The role of post-operative radiotherapy (PORT) is controversial for some cancer sites. In the absence of large randomized controlled trials, survival prediction models can help estimate the predicted benefit of PORT for specific settings. The purpose of this study was to compare the performance of two types of prediction models for estimating the benefit of PORT for two cancer sites. Using data from the Surveillance, Epidemiology, and End Results database, we constructed prediction models for gallbladder (GB) cancer and non-small cell lung cancer (NSCLC) using Cox proportional hazards (CPH) and Random Survival Forests (RSF). We compared validation measures for discrimination and found that both the CPH and RSF models had comparable C-indices. For GB cancer, PORT was associated with improved survival for node positive patients, and for NSCLC, PORT was associated with a survival benefit for patients with N2 disease.
To investigate the expression and prognostic significance of RSF-1 in gastric adenocarcinoma.
RSF-1 expression was analyzed using immunohistochemical staining on tissue samples from a consecutive series of 287 gastric adenocarcinoma patients who underwent tumor resections between 2003 and 2006.The relationship between RSF-1 expression, clinicopathological factors, and patient survival was investigated.
Immunohistochemical staining indicated that RSF-1 is highly expressed in 52.6% of gastric adenocarcinomas. RSF-1 expression levels were closely associated with tumor size, histological differentiation, tumor stage, and lymph node involvement. Kaplan-Meier survival analysis showed that high RSF-1 expression exhibited a significant correlation with poor prognosis for gastric adenocarcinoma patients. Multivariate analysis revealed that RSF-1 expression is an independent prognostic parameter for the overall survival rate of gastric adenocarcinoma patients.
Our data suggest that RSF-1 plays an important role in gastric adenocarcinoma progression and that high RSF-1 expression predicts an unfavorable prognosis in gastric adenocarcinoma patients.
RSF-1; gastric adenocarcinoma; prognosis; survival; diagnosis
Adenocarcinoma is the most common type of lung cancer, the leading cause of cancer deaths in the world. Early detection is the key to improve the survival of lung adenocarcinoma patients. We have previously shown that microRNAs were stably present in sputum and could be applied to diagnosis of lung cancer. The aim of this study was to develop a panel of microRNAs that can be used as highly sensitive and specific sputum markers for early detection of lung adenocarcinoma. This study contained three phases: (1) marker discovery using microRNA profiling on paired normal and tumor lung tissues from 20 patients with lung adenocarcinoma; (2) marker optimization by real-time RT-qPCR on sputum of a case-control cohort consisting of 36 cancer patients and 36 health individuals; and (3) validation on an independent set of 64 lung cancer patients and 58 cancer-free subjects. From the surgical tissues, seven microRNAs with significantly altered expression were identified, of which “four” were overexpressed and “three” were underexpressed in all 20 tumors. On the sputum samples of the case-control cohort, four (miR-21, miR-486, miR-375, and miR-200b) of the seven microRNAs were selected, which in combination produced the best prediction in distinguishing lung adenocarcinoma patients from normal subjects with 80.6% sensitivity and 91.7% specificity. Validation of the marker panel in the independent populations confirmed the sensitivity and specificity that provided a significant improvement over any single one alone. The sputum markers demonstrated the potential of translation to laboratory settings for improving the early detection of lung adenocarcinoma.
MicroRNA; sputum; lung adenocarcinoma; real-time RT-qPCR; diagnosis
Survival of patients with lung cancer could be significantly prolonged should the disease be diagnosed early. Growing evidence indicates that the immune response in the form of autoantibodies to developing cancer is present before clinical presentation. We used a phage-displayed antibody library to select for recombinant scFvs that specifically bind to lung cancer-associated IgM autoantibodies. We selected for scFv recombinant antibodies reactive with circulating IgM autoantibodies found in the serum of patients with early stage lung adenocarcinoma but not matched controls. Discriminatory performance of 6 selected scFvs was validated in an independent set of serum from stage 1 adenocarcinoma and matching control groups using two independent novel methods developed for this application. The panel of 6 selected scFvs predicted cancer based on seroreactivity value with sensitivity of 0.8 and specificity of 0.87. Receiver Operative Characteristic curve (ROC) for combined 6 scFv has an AUC of 0.88 (95%CI, 0.76–1.0) as determined by fluorometric microvolume assay technology (FMAT) The ROC curve generated using a homogeneous bridging Mesa Scale Discovery (MSD) assay had an AUC of 0.72 (95% CI, 0.59–0.85). The panel of all 6 antibodies demonstrated better discriminative power than any single scFv alone. The scFv panel also demonstrated the association between a high score - based on seroreactivity - with poor survival. Selected scFvs were able to recognize lung cancer associated IgM autoantibodies in patient serum as early as 21 months before the clinical presentation of disease. The panel of antibodies discovered represents a potential unique non-invasive molecular tool to detect an immune response specific to lung adenocarcinoma at an early stage of disease.
Although several prognostic signatures have been developed in lung cancer, their application in clinical practice has been limited because they have not been validated in multiple independent data sets. Moreover, the lack of common genes between the signatures makes it difficult to know what biological process may be reflected or measured by the signature. By using classical data exploration approach with gene expression data from patients with lung adenocarcinoma (n = 186), we uncovered two distinct subgroups of lung adenocarcinoma and identified prognostic 193-gene gene expression signature associated with two subgroups. The signature was validated in 4 independent lung adenocarcinoma cohorts, including 556 patients. In multivariate analysis, the signature was an independent predictor of overall survival (hazard ratio, 2.4; 95% confidence interval, 1.2 to 4.8; p = 0.01). An integrated analysis of the signature revealed that E2F1 plays key roles in regulating genes in the signature. Subset analysis demonstrated that the gene signature could identify high-risk patients in early stage (stage I disease), and patients who would have benefit of adjuvant chemotherapy. Thus, our study provided evidence for molecular basis of clinically relevant two distinct two subtypes of lung adenocarcinoma.
Lung cancer remains the leading cause of cancer-related deaths worldwide. The recurrence rate ranges from 35–50% among early stage non-small cell lung cancer patients. To date, there is no fully-validated and clinically applied prognostic gene signature for personalized treatment.
From genome-wide mRNA expression profiles generated on 256 lung adenocarcinoma patients, a 12-gene signature was identified using combinatorial gene selection methods, and a risk score algorithm was developed with Naïve Bayes. The 12-gene model generates significant patient stratification in the training cohort HLM & UM (n = 256; log-rank P = 6.96e-7) and two independent validation sets, MSK (n = 104; log-rank P = 9.88e-4) and DFCI (n = 82; log-rank P = 2.57e-4), using Kaplan-Meier analyses. This gene signature also stratifies stage I and IB lung adenocarcinoma patients into two distinct survival groups (log-rank P<0.04). The 12-gene risk score is more significant (hazard ratio = 4.19, 95% CI: [2.08, 8.46]) than other commonly used clinical factors except tumor stage (III vs. I) in multivariate Cox analyses. The 12-gene model is more accurate than previously published lung cancer gene signatures on the same datasets. Furthermore, this signature accurately predicts chemoresistance/chemosensitivity to Cisplatin, Carboplatin, Paclitaxel, Etoposide, Erlotinib, and Gefitinib in NCI-60 cancer cell lines (P<0.017). The identified 12 genes exhibit curated interactions with major lung cancer signaling hallmarks in functional pathway analysis. The expression patterns of the signature genes have been confirmed in RT-PCR analyses of independent tumor samples.
The results demonstrate the clinical utility of the identified gene signature in prognostic categorization. With this 12-gene risk score algorithm, early stage patients at high risk for tumor recurrence could be identified for adjuvant chemotherapy; whereas stage I and II patients at low risk could be spared the toxic side effects of chemotherapeutic drugs.
Tobacco smoking is responsible for over 90% of lung cancer cases, and yet the precise molecular alterations induced by smoking in lung that develop into cancer and impact survival have remained obscure.
We performed gene expression analysis using HG-U133A Affymetrix chips on 135 fresh frozen tissue samples of adenocarcinoma and paired noninvolved lung tissue from current, former and never smokers, with biochemically validated smoking information. ANOVA analysis adjusted for potential confounders, multiple testing procedure, Gene Set Enrichment Analysis, and GO-functional classification were conducted for gene selection. Results were confirmed in independent adenocarcinoma and non-tumor tissues from two studies. We identified a gene expression signature characteristic of smoking that includes cell cycle genes, particularly those involved in the mitotic spindle formation (e.g., NEK2, TTK, PRC1). Expression of these genes strongly differentiated both smokers from non-smokers in lung tumors and early stage tumor tissue from non-tumor tissue (p<0.001 and fold-change >1.5, for each comparison), consistent with an important role for this pathway in lung carcinogenesis induced by smoking. These changes persisted many years after smoking cessation. NEK2 (p<0.001) and TTK (p = 0.002) expression in the noninvolved lung tissue was also associated with a 3-fold increased risk of mortality from lung adenocarcinoma in smokers.
Our work provides insight into the smoking-related mechanisms of lung neoplasia, and shows that the very mitotic genes known to be involved in cancer development are induced by smoking and affect survival. These genes are candidate targets for chemoprevention and treatment of lung cancer in smokers.
Local recurrence is the major manifestation of treatment failure in patients with operable laryngeal carcinoma. Established clinicopathological factors cannot sufficiently predict patients that are likely to recur after treatment. Additional tools are therefore required to accurately identify patients at high risk for recurrence. This study attempts to identify and independently validate gene expression models, prognostic of disease-free survival (DFS) in operable laryngeal cancer.
Materials and Methods
Using Affymetrix U133A Genechips, we profiled fresh-frozen tumor tissues from 66 patients with laryngeal cancer treated locally with surgery. We applied Cox regression proportional hazards modeling to identify multigene predictors of recurrence. Gene models were then validated in two independent cohorts of 54 and 187 patients (fresh-frozen and formalin-fixed tissue validation sets, respectively).
We focused on genes univariately associated with DFS (p<0.01) in the training set. Among several models comprising different numbers of genes, a 30-probe set model demonstrated optimal performance in both the training (log-rank, p<0.001) and 1st validation (p = 0.010) sets. Specifically, in the 1st validation set, median DFS as predicted by the 30-probe set model, was 34 and 80 months for high- and low-risk patients, respectively. Hazard ratio (HR) for recurrence in the high-risk group was 3.87 (95% CI 1.28–11.73, Wald's p = 0.017). Testing the expression of selected genes from the above model in the 2nd validation set, with qPCR, revealed significant associations of single markers, such as ACE2, FLOT1 and PRKD1, with patient DFS. High PRKD1 remained an unfavorable prognostic marker upon multivariate analysis (HR = 2.00, 95% CI 1.28–3.14, p = 0.002) along with positive nodal status.
We have established and validated gene models that can successfully stratify patients with laryngeal cancer, based on their risk for recurrence. It seems worthy to prospectively validate PRKD1 expression as a laryngeal cancer prognostic marker, for routine clinical applications.
Chronic obstructive pulmonary disease (COPD) is a major public health problem. The aim of this study was to identify genes involved in emphysema severity in COPD patients.
Gene expression profiling was performed on total RNA extracted from non-tumor lung tissue from 30 smokers with emphysema. Class comparison analysis based on gas transfer measurement was performed to identify differentially expressed genes. Genes were then selected for technical validation by quantitative reverse transcriptase-PCR (qRT-PCR) if also represented on microarray platforms used in previously published emphysema studies. Genes technically validated advanced to tests of biological replication by qRT-PCR using an independent test set of 62 lung samples.
Class comparison identified 98 differentially expressed genes (p < 0.01). Fifty-one of those genes had been previously evaluated in differentiation between normal and severe emphysema lung. qRT-PCR confirmed the direction of change in expression in 29 of the 51 genes and 11 of those validated, remaining significant at p < 0.05. Biological replication in an independent cohort confirmed the altered expression of eight genes, with seven genes differentially expressed by greater than 1.3 fold, identifying these as candidate determinants of emphysema severity.
Gene expression profiling of lung from emphysema patients identified seven candidate genes associated with emphysema severity including COL6A3, SERPINF1, ZNHIT6, NEDD4, CDKN2A, NRN1 and GSTM3.
Metastasis-related recurrence often occurs in hepatocellular carcinoma (HCC) patients who receive curative therapies. At present, it is challenging to identify patients with high risk of recurrence, which would warrant additional therapies. In this study, we sought to analyze a recently developed metastasis-related gene signature for its utility in predicting HCC survival using two independent cohorts consisting of a total of 386 patients who received radical resection. Cohort-1 contained 247 predominantly HBV-positive cases analyzed with an Affymetrix platform, while cohort-2 contained 139 cases with mixed etiology analyzed with the NCI Oligo Set microarray platform. We employed a survival risk prediction algorithm with training, test, and independent cross-validation strategies and found that the gene signature is predictive of overall and disease-free survival. Importantly, risk was significantly predicted independently of clinical characteristics and microarray platform. In addition, survival prediction was successful in patients with early disease, such as small (<5 cm in diameter) and solitary tumors, and the signature predicted particularly well for early recurrence risk (<2 years), especially when combined with serum alpha fetoprotein or tumor staging. In conclusion, we have demonstrated in two independent cohorts with mixed etiologies and ethnicity that the metastasis gene signature is a useful tool to predict HCC outcome, suggesting the general utility of this classifier. We recommend the use of this classifier as a molecular diagnostic test to assess the risk that an HCC patient will develop tumor relaps within 2 years after surgical resection, particularly for those with early stage tumors and solitary presentation.
Early Recurrence; Prognosis; Gene Signature; HCC
More accurate prognostic assessment of patients with neuroblastoma is required to improve the choice of risk-related therapy. The aim of this study is to develop and validate a gene expression signature for improved outcome prediction.
Fifty-nine genes were carefully selected based on an innovative data-mining strategy and profiled in the largest neuroblastoma patient series (n=579) to date using RT-qPCR starting from only 20 ng of RNA. A multigene expression signature was built using 30 training samples, tested on 313 test samples and subsequently validated in a blind study on an independent set of 236 additional tumours.
The signature accurately classifies patients with respect to overall and progression-free survival (p<0·0001). The signature has a performance, sensitivity, and specificity of 85·4% (95%CI: 77·7–93·2), 84·4% (95%CI: 66·5–94·1), and 86·5% (95%CI: 81·1–90·6), respectively to predict patient outcome. Multivariate analysis indicates that the signature is a significant independent predictor after controlling for currently used riskfactors. Patients with high molecular risk have a higher risk to die from disease and for relapse/progression than patients with low molecular risk (odds ratio of 19·32 (95%CI: 6·50–57·43) and 3·96 (95%CI: 1·97–7·97) for OS and PFS, respectively). Patients with increased risk for adverse outcome can also be identified within the current treatment groups demonstrating the potential of this signature for improved clinical management. These results were confirmed in the validation study in which the signature was also independently statistically significant in a model adjusted for MYCN status, age, INSS stage, ploidy, INPC grade of differentiation, and MKI. The high patient/gene ratio (579/59) underlies the observed statistical power and robustness.
A 59-gene expression signature predicts outcome of neuroblastoma patients with high accuracy. The signature is an independent risk predictor, identifying patients with increased risk in the current clinical risk groups. The applied method and signature is suitable for routine lab testing and ready for evaluation in prospective studies.
The Belgian Foundation Against Cancer, found of public interest (project SCIE2006-25), the Children Cancer Fund Ghent, the Belgian Society of Paediatric Haematology and Oncology, the Belgian Kid’s Fund and the Fondation Nuovo-Soldati (JV), the Fund for Scientific Research Flanders (KDP, JH), the Fund for Scientific Research Flanders (grant number: G•0198•08), the Institute for the Promotion of Innovation by Science and Technology in Flanders, Strategisch basisonderzoek (IWT-SBO 60848), the Fondation Fournier Majoie pour l’Innovation, the Instituto Carlos III,RD 06/0020/0102 Spain, the Italian Neuroblastoma Foundation, the European Community under the FP6 (project: STREP: EET-pipeline, number: 037260), and the Belgian program of Interuniversity Poles of Attraction, initiated by the Belgian State, Prime Minister's Office, Science Policy Programming.
About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. The purpose of this study is to develop and validate a novel gene-expression signature that can predict tumor recurrence of stage I NSCLC patients. Cox proportional hazards regression analysis was performed to identify recurrence-related genes and a partial Cox regression model was used to generate a gene signature of recurrence in the training dataset −142 stage I lung adenocarcinomas without adjunctive therapy from the Director's Challenge Consortium. Four independent validation datasets, including GSE5843, GSE8894, and two other datasets provided by Mayo Clinic and Washington University, were used to assess the prediction accuracy by calculating the correlation between risk score estimated from gene expression and real recurrence-free survival time and AUC of time-dependent ROC analysis. Pathway-based survival analyses were also performed. 104 probesets correlated with recurrence in the training dataset. They are enriched in cell adhesion, apoptosis and regulation of cell proliferation. A 51-gene expression signature was identified to distinguish patients likely to develop tumor recurrence (Dxy = −0.83, P<1e-16) and this signature was validated in four independent datasets with AUC >85%. Multiple pathways including leukocyte transendothelial migration and cell adhesion were highly correlated with recurrence-free survival. The gene signature is highly predictive of recurrence in stage I NSCLC patients, which has important prognostic and therapeutic implications for the future management of these patients.
Lung cancer has one of the lowest survival outcomes of any cancer because more then two-thirds of patients are diagnosed when curative treatment is not possible. The challenge is to help earlier diagnosis of lung cancer and hence improve prognosis.
To derive and validate an algorithm incorporating information on symptoms, to estimate the absolute risk of having lung cancer
Design and setting
Cohort study of 375 UK QResearch® general practices for development, and 189 forvalidation.
Selected patients were aged 30-84 years and free of lung cancer at baseline and haemoptysis, loss of appetite, orweight loss in previous 12 months. Primary outcome was incident diagnosis of lung cancer recorded in the next 2 years. Risk factors examined were: haemoptysis, appetite loss, weight loss, cough, dyspnoea, tiredness, hoarseness, smoking, body mass index, deprivation score, family history of lung cancer, other cancers, asthma, chronic obstructive airways disease, pneumonia, asbestos exposure, and anaemia. Cox proportional hazards models with age as the underlying time variable were used to develop separate risk equations in males and females. Measures of calibration and discrimination assessed performance in the validation cohort.
There were 3785 incident cases of lung cancer arising from 4 289 282 person-years in the derivation cohort. Independent predictors were haemoptysis, appetite loss, weight loss, cough, body mass index, deprivation score, smoking status, chronic obstructive airways disease, anaemia, and prior cancer (females only). On validation, the algorithms explained 72% of the variation. The receiver operating characteristic (ROC) statistics were 0.92 for both females and males. The D statistic was 3.25 for females and 3.29 for males. The 10% of patients with the highest predicted risks included 77% of all lung cancers diagnosed over the subsequent 2 years.
The algorithm has good discrimination and calibration and could potentially be used to identify those at highest risk of lung cancer, to facilitate early referral and investigation.
diagnosis; lung cancer; primary care; qresearch; risk prediction; symptoms
Smoking is responsible for 90% of lung cancer cases. There is currently no clinically available gene test for early detection of lung cancer in smokers, or an effective patient selection strategy for adjuvant chemotherapy in lung cancer treatment. In this study, concurrent coexpression with multiple signaling pathways was modeled among a set of genes associated with smoking and lung cancer survival. This approach identified and validated a 7-gene signature for lung cancer diagnosis and prognosis in smokers using patient transcriptional profiles (n=847). The smoking-associated gene coexpression networks in lung adenocarcinoma tumors (n=442) were highly significant in terms of biological relevance (network precision = 0.91, FDR<0.01) when evaluated with numerous databases containing multi-level molecular associations. The gene coexpression network in smoking lung adenocarcinoma patients was confirmed in qRT-PCR assays of the identified biomarkers and involved signaling pathway genes in human lung adenocarcinoma cells (H23) treated with 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). Furthermore, the western blotting results of p53, phospho-p53, Rb and EGFR in NNK-treated H23 and transformed normal human lung epithelial cells (BEAS-2B) support their functional involvement in smoking-induced lung cancer carcinogenesis and progression.
smoking; lung cancer diagnosis and prognosis; gene signature; signaling pathway; coexpression networks
Smoking is responsible for 90% of lung cancer cases. There is currently no clinically available gene test for early detection of lung cancer in smokers, or an effective patient selection strategy for adjuvant chemotherapy in lung cancer treatment. In this study, concurrent coexpression with multiple signaling pathways was modeled among a set of genes associated with smoking and lung cancer survival. This approach identified and validated a 7-gene signature for lung cancer diagnosis and prognosis in smokers using patient transcriptional profiles (n=847). The smoking-associated gene coexpression networks in lung adenocarcinoma tumors (n=442) were highly significant in terms of biological relevance (network precision=0.91, FDR<0.01) when evaluated with numerous databases containing multi-level molecular associations. The gene coexpression network in smoking lung adenocarcinoma patients was confirmed in qRT-PCR assays of the identified biomarkers and involved signaling pathway genes in human lung adenocarcinoma cells (H23) treated with 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). Furthermore, the Western blotting results of p53, phospho-p53, Rb, and EGFR in NNK-treated H23 and transformed normal human lung epithelial cells (BEAS-2B) support their functional involvement in smoking induced lung cancer carcinogenesis and progression.
smoking; lung cancer diagnosis and prognosis; gene signature; signaling pathway; coexpression networks
Tumor recurrence is the major cause of death in lung cancer treatment. To date, there is no clinically applied gene expression-based model to predict the risk for tumor recurrence in non-small cell lung cancer (NSCLC). We sought to embed crosstalk with major signaling pathways into biomarker identification. Three approaches were used to identify prognostic gene signatures from 442 lung adenocarcinoma samples. Candidate genes co-expressed with 6 or 7 major NSCLC signaling hallmarks were identified from genome-wide coexpression networks specifically associated with different prognostic groups. From these candidate genes, the first approach selected genes significantly associated with disease-specific survival using univariate Cox model. The second approach used random forests to refine the gene signatures; and the third approach used Relief algorithm to form the final gene sets. A total of 21 gene signatures were identified using these three approaches. These gene signatures generated significant prognostic stratifications (log-rank P < 0.05 in Kaplan-Meier analyses; Hazard Ratio >1, P< 0.05) in all tumors, stage I only, and in stage I patients not receiving chemotherapy in all training and test sets. In multivariate analyses with age, gender, race, smoking history, cancer stage, and tumor differentiation, a 10-gene signature had a hazard ratio of 3.23 (95% CI: [1.48, 7.06]), which was a more significant prognostic factor than other clinical factors, except cancer stage (III vs. I; with no significant difference). All identified 21 gene signatures outperformed other lung cancer signatures evaluated in the Director's Challenge Study. This study is an important step toward personalized prognosis of tumor recurrence and patient selection for adjuvant chemotherapy, with significant impact on down-stream clinical applications.
lung adenocarcinoma; gene co-expression networks; biomarker identification; signaling pathways; prognostic stratification; tumor recurrence; metastasis; non-small cell lung cancer
Thymoma represents one of the rarest of all malignancies. Stage and completeness of resection have been used to ascertain postoperative therapeutic strategies albeit with limited prognostic accuracy. A molecular classifier would be useful to improve the assessment of metastatic behaviour and optimize patient management.
qRT-PCR assay for 23 genes (19 test and four reference genes) was performed on multi-institutional archival primary thymomas (n = 36). Gene expression levels were used to compute a signature, classifying tumors into classes 1 and 2, corresponding to low or high likelihood for metastases. The signature was validated in an independent multi-institutional cohort of patients (n = 75).
A nine-gene signature that can predict metastatic behavior of thymomas was developed and validated. Using radial basis machine modeling in the training set, 5-year and 10-year metastasis-free survival rates were 77% and 26% for predicted low (class 1) and high (class 2) risk of metastasis (P = 0.0047, log-rank), respectively. For the validation set, 5-year metastasis-free survival rates were 97% and 30% for predicted low- and high-risk patients (P = 0.0004, log-rank), respectively. The 5-year metastasis-free survival rates for the validation set were 49% and 41% for Masaoka stages I/II and III/IV (P = 0.0537, log-rank), respectively. In univariate and multivariate Cox models evaluating common prognostic factors for thymoma metastasis, the nine-gene signature was the only independent indicator of metastases (P = 0.036).
A nine-gene signature was established and validated which predicts the likelihood of metastasis more accurately than traditional staging. This further underscores the biologic determinants of the clinical course of thymoma and may improve patient management.
Background: Identification of appropriate markers for predicting clinical benefit with erlotinib in non-small-cell lung cancer (NSCLC) may be able to guide patient selection for treatment. This open-label, multicentre, phase II trial aimed to identify genes with potential use as biomarkers for clinical benefit from erlotinib therapy.
Methods: Adults with stage IIIb/IV NSCLC in whom one or more chemotherapy regimen had failed were treated with erlotinib (150 mg/day). Tumour biopsies were analysed using gene expression profiling with Affymetrix GeneChip® microarrays. Differentially expressed genes were verified using quantitative RT–PCR (qRT–PCR).
Results: A total of 264 patients were enrolled in the study. Gene expression profiles found no statistically significant differentially expressed genes between patients with and without clinical benefit. In an exploratory analysis in responding versus nonresponding patients, three genes on chromosome 7 were expressed at higher levels in the responding group [epidermal growth factor receptor (EGFR), phosphoserine phosphatase (PSPH) and Rap guanine nucleotide exchange factor 5 (RAPGEF5)]. Independent quantification using qRT–PCR validated the association between EGFR and PSPH overexpression, but not RAPGEF5 overexpression, and clinical outcome.
Conclusions: This study supports the use of erlotinib as an alternative to chemotherapy for patients with relapsed advanced NSCLC. Genetic amplification of the EGFR region of chromosome 7 may be associated with response to erlotinib therapy.
biomarkers; epidermal growth factor receptor; erlotinib; gene expression; non-small-cell lung cancer; PCR
Esophageal cancer is one of the most aggressive and deadly forms of cancer; highlighting the need to identify biomarkers for early detection and prognostic classification. Our recent studies have identified inflammatory gene and microRNA signatures derived from tumor and nontumor tissues as prognostic biomarkers of hepatocellular, lung, and colorectal adenocarcinoma. Here, we examine the relationship between expression of these inflammatory genes and miRNA expression in esophageal adenocarcinoma and patient survival.
We measured the expression of 23 inflammation-associated genes in tumors and adjacent normal tissues from 93 patients (58 Barrett's and 35 Sporadic adenocarcinomas) by quantitative reverse transcription-polymerase chain reaction. These data were used to build an inflammatory risk model, based on multivariate Cox regression, to predict survival in a training cohort (n=47). We then determined if this model could predict survival in a cohort of 46 patients. Expression data for miRNA-375 was available for these patients and was combined with inflammatory gene expression.
IFNγ, IL-1α, IL-8, IL-21, IL-23, and PRG expression in tumor and nontumor samples were each associated with poor prognosis based on Cox regression ([Z-score]>1.5) and therefore, were used to generate an inflammatory risk score (IRS). Patients with a high IRS had poor prognosis compared to those with a low IRS in the training (P=0.002) and test (P=0.012) cohorts. This association was stronger in the group with Barrett's history. When combining with miRNA-375, the combined IRS/miR signature was an improved prognostic classifier than either one alone.
Transcriptional profiling of inflammation-associated genes and miRNA expression in resected esophageal Barrett's associated adenocarcinoma tissues may have clinical utility as predictors of prognosis.
Inflammation; Cancer; Barrett's; Esophagus
Lung cancer is the commonest cause of cancer death in developed countries. Adenocarcinoma is becoming the most common form of lung cancer. Cigarette smoking is the main risk factor for lung cancer. Long-term cigarettes smoking may be characterized by genetic alteration and diffuse injury of the airways surface, named field cancerization, while cancer in non-smokers is usually clonally derived. Detecting specific genes expression changes in non-cancerous lung in smokers with adenocarcinoma may give us instrument for predicting smokers who are going to develop this malignancy.
We described the gene expression in non-cancerous lungs from 21 smoker patients with lung adenocarcinoma and compare it to gene expression in non-cancerous lung tissue from 10 non-smokers with primary lung adenocarcinoma.
Total RNA was isolated from peripheral non-cancerous lung tissue. The cDNA was hybridized to the U133A GeneChip array. Hierarchical clustering analysis on genes obtained from smokers and non-smokers, after subtracting were exported to the Ingenuity Pathway Analysis software for further analysis.
The genes subtraction resulted in disclosure of 36 genes with high score. They were subsequently mapped and sorted based on location, cellular components, and biochemical activity. The gene functional analysis disclosed 20 genes, which are involved in cancer process (P = 7.05E-5 to 2.92E-2).
Detected genes may serve as a predictor for smokers who may be at high risk of developing lung cancer. In addition, since these genes originating from non-cancerous lung, which is the major area of the lungs, a sample from an induced sputum may represent it.
Identification of melanoma patients at high risk for recurrence and monitoring for recurrence are critical for informed management decisions. We hypothesized that serum microRNAs (miRNAs) could provide prognostic information at the time of diagnosis unaccounted for by the current staging system and could be useful in detecting recurrence after resection.
We screened 355 miRNAs in sera from 80 melanoma patients at primary diagnosis (discovery cohort) using a unique quantitative reverse transcription-PCR (qRT-PCR) panel. Cox proportional hazard models and Kaplan-Meier recurrence-free survival (RFS) curves were used to identify a miRNA signature with prognostic potential adjusting for stage. We then tested the miRNA signature in an independent cohort of 50 primary melanoma patients (validation cohort). Logistic regression analysis was performed to determine if the miRNA signature can determine risk of recurrence in both cohorts. Selected miRNAs were measured longitudinally in subsets of patients pre-/post-operatively and pre-/post-recurrence.
A signature of 5 miRNAs successfully classified melanoma patients into high and low recurrence risk groups with significant separation of RFS in both discovery and validation cohorts (p = 0.0036, p = 0.0093, respectively). Significant separation of RFS was maintained when a logistic model containing the same signature set was used to predict recurrence risk in both discovery and validation cohorts (p < 0.0001, p = 0.033, respectively). Longitudinal expression of 4 miRNAs in a subset of patients was dynamic, suggesting miRNAs can be associated with tumor burden.
Our data demonstrate that serum miRNAs can improve accuracy in identifying primary melanoma patients with high recurrence risk and in monitoring melanoma tumor burden over time.
Melanoma; Serum microRNA; Prognostic biomarkers; Recurrence; Surveillance
Smoking is the leading cause of preventable death worldwide and has been shown to increase the risk of multiple diseases including coronary artery disease (CAD). We sought to identify genes whose levels of expression in whole blood correlate with self-reported smoking status.
Microarrays were used to identify gene expression changes in whole blood which correlated with self-reported smoking status; a set of significant genes from the microarray analysis were validated by qRT-PCR in an independent set of subjects. Stepwise forward logistic regression was performed using the qRT-PCR data to create a predictive model whose performance was validated in an independent set of subjects and compared to cotinine, a nicotine metabolite.
Microarray analysis of whole blood RNA from 209 PREDICT subjects (41 current smokers, 4 quit ≤ 2 months, 64 quit > 2 months, 100 never smoked; NCT00500617) identified 4214 genes significantly correlated with self-reported smoking status. qRT-PCR was performed on 1,071 PREDICT subjects across 256 microarray genes significantly correlated with smoking or CAD. A five gene (CLDND1, LRRN3, MUC1, GOPC, LEF1) predictive model, derived from the qRT-PCR data using stepwise forward logistic regression, had a cross-validated mean AUC of 0.93 (sensitivity=0.78; specificity=0.95), and was validated using 180 independent PREDICT subjects (AUC=0.82, CI 0.69-0.94; sensitivity=0.63; specificity=0.94). Plasma from the 180 validation subjects was used to assess levels of cotinine; a model using a threshold of 10 ng/ml cotinine resulted in an AUC of 0.89 (CI 0.81-0.97; sensitivity=0.81; specificity=0.97; kappa with expression model = 0.53).
We have constructed and validated a whole blood gene expression score for the evaluation of smoking status, demonstrating that clinical and environmental factors contributing to cardiovascular disease risk can be assessed by gene expression.
Smoking; Gene expression; Coronary artery disease; Whole blood
Degradation and chemical modification of RNA in formalin-fixed paraffin-embedded (FFPE) samples hamper their use in expression profiling studies. This study aimed to show that useful information can be obtained by Exon-array profiling archival FFPE tumour samples.
Nineteen cervical squamous cell carcinoma (SCC) and 9 adenocarcinoma (AC) FFPE samples (10–16-year-old) were profiled using Affymetrix Exon arrays. The gene signature derived was tested on a fresh-frozen non-small cell lung cancer (NSCLC) series. Exploration of biological networks involved gene set enrichment analysis (GSEA). Differential gene expression was confirmed using Quantigene, a multiplex bead-based alternative to qRT–PCR.
In all, 1062 genes were higher in SCC vs AC, and 155 genes higher in AC. The 1217-gene signature correctly separated 58 NSCLC into SCC and AC. A gene network centered on hepatic nuclear factor and GATA6 was identified in AC, suggesting a role in glandular cell differentiation of the cervix. Quantigene analysis of the top 26 differentially expressed genes correctly partitioned cervix samples as SCC or AC.
FFPE samples can be profiled using Exon arrays to derive gene expression signatures that are sufficiently robust to be applied to independent data sets, identify novel biology and design assays for independent platform validation.
cervix cancer; exon array; expression profiling; FFPE; histology
The thyroid transcription factor 1 (TTF-1) gene is associated with the differentiation of lung epithelial cells and has been reported to be an independent prognostic factor for lung adenocarcinoma patients. The aim of the present study was to detect the expression of TTF-1 in human lung cancer cell lines and to evaluate the association of overexpressed TTF-1 with Ki-67 and apoptosis in the A549 cell line. We also investigated the expression of TTF-1 and Ki-67 in Xuanwei lung adenocarcinoma. TTF-1 mRNA expression was evaluated in 10 non-small cell lung cancer (NSCLC) cell lines by quantitative real-time RT-PCR (qRT-PCR). Overexpression of TTF-1 in A549 cells was achieved by transient transfection. The TTF-1 and Ki-67 proteins were detected by immunohistochemistry and apoptosis was detected by flow cytometry. We also investigated immunohistochemically the expression of TTF-1 and Ki-67 in 62 resected cases of Xuanwei lung adenocarcinoma. Overall the expression of TTF-1 mRNA in the 10 cell lines was low. Overexpression of TTF-1 mRNA was found only in 3 (30%) of 10 NSCLC cell lines, including 1 (25%) of 4 adenocarcinoma cell lines. A549 cells overexpressing TTF-1 were found to have repressed expression of Ki-67 (P=0.012) and increased apoptosis (P=0.000). Immunohistochemical analysis of resected cases of Xuanwei lung adenocarcinoma (n=62) showed the expression of TTF-1 in 58 (93%) of 62 and Ki-67 in 22 (35%) of 62. Patients with strong immunohistochemical expression TTF-1 were statistically associated with well-differentiated phenotype (P=0.006) and inverse correlation with Ki-67 expression (P=0.016). These data suggest that TTF-1 may serve as a tumor suppressor gene based on its inverse correlation with Ki-67 proliferative activity and increase of cellular apoptosis.
thyroid transcription factor 1; Ki-67; apoptosis; non-small cell lung cancer lines; Xuanwei lung adenocarcinoma
Gene expression profiling may improve prognostic accuracy in patients with early breast cancer. Our objective was to demonstrate that it is possible to develop a simple molecular signature to predict distant relapse.
We included 153 patients with stage I-II hormonal receptor-positive breast cancer. RNA was isolated from formalin-fixed paraffin-embedded samples and qRT-PCR amplification of 83 genes was performed with gene expression assays. The genes we analyzed were those included in the 70-Gene Signature, the Recurrence Score and the Two-Gene Index. The association among gene expression, clinical variables and distant metastasis-free survival was analyzed using Cox regression models.
An 8-gene prognostic score was defined. Distant metastasis-free survival at 5 years was 97% for patients defined as low-risk by the prognostic score versus 60% for patients defined as high-risk. The 8-gene score remained a significant factor in multivariate analysis and its performance was similar to that of two validated gene profiles: the 70-Gene Signature and the Recurrence Score. The validity of the signature was verified in independent cohorts obtained from the GEO database.
This study identifies a simple gene expression score that complements histopathological prognostic factors in breast cancer, and can be determined in paraffin-embedded samples.