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. 2014 Apr 5:13:78.
doi: 10.1186/1476-4598-13-78.

A novel autoantibody test for the detection of pre-neoplastic lung lesions

Affiliations

A novel autoantibody test for the detection of pre-neoplastic lung lesions

Frazer J Lowe et al. Mol Cancer. .

Abstract

Background: Atypical adenomatous hyperplasia (AAH) and squamous cell dysplasia (SCD) are associated with the development of malignant lesions in the lung. Accurate diagnosis of AAH and SCD could facilitate earlier clinical intervention and provide useful information for assessing lung cancer risk in human populations. Detection of AAH and SCD has been achieved by imaging and bronchoscopy clinically, but sensitivity and specificity remain less than satisfactory. We utilized the ability of the immune system to identify lesion specific proteins for detection of AAH and SCD.

Methods: AAH and SCD tissue was surgically removed from six patients of Chinese descent (3 AAH and 3 SCD) with corresponding serum samples. Total RNA was extracted from the tissues and a cDNA library was generated and incorporated into a T7 bacteriophage vector. Following enrichment to remove "normal" reactive phages, a total of 200 AAH related and 200 SCD related phage clones were chosen for statistical classifier development and incorporation into a microarray. Microarray slides were tested with an independent double-blinded population consisting of 100 AAH subjects, 100 SCD subjects and 200 healthy control subjects.

Results: Sensitivity of 82% and specificity of 70% were achieved in the detection of AAH using a combination of 9 autoantibody biomarkers. Likewise, 86% sensitivity and 78% specificity were achieved in the detection of SCD using a combination of 13 SCD-associated markers. Sequencing analysis identified that most of these 22 autoantibody biomarkers had known malignant associations.

Conclusions: Both diagnostic values showed promising sensitivity and specificity in detection of pre-neoplastic lung lesions. Hence, this technology could be a useful non-invasive tool to assess lung cancer risk in human populations.

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Figures

Figure 1
Figure 1
Biopanning enrichment of immunogenic tumor-associated proteins. To confirm the enrichment of the biopanning, the outputs of biopans 1–4 (BP1-BP4) were plated onto LB-Agar plates in limiting dilution and plaque lifts were performed. (A). the output of BP3 revealed an increasing number of immunoreactive phage clones than (B). BP4, and illustrates the ability of the sequential biopans to enrich the concentration of tumor-associated proteins recognized by antibodies in patient serum. To confirm the specificity of enriched phage proteins, two nitrocellulose members were lifted twice on the same LB-Agar plate of BP4. One membrane was probed with pooled AAH/SCD patient sera (D) while the other was probed with pooled normal sera (C). Numerous immunoreactive clones show on the membrane incubated with patient sera (D) that are not seen in the identical membrane incubated with normal serum (C).
Figure 2
Figure 2
High-throughput screening of tumor-associated phage proteins. Biopanned phage clones were spotted on microarray slides and tested with patient serum samples. The partial array images show reactivity patterns for (A) AAH and (C) SCD patient samples, and the corresponding scatter plots show possible disease-associated phage clones (X-axis is Cy3 signal, Y-axis is Cy5 signal) in (B) and (D) respectively. The computer-generated regression line and standard deviation lines on the scatter plots assist in identifying candidate marker phages (yellow dots) for diagnostic chip construction.
Figure 3
Figure 3
Diagnostic accuracy for predicting the pre-neoplastic samples in validation test. Classifiers generated in the training tests were used to predict the samples statures in the blinded validation test. Corresponding receiver operating characteristics (ROC) curves and the value of the area under the curve (AUC) were calculated. Panel A was generated in the validation test based on the values from 100 AAH samples and 100 control samples. Panel B was generated based on the values from 100 SCD samples and 100 control samples.

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