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.
Highlights
Lung cancer is the leading cause of cancer-related death worldwide, responsible for around 1.59 million deaths in 2012 [1]
Samples from 100 lung cancer patients and 100 lung cancer-free controls were processed on the 16k protein microarray comprising 5449 recombinantly expressed human proteins derived from 15,417 cDNA clones
There is a great need for improving the sensitivity of such assays to cover the whole molecular complexity of lung cancer. This could be achieved by combining already established assays like the EarlyCDT®-Lung assay with our identified immuno-profiles which yielded high sensitivity rates since we achieved correct classification of 85%
Summary
Lung cancer is the leading cause of cancer-related death worldwide, responsible for around 1.59 million deaths in 2012 [1] This disease is characterized by high heterogeneity in terms of pathological features and is commonly classified into two major groups, small-cell lung carcinoma (SCLC), accounting for approximately 15% of lung cancer cases, and non-small cell lung carcinoma (NSCLC). Traditional diagnostic methods like computer tomography (CT) or X-ray often yield low positive predictive values (PPV), indicating that only few patients with a positive test result were confirmed with lung cancer after additional biopsy. This circumstance results in unsatisfactory performance of many diagnostic tests, high downstream costs and unpleasant procedures for the patients [5]. It is important to find novel markers for early detection of lung cancer by means of minimal invasive biomarker screenings using material such as blood, sputum, or exhaled breath [6]
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