Ying-Wooi Wan

The microRNA-200 family targets multiple non-small cell lung cancer prognostic markers in H1299 cells and BEAS-2B cells

Lung cancer remains the leading cause of cancer-related mortality for both men and women. Tumor recurrence and metastasis is the major cause of lung cancer treatment failure and death. The microRNA-200 (miR-200) family is a powerful regulator of the epithelial-mesenchymal transition (EMT) process, which is essential in tumor metastasis. Nevertheless, miR-200 family target genes that promote metastasis in non-small cell lung cancer (NSCLC) remain largely unknown. Here, we sought to investigate whether the microRNA-200 family regulates our previously identified NSCLC prognostic marker genes associated with metastasis, as potential molecular targets. Novel miRNA targets were predicted using bioinformatics tools based on correlation analyses of miRNA and mRNA expression in 57 squamous cell lung cancer tumor samples. The predicted target genes were validated with quantitative RT-PCR assays and western blot analysis following re-expression of miR-200a, -200b and -200c in the metastatic NSCLC H1299 cell line. The results show that restoring miR-200a or miR-200c in H1299 cells induces downregulation of DLC1, ATRX and HFE. Reinforced miR-200b expression results in downregulation of DLC1, HNRNPA3 and HFE. Additionally, miR-200 family downregulates HNRNPR3, HFE and ATRX in BEAS-2B immortalized lung epithelial cells in quantitative RT-PCR and western blot assays. The miR-200 family and these potential targets are functionally involved in canonical pathways of immune response, molecular mechanisms of cancer, metastasis signaling, cell-cell communication, proliferation and DNA repair in Ingenuity pathway analysis (IPA). These results indicate that re-expression of miR-200 downregulates our previously identified NSCLC prognostic biomarkers in metastatic NSCLC cells. These results provide new insights into miR-200 regulation in lung cancer metastasis and consequent clinical outcome, and may provide a potential basis for innovative therapeutic approaches for the treatment of this deadly disease.

Multiwalled Carbon Nanotube-Induced Gene Signatures in the Mouse Lung: Potential Predictive Value for Human Lung Cancer Risk and Prognosis

Concerns over the potential for multiwalled carbon nanotubes (MWCNT) to induce lung carcinogenesis have emerged. This study sought to (1) identify gene expression signatures in the mouse lungs following pharyngeal aspiration of well-dispersed MWCNT and (2) determine if these genes were associated with human lung cancer risk and progression. Genome-wide mRNA expression profiles were analyzed in mouse lungs (n = 160) exposed to 0, 10, 20, 40, or 80 μg of MWCNT by pharyngeal aspiration at 1, 7, 28, and 56 d postexposure. By using pairwise statistical analysis of microarray (SAM) and linear modeling, 24 genes were selected, which have significant changes in at least two time points, have a more than 1.5-fold change at all doses, and are significant in the linear model for the dose or the interaction of time and dose. Additionally, a 38-gene set was identified as related to cancer from 330 genes differentially expressed at d 56 postexposure in functional pathway analysis. Using the expression profiles of the cancer-related gene set in 8 mice at d 56 postexposure to 10 μg of MWCNT, a nearest centroid classification accurately predicts human lung cancer survival with a significant hazard ratio in training set (n = 256) and test set (n = 186). Furthermore, both gene signatures were associated with human lung cancer risk (n = 164) with significant odds ratios. These results may lead to development of a surveillance approach for early detection of lung cancer and prognosis associated with MWCNT in the workplace.

Multi-Walled Carbon Nanotube-Induced Gene Expression in the Mouse Lung: Association with Lung Pathology

Due to the fibrous shape and durability of multi-walled carbon nanotubes (MWCNT), concerns regarding their potential for producing environmental and human health risks, including carcinogenesis, have been raised. This study sought to investigate how previously identified lung cancer prognostic biomarkers and the related cancer signaling pathways are affected in the mouse lung following pharyngeal aspiration of well-dispersed MWCNT. A total of 63 identified lung cancer prognostic biomarker genes and major signaling biomarker genes were analyzed in mouse lungs (n=80) exposed to 0, 10, 20, 40, or 80μg of MWCNT by pharyngeal aspiration at 7 and 56days post-exposure using quantitative PCR assays. At 7 and 56days post-exposure, a set of 7 genes and a set of 11 genes, respectively, showed differential expression in the lungs of mice exposed to MWCNT vs. the control group. Additionally, these significant genes could separate the control group from the treated group over the time series in a hierarchical gene clustering analysis. Furthermore, 4 genes from these two sets of significant genes, coiled-coil domain containing-99 (Ccdc99), muscle segment homeobox gene-2 (Msx2), nitric oxide synthase-2 (Nos2), and wingless-type inhibitory factor-1 (Wif1), showed significant mRNA expression perturbations at both time points. It was also found that the expression changes of these 4 overlapping genes at 7days post-exposure were attenuated at 56days post-exposure. Ingenuity Pathway Analysis (IPA) found that several carcinogenic-related signaling pathways and carcinogenesis itself were associated with both the 7 and 11 gene signatures. Taken together, this study identifies that MWCNT exposure affects a subset of lung cancer biomarkers in mouse lungs.

Hybrid Models Identified a 12-Gene Signature for Lung Cancer Prognosis and Chemoresponse Prediction

Background 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. Methodology/Principal Findings 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. Conclusions/Significance 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.

A 12-gene genomic instability signature predicts clinical outcomes in multiple cancer types.

Background and aims Genomic instability, as reflected in specific chromosomal aneuploidies and variation in the nuclear DNA content, is a defining feature of human carcinomas. It is solidly established that the degree of genomic instability influences clinical outcome. We have recently identified a 12-gene expression signature that discerned genomically stable from unstable breast carcinomas. This gene expression signature was also useful to predict, with high accuracy, the clinical course in independent multiple published breast cancer cohorts. From a biological point of view, this result confirmed the central role of genomic instability for a tumors ability to adapt to external challenges and selective pressure, and hence for continued survival fitness. This prompted us to investigate whether this genomic instability signature could also predict clinical outcome in other cancer types of epithelial origin, including colorectal tumors, non-small cell lung carcinomas, and ovarian cancer. Results The results show that the gene expression signature that defines genomic instability and poor outcome in breast cancer contributes significantly more accurate (p<0.05 compared with random prediction) prognostic information in multiple cancer types independent of established clinical parameters. The 12-gene genomic instability signature stratified patients into high- and low-risk groups with distinct postoperative survival in three non-small cell lung cancer cohorts (n=637) in KaplanMeier analyses (log-rank p<0.05). It predicted recurrence in colon cancer patients (n=92) with an overall accuracy greater than 69% (p=0.04) in cross-cohort validation. It quantified relapse-free survival in ovarian cancer (n=124; log-rank p<0.05). Functional pathway analysis revealed interactions between the 12 signature genes and well-known cancer hallmarks. Conclusion The degree of genomic instability has diagnostic and prognostic implications. It is tempting to speculate that pursuing genomic instability therapeutically could provide entry points for a target that is unique to cancer cells.

Pathway-based identification of a smoking associated 6-gene signature predictive of lung cancer risk and survival

OBJECTIVE Smoking is a prominent risk factor for lung cancer. However, it is not an established prognostic factor for lung cancer in clinics. To date, no gene test is available for diagnostic screening of lung cancer risk or prognostication of clinical outcome in smokers. This study sought to identify a smoking associated gene signature in order to provide a more precise diagnosis and prognosis of lung cancer in smokers. METHODS AND MATERIALS An implication network based methodology was used to identify biomarkers by modeling crosstalk with major lung cancer signaling pathways. Specifically, the methodology contains the following steps: (1) identifying genes significantly associated with lung cancer survival; (2) selecting candidate genes which are differentially expressed in smokers versus non-smokers from the survival genes identified in Step 1; (3) from these candidate genes, constructing gene coexpression networks based on prediction logic for the smoker group and the non-smoker group, respectively; (4) identifying smoking-mediated differential components, i.e., the unique gene coexpression patterns specific to each group; and (5) from the differential components, identifying genes directly co-expressed with major lung cancer signaling hallmarks. RESULTS A smoking-associated 6-gene signature was identified for prognosis of lung cancer from a training cohort (n=256). The 6-gene signature could separate lung cancer patients into two risk groups with distinct post-operative survival (log-rank P<0.04, Kaplan-Meier analyses) in three independent cohorts (n=427). The expression-defined prognostic prediction is strongly related to smoking association and smoking cessation (P<0.02; Pearsons Chi-squared tests). The 6-gene signature is an accurate prognostic factor (hazard ratio=1.89, 95% CI: [1.04, 3.43]) compared to common clinical covariates in multivariate Cox analysis. The 6-gene signature also provides an accurate diagnosis of lung cancer with an overall accuracy of 73% in a cohort of smokers (n=164). The coexpression patterns derived from the implication networks were validated with interactions reported in the literature retrieved with STRING8, Ingenuity Pathway Analysis, and Pathway Studio. CONCLUSIONS The pathway-based approach identified a smoking-associated 6-gene signature that predicts lung cancer risk and survival. This gene signature has potential clinical implications in the diagnosis and prognosis of lung cancer in smokers.

A smoking-associated 7-gene signature for lung cancer diagnosis and prognosis

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.

A novel network model for molecular prognosis

Network-based genome-wide association studies (NWAS) utilize the molecular interactions between genes and functional pathways in biomarker identification. This study presents a novel network-based methodology for identifying prognostic gene signatures to predict cancer recurrence. The methodology contains the following steps: 1) Constructing genome-wide coexpression networks for different disease states (metastatic vs. non-metastatic). Prediction logic is used to induct valid implication relations between each pair of gene expression profiles in terms of formal logic rules. 2) Identifying differential components associated with specific disease states from the genome-wide coexpression networks. 3) Dissecting network modules that are tightly connected with major disease signal hallmarks from the disease specific differential components. 4) Identifying most significant genes/probes associated with clinical outcome from the pathway connected network modules. Using this methodology, a 14-gene prognostic signature was identified for accurate patient stratification in early stage lung cancer.

Network-based identification of smoking-associated gene signature for lung cancer

This study presents a novel computational approach to identifying a smoking-associated gene signature. The methodology contains the following steps: 1) identifying genes significantly associated with lung cancer survival, 2) selecting genes which are differentially expressed in smoker versus non-smoker groups from the survival genes, 3) from these candidate genes, constructing gene co-expression networks based on prediction logic for smokers and non-smokers, 4) identifying smoking-mediated differential components, i.e., the unique gene co-expression patterns specific to each group, and 5) from the differential components, identifying genes directly co-expressed with major lung cancer hallmarks. The identified 7-gene signature could separate lung cancer patients into two risk groups with distinct postoperative survival (log-rank P < 0.05, Kaplan-Meier analysis) in four independent cohorts (n=427). It also has implications in the diagnosis of lung cancer (accuracy = 74%) in a cohort of smokers (n=164). Computationally derived co-expression patterns were validated with Pathway Studio and STRING 8.

Constructing gene-expression based survival prediction model for Non-Small Cell Lung Cancer (NSCLC) in all stages and early stages

Lung cancer has been the top leading cancer type for the past two decades and the overall survival rate for Non-Small Cell Lung Cancer (NSCLC) remains at a low rate of 15 percents. Current lung cancer prognosis using staging system has been studied and proven to be not accurate enough, especially on early stages. Therefore, a new prognostic model is desired. Using gene expression values of 442 Affymetrix U133A microarray data, we identified a 15-gene and a 12-gene signatures and constructed prediction models for NSCLC overall survival. The set of 442 samples were separated into three sets: UM/HLM as training set (n=256), MSK as first test set (n=104), and DFCI as second test set (n=82). The 15-gene signature was identified from combination of t-test and RFLIEFF feature selection. By fitting the 15 genes into Cox proportional hazard model and using the median risk scores as the cut-off, patients in training set and both testing sets were stratified into two distinct survival groups significantly (log-rank P £ 0.02) in KM analysis. This model was further studied on early stage patients. In KM analysis, the stratification was not significant on MSK stage 1 (log-rank P = 0.12) but significant on DFCI stage 1 (log-rank P = 0.02). The model was able to further stratified stage 1B patients into two distinct risk groups (log-rank P = 0.00765) as well. The 12-gene signature was identified from combination of t-test, SAM statistics, and RELIEFF feature selection. Naïve-Bayes classifier was used to construct the prognostic model based on 5-year survival data. The 12-gene model also provided significant stratifications (log-rank P £ 0.001) in KM analysis on both training and test sets. Furthermore, the 12-gene model significantly stratified stage 1 patients on both test sets (log-rank P £ 0.04) in KM analysis. Consistent to 15-gene, this 12-gene model was able to significantly stratified stage 1B patients (log-rank P = 0.0047) but not stage 1A patients. Results showed that both signatures were as significant as other published gene signatures of larger size. Also, these two gene signatures shared two common genes and the performances on the two prognostic models were comparative. Therefore, in addition to comparing the predictive performance of the models, we used IPA to further study the biological relevance of both signatures.