Serum Protein Biomarkers of Lung Cancer

There is an enormous unmet medical need related to the diagnosis of lung cancer in the earliest stages when it is most treatable but no approved blood test for lung cancer is yet available. Serum biomarkers that could aid clinicians in making case management decisions about lung cancer would be extremely useful. Two proteomic platforms and literature search have enabled selection of candidate serum biomarkers for the diagnosis of lung cancer (Patz et al. 2007). Classification and Regression Tree (CART) analysis was used to select a panel of four serum protein biomarkers for prediction of lung cancer in patients: (1) carcinoembryonic antigen; (2) retinol binding protein; (3) alpha1-antitrypsin; and (4) squamous cell carcinoma antigen. These were collectively found to correctly classify the majority of lung cancer and control patients in the training set (sensitivity 89.3% and specificity 84.7%). These biomarkers also accurately classified patients in the independent validation set (sensitivity 77.8% and specificity 75.4%). Approximately 90% of patients who fell into any one of three groupings in the CART analysis had lung cancer. Thus the panel of four serum proteins is valuable in the diagnosis of lung cancer. The data may be useful for treating patients with an indeterminate pulmonary lesion, and potentially in predicting individuals at high risk for lung cancer. Laboratory Corporation of America plans to market the serum protein assay, which could serve as a useful complement to imaging studies such as CT scan to differentiate cancers from benign nodules.

An efficient strategy, consisting of SELDI-TOF-MS analysis, HPLC purification, MALDI-TOF-MS trace and LC-MS/MS identification is useful for detection of protein biomarkers. Apolipoprotein C-I, haptoglobin alpha-1 chain, and S100A4 have been identified as potential proteomic biomarkers of NSCLC but further studies with larger sample sizes will be needed to validify these (Yang et al. 2009a).

Each year, millions of pulmonary nodules are discovered by CT and subsequently biopsied. Because most of these nodules are benign, many patients undergo unnecessary and costly invasive procedures. A 13-protein blood-based classifier helps to differentiate malignant and benign nodules with high confidence, thereby providing a diagnostic tool to avoid invasive biopsy on benign nodules (Li et al. 2013). Using a systems biology strategy, the authors of the study identified several protein candidates and developed a multiple reaction monitoring (MRM) assay for each, which was applied in a discovery study 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 (set of biomarkers) was validated on an independent set of plasma samples, 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. This test is commercialized as Xpresys Lung (Integrated Diagnostics) to simultaneously measure multiple circulating proteins associated with lung cancer by the most advanced mass spectrometry. This test is a complementary tool in diagnosis of lung cancer.

Human primary lung adenocarcinoma tumors have been analyzed using global MS to elucidate the biological mechanisms behind relapse after surgery (Pernemalm et al. 2013). In total, >3000 proteins were identified with high confidence and supervised multivariate analysis was used to select 132 proteins separating the prognostic groups. Based on in-depth bioinformatics analysis, the authors hypothesized that the tumors with poor prognosis had a higher glycolytic activity and HIF activation. By measuring the bioenergetic cellular index of the tumors, they could detect a higher dependency of glycolysis among the tumors with poor prognosis. Further, they could also detect an up-regulation of HIF1a mRNA expression in tumors with early relapse. Finally, they selected three proteins that were upregulated in the poor prognosis group (cathepsin D, ENO1, and VDAC1) to confirm that the proteins indeed originated from the tumor and not from a stromal or inflammatory component. Overall, these findings show how in-depth analysis of clinical material can lead to an increased understanding of the molecular mechanisms underlying tumor progression. This study shows a functional coupling between high glycolytic activity and postsurgical relapse of adenocarcinoma of the lung. Protein level changes detected in this study could serve as starting point for discovery of predictive biomarkers for metabolic treatment options in lung cancer.

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