Screening for lung cancer

Radioimmunoimaging with mixed monoclonal antibodies of nude mice bearing human lung adenocarcinoma xenografts

The present study was conducted to evaluate radioimmunoimaging (RII) and in vivo distribution of mixed antibodies 99mTc-EGFR-mAb and 99mTc-CD44- mAb in nude mice bearing human lung adenocarcinoma xenografts. Single and mixed applications of the two radiolabeled monoclonal antibodies (mAbs) were compared. Direct labeling of 99mTc was applied to radiolabel the EGFR and CD44 mAbs. The properties of the radiolabeled antibodies were then characterized. RII and assessment of the distribution of the antibodies in nude mice bearing lung adenocarcinoma xenografts were achieved by applying separate and combined doses of 99mTc-EGFR-mAb and 99mTc-CD44-mAb. The labeling rates of 99mTc for EGFR-mAb and CD44-mAb were 91.5% ±3.8% and 92.3% ± 4.1% respectively, with specific activities of 2.8 and 2.9 MBq/μg, respectively, and radiochemical purities (RCP) of 96.5% and 96.2%. The radioactivity uptake of the combined application of both radiolabeled antibodies was clearly higher than with a single application of either alone. The relative values of target-to-nontarget (T/ NT) measured through the regional interest (ROI) technique were 5.59 ± 0.42 (mixed antibodies), 2.78 ±0.20 (99mTc-EGFR-mAb), and 2.28 ± 0.16 (99mTc-CD44-mAb) in the RII. The body distribution of the radiolabeled antibodies and their imaging results were basically identical. Application of the mixed antibodies with 99mTc- EGFR-mAb and 99mTc-CD44-mAb can increase the radioactivity uptake of tumor tissue, leading to more ideal target-to-nontarget ratios, and therefore superior results.

Serum microRNA biomarkers for detection of non-small cell lung cancer

Non small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality world-wide and the majority of cases are diagnosed at late stages of disease. There is currently no cost-effective screening test for NSCLC, and the development of such a test is a public health imperative. Recent studies have suggested that chest computed tomography screening of patients at high risk of lung cancer can increase survival from disease, however, the cost effectiveness of such screening has not been established. In this Phase I/II biomarker study we examined the feasibility of using serum miRNA as biomarkers of NSCLC using RT-qPCR to examine the expression of 180 miRNAs in sera from 30 treatment naive NSCLC patients and 20 healthy controls. Receiver operating characteristic curves (ROC) and area under the curve were used to identify differentially expressed miRNA pairs that could distinguish NSCLC from healthy controls. Selected miRNA candidates were further validated in sera from an additional 55 NSCLC patients and 75 healthy controls. Examination of miRNA expression levels in serum from a multi-institutional cohort of 50 subjects (30 NSCLC patients and 20 healthy controls) identified differentially expressed miRNAs. A combination of two differentially expressed miRNAs miR-15b and miR-27b, was able to discriminate NSCLC from healthy controls with sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 100% in the training set. Upon further testing on additional 130 subjects (55 NSCLC and 75 healthy controls), this miRNA pair predicted NSCLC with a specificity of 84% (95% CI 0.73–0.91), sensitivity of 100% (95% CI; 0.93–1.0), NPV of 100%, and PPV of 82%. These data provide evidence that serum miRNAs have the potential to be sensitive, cost-effective biomarkers for the early detection of NSCLC. Further testing in a Phase III biomarker study in is necessary for validation of these results.

Identification of lung cancer patients by serum protein profiling using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

OBJECTIVE

Diagnosis of lung cancer at an early disease stage is important for successful treatment and improving the outcome of patients. To improve its prognosis, we attempted to explore novel tools for screening serum biomarkers to distinguish lung cancer from healthy individuals by serum protein profiles and a classification tree algorithm.

METHODS

Serum samples were applied to metal affinity protein chips to generate mass spectra by surface-enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry. Protein peak identification and clustering were performed using the Biomarker Wizard software. Proteomic spectra of serum samples from 89 lung cancer patients and age- and sex-matched 68 healthy individuals were used as a training set and a classification tree with 3 distinct protein masses was generated by using Biomarker Pattern software. The validity of the classification tree was then challenged with a blind test set including another 62 lung cancer patients and 34 healthy individuals. We additionally determined Cyfra21-1 and carcinoembryonic antigen in all the serum samples included in this study using an electrochemiluminescent immunoassay.

RESULTS

The software identified an average of 48 mass peaks/spectrum and 3 of the identified peaks at 5808, 5971, and 7779 d were used to construct the classification tree. The classification tree separated effectively lung cancer from healthy individuals, achieving a sensitivity of 91% (81 of 89) and a specificity of 97% (66 of 68). The blind test challenged the model with a sensitivity of 89% (55 of 62) and a specificity of 91% (31 of 34), and a positive predictive value of 90% (86 of 96), respectively. The specificity of Cyfra21-1 and the sensitivity provided by Cyfra21-1 and carcinoembryonic antigen used individually or in combination were significantly lower than that of the SELDI marker pattern (P < 0.05 or P < 0.005, respectively).

CONCLUSION

The results suggest that SELDI time-of-flight mass spectrometry technique can correctly distinguish lung cancer patients from healthy individuals and shows great potential for the development of a screening test for the detection of lung cancer.