Using Protein Microarray as a Diagnostic Assay for Non–Small Cell Lung Cancer

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

doi:10.1164/rccm.200505-830oc

Cited by 90 publications

(82 citation statements)

References 39 publications

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“…We and others have previously used this technology and showed a high degree of diagnostic accuracy for cancer detection in ovarian (21), lung (45,46), and prostate (22) cancers. We found this pattern of immunoreactivity to be highly reproducible.…”

Section: Discussionmentioning

confidence: 99%

Autoantibody Approach for Serum-Based Detection of Head and Neck Cancer

Lin

Talwar

Tarca

et al. 2007

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Currently, no effective tool exists for screening or early diagnosis of head and neck squamous cell carcinoma (HNSCC). Here, we describe an approach for cancer detection based on analysis of patterns of serum immunoreactivity against a panel of biomarkers selected using microarray-based serologic profiling and specialized bioinformatics. We biopanned phage display libraries derived from three different HNSCC tissues to generate 5,133 selectively cloned tumor antigens. Based on their differential immunoreactivity on protein microarrays against serum immunoglobulins from 39 cancer and 41 control patients, we reduced the number of clones to 1,021. The performance of a neural network model (Multilayer Perceptron) for cancer classification on a data set of 80 HNSCC and 78 control samples was assessed using 10-fold crossvalidation repeated 100 times. A panel of 130 clones was found to be adequate for building a classifier with sufficient sensitivity and specificity. Using these 130 markers on a completely new and independent set of 80 samples, an accuracy of 84.9% with sensitivity of 79.8% and specificity of 90.1% was achieved. Similar performance was achieved by reshuffling of the data set and by using other classification models. The performance of this classification approach represents a significant improvement over current diagnostic accuracy (sensitivity of 37% to 46% and specificity of 24%) in the primary care setting. The results shown here are promising and show the potential use of this approach toward eventual development of diagnostic assay with sufficient sensitivity and specificity suitable for detection of early-stage HNSCC in high-risk populations. (Cancer Epidemiol Biomarkers Prev 2007;16(11):2396 -405)

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Section: Discussionmentioning

confidence: 99%

Autoantibody Approach for Serum-Based Detection of Head and Neck Cancer

Lin

Talwar

Tarca

et al. 2007

Cancer Epidemiology, Biomarkers &Amp; Prevention

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“…Chen et al (4) separated sera of lung adenocarcinoma patients from healthy controls with a specificity of 86% and a sensitivity of 85%. Sera of patients with non-small lung cancer were separated from normal sera with a specificity of 95% and a sensitivity of 90% (5). A study on ovarian cancer reached only an average sensitivity of 32% at a specificity of 94% (6), whereas a study on epithelial ovarian cancer patients yielded a value of 0.86 for AUC without providing the values for specificity and sensitivity (7).…”

Section: Discussionmentioning

confidence: 99%

Pattern of Serum Autoantibodies Allows Accurate Distinction between a Tumor and Pathologies of the Same Organ

Ludwig¹,

Keller

Comtesse³

et al. 2008

Clinical Cancer Research

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Purpose: Recent studies impressively showed the diagnostic potential of seroreactivity patterns for different tumor types, offering the prospect for low-cost screening of numerous tumor types simultaneously. One of the major challenges toward this goal is to prove that seroreactivity profiles do not only allow for identifying a tumor but also allow for distinguishing tumors from other pathologies of the same organ. Experimental Design: We chose glioma as a model system and tested 325 sera (88 glioma, 95 intracranial tumors, 60 other brain pathologies, and 82 healthy controls) for seroreactivity on a panel of 35 antigens. Results: We were able to discriminate between glioma and all other sera with cross-validated specificity of 86.1%, sensitivity of 85.2%, and accuracy of 85.8%.We obtained comparably good results for the separation of glioma versus nontumor brain pathologies and glioma versus other intracranial tumors. Conclusion: Our study provides first evidence that seroreactivity patterns allow for an accurate discrimination between a tumor and pathologies of the same organ even between different tumor types of the same organ.

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“…Although there are many examples of gene expression profiles that can be used for molecular classification of cancer (3)(4)(5)(26)(27)(28), a global perspective to analyze and identify autoantibody repertoires in response to tumor antigens has only recently been developed (29)(30)(31). In this study, we combine phage display technology with protein microarrays to develop a powerful platform to identify and characterize an autoantibody signature for lung adenocarcinoma patients that can be evaluated, in multiplex, to develop diagnostic biomarkers.…”

Section: Introductionmentioning

confidence: 99%

Autoantibody Profiles Reveal Ubiquilin 1 as a Humoral Immune Response Target in Lung Adenocarcinoma

Chen¹,

Wang

Yu³

et al. 2007

Cancer Research

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There is considerable evidence that the presence of cancer can elicit a humoral immune response to specific proteins in the host, and these resulting autoantibodies may have potential as noninvasive biomarkers. To characterize the autoantibody repertoire present in the sera of patients with lung adenocarcinoma, we developed a high-density peptide microarray derived from biopanning a lung cancer phage display library. Using a 2,304-element microarray, we interrogated a total of 250 sera from Michigan lung cancer patients and noncancer controls to develop an ''autoantibody profile'' of lung adenocarcinoma. A set of 22 discriminating peptides derived from a training set of 125 serum samples from lung adenocarcinoma patients and control subjects was found to predict cancer status with 85% sensitivity and 86% specificity in an independent test set of 125 sera. Sequencing of the immunoreactive phage-peptide clones identified candidate humoral immune response targets in lung adenocarcinoma, including ubiquilin 1, a protein that regulates the degradation of several ubiquitin-dependent proteasome substrates. An independent validation set of 122 serum samples from Pittsburgh was examined using two overlapping clones of ubiquilin 1 that showed 0.79 and 0.74 of the area under the receiver operating characteristics curve, respectively. Significantly increased levels of both ubiquilin 1 mRNA and protein, as well as reduced levels of the phosphorylated form of this protein, were detected in lung tumors. Immunofluorescence using anti-ubiquilin 1 antibodies confirmed intracellular expression within tumors cells. These studies indicate that autoantibody profiles, as well as individual candidates, may be useful for the noninvasive detection of lung adenocarcinoma. [Cancer Res 2007;67(7):3461-7]

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Using Protein Microarray as a Diagnostic Assay for Non–Small Cell Lung Cancer

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

Cited by 90 publications

References 39 publications

Autoantibody Approach for Serum-Based Detection of Head and Neck Cancer

Autoantibody Approach for Serum-Based Detection of Head and Neck Cancer

Pattern of Serum Autoantibodies Allows Accurate Distinction between a Tumor and Pathologies of the Same Organ

Autoantibody Profiles Reveal Ubiquilin 1 as a Humoral Immune Response Target in Lung Adenocarcinoma

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