Each year, millions of pulmonary nodules are discovered by computed tomography and subsequently biopsied. Because most of these nodules are benign, many patients undergo unnecessary and costly invasive procedures. We present a 13-protein blood-based classifier that differentiates malignant and benign nodules with high confidence, thereby providing a diagnostic tool to avoid invasive biopsy on benign nodules. Using a systems biology strategy, we identified 371 protein candidates and developed a multiple reaction monitoring (MRM) assay for each. The MRM assays were applied in a three-site discovery study (n = 143) on plasma samples from patients with benign and stage IA lung cancer matched for nodule size, age, gender, and clinical site, producing a 13-protein classifier. The classifier was validated on an independent set of plasma samples (n = 104), exhibiting a negative predictive value (NPV) of 90%. Validation performance on samples from a nondiscovery clinical site showed an NPV of 94%, indicating the general effectiveness of the classifier. A pathway analysis demonstrated that the classifier proteins are likely modulated by a few transcription regulators (NF2L2, AHR, MYC, and FOS) that are associated with lung cancer, lung inflammation, and oxidative stress networks. The classifier score was independent of patient nodule size, smoking history, and age, which are risk factors used for clinical management of pulmonary nodules. Thus, this molecular test provides a potential complementary tool to help physicians in lung cancer diagnosis.

/pdf/a-blood-based-proteomic-classifier-for-the-molecular-45iu11wdd4.pdf

A Blood-Based Proteomic Classifier for the Molecular Characterization of Pulmonary Nodules

Diagnosis of lung cancer is performed using a plasmonic gold (pGOLD) chip through multiplexed near-infrared (NIR) detection of carcino-embryonic antigen (CEA), Cyfra21-1, and neuron-specific enolase (NSE) in the serum samples of patients. With ≈50-fold enhancement of NIR fluorescence, multiplexed microarray analysis of CEA, Cyfra21-1, and NSE in 10 μL of human serum or whole blood samples on pGOLD chip leads to markedly improved limit-of-quantification, limit-of-detection, reproducibility, and higher diagnostic sensitivity and specificity compared to traditional biochips and Luminex technology currently in use in hospitals.

High Performance, Multiplexed Lung Cancer Biomarker Detection on a Plasmonic Gold Chip

“PAULA’s” test (Protein Assays Utilizing Lung cancer Analytes) is a novel multiplex immunoassay blood test that incorporates both tumor antigens and autoantibodies to determine the risk that lung cancer (LC) is present in individuals from a high-risk population. The test’s performance characteristics were evaluated in a study using 380 retrospective clinical serum samples. PAULA’s test is performed on the Luminex xMAP technology platform, and detects a panel of 3 tumor antigens (CEA, CA-125, and CYFRA 21–1) and 1 autoantibody marker (NY-ESO-1). A training set (n = 230) consisting of 115 confirmed diagnoses of non-small cell lung carcinoma (NSCLC) cases and 115 age- and smoking history-matched controls was used to develop the LC predictive model. Data from an independent matched validation set (n = 150) was then used to evaluate the model developed, and determine the ability of the test to distinguish NSCLC cases from controls. The 4-biomarker panel was able to discriminate NSCLC cases from controls with 74% sensitivity, 80% specificity, and 0.81 AUC in the training set and with 77% sensitivity, 80% specificity, and 0.85 AUC in the independent validation set. The use of NY-ESO-1 autoantibodies substantially increased the overall sensitivity of NSCLC detection as compared to the 3 tumor markers alone. Overall, the multiplexed 4-biomarker panel assay demonstrated comparable performance to a previously employed 8-biomarker non-multiplexed assay. These studies confirm the value of using a mixed panel of tumor antigens and autoantibodies in the early detection of NSCLC in high-risk individuals. The results demonstrate that the performance of PAULA’s test makes it suitable for use as an aid to determine which high-risk patients need to be directed to appropriate noninvasive diagnostic follow-up testing, especially low-dose CT (LDCT).

/pdf/performance-of-a-multiplexed-dual-analyte-immunoassay-for-2473ti5ba1.pdf

Performance of a multiplexed dual analyte immunoassay for the early detection of non-small cell lung cancer.

Urine Proteome Profiling Predicts Lung Cancer from Control Cases and Other Tumors.

Accumulating evidence supports a role for exosomal protein in diagnosis. The purpose of this study was to identify the tumor-derived exosomal biomarkers in the serum that improve the diagnostic value in Chinese non-small cell lung cancer (NSCLC) patients. Serum exosomes were isolated from healthy donors (n = 46) and NSCLC patients (n = 125) by ultracentrifugation and were characterized using transmission electron microscopy, qNano, and immunoblotting. Proteomic profiles (by mass spectrometry) revealed multiple differentially expressed proteins in the healthy and NSCLC groups. The exosomal expression levels of alpha-2-HS-glycoprotein (AHSG) and extracellular matrix protein 1 (ECM1) increased significantly in the NSCLC patients compared to the healthy group. Alpha-2-HS-glycoprotein showed diagnostic values with a maximum area under the receiver operating characteristic curve (AUC) as 0.736 for NSCLC vs healthy individuals (P < .0001) and 0.682 for early stage NSCLC vs healthy individuals (P < .01). Extracellular matrix protein 1 showed the diagnostic capacity with AUC values of 0.683 (P < .001) and 0.656 (P < .05) in cancer and early stage NSCLC compared to healthy individuals. When AHSG was combined with ECM1, the AUCs were 0.795 and 0.739 in NSCLC and early stage patients, respectively. Taken together, the combination of AHSG, ECM1, and carcinoembryonic antigen improved the diagnostic potential of NSCLC. The diagnosis values were AUC of 0.938 for NSCLC and 0.911 for early stage NSCLC vs healthy individuals. Our results suggest that novel proteomic signatures found in serum exosomes of NSCLC patients show potential usefulness as diagnostic tools.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/cas.13862

Tumor-derived exosomal proteins as diagnostic biomarkers in non-small cell lung cancer.

The propensity for tumor biomarkers to be detected in serum at an early disease stage has become an area of interest for clinicians. This study aimed to evaluate the efficiency of 7 tumor biomarkers, namely, carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin 19 (CYFRA-21-1), alpha-fetoprotein, carbohydrate antigen-125 (CA-125), carbohydrate antigen-19.9 (CA-19.9), and ferritin, independently or in combination for the diagnosis of lung cancer. Electrochemiluminescence immunization was used to determine biomarker levels expressed in 530 patients with pulmonary disease and 229 healthy subjects. The observed levels of CEA, NSE, CYFRA-21-1, CA-125, and CA-19.9 in patients with pathologically confirmed lung cancer were significantly higher than those in patients with benign pulmonary disease or control subjects. Adenocarcinoma, squamous cell carcinoma, and small cell carcinoma of the lung were associated with the highest observed levels of CA-125, CYFRA-21-1, and NSE, respectively. Combining biomarkers successfully led to the diagnosis of lung cancer. CEA + NSE + CA-125 showed the highest sensitivity for small cell carcinoma, at 83.33%, whereas CEA + NSE + CYFRA-21-1 + CA-125 showed 94.11% sensitivity for squamous cell carcinoma. The combination of 6 biomarkers, namely, CEA + NSE + CYFRA-21-1 + CA-125 + ferritin + CA-19.9, showed 80.49% sensitivity for adenocarcinoma. Combining biomarkers significantly aided in the diagnosis of lung cancer. However, this increased sensitivity on combination was accompanied by a decreased specificity for lung cancer subtypes. Combining biomarkers appropriately increases their sensitivity and helps with the diagnosis of lung cancer.

/pdf/biomarkers-in-the-lung-cancer-diagnosis-a-clinical-3liacjl8ek.pdf

Li X

Papers

Biomarkers in the lung cancer diagnosis: a clinical perspective.