Citations

Abstract DNA microarray has been widely used in cancer research to better predict clinical outcomes and potentially improve patient management. The new approach provides accurate tumor classification and outcome predictions, such as tumor stage, metastatic status, and patient survival, and offers some hope for individualized medicine. However, growing evidence suggests that gene-based prediction is not stable and little is known about the prediction power of gene expression profiles compared with well-known clinical and pathologic predictors. This review summarized up-to-date publications in microarray-based lung cancer clinical outcome prediction and conducted secondary analyses for those with sufficient sample sizes and associated clinical information. Among the most commonly used analytic approaches, unsupervised clustering mainly recaptures tumor histology and provides variable degrees of prediction for tumor stage, lymph node status, or survival. Overall, most studies lack an independent validation. Supervised learning and testing generally offer a better prediction. Noted is that when conventional predictors of age, gender, stage, cell type, and tumor grade are considered collectively, the predictive advantage of the gene expression profiles diminishes. We conclude that outcome prediction from gene expression signatures selected by current analytic approaches can be mostly explained by well-known conventional predictors, particularly histologic subtype and grade of differentiation. A strategy for establishing independent or more accurate signatures is commented. (Cancer Epidemiol Biomarkers Prev 2006;15(11):2063–8)

Abstract A variety of melanoma antigen A (MAGE-A) genes are commonly detected in non–small cell lung cancers. Their biological function is not well characterized but may involve the regulation of apoptosis and cell cycle progression. We hypothesized that MAGE-A4 is involved in the regulation of apoptosis. To investigate this, expression of MAGE-A was evaluated. MAGE-A4 was expressed in 48% of non–small cell lung carcinomas. Ninety percent of lung carcinomas expressing MAGE-A4 were classified as squamous cell carcinomas and 10% were adenocarcinomas. Tumor-free surrounding lung tissue was negative for MAGE-A4. A molecular clone of MAGE-A4 derived from human lung cancer was stably expressed in human embryonic kidney cells (293 cells) to evaluate effects on cell death. Overexpression of MAGE-A4 increased apoptosis as measured by the apoptotic index (P < 0.0001) and caspase-3 activity (P < 0.002). Exposure to 25 μmol/L etoposide, a chemotherapeutic agent, increased the apoptotic effect (P < 0.0001). Furthermore, we show that MAGE-A4 silencing using a small interfering RNA approach results in decreased caspase-3 activity in the squamous cell lung cancer cell line H1703 by 58% (P = 0.0027) and by 24% (P = 0.028) in 293/MAGE-A4 cells. These findings suggest that MAGE-A4 expression may promote tumor cell death, sensitize malignancies to apoptotic stimuli, such as chemotherapeutic agents, and therefore may represent a tumor suppressor protein. (Cancer Res 2006; 66(9): 4693-700)