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From the third issue of 2017, Microarrays has changed its name to High-Throughput.

Open AccessArticle
Microarrays 2015, 4(2), 162-187; doi:10.3390/microarrays4020162

Immune-Signatures for Lung Cancer Diagnostics: Evaluation of Protein Microarray Data Normalization Strategies

1
Molecular Diagnostics, Health & Environment Department, AIT Austrian Institute of Technology GmbH, Muthgasse 11, 1190 Vienna, Austria
2
Department of Medicine I, Institute of Cancer Research, Comprehensive Cancer Center, Medical University Vienna, Borschkegasse 8a, 1090 Vienna, Austria
These authors contributed equally to this work and are listed alphabetically.
*
Author to whom correspondence should be addressed.
Academic Editor: Shu-Kay Ng
Received: 30 January 2015 / Revised: 23 March 2015 / Accepted: 25 March 2015 / Published: 2 April 2015
(This article belongs to the Special Issue Advanced Methods in Microarrays for Cancer Research)
View Full-Text   |   Download PDF [1369 KB, uploaded 2 April 2015]   |  

Abstract

New minimal invasive diagnostic methods for early detection of lung cancer are urgently needed. It is known that the immune system responds to tumors with production of tumor-autoantibodies. Protein microarrays are a suitable highly multiplexed platform for identification of autoantibody signatures against tumor-associated antigens (TAA). These microarrays can be probed using 0.1 mg immunoglobulin G (IgG), purified from 10 µL of plasma. We used a microarray comprising recombinant proteins derived from 15,417 cDNA clones for the screening of 100 lung cancer samples, including 25 samples of each main histological entity of lung cancer, and 100 controls. Since this number of samples cannot be processed at once, the resulting data showed non-biological variances due to “batch effects”. Our aim was to evaluate quantile normalization, “distance-weighted discrimination” (DWD), and “ComBat” for their effectiveness in data pre-processing for elucidating diagnostic immune‑signatures. “ComBat” data adjustment outperformed the other methods and allowed us to identify classifiers for all lung cancer cases versus controls and small-cell, squamous cell, large-cell, and adenocarcinoma of the lung with an accuracy of 85%, 94%, 96%, 92%, and 83% (sensitivity of 0.85, 0.92, 0.96, 0.88, 0.83; specificity of 0.85, 0.96, 0.96, 0.96, 0.83), respectively. These promising data would be the basis for further validation using targeted autoantibody tests. View Full-Text
Keywords: protein microarrays; cancer research; lung cancer; bioinformatics; biomarkers protein microarrays; cancer research; lung cancer; bioinformatics; biomarkers
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Brezina, S.; Soldo, R.; Kreuzhuber, R.; Hofer, P.; Gsur, A.; Weinhaeusel, A. Immune-Signatures for Lung Cancer Diagnostics: Evaluation of Protein Microarray Data Normalization Strategies. Microarrays 2015, 4, 162-187.

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