CTCs as Biomarkers of Lung Cancer
In lung cancer, the biological assessment of CTCs therapeutic target biomarkers, such as mutations of EGFR, could be a valid alternative to its determination in tumor samples. EGFR mutation analysis in CTC was shown to be concordant with data from tumoral samples (Maheswaran and Haber 2010). CTCs characteristics in lung cancer could help to stratify the patients and possibly drive future therapeutic strategies. CTC numbers correlate with prognosis in both early and advanced lung cancer. The availability of this noninvasive virtual biopsy could solve the problem of the increasing need of lung cancer biological samples for molecular studies.
This would help avoid the use of invasive procedures in order to obtain cytological specimens or small biopsies (Franco et al. 2012).
Gene Expression Profiling for Biomarkers of Lung Cancer
The most common genetic changes associated with lung cancer involve abnormalities of the genes and the proteins expressed by them, which regulate the cell cycle. Molecular networking of P53 and P16 tumor suppressor genes and K-Ras oncogene exerts a crucial impact on cell cycle regulation and appears to be of major clinical significance for lung cancer evaluation. P53, P16 and K-Ras evaluations have been used in lung cancer with particular focus on biological and clinical implications, as well as on new molecular approaches to the study of these genes.
Sixteen genes that correlated with survival among patients with NSCLC were identified by analyzing microarray data and risk scores (DUSP6, MMD, STAT1, ERBB3, and LCK) were selected for RT-PCR and decision-tree analysis (Chen et al. 2007). The five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC. BRCA1 and Xeroderma pigmentosum group G (XPG) are independent prognostic factors for both median survival and disease-free survival. High BRCA1 mRNA expression confers poor prognosis in early NSCLC, and the combination of high BRCA1 and low XPG expression still further increases the risk of shorter survival (Bartolucci et al. 2009). These findings can help optimize the customization of adjuvant chemotherapy.
A subset of 11 genes has been identified as a prognostic gene-expression signature and validated in multiple independent NSCLC microarray datasets (Navab et al. 2011). Functional annotation using protein-protein interaction analyses of these and published cancer gene-expression changes revealed prominent involvement of the focal adhesion and MAPK signaling pathways. Fourteen of the 46 genes were also differentially expressed in LCM primary tumor stroma compared with the matched normal lung. Six of these 14 genes could be induced by TGF-p1 in normal fibroblasts. These results establish the prognostic impact of changes in gene- expression of carcinoma-associated fibroblasts in NSCLC patients.
Because cigarette smoke injures the airway, efforts have been made to determine if gene expression in histologically normal large-airway epithelial cells obtained at bronchoscopy from smokers with suspicion of lung cancer could be used as a lung cancer biomarker. Using a training set and gene-expression profiles from Affymetrix HG-U133A microarrays, an 80-gene biomarker was identified that distinguishes smokers with and without lung cancer (Spira et al. 2007). This biomarker had ~90% sensitivity for stage 1 cancer across all subjects. Combining cytopathology of lower airway cells obtained at bronchoscopy with the biomarker yielded 95% sensitivity and a 95% negative predictive value. These findings indicate that gene expression in cytologically normal large-airway epithelial cells can serve as a lung cancer biomarker, potentially owing to a cancer-specific airwaywide response to cigarette smoke.