Predicting Metastatic Behaviour in Lung Adenocarcinoma

Ioannis Boukovinas, Paris A Kosmidis
2008 European Oncology & Haematology  
Lung cancer is the leading cause of cancer-related deaths for both men and women in industrialised countries. The relapse rate among patients with early-stage non-small-cell lung cancer (NSCLC) is 40% within five years after potentially curative treatment. 1 The current staging system for NSCLC is inadequate for predicting the outcome of treatment. To guide clinical decisions on the optimum treatment regimen, there is clearly a need to accurately identify patients at high risk of recurrent or
more » ... tastatic disease. Adenocarcinomas comprise 40% of NSCLC and they have shown an increasing incidence, particularly in females and nonsmokers. 2 Adenocarcinomas represent a heterogenous group of tumours and are known to exhibit the highest degree of morphological and clinical diversitiy, which reflects patient survival. 3 This review highlights the molecular characteristics that may influence metastatic behaviour in lung adenocarcinoma. Molecular Signatures It is now recognised that the ability of cells to gain metastatic potential is an intrinsic property of the primary tumour, which is substantiated by the high correlations between clinical outcome and gene expression profiles of a variety of primary tumours. Gene expression profiling with the use of microarrays or polymerase chain reaction (PCR) has been shown to estimate the prognosis for patients with lung cancer. Moreover, based on recent investigations into the nature of adenocarcinoma of the lung, it has become obvious that several distinct molecular diseases are lumped together under this morphological entity. More than one molecular signature of lung adenocarcinoma associated with metastasis and survival has been reported. 4-14 Chen et al. identified a five-gene signature that was significantly associated with relapse-free and overall survival among patients with NSCLC. 4 Also, Potti et al. developed a lung metagene model capable of predicting
doi:10.17925/eoh.2008.04.1.60 fatcat:bbuhlsqzfza7ncp6f5v4k4ahzq