Volume 129, Issue 1 pp. 133-142
Early Detection and Diagnosis

Enhancement of a multianalyte serum biomarker panel to identify lymph node metastases in non-small cell lung cancer with circulating autoantibody biomarkers

Kalpa Patel

Kalpa Patel

Department of Biochemistry, Rush University Medical Center, Chicago, IL

K.P. and E.C. contributed equally to this work.

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Erin C. Farlow

Erin C. Farlow

Department of Surgery, Rush University Medical Center, Chicago, IL

K.P. and E.C. contributed equally to this work.

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Anthony W. Kim

Anthony W. Kim

Department of Thoracic Surgery, Rush University Medical Center, Chicago, IL

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Bao-Shiang Lee

Bao-Shiang Lee

Protein Research Laboratory, Research Resources Center, University of Illinois at Chicago, Chicago, IL

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Sanjib Basu

Sanjib Basu

Department of Oncology, Rush University Medical Center, Chicago, IL

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John S. Coon

John S. Coon

Department of Pathology, Rush University Medical Center, Chicago, IL

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David DeCresce

David DeCresce

Department of Biochemistry, Rush University Medical Center, Chicago, IL

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Lida Thimothy

Lida Thimothy

Department of Biochemistry, Rush University Medical Center, Chicago, IL

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Kelly A. Walters

Kelly A. Walters

Department of Pathology, Rush University Medical Center, Chicago, IL

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Cristina Fhied

Cristina Fhied

Department of Biochemistry, Rush University Medical Center, Chicago, IL

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Christopher Chang

Christopher Chang

Abnova Corporation, Taipei City, Taiwan

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Ssu-Hsien Chen

Ssu-Hsien Chen

Abnova Corporation, Taipei City, Taiwan

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L. Penfield Faber

L. Penfield Faber

Department of Thoracic Surgery, Rush University Medical Center, Chicago, IL

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Philip Bonomi

Philip Bonomi

Department of Oncology, Rush University Medical Center, Chicago, IL

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Michael J. Liptay

Michael J. Liptay

Department of Thoracic Surgery, Rush University Medical Center, Chicago, IL

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Jeffrey A. Borgia

Corresponding Author

Jeffrey A. Borgia

Department of Biochemistry, Rush University Medical Center, Chicago, IL

Department of Pathology, Rush University Medical Center, Chicago, IL

Proteomics and Biomarkers Core, Rush University Medical Center, Chicago, IL

Tel.: 312-942-7837, Fax: 312-942-3053

Department of Biochemistry, Rush University Medical Center, 558 Cohn Research Building, 1735 W. Harrison St., Chicago, IL 60612, USASearch for more papers by this author
First published: 07 September 2010
Citations: 25

Abstract

We recently reported the development of a multianalyte serum algorithm to identify nodal status in non-small cell lung cancer (NSCLC) patients facing an anatomic resection with curative intent. This study aims to enhance the overall performance characteristics of this test by adding autoantibody biomarkers identified through immunoproteomic discovery. More specifically, we used sera from 20 NSCLC patients to probe 2-D immunoblots of HCC827 lysates for tumor-associated autoantigens. Relevant differences in immunoreactivity associated with pathological nodal status were then identified via tandem mass spectrometry. Identified autoantigens were then developed into Luminex immunobead assays alongside a series of autoantigen targets relevant to early-disease detection. These assays were then used to measure circulating autoantibody levels in the identical cohort of NSCLC patients used in our original study. This strategy identified 11 autoantigens found primarily in patients with disease progression to the locoregional lymph nodes. Custom Luminex-based “direct-capture” assays (25 total; including autoantibody targets relevant to early-disease detection) were assembled to measure autoantibody levels in sera from 107 NSCLC patients. Multivariate classification algorithms were then used to identify the optimal combination of biomarkers when considered collectively with our original 6-analyte serum panel. The new algorithm resulting from this analysis consists of TNF-α, TNF-RI, MIP-1α and autoantibodies against Ubiquilin-1, hydroxysteroid-(17-β)-dehydrogenase, and triosephosphate isomerase. The inclusion of autoantibody biomarkers provided a dramatic improvement in the overall test performance characteristics, relative to the original test panel, including an 11% improvement in the classification efficiency.

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