Prediction of recurrence for non-small cell lung cancer by combined analysis of molecular markers and 18F 2-fluoro-2-deoxy-D-glucose positron emission tomography.

ABSTRACT

PURPOSE: Numerous biomarkers have been reported to reflect prognosis in patients with non-small cell lung cancer, but most of them remain controversial in terms of the clinical benefits. The aim of this study is to establish a novel procedure in combined analyses of molecular markers and biomedical image for precise prediction for patient prognosis of non-small cell lung cancer. EXPERIMENTAL DESIGN: Molecular markers related to cell cycle and proliferation and (18)F 2-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) were retrospectively analyzed for their utility as prognostic parameters in 54 patients with non-small cell lung cancer. Expression of ten representative molecular markers (Glut-1, proliferating cell nuclear antigen, Ki-67, cyclin B1, cyclin D1, cyclin E, E2F-1, p21, p27, and p53) were immunohistochemically analyzed using tissue microarray. The maximum standardized uptake value (SUVmax) on FDG-PET was analyzed as a semiquantitative value of FDG uptake of the primary tumor. RESULTS: Several molecular markers were significantly correlated with some of clinicopathological parameters, whereas none of each marker were correlated with recurrence or survival. Hierarchical clustering analysis in combination of immunohistochemical analysis of molecular expressions and SUVmax divided them into three subgroups significantly different in two-year recurrent-free survival (Cluster A, 56.3%; B, 100%; C 93.8%). These clustering subgroups were also significantly correlated with disease recurrence (p=0.0282). CONCLUSIONS: Hierarchical clustering analysis, based on molecular markers and FDG accumulation, could be an efficient tool for prediction of recurrence and survival in patients with non-small cell lung cancer.