Use tumor suppressor genes as biomarkers for diagnosis of non-small cell lung cancer

Lung cancer is the leading cause of death worldwide. Especially, non-small cell lung cancer (NSCLC) has higher mortality rate than the other cancers. The high mortality rate is partially due to lack of efficient biomarkers for detection, diagnosis and prognosis. To find high efficient biomarkers for clinical diagnosis of NSCLC patients, we used gene differential expression and gene ontology (GO) to define a set of 26 tumor suppressor (TS) genes. The 26 TS genes were down-expressed in tumor samples in cohorts GSE18842, GSE40419, and GSE21933 and at stages 2 and 3 in GSE19804, and 15 TS genes were significantly down-expressed in tumor samples of stage 1. We used S-scores and N-scores defined in correlation networks to evaluate positive and negative influences of these 26 TS genes on expression of other functional genes in the four independent cohorts and found that SASH1, STARD13, CBFA2T3 and RECK were strong TS genes that have strong accordant/discordant effects and network effects globally impacting the other genes in expression and hence can be used as specific biomarkers for diagnosis of NSCLC cancer. Weak TS genes EXT1, PTCH1, KLK10 and APC that are associated with a few genes in function or work in a special pathway were not detected to be differentially expressed and had very small S-scores and N-scores in all collected datasets and can be used as sensitive biomarkers for diagnosis of early cancer. Our findings are well consistent with functions of these TS genes. GSEA analysis found that these 26 TS genes as a gene set had high enrichment scores at stages 1, 2, 3 and all stages.

Differences in the early stage gene expression profiles of lung adenocarcinoma and lung squamous cell carcinoma

The discovery of lung carcinoma subtype-specific gene expression changes has the potential to elucidate the molecular differences and provide personalized therapeutic targets for these pathologies. The aim of the present study was to characterize the genetic profiles of the early stages (IA/IB) of two non-small cell lung cancer subtypes, adenocarcinoma (AD) and squamous cell carcinoma (SC). RNA-Seq gene expression data from The Cancer Genome Atlas was analyzed to compare the gene expression differences between AD and SC. The gene sets specific to each subtype were further analyzed to identify the enriched Gene Ontology terms, Kyoto Encyclopedia of Genes and Genomes pathways and biological functions. The results demonstrated that a unique set of genes (145 upregulated and 27 downregulated) was altered in AD, but not in SC; another set of genes (146 upregulated and 103 downregulated) was significantly altered in SC, but not in AD. Genes highly upregulated specifically in AD included albumin (1,732-fold), protein lin-28 homolog A, which is a positive regulator of cyclin-dependent kinase 2 (150-fold) and gastric lipase (81-fold). Genes highly upregulated specifically in SC included amelotin (618-fold), alcohol dehydrogenase 7 (57-fold), aclerosteosis (55-fold) and claudin-22 (54-fold). Several cancer/testis antigen family genes were notably upregulated in SC, but not in AD, whereas mucins were upregulated only in AD. Functional pathway analysis demonstrated that the dysregulation of genes associated with retinoid X receptors was common in AD and SC, genes associated with ‘lipid metabolism’ and ‘drug metabolism’ were dysregulated only in SC, whereas genes associated with ‘molecular transport’ and ‘cellular growth and proliferation’ were significantly enriched in AD specifically. These results reveal fundamental differences in the gene expression profiles of early-stage AD and SC. In addition, the present study identified molecular pathways that are uniquely associated with the pathogenesis of these subtypes.

Consensus of gene expression phenotypes and prognostic risk predictors in primary lung adenocarcinoma

Transcriptional profiling of lung adenocarcinomas has identified numerous gene expression phenotype (GEP) and risk prediction (RP) signatures associated with patient outcome. However, classification agreement between signatures, underlying transcriptional programs, and independent signature validation are less studied. We classified 2395 transcriptional adenocarcinoma profiles, assembled from 17 public cohorts, using 11 GEP and seven RP signatures, finding that 16 signatures were associated with patient survival in the total cohort and in multiple individual cohorts. For significant signatures, total cohort hazard ratios were ~2 in univariate analyses (mean=1.95, range=1.4-2.6). Strong classification agreement between signatures was observed, especially for predicted low-risk patients by adenocarcinoma-derived signatures. Expression of proliferation-related genes correlated strongly with GEP subtype classifications and RP scores, driving the gene signature association with prognosis. A three-group consensus definition of samples across 10 GEP classifiers demonstrated aggregation of samples with specific smoking patterns, gender, and EGFR/KRAS mutations, while survival differences were only significant when patients were divided into low- or high-risk. In summary, our study demonstrates a consensus between GEPs and RPs in lung adenocarcinoma through a common underlying transcriptional program. This consensus generalizes reported problems with current signatures in a clinical context, stressing development of new adenocarcinoma-specific single sample predictors for clinical use.

AURKA mRNA expression is an independent predictor of poor prognosis in patients with non-small cell lung cancer

Deregulation of mitotic spindle genes has been reported to contribute to the development and progression of malignant tumours. The aim of the present study was to explore the association between the expression profiles of Aurora kinases (AURKA, AURKB and AURKC), cytoskeleton-associated protein 5 (CKAP5), discs large-associated protein 5 (DLGAP5), kinesin-like protein 11 (KIF11), microtubule nucleation factor (TPX2), monopolar spindle 1 kinase (TTK), and β-tubulins (TUBB) and (TUBB3) genes and clinicopathological characteristics in human non-small cell lung carcinoma (NSCLC). Reverse transcription-quantitative polymerase chain reaction-based RNA gene expression profiles of 132 NSCLC and 44 adjacent wild-type tissues were generated, and Cox's proportional hazard regression was used to examine associations. With the exception of AURKC, all genes exhibited increased expression in NSCLC tissues. Of the 10 genes examined, only AURKA was significantly associated with prognosis in NSCLC. Multivariate Cox's regression analysis demonstrated that AURKA mRNA expression [hazard ratio (HR), 1.81; 95% confidence interval (CI), 1.16-2.84; P=0.009], age (HR, 1.03; 95% CI, 1.00-1.06; P=0.020), pathological tumour stage 2 (HR, 2.43; 95% CI, 1.16-5.10; P=0.019) and involvement of distal nodes (pathological node stage 2) (HR, 3.14; 95% CI, 1.24-7.99; P=0.016) were independent predictors of poor prognosis in patients with NSCLC. Poor prognosis of patients with increased AURKA expression suggests that those patients may benefit from surrogate therapy with AURKA inhibitors.

[Development of New Molecular EZH2 on Lung Cancer Invasion and Metastasis]

Lung cancer is a serious threat to human health malignancies upward trend in morbidity and mortality. It is hot topic to investigate the molecular mechanisms of lung cancer development and explore the new therapeutic targets. The underlying mechanism of EZH2 on lung cancer development will demonstrate the new pathway of lung cancer development, invasion and metastasis. The exploration and application of new targeted molecular will improve the survival rate and living quality of lung cancer patients in future.

Epithelial-mesenchymal transition-associated secretory phenotype predicts survival in lung cancer patients

In cancer cells, the process of epithelial-mesenchymal transition (EMT) confers migratory and invasive capacity, resistance to apoptosis, drug resistance, evasion of host immune surveillance and tumor stem cell traits. Cells undergoing EMT may represent tumor cells with metastatic potential. Characterizing the EMT secretome may identify biomarkers to monitor EMT in tumor progression and provide a prognostic signature to predict patient survival. Utilizing a transforming growth factor-β-induced cell culture model of EMT, we quantitatively profiled differentially secreted proteins, by GeLC-tandem mass spectrometry. Integrating with the corresponding transcriptome, we derived an EMT-associated secretory phenotype (EASP) comprising of proteins that were differentially upregulated both at protein and mRNA levels. Four independent primary tumor-derived gene expression data sets of lung cancers were used for survival analysis by the random survival forests (RSF) method. Analysis of 97-gene EASP expression in human lung adenocarcinoma tumors revealed strong positive correlations with lymph node metastasis, advanced tumor stage and histological grade. RSF analysis built on a training set (n = 442), including age, sex and stage as variables, stratified three independent lung cancer data sets into low-, medium- and high-risk groups with significant differences in overall survival. We further refined EASP to a 20 gene signature (rEASP) based on variable importance scores from RSF analysis. Similar to EASP, rEASP predicted survival of both adenocarcinoma and squamous carcinoma patients. More importantly, it predicted survival in the early-stage cancers. These results demonstrate that integrative analysis of the critical biological process of EMT provides mechanism-based and clinically relevant biomarkers with significant prognostic value.

Bronchial airway gene expression in smokers with lung or head and neck cancer

Cigarette smoking is the major cause of cancers of the respiratory tract, including non-small cell lung cancer (NSCLC) and head and neck cancer (HNC). In order to better understand carcinogenesis of the lung and upper airways, we have compared the gene expression profiles of tumor-distant, histologically normal bronchial biopsy specimens obtained from current smokers with NSCLC or HNC (SC, considered as a single group), as well as nonsmokers (NS) and smokers without cancer (SNC). RNA from a total of 97 biopsies was used for gene expression profiling (Affymetrix HG-U133 Plus 2.0 array). Differentially expressed genes were used to compare NS, SNC, and SC, and functional analysis was carried out using Ingenuity Pathway Analysis (IPA). Smoking-related cancer of the respiratory tract was found to affect the expression of genes encoding xenobiotic biotransformation proteins, as well as proteins associated with crucial inflammation/immunity pathways and other processes that protect the airway from the chemicals in cigarette smoke or contribute to carcinogenesis. Finally, we used the prediction analysis for microarray (PAM) method to identify gene signatures of cigarette smoking and cancer, and uncovered a 15-gene signature that distinguished between SNC and SC with an accuracy of 83%. Thus, gene profiling of histologically normal bronchial biopsy specimens provided insight into cigarette-induced carcinogenesis of the respiratory tract and gene signatures of cancer in smokers.

Gene expression profile of peripheral blood lymphocytes from renal cell carcinoma patients treated with IL-2, interferon-α and dendritic cell vaccine

Lymphocytes are a key component of the immune system and their differentiation and function are directly influenced by cancer. We examined peripheral blood lymphocyte (PBL) gene expression as a biomarker of illness and treatment effect using the Affymetrix Human Gene ST1 platform in patients with metastatic renal cell carcinoma (mRCC) who received combined treatment with IL-2, interferon-?-2a and dendritic cell vaccine. We examined gene expression, cytokine levels in patient serum and lymphocyte subsets as determined by flow cytometry (FCM). Pre-treatment PBLs from patients with mRCC exhibit a gene expression profile and serum cytokine profile consistent with inflammation and proliferation not found in healthy donors (HD). PBL gene expression from patients with mRCC showed increased mRNA of genes involved with T-cell and T_REG-cell activation pathways, which was also reflected in lymphocyte subset distribution. Overall, PBL gene expression post-treatment (POST) was not significantly different than pre-treatment (PRE). Nevertheless, treatment related changes in gene expression (post-treatment minus pre-treatment) revealed an increased expression of T-cell and B-cell receptor signaling pathways in responding (R) patients compared to non-responding (NR) patients. In addition, we observed down-regulation of T_REG-cell pathways post-treatment in R vs. NR patients. While exploratory in nature, this study supports the hypothesis that enhanced inflammatory cytotoxic pathways coupled with blunting of the regulatory pathways is necessary for effective anti-cancer activity associated with immune therapy. This type of analysis can potentially identify additional immune therapeutic targets in patients with mRCC.

Incorporating network structure in integrative analysis of cancer prognosis data

In high-throughput cancer genomic studies, markers identified from the analysis of single datasets may have unsatisfactory properties because of low sample sizes. Integrative analysis pools and analyzes raw data from multiple studies, and can effectively increase sample size and lead to improved marker identification results. In this study, we consider the integrative analysis of multiple high-throughput cancer prognosis studies. In the existing integrative analysis studies, the interplay among genes, which can be described using the network structure, has not been effectively accounted for. In network analysis, tightly connected nodes (genes) are more likely to have related biological functions and similar regression coefficients. The goal of this study is to develop an analysis approach that can incorporate the gene network structure in integrative analysis. To this end, we adopt an AFT (accelerated failure time) model to describe survival. A weighted least squares approach, which has low computational cost, is adopted for estimation. For marker selection, we propose a new penalization approach. The proposed penalty is composed of two parts. The first part is a group MCP penalty, and conducts gene selection. The second part is a Laplacian penalty, and smoothes the differences of coefficients for tightly connected genes. A group coordinate descent approach is developed to compute the proposed estimate. Simulation study shows satisfactory performance of the proposed approach when there exist moderate-to-strong correlations among genes. We analyze three lung cancer prognosis datasets, and demonstrate that incorporating the network structure can lead to the identification of important genes and improved prediction performance.

In vitro and in vivo antitumor activity of a novel pH-activated polymeric drug delivery system for doxorubicin

Background

Conventional chemotherapy agent such as doxorubicin (DOX) is of limited clinical use because of its inherently low selectivity, which can lead to systemic toxicity in normal healthy tissue.

Methods

A pH stimuli-sensitive conjugate based on polyethylene glycol (PEG) with covalently attachment doxorubicin via hydrazone bond (PEG-hyd-DOX) was prepared for tumor targeting delivery system. While PEG-DOX conjugates via amid bond (PEG-ami-DOX) was synthesized as control.

Results

The synthetic conjugates were confirmed by proton nuclear magnetic resonance (NMR) spectroscopy, the release profile of DOX from PEG-hyd-DOX was acid-liable for the hydrazone linkage between DOX and PEG, led to different intracellular uptake route; intracellular accumulation of PEG-hyd-DOX was higher than PEG-ami-DOX due to its pH-triggered profile, and thereby more cytotoxicity against MCF-7, MDA-MB-231 (breast cancer models) and HepG2 (hepatocellular carcinoma model) cell lines. Following the in vitro results, we xenografted MDA-MB-231 cell onto SCID mice, PEG-hyd-DOX showed stronger antitumor efficacy than free DOX and was tumor-targeting.

Conclusions

Results from these in vivo experiments were consistent with our in vitro results; suggested this pH-triggered PEG-hyd-DOX conjugate could target DOX to tumor tissues and release free drugs by acidic tumor environment, which would be potent in antitumor drug delivery.

Functional drug-gene interactions in lung cancer

Despite the dawn of the genomic information era, the challenges of cancer treatment remain formidable. Particularly for the most prevalent cancer types, including lung cancer, successful treatment of metastatic disease is rare and escalating costs for modern targeted drugs place an increasing strain on healthcare systems. Although powerful diagnostic tools to characterize individual tumor samples in great molecular detail are becoming rapidly available, the transformation of this information into therapy provides a major challenge. A fundamental difficulty is the molecular complexity of cancer cells that often causes drug resistance, but can also render tumors exquisitely sensitive to targeted agents. By using lung cancer as an example, we outline the principles that govern drug sensitivity and resistance from a genetic perspective and discuss how in vitro chemical-genetic screens can impact on patient stratification in the clinic.

Integrating contextual miRNA and protein signatures for diagnostic and treatment decisions in cancer

The promise of personalized medicine is highly dependent on the identification of biomarkers that inform diagnostic decisions and treatment options, as well as on the accurate, rapid and cost-effective detection and interpretation of these biomarkers. miRNAs, which are short noncoding regulatory RNAs, are rapidly emerging as a novel class of biomarkers with a unique set of biological and chemical properties that makes them very appealing candidates for theranostic applications in cancer. Since the utility of some protein-encoding gene biomarkers is already exploited in routine clinical practice, it will be important to identify areas in which miRNAs provide complementary or superior information to these existing (and other translational) biomarkers to enhance the diagnostic, prognostic and predictive power of molecular characterization of tumors. In this article, the challenges and opportunities for integration of miRNA-based assays in the clinical toolkit to improve care and management of patients afflicted with solid tumors will be discussed.

Impact of collection and storage of lung tumor tissue on whole genome expression profiling

Gene expression profiling could assist in revealing biomarkers of lung cancer prognosis and progression. The handling of biological samples may strongly influence global gene expression, a fact that has not been addressed in many studies. We sought to investigate the changes in gene expression that may occur as a result of sample processing time and conditions. Using Illumina Human WG-6 arrays, we quantified gene expression in lung carcinoma samples from six patients obtained at chest opening before and immediately after lung resection with storage in RNAlater [T1a((CO)) and T1b((LR))], after receipt of the sample for histopathology, placed in RNAlater [T2a((HP))]; snap frozen [T2b((HP.SF))]; or snap frozen and stored for 1 week [T2c((HP.SFA))], as well as formalin-fixed, paraffin-embedded (FFPE) block samples. Sampling immediately after resection closely represented the tissue obtained in situ, with only 1% of genes differing more than twofold [T1a((CO)) versus T1b((LR))]. Delaying tissue harvest for an average of 30 minutes from the operating theater had a significant impact on gene expression, with approximately 25% of genes differing between T1a((CO)) and T2a((HP)). Many genes previously identified as lung cancer biomarkers were altered during this period. Examination of FFPE specimens showed minimal correlation with fresh samples. This study shows that tissue collection immediately after lung resection with conservation in RNAlater is an optimal strategy for gene expression profiling.

Tissue sampling in lung cancer: a review in light of the MERIT experience

Lung cancer continues to present an enormous global burden of morbidity and mortality, despite an increasing therapeutic armamentarium of chemotherapy and targeted agents. Recent research efforts have been directed towards identifying predictors of response to treatment, in order to facilitate the selection of patients likely to obtain the greatest benefit from specific therapeutic interventions, with the ultimate goal of providing customized therapy. A strong scientific basis exists for the use of markers to identify patients who are most likely to respond to biological and targeted therapies, based on characteristics such as tumour genotype and histology. Biomarkers have the potential to aid in patient stratification (risk assessment), treatment-response identification (surrogate markers), or differential diagnosis (identifying individuals who are likely to respond well to specific therapies). Numerous trials have demonstrated correlations between molecular biomarkers and the outcome of treatment with targeted therapies such as epidermal growth factor inhibitor tyrosine-kinase inhibitors in patients with non-small-cell lung cancer (NSCLC). The recently completed MarkER Identification Trial (MERIT) found some evidence of a link between the molecular profile of a tumour and the clinical response to erlotinib in patients with relapsed NSCLC. However, MERIT also highlighted the difficulties in obtaining adequate samples for the various procedures involved in genetic analyses in clinical trials. Routine clinical practice brings its own challenges relating to biopsy techniques and tissue availability and this has implications for the application of molecular analyses in treatment decision-making. Applying the lessons learned from tissue sampling and molecular testing in MERIT and other major NSCLC trials will be essential in paving the way for the routine use of biomarker analyses in clinical practice.

Chronic obstructive pulmonary disease and lung cancer: new molecular insights

Both chronic obstructive pulmonary disease (COPD) and lung cancer are major causes of death worldwide. In most cases this reflects cigarette smoke exposure which is able to induce an inflammatory response in the airways of smokers. Indeed, COPD is characterized by lower airway inflammation, and importantly, the presence of COPD is by far the greatest risk factor for lung cancer amongst smokers. Cigarette smoke induces the release of many inflammatory mediators and growth factors including TGF-β, EGFR, IL-1, IL-8 and G-CSF through oxidative stress pathways and this inflammation may persist for decades after smoking cessation. Mucus production is also increased by these inflammatory mediators, further linking airway inflammation to an important mechanism of lung cancer. A greater understanding of the molecular and cellular pathobiology that distinguishes smokers with lung cancer from smokers with and without COPD is needed to unravel the complex molecular interactions between COPD and lung cancer. By understanding the common signalling pathways involved in COPD and lung cancer the hope is that treatments will be developed that not only treat the underlying disease process in COPD, but also reduce the currently high risk of developing lung cancer in these patients.

Microarray analysis of cutaneous squamous cell carcinomas reveals enhanced expression of epidermal differentiation complex genes

Gene expression profiles were determined for 12 cutaneous squamous cell carcinomas (SCC) removed from sun-exposed sites on nonimmunosuppressed patients. Gene expression in each SCC was compared to that in sun-exposed skin from the same patient using the Affymetrix HGU133 2.0 PlusGeneChip. We identified 440 genes with increased expression in SCC and 738 with decreased expression; overall we identified a large number of small changes in gene expression rather than a few marked changes that distinguished SCC from sun-exposed skin. Analyzing this robust data set according to biofunctional pathways using DAVID, transcriptional control elements using oPOSSUM, and chromosomal location using GSEA suggested genetic and epigenetic mechanisms of gene expression regulation in SCC. Some altered patterns of gene expression in SCC were consistent with regulation of spatially separated genes by a number of developmentally important transcription factors (forkhead, HMG, and homeo factors) that negatively regulated gene expression and to a few factors that positively regulated expression (Creb-1, NFkappaB, RelA, and Sp-1). We also found that coordinately enhanced expression of epidermal differentiation complex genes on chromosome 1q21 was a hallmark of SCC. A novel finding in our study was enhanced expression of keratin 13 in SCC, a result validated by immunohistochemical staining of an SCC tumor tissue array.

Understanding prognostic gene expression signatures in lung cancer

In non-small-cell lung cancer (NSCLC), molecular profiling of tumors has led to the identification of gene expression patterns that are associated with specific phenotypes and prognosis. Such correlations could identify early-stage patients who are at increased risk of disease recurrence and death after complete surgical resection and who might benefit from adjuvant therapy. Profiling may also identify aberrant molecular pathways that might lead to specific molecularly targeted therapies. The technology behind the capturing and correlating of molecular profiles with clinical and biologic endpoints have evolved rapidly since microarrays were first developed a decade ago. In this review, we discuss multiple methods that have been used to derive prognostic gene expression signatures in NSCLC. Despite the diversity in the approaches used, 3 main steps are followed. First, the expression levels of several hundred to tens of thousands of genes are quantified by microarray or quantitative polymerase chain reaction techniques; the data are then preprocessed, normalized, and possibly filtered. In the second step, expression data are combined and grouped by clustering, risk score generation, or other means, to generate a gene signature that correlates with a clinical outcome, usually survival. Finally, the signature is validated in datasets of independent cohorts. This review discusses the concepts and methodologies involved in these analytical steps, primarily to facilitate the understanding of reports on large dataset gene expression studies that focus on prognostic signatures in NSCLC.

From transcriptome analysis to therapeutic anti-CD40L treatment in the SOD1 model of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive loss of motor neurons. Using unbiased transcript profiling in an ALS mouse model, we identified a role for the co-stimulatory pathway, a key regulator of immune responses. Furthermore, we observed that this pathway is upregulated in the blood of 56% of human patients with ALS. A therapy using a monoclonal antibody to CD40L was developed that slows weight loss, delays paralysis and extends survival in an ALS mouse model. This work demonstrates that unbiased transcript profiling can identify cellular pathways responsive to therapeutic intervention in a preclinical model of human disease.

Smoking-Related Gene Expression in Laser Capture-Microdissected Human Lung

PURPOSE: Interindividual differences in quantitative expression could underlie a propensity for lung cancer. To determine precise individual gene expression signatures on a lung compartment-specific basis, we investigated the expression of carcinogen metabolism genes encoding cytochromes P450 (CYP) 1B1, 2A13, GSTP1, and a tumor suppressor gene p16 in laser capture-microdissected samples of human alveolar compartment (AC) and bronchial epithelial compartment (BEC) lung tissue from 62 smokers and nonsmokers. EXPERIMENTAL DESIGN: Tobacco exposure was determined by plasma nicotine, cotinine, and smoking history. Precise mRNA expression was determined using our RNA-specific qRT-PCR strategy, and correlated with detailed demographic and clinical characteristics. RESULTS: Several correlations of mRNA expression included (a) CYP1B1 in AC (positively with plasma nicotine level, P = 0.008; plasma cotinine level, P = 0.001), (b) GSTP1 in AC (positively with plasma cotinine level, P = 0.003), and (c) GSTP1 in BEC (negatively with smoke dose, P = 0.043; occupational risk, P = 0.019). CYP2A13 was rarely expressed in AC and not expressed in BEC. p16 expression was not correlated with any measured factor. For each gene, subjects showed expression that was individually concordant between these compartments. No clear association of mRNA expression with lung cancer risk was observed in this pilot analysis. CONCLUSIONS: The association between lung mRNA expression and tobacco exposure implies that gene-tobacco interaction is a measurable quantitative trait, albeit with wide interindividual variation. Gene expression tends to be concordant for alveolar and bronchial compartments for these genes in an individual, controlling for proximate tobacco exposure. (Clin Cancer Res 2009;15(24):7562-70).

Molecular predictive and prognostic markers in non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer death in the developed world. Platinum-based chemotherapy is the therapeutic foundation of treatment both in the metastatic and adjuvant setting and targeted therapies are entering standard treatment paradigms. However, many patients do not obtain benefit from cytotoxic agents or newer targeted therapies, but are still exposed to their toxic effects. Reliable biomarkers to select treatments for patients most likely to obtain benefit have, therefore, been an important focus for many research groups. In this paper, we review current predictive and prognostic biomarkers in NSCLC. We assess their potential clinical use and explore recent data pertaining to genome-wide approaches for treatment selection in NSCLC.

GWAS meets microarray: are the results of genome-wide association studies and gene-expression profiling consistent? Prostate cancer as an example

BACKGROUND

Genome-wide association studies (GWASs) and global profiling of gene expression (microarrays) are two major technological breakthroughs that allow hypothesis-free identification of candidate genes associated with tumorigenesis. It is not obvious whether there is a consistency between the candidate genes identified by GWAS (GWAS genes) and those identified by profiling gene expression (microarray genes).

METHODOLOGY/PRINCIPAL FINDINGS

We used the Cancer Genetic Markers Susceptibility database to retrieve single nucleotide polymorphisms from candidate genes for prostate cancer. In addition, we conducted a large meta-analysis of gene expression data in normal prostate and prostate tumor tissue. We identified 13,905 genes that were interrogated by both GWASs and microarrays. On the basis of P values from GWASs, we selected 1,649 most significantly associated genes for functional annotation by the Database for Annotation, Visualization and Integrated Discovery. We also conducted functional annotation analysis using same number of the top genes identified in the meta-analysis of the gene expression data. We found that genes involved in cell adhesion were overrepresented among both the GWAS and microarray genes.

CONCLUSIONS/SIGNIFICANCE

We conclude that the results of these analyses suggest that combining GWAS and microarray data would be a more effective approach than analyzing individual datasets and can help to refine the identification of candidate genes and functions associated with tumor development.

Molecular targets and gene therapy of lung cancer

Lung cancer is of great interest in human pathology because its apparent aggressiveness cannot be stopped by applied treatment procedures. The lack of highly specific screening tests prevents an early diagnosis of the disease. Insidious beginning and diverse and unclear clinical picture are responsible for the fact that most cases are diagnosed at advanced stages. An increasing number of patients and a short length of survival are additional factors that make this disease an imperative in the clinical practice, while vague and mutually dependent etiological factors represent a challenge in laboratory studies of the pathogenesis. The objective of this review is to describe some of the potential molecular targets available for manipulation in lung cancer; vector currently used by thoracic investigators to deliver therapy, and illustrated the experience with clinical trials of gene therapy in lung cancer. While gene therapy offers new hopes for lung cancer treatment, it is the need to develop valid clinical protocols of randomized trials before safety using to various lung cancer patient populations.