Constructing Molecular Classifiers for the Accurate Prognosis of Lung Adenocarcinoma

Identification and prognostic analysis of ferroptosis‑related gene HSPA5 to predict the progression of lung squamous cell carcinoma

Ferroptosis, an iron-dependent form of regulated cell death driven by excessive lipid peroxidation, is implicated in the development and therapeutic responses of cancer. However, the role of ferroptosis-related gene profiles in lung squamous cell carcinoma (LSCC) remains largely unknown. The present study aimed to identify the prognostic roles of ferroptosis-related genes in LSCC. Sequencing data from the Cancer Genome Atlas were analyzed and ferroptosis-related gene expression between tumor and para-tumor tissue was identified. The prognostic role of these genes was also assessed using Kaplan-Meier analyses and univariate and multivariate Cox proportional hazards regression model analyses. Immunological correlation, tumor stemness, drug sensitivity and the transcriptional differences of heat shock protein (HSP)A5 in LSCC were also analyzed. Thereafter, the expression of HSPA5 in 100 patients with metastatic LSCC was evaluated using immunohistochemistry (IHC) and the clinical significance of these markers with different risk factors was assessed. Of the 22 ferroptosis-related genes, the expression of HSPA5, HSPB1, glutathione peroxidase 4, Fanconi anemia complementation group D2, CDGSH iron sulfur domain 1, farnesyl-diphosphate farnesyltransferase 1, nuclear factor erythroid 2 like 2, solute carrier (SLC)1A5, ribosomal protein L8, nuclear receptor coactivator 4, transferrin receptor and SLC7A11 was significantly increased in LSCC compared with adjacent tissues. However, only high expression of HSPA5 was able to predict progression-free survival (PFS) and disease-free survival in LSCC. Although HSPA5 was also significantly elevated in patients with lung adenocarcinoma, HSPA5 expression did not predict the prognosis of patients with lung adenocarcinoma. Of note, a higher expression of HSPA5 was related to higher responses to chemotherapy but not to immunotherapy. In addition, HSPA5 expression was positively correlated with 'ferroptosis', 'cellular responses to hypoxia', 'tumor proliferation signature', 'G2M checkpoint', 'MYC targets' and 'TGFB'. IHC analysis also demonstrated that a high expression of HSPA5 in patients with metastatic LSCC in the study cohort was associated with shorter PFS and overall survival. In conclusion, the present study demonstrated that the expression of the ferroptosis-related gene HSPA5 may be a negative prognostic marker for LSCC.

Effects of different KRAS mutants and Ki67 expression on diagnosis and prognosis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a prevalent form of non-small cell lung cancer with a rising incidence in recent years. Understanding the mutation characteristics of LUAD is crucial for effective treatment and prediction of this disease. Among the various mutations observed in LUAD, KRAS mutations are particularly common. Different subtypes of KRAS mutations can activate the Ras signaling pathway to varying degrees, potentially influencing the pathogenesis and prognosis of LUAD. This study aims to investigate the relationship between different KRAS mutation subtypes and the pathogenesis and prognosis of LUAD. A total of 63 clinical samples of LUAD were collected for this study. The samples were analyzed using targeted gene sequencing panels to obtain sequencing data. To complement the dataset, additional clinical and sequencing data were obtained from TCGA and MSK. The analysis revealed significantly higher Ki67 immunohistochemical scores in patients with missense mutations compared to controls. Moreover, the expression level of KRAS was found to be significantly correlated with Ki67 expression. Enrichment analysis indicated that KRAS missense mutations activated the SWEET_LUNG_CANCER_KRAS_DN and CREIGHTON_ENDOCRINE_THERAPY_RESISTANCE_2 pathways. Additionally, patients with KRAS missense mutations and high Ki67 IHC scores exhibited significantly higher tumor mutational burden levels compared to other groups, which suggests they are more likely to be responsive to ICIs. Based on the data from MSK and TCGA, it was observed that patients with KRAS missense mutations had shorter survival compared to controls, and Ki67 expression level could more accurately predict patient prognosis. In conclusion, when utilizing KRAS mutations as biomarkers for the treatment and prediction of LUAD, it is important to consider the specific KRAS mutant subtypes and Ki67 expression levels. These findings contribute to a better understanding of LUAD and have implications for personalized therapeutic approaches in the management of this disease.

Improved outcomes and staging in non-small-cell lung cancer guided by a molecular assay

There remains a critical need for improved staging of non-small-cell lung cancer, as recurrence and mortality due to undetectable metastases at the time of surgery remain high even after complete resection of tumors currently categorized as 'early stage.' A 14-gene quantitative PCR-based expression profile has been extensively validated to better identify patients at high-risk of 5-year mortality after surgical resection than conventional staging - mortality that almost always results from previously undetectable metastases. Furthermore, prospective studies now suggest a predictive benefit in disease-free survival when the assay is used to guide adjuvant chemotherapy decisions in early-stage non-small-cell lung cancer patients.

Large-scale gene expression analysis reveals robust gene signatures for prognosis prediction in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. High mortality in LUAD motivates us to stratify the patients into high- and low-risk groups, which is beneficial for the clinicians to design a personalized therapeutic regimen. To robustly predict the risk, we identified a set of robust prognostic gene signatures and critical pathways based on ten gene expression datasets by the meta-analysis-based Cox regression model, 25 of which were selected as predictors of multivariable Cox regression model by MMPC algorithm. Gene set enrichment analysis (GSEA) identified the Aurora-A pathway, the Aurora-B pathway, and the FOXM1 transcription factor network as prognostic pathways in LUAD. Moreover, the three prognostic pathways were also the biological processes of G2-M transition, suggesting that hyperactive G2-M transition in cell cycle was an indicator of poor prognosis in LUAD. The validation in the independent datasets suggested that overall survival differences were observed not only in all LUAD patients, but also in those with a specific TNM stage, gender, and age group. The comprehensive analysis demonstrated that prognostic signatures and the prognostic model by the large-scale gene expression analysis were more robust than models built by single data based gene signatures in LUAD overall survival prediction.

Differential gene methylation patterns in cancerous and non‑cancerous cells

Large-scale projects, such as The Cancer Genome Atlas (TCGA), Human Epigenome Project (HEP) and Human Epigenome Atlas (HEA), provide an insight into DNA methylation and histone modification markers. Changes in the epigenome significantly contribute to the initiation and progression of cancer. The goal of the present study was to characterize the prostate cancer malignant transformation model using the CpG island methylation pattern. The Human Prostate Cancer EpiTect Methyl II Signature PCR Array was used to evaluate the methylation status of 22 genes in prostate cancer cell lines: PC3, PC3M, PC3MPro4 and PC3MLN4, each representing different metastatic potential in vivo. Subsequently, it was ascertained whether DNA methylation plays a role in the expression of these genes in prostate cancer cells. Hypermethylation of APC, DKK3, GPX3, GSTP1, MGMT, PTGS2, RASSF1, TIMP2 and TNFRSF10D resulted in downregulation of their expression in prostate cancer cell lines as compared to WT fibroblasts. Mining of the TCGA data deposited in the MetHC database found increases in the methylation status of these 9 genes in prostate cancer patients, further supporting the role of methylation in altering the expression of these genes in prostate cancer. Future studies are warranted to investigate the role of these proteins in prostate cancer development.

A Predictive 7-Gene Assay and Prognostic Protein Biomarkers for Non-small Cell Lung Cancer

PURPOSE

This study aims to develop a multi-gene assay predictive of the clinical benefits of chemotherapy in non-small cell lung cancer (NSCLC) patients, and substantiate their protein expression as potential therapeutic targets.

PATIENTS AND METHODS

The mRNA expression of 160 genes identified from microarray was analyzed in qRT-PCR assays of independent 337 snap-frozen NSCLC tumors to develop a predictive signature. A clinical trial JBR.10 was included in the validation. Hazard ratio was used to select genes, and decision-trees were used to construct the predictive model. Protein expression was quantified with AQUA in 500 FFPE NSCLC samples.

RESULTS

A 7-gene signature was identified from training cohort (n = 83) with accurate patient stratification (P = 0.0043) and was validated in independent patient cohorts (n = 248, P < 0.0001) in Kaplan-Meier analyses. In the predicted benefit group, there was a significantly better disease-specific survival in patients receiving adjuvant chemotherapy in both training (P = 0.035) and validation (P = 0.0049) sets. In the predicted non-benefit group, there was no survival benefit in patients receiving chemotherapy in either set. The protein expression of ZNF71 quantified with AQUA scores produced robust patient stratification in separate training (P = 0.021) and validation (P = 0.047) NSCLC cohorts. The protein expression of CD27 quantified with ELISA had a strong correlation with its mRNA expression in NSCLC tumors (Spearman coefficient = 0.494, P < 0.0088). Multiple signature genes had concordant DNA copy number variation, mRNA and protein expression in NSCLC progression.

CONCLUSIONS

This study presents a predictive multi-gene assay and prognostic protein biomarkers clinically applicable for improving NSCLC treatment, with important implications in lung cancer chemotherapy and immunotherapy.

Elevation of CA19-9 and CEA is associated with a poor prognosis in patients with resectable gallbladder carcinoma

AIMS

The aim of this study was to determine whether a combination of the tumour markers carcinoembryonic (CEA) and carbohydrate antigen 19-9 (CA19-9) would be helpful in predicting the prognosis of patients with gallbladder carcinoma (GBC) who underwent resection.

METHODS

A retrospective analysis of clinico-pathological features and survival of 390 patients with GBC who were treated between January 2003 and December 2013. Time-dependent receiver operating characteristic (ROC) was used to evaluate the prognostic ability of tumour markers. Combinations of preoperative CEA and CA19-9 were tested as potential prognostic factors.

RESULTS

The evaluation of preoperative CEA and CA19-9 showed that patients with both tumour markers within the normal range had the best prognosis with a median survival of 27 months and R0 rate of 86%. Patients with both tumour markers elevated had the poorest prognosis and lower R0 rate (p < 0.001). The combination of CEA and CA19-9 was an independent risk factor for overall survival. The AUROC at 5 years of combination of CEA and CA19-9 was 0.798, which was similar to CEA (0.765) or CA19-9 (0.771) alone (p = 0.103, p = 0.147).

CONCLUSIONS

A combination of an elevated preoperative CEA and CA19-9 was associated with a worse prognosis for patients with GBC who underwent resection.

Generating a robust prediction model for stage I lung adenocarcinoma recurrence after surgical resection

Lung cancer mortality remains high even after successful resection. Adjuvant treatment benefits stage II and III patients, but not stage I patients, and most studies fail to predict recurrence in stage I patients. Our study included 211 lung adenocarcinoma patients (stages I-IIIA; 81% stage I) who received curative resections at Taipei Veterans General Hospital between January 2001 and December 2012. We generated a prediction model using 153 samples, with validation using an additional 58 clinical outcome-blinded samples. Gene expression profiles were generated using formalin-fixed, paraffin-embedded tissue samples and microarrays. Data analysis was performed using a supervised clustering method. The prediction model generated from mixed stage samples successfully separated patients at high vs. low risk for recurrence. The validation tests hazard ratio (HR = 4.38) was similar to that of the training tests (HR = 4.53), indicating a robust training process. Our prediction model successfully distinguished high- from low-risk stage IA and IB patients, with a difference in 5-year disease-free survival between high- and low-risk patients of 42% for stage IA and 45% for stage IB (p < 0.05). We present a novel and effective model for identifying lung adenocarcinoma patients at high risk for recurrence who may benefit from adjuvant therapy. Our prediction performance of the difference in disease free survival between high risk and low risk groups demonstrates more than two fold improvement over earlier published results.

Proteomics analysis to reveal biological pathways and predictive proteins in the survival of high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSC) is an aggressive cancer with a worse clinical outcome. Therefore, studies about the prognosis of HGSC may provide therapeutic avenues to improve patient outcomes. Since genome alteration are manifested at the protein level, we integrated protein and mRNA data of ovarian cancer from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) and utilized the sparse overlapping group lasso (SOGL) method, a new mechanism-driven variable selection method, to select dysregulated pathways and crucial proteins related to the survival of HGSC. We found that biosynthesis of amino acids was the main biological pathway with the best predictive performance (AUC = 0.900). A panel of three proteins, namely EIF2B1, PRPS1L1 and MAPK13 were selected as potential predictive proteins and the risk score consisting of these three proteins has predictive performance for overall survival (OS) and progression free survival (PFS), with AUC of 0.976 and 0.932, respectively. Our study provides additional information for further mechanism and therapeutic avenues to improve patient outcomes in clinical practice.

A Novel Adaptive Penalized Logistic Regression for Uncovering Biomarker Associated with Anti-Cancer Drug Sensitivity

We propose a novel adaptive penalized logistic regression modeling strategy based on Wilcoxon rank sum test (WRST) to effectively uncover driver genes in classification. In order to incorporate significance of gene in classification, we first measure significance of each gene by gene ranking method based on WRST, and then the adaptive L ₁-type penalty is discriminately imposed on each gene depending on the measured importance degree of gene. The incorporating significance of genes into adaptive logistic regression enables us to impose a large amount of penalty on low ranking genes, and thus noise genes are easily deleted from the model and we can effectively identify driver genes. Monte Carlo experiments and real world example are conducted to investigate effectiveness of the proposed approach. In Sanger data analysis, we introduce a strategy to identify expression modules indicating gene regulatory mechanisms via the principal component analysis (PCA), and perform logistic regression modeling based on not a single gene but gene expression modules. We can see through Monte Carlo experiments and real world example that the proposed adaptive penalized logistic regression outperforms feature selection and classification compared with existing L ₁ -type regularization. The discriminately imposed penalty based on WRST effectively performs crucial gene selection, and thus our method can improve classification accuracy without interruption of noise genes. Furthermore, it can be seen through Sanger data analysis that the method for gene expression modules based on principal components and their loading scores provides interpretable results in biological viewpoints.

Smoking and Lung Cancer: Future Research Directions

The aim of future research in this area is to provide the mechanistic understanding and the tools for effective prevention, early diagnosis, and therapy of lung cancer. With the established causal link between cigarette smoking and the risk of developing lung cancer, the most effective prevention is certainly not to smoke. A much better mechanistic understanding of lung cancer and its variability will support the development and evaluation of potentially reduced risk products for those who maintain smoking as well as for the development of early diagnostic tools and targeted therapies. Because of the complexity of lung cancer and the long duration for its development, nonclinical and clinical research efforts need to complement each other. Recent promising advances in this research area are the understanding of the interaction between genotoxic and epigenetic effects of smoking, the development of laboratory animal models for lung tumorigenesis by smoke inhalation, the unraveling of molecular pathways and signatures in clinical lung cancer research useful for developing diagnostic tools and therapeutic approaches, and the first successful therapy for lung cancer—although less suitable for smokers. The above—in combination with emerging data sets from explorative non-clinical and clinical studies as well as improved modeling approaches—are setting the stage for accelerated progress towards developing successful early diagnostic tools and therapies as well as for the assessment of new consumer products with potentially reduced risk.

Association of PDCD1 and CTLA-4 Gene Expression with Clinicopathological Factors and Survival in Non-Small-Cell Lung Cancer: Results from a Large and Pooled Microarray Database

INTRODUCTION

Immune checkpoint blockade is being investigated in clinical trials and showed great potential in lung cancer. The prognostic roles of and clinicopathological factors associated with immune checkpoint gene expression, CTLA-4 and PDCD1 remain largely undefined, which encodes cytotoxic-lymphocyte antigen 4 (CTLA-4) and programmed cell death 1 (PD-1), respectively.

METHODS

We used a lung cancer database of 1715 patients measured by Affymetrix microarrays to analyze the association of gene expression with clinicopathological factors and survival. Hazard ratio (HR) and 95% confidence interval (CI) for overall survival (OS) were calculated. Cutoffs were determined by median across the entire database.

RESULTS

In 909 patients with histology information, significantly higher PDCD1 and CTLA-4 expression were found in squamous carcinoma than adenocarcinoma. In 848 patients with known smoking history, current/former smokers were found to have significantly elevated gene expression compared with nonsmokers. Significant higher expression of both genes were found in TNM stage II versus I. Higher expression of PDCD1 predicted worse OS in univariate analysis, but not in multivariate (HR: 1.22; 95% CI: 0.53-2.79). CTLA-4 was marginally significant in univariate analysis of the entire set (HR: 1.15; 95% CI: 0.99-1.34). In patients with information for multivariate analysis, higher expression of CTLA-4 was associated with worse OS (HR: 1.96; 95% CI: 1.18-3.31).

CONCLUSIONS

In this study with large number of patients, PDCD1 and CTLA-4 expression is significantly higher in squamous carcinoma and current/former smokers. Higher expression of CTLA-4, but not PDCD1 predicts worse survival.

High-dimensional genomic data bias correction and data integration using MANCIE

High-dimensional genomic data analysis is challenging due to noises and biases in high-throughput experiments. We present a computational method matrix analysis and normalization by concordant information enhancement (MANCIE) for bias correction and data integration of distinct genomic profiles on the same samples. MANCIE uses a Bayesian-supported principal component analysis-based approach to adjust the data so as to achieve better consistency between sample-wise distances in the different profiles. MANCIE can improve tissue-specific clustering in ENCODE data, prognostic prediction in Molecular Taxonomy of Breast Cancer International Consortium and The Cancer Genome Atlas data, copy number and expression agreement in Cancer Cell Line Encyclopedia data, and has broad applications in cross-platform, high-dimensional data integration.

Combining COPD with clinical, pathological and demographic information refines prognosis and treatment response prediction of non-small cell lung cancer

Background

Accurate assessment of a patient’s risk of recurrence and treatment response is an important prerequisite of personalized therapy in lung cancer. This study extends a previously described non-small cell lung cancer prognostic model by the addition of chemotherapy and co-morbidities through the use of linked SEER-Medicare data.

Methodology/Principal Findings

Data on 34,203 lung adenocarcinoma and 26,967 squamous cell lung carcinoma patients were used to determine the contribution of Chronic Obstructive Pulmonary Disease (COPD) to prognostication in 30 treatment combinations. A Cox model including COPD was estimated on 1,000 bootstrap samples, with the resulting model assessed on ROC, Brier Score, Harrell’s C, and Nagelkerke’s R² metrics in order to evaluate improvements in prognostication over a model without COPD. The addition of COPD to the model incorporating cancer stage, age, gender, race, and tumor grade was shown to improve prognostication in multiple patient groups. For lung adenocarcinoma patients, there was an improvement on the prognostication in the overall patient population and in patients without receiving chemotherapy, including those receiving surgery only. For squamous cell carcinoma, an improvement on prognostication was seen in both the overall patient population and in patients receiving multiple types of chemotherapy. COPD condition was able to stratify patients receiving the same treatments into significantly (log-rank p<0.05) different prognostic groups, independent of cancer stage.

Conclusion/Significance

Combining patient information on COPD, cancer stage, age, gender, race, and tumor grade could improve prognostication and prediction of treatment response in individual non-small cell lung cancer patients. This model enables refined prognosis and estimation of clinical outcome of comprehensive treatment regimens, providing a useful tool for personalized clinical decision-making.

Analytical validation of a practical molecular assay prognostic of survival in nonsquamous non-small cell lung cancer

A molecular assay prognostic of survival in resected nonsquamous non-small cell lung cancer designed to meet the need for improved risk stratification in early-stage disease has recently been described. This assay measures the expression levels of 14 genes using RNA extracted from formalin-fixed, paraffin-embedded (FFPE) tissues. The assay underwent blinded clinical validation in 2 large international cohorts involving approximately 1500 patients; the analytical precision and reproducibility of this assay, however, have not yet been reported. For each of the 14 TaqMan quantitative polymerase chain reaction (PCR) primer and probe sets used in the molecular prognostic assay, the linear range, PCR efficiency, limits of blank, limits of quantitation, and quantitative bias were determined using serial dilutions of pooled RNA extracted from FFPE samples. The reproducibility of the entire molecular assay was determined by performing repeat testing of FFPE samples over multiple days. The linear range of individual quantitative TaqMan PCR primer and probe sets was between 2(10)- and 2(15)-fold input RNA. The median C(T) of the quantitative PCR primer and probe sets at 10 ng of input RNA was 24.3; the median efficiency was 91.2%. The median quantitative bias across all quantitative PCR primer and probe sets was 0.75% (range, 0.32% to 1.32%). In repeat testing, the mean SD of the risk score (scaled from 1 to 100) was 2.18, with a mean coefficient of variation of 0.08. The molecular prognostic assay presented in this study demonstrates high precision and reproducibility, validating its clinical utility as a reliable prognostic tool that can contribute to the management of patients with early-stage disease.

Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer

In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: www.kmplot.com/lung. All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer.

Yin Yang gene expression ratio signature for lung cancer prognosis

Many studies have established gene expression-based prognostic signatures for lung cancer. All of these signatures were built from training data sets by learning the correlation of gene expression with the patients' survival time. They require all new sample data to be normalized to the training data, ultimately resulting in common problems of low reproducibility and impracticality. To overcome these problems, we propose a new signature model which does not involve data training. We hypothesize that the imbalance of two opposing effects in lung cancer cells, represented by Yin and Yang genes, determines a patient's prognosis. We selected the Yin and Yang genes by comparing expression data from normal lung and lung cancer tissue samples using both unsupervised clustering and pathways analyses. We calculated the Yin and Yang gene expression mean ratio (YMR) as patient risk scores. Thirty-one Yin and thirty-two Yang genes were identified and selected for the signature development. In normal lung tissues, the YMR is less than 1.0; in lung cancer cases, the YMR is greater than 1.0. The YMR was tested for lung cancer prognosis prediction in four independent data sets and it significantly stratified patients into high- and low-risk survival groups (p = 0.02, HR = 2.72; p = 0.01, HR = 2.70; p = 0.007, HR = 2.73; p = 0.005, HR = 2.63). It also showed prediction of the chemotherapy outcomes for stage II & III. In multivariate analysis, the YMR risk factor was more successful at predicting clinical outcomes than other commonly used clinical factors, with the exception of tumor stage. The YMR can be measured in an individual patient in the clinic independent of gene expression platform. This study provided a novel insight into the biology of lung cancer and shed light on the clinical applicability.

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.

Molecular classification of non-small-cell lung cancer: diagnosis, individualized treatment, and prognosis

Non-small-cell lung cancer (NSCLC) is the most common cause of premature death among the malignant diseases worldwide. The current staging criteria do not fully capture the complexity of this disease. Molecular biology techniques, particularly gene expression microarrays, proteomics, and next-generation sequencing, have recently been developed to facilitate effectively its molecular classification. The underlying etiology, pathogenesis, therapeutics, and prognosis of NSCLC based on an improved molecular classification scheme may promote individualized treatment and improve clinical outcomes. This review focuses on the molecular classification of NSCLC based on gene expression microarray technology reported during the past decade, as well as their applications for improving the diagnosis, staging and treatment of NSCLC, including the discovery of prognostic markers or potential therapeutic targets. We highlight some of the recent studies that may refine the identification of NSCLC subtypes using novel techniques such as epigenetics, proteomics, or deep sequencing.

Systematic analysis of multiwalled carbon nanotube-induced cellular signaling and gene expression in human small airway epithelial cells

Multiwalled carbon nanotubes (MWCNT) are one of the most commonly produced nanomaterials, and pulmonary exposure during production, use, and disposal is a concern for the developing nanotechnology field. The airway epithelium is the first line of defense against inhaled particles. In a mouse model, MWCNT were reported to reach the alveolar space of the lung after in vivo exposure, penetrate the epithelial lining, and result in inflammation and progressive fibrosis. This study sought to determine the cellular and gene expression changes in small airway epithelial cells (SAEC) after in vitro exposure to MWCNT in an effort to elucidate potential toxicity mechanisms and signaling pathways. A direct interaction between SAEC and MWCNT was confirmed by both internalization of MWCNT and interaction at the cell periphery. Following exposure, SAEC showed time-dependent increases in reactive oxygen species production, total protein phosphotyrosine and phosphothreonine levels, and migratory behavior. Analysis of gene and protein expression suggested altered regulation of multiple biomarkers of lung damage, carcinogenesis, and tumor progression, as well as genes involved in related signaling pathways. These results demonstrate that MWCNT exposure resulted in the activation of SAEC. Gene expression data derived from MWCNT exposure provide information that may be used to elucidate the underlying mode of action of MWCNT in the small airway and suggest potential prognostic gene signatures for risk assessment.

Exploiting the noise: improving biomarkers with ensembles of data analysis methodologies

BACKGROUND

The advent of personalized medicine requires robust, reproducible biomarkers that indicate which treatment will maximize therapeutic benefit while minimizing side effects and costs. Numerous molecular signatures have been developed over the past decade to fill this need, but their validation and up-take into clinical settings has been poor. Here, we investigate the technical reasons underlying reported failures in biomarker validation for non-small cell lung cancer (NSCLC).

METHODS

We evaluated two published prognostic multi-gene biomarkers for NSCLC in an independent 442-patient dataset. We then systematically assessed how technical factors influenced validation success.

RESULTS

Both biomarkers validated successfully (biomarker #1: hazard ratio (HR) 1.63, 95% confidence interval (CI) 1.21 to 2.19, P = 0.001; biomarker #2: HR 1.42, 95% CI 1.03 to 1.96, P = 0.030). Further, despite being underpowered for stage-specific analyses, both biomarkers successfully stratified stage II patients and biomarker #1 also stratified stage IB patients. We then systematically evaluated reasons for reported validation failures and find they can be directly attributed to technical challenges in data analysis. By examining 24 separate pre-processing techniques we show that minor alterations in pre-processing can change a successful prognostic biomarker (HR 1.85, 95% CI 1.37 to 2.50, P < 0.001) into one indistinguishable from random chance (HR 1.15, 95% CI 0.86 to 1.54, P = 0.348). Finally, we develop a new method, based on ensembles of analysis methodologies, to exploit this technical variability to improve biomarker robustness and to provide an independent confidence metric.

CONCLUSIONS

Biomarkers comprise a fundamental component of personalized medicine. We first validated two NSCLC prognostic biomarkers in an independent patient cohort. Power analyses demonstrate that even this large, 442-patient cohort is under-powered for stage-specific analyses. We then use these results to discover an unexpected sensitivity of validation to subtle data analysis decisions. Finally, we develop a novel algorithmic approach to exploit this sensitivity to improve biomarker robustness.

Development and validation of a prognostic gene-expression signature for lung adenocarcinoma

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.

Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?

In the Life Sciences 'omics' data is increasingly generated by different high-throughput technologies. Often only the integration of these data allows uncovering biological insights that can be experimentally validated or mechanistically modelled, i.e. sophisticated computational approaches are required to extract the complex non-linear trends present in omics data. Classification techniques allow training a model based on variables (e.g. SNPs in genetic association studies) to separate different classes (e.g. healthy subjects versus patients). Random Forest (RF) is a versatile classification algorithm suited for the analysis of these large data sets. In the Life Sciences, RF is popular because RF classification models have a high-prediction accuracy and provide information on importance of variables for classification. For omics data, variables or conditional relations between variables are typically important for a subset of samples of the same class. For example: within a class of cancer patients certain SNP combinations may be important for a subset of patients that have a specific subtype of cancer, but not important for a different subset of patients. These conditional relationships can in principle be uncovered from the data with RF as these are implicitly taken into account by the algorithm during the creation of the classification model. This review details some of the to the best of our knowledge rarely or never used RF properties that allow maximizing the biological insights that can be extracted from complex omics data sets using RF.

Expression patterns of USP22 and potential targets BMI-1, PTEN, p-AKT in non-small-cell lung cancer

BACKGROUND

Recent researches document that an oncogenic role of USP22 activation may contribute to progression and predict the prognosis. We have reported that USP22 mediates cell survival and proliferation by promoting the expression of BMI-1 and upregulation of activated AKT pathway in colon cancer cells. However, little is known about its mechanisms in non-small-cell lung cancer (NSCLC). Here the authors investigated the significance of activation of USP22 and potential targets BMI-1, PTEN and phospho-AKT (p-AKT) in NSCLC.

METHODS

Expression levels of USP22, BMI-1, PTEN and p-AKT in samples from 114 patients with NSCLC were evaluated immunohistochemically using the tissue microarray method. Clinical significance was analyzed by multivariate Cox regression analysis, Kaplan-Meier curves and the log-rank test.

RESULTS

Immunohistochemically, USP22, BMI-1, p-AKT and PTEN were positive in 66.66%, 78.07%, 71.92% and 43.85% of NSCLC samples, respectively. Statistical correlation analysis showed USP22 to be significantly correlated with BMI-1 (r=0.315, P=0.001), p-AKT (r=0.271, P=0.003), and PTEN (r=-0.384, P<0.0001). NSCLCs with positive expression of USP22, BMI-1, p-AKT, and negative expression of PTEN were significantly correlated to tumor size (P=0.0240), differentiation (P=0.0457), pT classification (P=0.0077), pN classification (P=0.0064), and AJCC stage (P=0.0363) and poor overall survival (P<0.001). Multivariate Cox proportional hazards model analysis showed that the combined 4 markers was the independent prognostic indicator of overall survival (P<0.001; HR, 5.974; 95% CI, 3.307-10.791).

CONCLUSIONS

The simultaneous targeting of USP22, and its downstream signal transduction molecules seem highly informative in stratification of the cancer into subgroups with distinct likelihood of therapy failure, which contribute to make decision process regarding the individualized therapy selection and optimization.

Molecular signatures of lung cancer: defining new diagnostic and therapeutic paradigms

Molecular profiling holds great promise for improving our ability to diagnose, prognosticate, and select individualized treatments for lung cancer patients. However, using multidimensional data and novel technologies to derive these profiles is limited by our ability to employ the assay in a clinical scenario where it can impact the course of disease. Although many molecular signatures have been reported in lung cancer, as of yet, few have been sufficiently validated for widespread clinical use. Recently, several novel signatures have been reported, which address critical aspects of patient care and/or demonstrate improved efforts for appropriate clinical validation. Here, we present our opinion on the current state of the field of molecular signatures in lung cancer.

Molecular profiling to optimize treatment in non-small cell lung cancer: a review of potential molecular targets for radiation therapy by the translational research program of the radiation therapy oncology group

Therapeutic decisions in non-small cell lung cancer (NSCLC) have been mainly based on disease stage, performance status, and co-morbidities, and rarely on histological or molecular classification. Rather than applying broad treatments to unselected patients that may result in survival increase of only weeks to months, research efforts should be, and are being, focused on identifying predictive markers for molecularly targeted therapy and determining genomic signatures that predict survival and response to specific therapies. The availability of such targeted biologics requires their use to be matched to tumors of corresponding molecular vulnerability for maximum efficacy. Molecular markers such as epidermal growth factor receptor (EGFR), K-ras, vascular endothelial growth factor (VEGF), mammalian target of rapamycin (mTOR), and anaplastic lymphoma kinase (ALK) represent potential parameters guide treatment decisions. Ultimately, identifying patients who will respond to specific therapies will allow optimal efficacy with minimal toxicity, which will result in more judicious and effective application of expensive targeted therapy as the new paradigm of personalized medicine develops.

A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies

BACKGROUND

The frequent recurrence of early-stage non-small-cell lung cancer (NSCLC) is generally attributable to metastatic disease undetected at complete resection. Management of such patients depends on prognostic staging to identify the individuals most likely to have occult disease. We aimed to develop and validate a practical, reliable assay that improves risk stratification compared with conventional staging.

METHODS

A 14-gene expression assay that uses quantitative PCR, runs on formalin-fixed paraffin-embedded tissue samples, and differentiates patients with heterogeneous statistical prognoses was developed in a cohort of 361 patients with non-squamous NSCLC resected at the University of California, San Francisco. The assay was then independently validated by the Kaiser Permanente Division of Research in a masked cohort of 433 patients with stage I non-squamous NSCLC resected at Kaiser Permanente Northern California hospitals, and on a cohort of 1006 patients with stage I-III non-squamous NSCLC resected in several leading Chinese cancer centres that are part of the China Clinical Trials Consortium (CCTC).

FINDINGS

Kaplan-Meier analysis of the Kaiser validation cohort showed 5 year overall survival of 71·4% (95% CI 60·5-80·0) in low-risk, 58·3% (48·9-66·6) in intermediate-risk, and 49·2% (42·2-55·8) in high-risk patients (p(trend)=0·0003). Similar analysis of the CCTC cohort indicated 5 year overall survivals of 74·1% (66·0-80·6) in low-risk, 57·4% (48·3-65·5) in intermediate-risk, and 44·6% (40·2-48·9) in high-risk patients (p(trend)<0·0001). Multivariate analysis in both cohorts indicated that no standard clinical risk factors could account for, or provide, the prognostic information derived from tumour gene expression. The assay improved prognostic accuracy beyond National Comprehensive Cancer Network criteria for stage I high-risk tumours (p<0·0001), and differentiated low-risk, intermediate-risk, and high-risk patients within all disease stages.

INTERPRETATION

Our practical, quantitative-PCR-based assay reliably identified patients with early-stage non-squamous NSCLC at high risk for mortality after surgical resection.

FUNDING

UCSF Thoracic Oncology Laboratory and Pinpoint Genomics.

MicroRNA profiling and prediction of recurrence/relapse-free survival in stage I lung cancer

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. MicroRNAs (miRNAs) are a class of small non-coding RNAs of 19-25 nt and play important roles in gene regulation in human cancers. The purpose of this study is to identify miRNA expression profiles that would better predict prognosis of stage I NSCLC. MiRNAs extracted from 527 stage I NSCLC patients were profiled on the human miRNA expression profiling v2 panel (Illumina). The expression profiles were analyzed for their association with cancer subtypes, lung cancer brain metastasis and recurrence/relapse free survival (RFS). MiRNA expression patterns between lung adenocarcinoma and squamous cell carcinoma differed significantly with 171 miRNAs, including Let-7 family members and miR-205. Ten miRNAs associated with brain metastasis were identified including miR-145*, which inhibit cell invasion and metastasis. Two miRNA signatures that are highly predictive of RFS were identified. The first contained 34 miRNAs derived from 357 stage I NSCLC patients independent of cancer subtype, whereas the second containing 27 miRNAs was adenocarcinoma specific. Both signatures were validated using formalin-fixed paraffin embedded and/or fresh frozen tissues in independent data set with 170 stage I patients. Our findings have important prognostic or therapeutic implications for the management of stage I lung cancer patients. The identified miRNAs hold great potential as targets for histology-specific treatment or prevention and treatment of recurrent 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.

Development and validation of a quantitative real-time polymerase chain reaction classifier for lung cancer prognosis

INTRODUCTION

This prospective study aimed to develop a robust and clinically applicable method to identify patients with high-risk early-stage lung cancer and then to validate this method for use in future translational studies.

METHODS

Three published Affymetrix microarray data sets representing 680 primary tumors were used in the survival-related gene selection procedure using clustering, Cox model, and random survival forest analysis. A final set of 91 genes was selected and tested as a predictor of survival using a quantitative real-time polymerase chain reaction-based assay using an independent cohort of 101 lung adenocarcinomas.

RESULTS

The random survival forest model built from 91 genes in the training set predicted patient survival in an independent cohort of 101 lung adenocarcinomas, with a prediction error rate of 26.6%. The mortality risk index was significantly related to survival (Cox model p < 0.00001) and separated all patients into low-, medium-, and high-risk groups (hazard ratio = 1.00, 2.82, 4.42). The mortality risk index was also related to survival in stage 1 patients (Cox model p = 0.001), separating patients into low-, medium-, and high-risk groups (hazard ratio = 1.00, 3.29, 3.77).

CONCLUSIONS

The development and validation of this robust quantitative real-time polymerase chain reaction platform allows prediction of patient survival with early-stage lung cancer. Utilization will now allow investigators to evaluate it prospectively by incorporation into new clinical trials with the goal of personalized treatment of patients with lung cancer and improving patient survival.

Molecular profiles and clinical outcome of stage UICC II colon cancer patients

PURPOSE

Published multigene classifiers suggesting outcome prediction for patients with stage UICC II colon cancer have not been translated into a clinical application so far. Therefore, we aimed at validating own and published gene expression signatures employing methods which enable their reconstruction in routine diagnostic specimens.

METHODS

Immunohistochemistry was applied to 68 stage UICC II colon cancers to determine the protein expression of previously published prognostic classifier genes (CDH17, LAT, CA2, EMR3, and TNFRSF11A). RNA from macrodissected tumor samples from 53 of these 68 patients was profiled on Affymetrix GeneChips (HG-U133 Plus 2.0). Prognostic signatures were generated by "nearest shrunken centroids" with cross-validation. Previously published gene signatures were applied to our data set using "global tests" and leave-one-out cross-validation

RESULTS

Correlation of protein expression with clinical outcome failed to separate patients with disease-free follow-up (group DF) and relapse (group R). Although gene expression profiling allowed the identification of differentially expressed genes ("DF" vs. "R"), a stable classification/prognosis signature was not discernable. Furthermore, the application of previously published gene signatures to our data was unable to predict clinical outcome (prediction rate 75.5% and 64.2%; n.s.). T-stage was the only independent prognostic factor for relapse with established clinical and pathological parameters including microsatellite status (multivariate analysis).

CONCLUSIONS

Our protein and gene expression analyses do not support application of molecular classifiers for prediction of clinical outcome in current routine diagnostic as a basis for patient-orientated therapy in stage UICC II colon cancer. Further studies are needed to develop prognosis signatures applicable in patient care.

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.

Genomic medicine in non-small cell lung cancer: paving the path to personalized care

Lung cancer is the commonest cause of cancer-related mortality and non-small cell lung cancer (NSCLC) accounts for 80% of all lung cancer. The prognosis of NSCLC remains poor across all stages, despite advances in staging techniques and treatments. The findings of recent high-throughput mRNA microarray studies have shown potential in refining current NSCLC diagnosis, classification, prognosis and treatment paradigms. Emerging microarray studies of microRNA, DNA copy number and methylation profiles are also providing novel insights into the biology of NSCLC. Currently there are several challenges, such as the reproducibility and cost of microarray platforms that will need to be addressed prior to the implementation of these genomic technologies to routine thoracic oncology practice. In addition, genomic tests (such as prognosis and prediction gene expression signatures) will need to be validated in well designed prospective studies that aim to answer clinically relevant questions. If successful, the integration of microarray-based genomic information with existing clinicopathological models may enhance the ability of clinicians to match the most effective treatment to an individual patient. Such a strategy may improve survival and reduce treatment-related morbidity in NSCLC patients.

Downregulation of EMX2 is associated with clinical outcomes in lung adenocarcinoma patients

BACKGROUND

The 5-year survival rate for stage I non-small-cell lung cancer (NSCLC) of 50% to 70% indicates that our current staging methods do not adequately predict outcome. Empty spiracles homeobox 2 (EMX2) is a homeo-domain-containing transcription factor that regulates a key developmental pathway known to promote lung tumorigenesis. This study assessed the significance of EMX2 as a prognostic biomarker in lung adenocarcinoma including bronchioloalveolar carcinoma (BAC).

PATIENTS AND METHODS

144 patients with lung adenocarcinoma undergoing surgical resection were studied. Quantitative real-time reverse transcriptase polymerase chain reaction and Immunohistochemistry were used to analyze EMX2 mRNA and protein expression, respectively. Association of EMX2 mRNA expression levels with clinical outcomes was evaluated using the Kaplan-Meier method and a multivariate Cox proportional hazards regression model.

RESULTS

EMX2 mRNA expression was significantly downregulated in lung adenocarcinoma compared with matched adjacent normal tissue (P < .001). EMX2 protein expression was similarly found to be downregulated in lung adenocarcinoma. The EMX2-high mRNA expressing group had statistically significant better overall survival (OS) than the EMX2-low mRNA expressing group (P = .005). Subgroup analysis also demonstrated improved survival in stage I patients (P = .01) and patients with BAC (P = .03). Lastly, the EMX2-high mRNA expressing group had statistically significant better recurrence-free survival (RFS) than the EMX2-low mRNA expression group in patients with adenocarcinoma (P < .001).

CONCLUSION

EMX2 expression is downregulated in lung adenocarcinoma. Low EMX2 mRNA expression is significantly associated with decreased OS and RFS in patients with lung adenocarcinoma, particularly with stage I disease and BAC.

Lung cancer: New biological insights and recent therapeutic advances

Approximately 1.6 million new cases of lung cancer are diagnosed each year throughout the world. In many countries, the mortality related to lung cancer continues to rise. The outcomes for patients with all stages of lung cancer have improved in recent years. The use of systemic therapy in conjunction with local therapy has led to improved cure rates in both resectable and unresectable patient groups. For patients with advanced stage disease, modest but real improvements in overall survival and quality of life have been achieved with systemic chemotherapy. A major focus of research has been the development of molecularly targeted agents and the identification of biomarkers for patient selection. Patients with non-small cell lung cancer with mutations in the epidermal growth factor receptor (EGFR) tyrosine kinase domain achieve response rates of greater than 70% and superior progression-free survival when treated with an EGFR tyrosine kinase inhibitor compared with standard chemotherapy. This has now emerged as the preferred therapeutic approach for the subset of patients with a mutation in exons 19 or 21 of the EGFR. Another promising targeted approach involves the use of an anaplastic lymphoma kinase (ALK) inhibitor in patients with a translocation involving the echinoderm microtubule-associated protein-like 4 (EML4) and -ALK genes. Finally, a paradigm shift in favor of maintenance therapy for patients with advanced stage disease has gained strength from recent data. All of these advances have been made possible by developing a greater understanding of the biology, the discovery of novel anticancer agents, and improved supportive care measures. This article reviews the major strides made in the treatment of lung cancer in the recent past.

Combining clinical, pathological, and demographic factors refines prognosis of lung cancer: a population-based study

Background

In the treatment of lung cancer, an accurate estimation of patient clinical outcome is essential for choosing an appropriate course of therapy. It is important to develop a prognostic stratification model which combines clinical, pathological and demographic factors for individualized clinical decision making.

Methodology/Principal Findings

A total of 234,412 patients diagnosed with adenocarcinomas or squamous cell carcinomas of the lung or bronchus between 1988 and 2006 were retrieved from the SEER database to construct a prognostic model. A model was developed by estimating a Cox proportional hazards model on 500 bootstrapped samples. Two models, one using stage alone and another comprehensive model using additional covariates, were constructed. The comprehensive model consistently outperformed the model using stage alone in prognostic stratification and on Harrell's C, Nagelkerke's R², and Brier Scores in the whole patient population as well as in specific treatment modalities. Specifically, the comprehensive model generated different prognostic groups with distinct post-operative survival (log-rank P<0.001) within surgical stage IA and IB patients in Kaplan-Meier analyses. Two additional patient cohorts (n = 1,991) were used as an external validation, with the comprehensive model again outperforming the model using stage alone with regards to prognostic stratification and the three evaluated metrics.

Conclusion/Significance

These results demonstrate the feasibility of constructing a precise prognostic model combining multiple clinical, pathologic, and demographic factors. The comprehensive model significantly improves individualized prognosis upon AJCC tumor staging and is robust across a range of treatment modalities, the spectrum of patient risk, and in novel patient cohorts.

International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma

INTRODUCTION

Adenocarcinoma is the most common histologic type of lung cancer. To address advances in oncology, molecular biology, pathology, radiology, and surgery of lung adenocarcinoma, an international multidisciplinary classification was sponsored by the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society. This new adenocarcinoma classification is needed to provide uniform terminology and diagnostic criteria, especially for bronchioloalveolar carcinoma (BAC), the overall approach to small nonresection cancer specimens, and for multidisciplinary strategic management of tissue for molecular and immunohistochemical studies.

METHODS

An international core panel of experts representing all three societies was formed with oncologists/pulmonologists, pathologists, radiologists, molecular biologists, and thoracic surgeons. A systematic review was performed under the guidance of the American Thoracic Society Documents Development and Implementation Committee. The search strategy identified 11,368 citations of which 312 articles met specified eligibility criteria and were retrieved for full text review. A series of meetings were held to discuss the development of the new classification, to develop the recommendations, and to write the current document. Recommendations for key questions were graded by strength and quality of the evidence according to the Grades of Recommendation, Assessment, Development, and Evaluation approach.

RESULTS

The classification addresses both resection specimens, and small biopsies and cytology. The terms BAC and mixed subtype adenocarcinoma are no longer used. For resection specimens, new concepts are introduced such as adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) for small solitary adenocarcinomas with either pure lepidic growth (AIS) or predominant lepidic growth with ≤ 5 mm invasion (MIA) to define patients who, if they undergo complete resection, will have 100% or near 100% disease-specific survival, respectively. AIS and MIA are usually nonmucinous but rarely may be mucinous. Invasive adenocarcinomas are classified by predominant pattern after using comprehensive histologic subtyping with lepidic (formerly most mixed subtype tumors with nonmucinous BAC), acinar, papillary, and solid patterns; micropapillary is added as a new histologic subtype. Variants include invasive mucinous adenocarcinoma (formerly mucinous BAC), colloid, fetal, and enteric adenocarcinoma. This classification provides guidance for small biopsies and cytology specimens, as approximately 70% of lung cancers are diagnosed in such samples. Non-small cell lung carcinomas (NSCLCs), in patients with advanced-stage disease, are to be classified into more specific types such as adenocarcinoma or squamous cell carcinoma, whenever possible for several reasons: (1) adenocarcinoma or NSCLC not otherwise specified should be tested for epidermal growth factor receptor (EGFR) mutations as the presence of these mutations is predictive of responsiveness to EGFR tyrosine kinase inhibitors, (2) adenocarcinoma histology is a strong predictor for improved outcome with pemetrexed therapy compared with squamous cell carcinoma, and (3) potential life-threatening hemorrhage may occur in patients with squamous cell carcinoma who receive bevacizumab. If the tumor cannot be classified based on light microscopy alone, special studies such as immunohistochemistry and/or mucin stains should be applied to classify the tumor further. Use of the term NSCLC not otherwise specified should be minimized.

CONCLUSIONS

This new classification strategy is based on a multidisciplinary approach to diagnosis of lung adenocarcinoma that incorporates clinical, molecular, radiologic, and surgical issues, but it is primarily based on histology. This classification is intended to support clinical practice, and research investigation and clinical trials. As EGFR mutation is a validated predictive marker for response and progression-free survival with EGFR tyrosine kinase inhibitors in advanced lung adenocarcinoma, we recommend that patients with advanced adenocarcinomas be tested for EGFR mutation. This has implications for strategic management of tissue, particularly for small biopsies and cytology samples, to maximize high-quality tissue available for molecular studies. Potential impact for tumor, node, and metastasis staging include adjustment of the size T factor according to only the invasive component (1) pathologically in invasive tumors with lepidic areas or (2) radiologically by measuring the solid component of part-solid nodules.

A five-gene and corresponding protein signature for stage-I lung adenocarcinoma prognosis

PURPOSE

Identification of effective markers for outcome is expected to improve the clinical management of non-small cell lung cancer (NSCLC). Here, we assessed in NSCLC the prognostic efficacy of genes, which we had previously found to be differentially expressed in an in vitro model of human lung carcinogenesis.

EXPERIMENTAL DESIGN

Prediction algorithms and risk-score models were applied to the expression of the genes in publicly available NSCLC expression data sets. The prognostic capacity of the immunohistochemical expression of proteins encoded by these genes was also tested using formalin-fixed paraffin-embedded (FFPE) tissue specimens from 156 lung adenocarcinomas and 79 squamous cell carcinomas (SCCs).

RESULTS

The survival of all-stages (P < 0.001, HR = 2.0) or stage-I (P < 0.001, HR = 2.84) adenocarcinoma patients that expressed the five-gene in vitro lung carcinogenesis model (FILM) signature was significantly poorer than that of patients who did not. No survival differences were observed between SCCs predicted to express or lack FILM signature. Moreover, all stages (P < 0.001, HR = 1.95) or stage-I (P = 0.001, HR = 2.6) adenocarcinoma patients predicted to be at high risk by FILM transcript exhibited significantly worse survival than patients at low risk. Furthermore, the corresponding protein signature was associated with poor survival (all stages, P < 0.001, HR = 3.6; stage-I, P < 0.001, HR = 3.5; stage-IB, P < 0.001, HR = 4.6) and mortality risk (all stages, P = 0.001, HR = 4.0; stage-I, P = 0.01, HR = 3.4; stage-IB, P < 0.001, HR = 7.2) in lung adenocarcinoma patients.

CONCLUSIONS

Our findings highlight a gene and corresponding protein signature with effective capacity for identification of stage-I lung adenocarcinoma patients with poor prognosis that are likely to benefit from adjuvant therapy.

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.

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.

Gene expression-based prognostic signatures in lung cancer: ready for clinical use?

A substantial number of studies have reported the development of gene expression-based prognostic signatures for lung cancer. The ultimate aim of such studies should be the development of well-validated clinically useful prognostic signatures that improve therapeutic decision making beyond current practice standards. We critically reviewed published studies reporting the development of gene expression-based prognostic signatures for non-small cell lung cancer to assess the progress made toward this objective. Studies published between January 1, 2002, and February 28, 2009, were identified through a PubMed search. Following hand-screening of abstracts of the identified articles, 16 were selected as relevant. Those publications were evaluated in detail for appropriateness of the study design, statistical validation of the prognostic signature on independent datasets, presentation of results in an unbiased manner, and demonstration of medical utility for the new signature beyond that obtained using existing treatment guidelines. Based on this review, we found little evidence that any of the reported gene expression signatures are ready for clinical application. We also found serious problems in the design and analysis of many of the studies. We suggest a set of guidelines to aid the design, analysis, and evaluation of prognostic signature studies. These guidelines emphasize the importance of focused study planning to address specific medically important questions and the use of unbiased analysis methods to evaluate whether the resulting signatures provide evidence of medical utility beyond standard of care-based prognostic factors.

How is gene-expression profiling going to challenge the future management of lung cancer?

Lung cancer has a very high recurrence rate and mortality, even in early stages of the disease. Current clinical staging techniques have limitations in terms of predicting which patients have an increased risk of recurrence, and they are not capable of sorting out who will benefit most from adjuvant therapy in term of survival advantage. The study of genomics has revolutionized how researchers are able to identify new molecular targets and improve patient care through the identification of 'genetic fingerprints or profiles' that might be able to predict responsiveness to therapy or prognosis. Techniques such as microarray-based gene-expression profiling have also allowed investigators to reveal different non-small-cell lung cancer subtypes that have been associated with different clinical outcomes, independently of histology and current clinical staging techniques. We review the current advances in gene-expression profiling and its potential role as a diagnostic and prognostic/predictive biomarker, and how this may translate into a 'personalized medicine' for lung cancer.

High-resolution computed tomography screening for lung cancer: unexpected findings and new controversies regarding adenocarcinogenesis

CONTEXT

Recent advances in human imaging technologies reawakened interest in lung cancer screening. Although historic and current preliminary and noncontrolled studies have not shown a decrease in lung cancer mortality in screened populations, many explanations have been proffered while the lung cancer community awaits the results of several large controlled population studies.

OBJECTIVE

To critically review the current model of adenocarcinoma development against the background of lung cancer screening results combined with observational pathologic and radiographic studies.

DATA SOURCES

Published articles pertaining to lung cancer screening, lung adenocarcinoma pathology, and radiology accessible through PubMed form the basis for this review.

CONCLUSIONS

The current adenocarcinogenesis model is probably valid for many but not all lung adenocarcinomas. Screening data combined with radiographic and pathologic studies suggest that not all lung adenocarcinomas are clinically aggressive, and it is uncertain whether all aggressive adenocarcinomas arise from identified precursors.

High-throughput molecular analysis in lung cancer: insights into biology and potential clinical applications

During the last decade, high-throughput technologies including genomic, epigenomic, transcriptomic and proteomic have been applied to further our understanding of the molecular pathogenesis of this heterogeneous disease, and to develop strategies that aim to improve the management of patients with lung cancer. Ultimately, these approaches should lead to sensitive, specific and noninvasive methods for early diagnosis, and facilitate the prediction of response to therapy and outcome, as well as the identification of potential novel therapeutic targets. Genomic studies were the first to move this field forward by providing novel insights into the molecular biology of lung cancer and by generating candidate biomarkers of disease progression. Lung carcinogenesis is driven by genetic and epigenetic alterations that cause aberrant gene function; however, the challenge remains to pinpoint the key regulatory control mechanisms and to distinguish driver from passenger alterations that may have a small but additive effect on cancer development. Epigenetic regulation by DNA methylation and histone modifications modulate chromatin structure and, in turn, either activate or silence gene expression. Proteomic approaches critically complement these molecular studies, as the phenotype of a cancer cell is determined by proteins and cannot be predicted by genomics or transcriptomics alone. The present article focuses on the technological platforms available and some proposed clinical applications. We illustrate herein how the "-omics" have revolutionised our approach to lung cancer biology and hold promise for personalised management of lung cancer.

An embryonic stem cell-like signature identifies poorly differentiated lung adenocarcinoma but not squamous cell carcinoma

PURPOSE

An embryonic stem cell (ESC) profile correlates with poorly differentiated breast, bladder, and glioma cancers. In this article, we assess the correlation between the ESC profile and clinical variables in lung cancer.

EXPERIMENTAL DESIGN

Microarray gene expression analysis was done using Affymetrix Human Genome U133A on 443 samples of human lung adenocarcinoma and 130 samples of squamous cell carcinoma (SCC). To identify gene set enrichment patterns, we used the Genomica software.

RESULTS

Our analysis showed that an increased expression of the ESC gene set and a decreased expression of the Polycomb target gene set identified poorly differentiated lung adenocarcinoma. In addition, this gene expression signature was associated with markers of poor prognosis and worse overall survival in lung adenocarcinoma. However, there was no correlation between this ESC gene signature and any histologic or clinical variable assessed in lung SCC.

CONCLUSIONS

This work suggests that not all poorly differentiated non-small cell lung cancers exhibit a gene expression profile similar to that of ESC, and that other characteristics may play a more important role in the determination of differentiation and survival in SCC of the lung.