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W. Meng
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ORAL 39 - Potential Biomarkers for CT Screening (ID 149)
- Event: WCLC 2015
- Type: Oral Session
- Track: Screening and Early Detection
- Presentations: 2
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ORAL39.01 - Multiplexing Serum Proteins and Circulating Autoantibody for Detection of Lung Cancer (ID 570)
16:45 - 16:56 | Author(s): W. Meng
- Abstract
Background:
Currently, a blood test for lung cancer does not exist. Low-dose spiral computed tomography (CT) has been proposed as an early detection screening tool. However, despite its high sensitivity, the specificity of CT in lung cancer detection is poor. In addition, the necessity for repeated CT scans to determine growth rates over time can expose patients to potentially harmful radiation. Therefore, a minimally-invasive biomarker-based test that could further characterize CT-positive patients based on risk of malignancy would greatly enhance its clinical efficacy.
Methods:
From 2009 through 2013, six hospitals enrolled 1148 patients with lung cancer, 889 blood donors as healthy participants and 36 patients with other lung diseases. The lung cancer associated biomarker panels were identified from the pretreated serum samples and subsequently analyzed in three randomly determined subgroups, the discovery cohort (40 patients with lung cancer, and 45 healthy participants), test cohort (204 patients with lung cancer, and 120 healthy participants), and validation cohort (904 patients with lung cancer, 724 healthy participants, and 36 patients with other lung diseases). Finally the panel of biomarkers were used to predict 12 prospective patients with a suspicious pulmonary nodule by CT images.
Results:
The discovery cohort demonstrated that 4 serum biomarkers (C-reactive protein, prolactin, hepatocyte growth factor, and NY-ESO-1 autoantibody) were significantly higher in patients with lung cancer compared to healthy controls. The 4-biomarker panel was independently investigated in the test cohort and validation cohort. The test characteristics were area under the curve (AUC) of 0.835 (95% CI 0.79-0.873, sensitivity 70.1%, specificity 88.3%) in the test cohort, and 0.818 (95% CI 0.798-0.836, sensitivity 70.0%, specificity 79.6%) in the expanded validation cohort. The 4 biomarkers had no discriminatory power for detecting other benign lung diseases. The performance of the panels in patients with stage I-II lung cancer was AUC of 0.774 (95% CI 0.746-0.801) in the combined test and validation cohorts. Remarkably, analysis model generated by the biomarkers correctly predicted 7 out of 9 prospective patients having lung cancer, and 2 out of 3 patients as benign, which were further verified by the pathologist.
Conclusion:
This study identified four diagnostic biomarkers in serum samples with the potential to distinguish patients with lung cancer from healthy controls. This panel of serum proteins is valuable in suggesting the diagnosis of patients with an indeterminate pulmonary lesion, and potentially in predicting individuals at high risk for lung cancer. Further research is necessary to understand whether these have clinical implications for early detection of lung cancer.
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ORAL39.05 - Identification of miRNAs as Biomarkers for Early Diagnosis of Lung Cancers (ID 808)
17:28 - 17:39 | Author(s): W. Meng
- Abstract
Background:
Current clinical diagnostic methods lack the specificity in detecting lung cancer patients. The issue is critical for stage I & II patients as there are no available biomarkers to indicate which high-risk patients should undergo adjuvant therapy. There is considerable evidence that microRNA plays a very important role in lung carcinogenesis. We postulated that the expression pattern of multiple microRNAs (miRNAs) could aid clinicians in detecting cancer patients thus reducing the mortality of lung cancer.
Methods:
Differential expressed miRNAs were analyzed by miRNA microarrays in 15 paired non-small-cell lung cancer (NSCLC) tumors and distant normal tissues. The identified miRNAs were further validated by qRT-PCR using snap-frozen lung tissue samples collected from independent 22 patients with NSCLC. Classification analyses of miRNA expression data were performed by the Bayesian Compound Covariate predictor (BCCP). The expression levels of miR-141-5p, miR-301a-3p and miR-1244 were also analyzed by qRT-PCR in serum samples collected from 60 patients with lung cancer and 50 healthy controls.
Results:
A total of 41 miRNAs was identified with significantly elevated levels in patients with lung cancer by profiling microRNA array, of which 12 miRNAs were further validated in the independent sample cohort. Multiplexing analysis with the panel of 12 miRNAs generated the highest discriminatory power in separating NSCLC from normal tissues with an AUC of 0.915 (95% CI = 0.894-1.000; P <0.001). Leave-one-out cross-validation revealed the 85% sensitivity and 95% specificity at a cutoff score of 0.5. In addition, serum miR-1244 was significantly upregulated in an independent trial and could distinguish NSCLC from controls with 77.6% sensitivity and 78.7% specificity.
Conclusion:
Our 12-miRNA classifier might have potential clinical utility in discriminating NSCLC from healthy population.
