci USA 2001:98:98249829.

16. Brichory F, Beer D, LeNaour F, Giordano T, Hanash S. Proteomicsbased identification of protein gene product 9.5 as a tumor antigen that induces a humoral immune response in lung cancer. Cancer Res 2001:61:7908-7912.

17. Zhong L, Peng X, Hidalgo GE, Doherty DE, Stromberg AJ, Hirschowitz EA. Identification of circulating antibodies to tumor-associated proteins for combined use as markers of non-small cell lung cancer. Proteomics 2004:4:1216-1225.

18. Brasseur F, Chomez P, Van den Eynde B, Boon T, van der Bruggen P. Characterization of the GAGE genes that are expressed in various human cancers and in normal testis. Cancer Res 1999:59:3157-3165.

19. Melloni G, Ferreri AJ, Russo V, Gattinoni L, Arrigoni G, Ceresoli GL, Zannini P, Traversari C. Prognostic significance of cancer-testis gene expression in resected non-small cell lung cancer patients. Oncol Rep 2004:12:145-151.

20. Lamberti A, Caraglia M, Longo O, Marra M, Abbruzzese A, Arcari P. The translation elongation factor 1A in tumorigenesis. signal transduction and apoptosis: review article. Amino Acids 2004:26:443-448.

21. Joseph P, O'Kernick CM, Othumpangat S, Lei YX, Yuan BZ, Ong TM. Expression profile of eukaryotic translation factors in human cancer tissues and cell lines. Mol Carcinog 2004:40:171-179.

22. Grant AG, Flomen RM, Tizard ML, Grant DA. Differential screening of a human pancreatic adenocarcinoma lambda gt11 expression library has identified increased transcription of elongation factor EF-1 α in tumour cells. Int J Cancer 1992:50:740-745.

23. Chen HY, Shen CH, Tsai YT, Lin FC, Huang YP, Chen RH. Brk activates rac1 and promotes cell migration and invasion by phosphorylating paxillin. Mol Cell Biol 2004:24:10558-10572.

24. Crowe DL, Ohannessian A. Recruitment of focal adhesion kinase and paxillin to β^sub 1^ integrin promotes cancer cell migration via mitogen activated protein kinase activation. BMC Cancer 2004:4:18.

25. Zuo W, Li H. Relationship of the expression of CD44v6 and paxillin to the prognosis of non-small cell lung carcinoma. Sichuan Da Xue Xue Bao Yi Xue Ban 2003:34:484-485.

26. Nagata M, Fujita H, Ida H, Hoshina H, Inoue T, Seki Y, Ohnishi M, Ohyama T, Shingaki S, Kaji M, et al. Identification of potential biomarkers of lymph node metastasis in oral squamous cell carcinoma by cDNA microarray analysis. Int J Cancer 2003:106:683-689.

27. Azuma K, Tanaka M, Uekita T, Inoue S, Yokota J, Ouchi Y, Sakai R. Tyrosine phosphorylation of paxillin affects the metastatic potential of human osteosarcoma. Oncogens 2005:24:4754-4764.

28. Li HG, Xie DR, Shen XM, Li HH, Zeng H, Zeng YJ. Clinicopathological significance of expression of paxillin, syndecan-1 and EMMPRIN in hepatocellular carcinoma. World J Gastroentenol 2005;11:1445-1451.

29. Nicolaides NC. Carter KC, Shell BK, Papadopoulos N, Vogelstein B, Kinzler KW. Genomic organization of the human PMS2 gene family. Genomics 1995:30:195-206.

30. Zhang XM, Ellis S. Sriratana A, Milchell CA, Rowe T. Sec15 is an effector for the Rab11 GTPase in mammalian cells. J Biol Chem 2004; 279:43027-43034.

31. Miau LH, Chang CJ, Tsai WH, Lee SC. Identification and characterization of a nucleolar phosphoprotein. Nopp140, as a transcription factor. Mol Cell Biol 1997:17:230-239.

32. Foa P, Fornier M, Miceli R, Seregni E, Santambrogio L, Nosotti M, Cataldo I, Sala M, Caldiera S, Bomhardieri E. Tumour markers CEA. NSE, SCC, TPA and CYFRA 21.1 in resectable non-small cell lung cancer. Anticancer Res 1999;19:3613-3618.

33. Buccheri G, Ferrigno D. Lung ltumor markers of cytokeratin origin: an overview. Lung Cancer 2001;34:S65-S69.

34. Ebert W, Muley T, Drings P. Does the assessment of serum markers in patients with lung cancer aid in the clinical decision making process? Anticancer Res 1996:16:2161-2168.

35. Buccheri G, Violante B, Sartoris AM, Ferrigno D, Curcio A. Clinical value of a multiple biomarker assay in patients with bronchogenic carcinoma. Cancer 1986:57:2389-2396.

36. Lombardi C, Tassi GF, Pizzocolo G, Donato F. Clinical significance of a multiple biomarker assay in patients with lung cancer: a study with logistic regression analysis. Chest 1990:97:639-644.

37. Ando S, Kimura H, Iwai N. Shima M, Ando M, Kuriyama T. Optimal combination of seven tumour markers in prediction of advanced stage at first examination of patients with non-small cell lung cancer. Anticancer Res 2001 ;21:3085-3092.

38. Mizushima Y, Tsuji H, Izumi S, Hirata H, Kin Y, Kawasaki A, Matsui S, Yano S. Clinical evaluation of five tumor marker assays in patients with lung cancer. Anticancer Res 1991:11:91-96.

39. Pei J, Balsara BR, Li W, Litwin S, Gabrielson E, Feder M, Jen J, Testa JR. Genomic imbalances in human lung adenocarcinomas and squamous cell carcinomas. Genes Chromosomes Cancer 2001:31:282-287.

40. Bach PB, Kelley MJ, Tale RC, McCrory DC. Screening for lung cancer: a review of the current literature. Chest 2003:123:728-828.

41. MacRedmond R, Logan PM, Lee M, Kenny D, Foley C, Costello RW. Screening for lung cancer using low-dose CT scanning. Thorax 2004:59: 237-241.

42. McWilliams A, Mayo J, MacDonald S, leRiche JC, Palcic B, Szabo E, Lam S. Lung cancer screening: a different paradigm. Am J Respir Cm Care Med 2003:168:1143-1144.

Li Zhong, Giovanna E. Hidalgo, Arnold J. Stromberg, Nada H. Khattar, James R. Jett, and Edward A. Hirschowitz

Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, and Department of Statistics, University of Kentucky; Lexington Veteran's Administration Medical Center, Lexington, Kentucky; and Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota

(Received in original form May 27, 2005; accepted in final form August 18, 2005)

Supported by a National Institutes of Health grant (R01, CA10032-01), the Veteran's Administration Merit Review Program, and the Kentucky Lung Cancer Association.

Correspondence and requests for reprints should be addressed to Li Zhong, Ph.D., Division of Pulmonary and Critical Care Medicine, University of Kentucky, Chandler Medical Center, K528 Kentucky Clinic, 740 S. Limestone, Lexington, KY 40536. E-mail: Izhon2@uky.edu

Am I Respir Crit Care Med Vol 172. pp 1308-1314, 2005

Originally Published in Press as DOI: 10.1164/rccm.200505-830OC on August 18, 2005

Internet address: www.atsjournals.org

Copyright American Thoracic Society Nov 15, 2005
Provided by ProQuest Information and Learning Company. All rights Reserved

Return to Carbenicillin
Home Contact Resources Exchange Links ebay