Detection method has independent prognostic significance in the PLCO lung screening trial

Prognostic models in cancer use patient demographic and tumor characteristics to predict survival and dynamic disease prognosis. Past work in breast cancer has shown that cancer detection method, screen-detected or symptom-detected, has prognostic significance. We investigate this phenomenon in the lung component of the Prostate, Lung, Colorectal, and Ovarian (PLCO) screening trial. Patients were randomized to intervention, receiving four annual chest x-rays (CXRs), or to control, receiving usual care. Patients were followed for a total of approximately 13 years. In PLCO, lung cancer detection method has independent prognostic value exceeding that of variables commonly used in lung cancer prognostic models, including sex, histology, and age. Results are robust to cohort selection and type of predictive model. These results imply that detection method should be considered when developing prognostic models in lung cancer studies, and cancer registries should routinely collect cancer detection method.

Radiomics and artificial intelligence for precision medicine in lung cancer treatment

Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.

Prognostic value of Beclin 1, EGFR and ALK in non-squamous non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is one of the most malignant tumors. The study was carried out to investigate the prognostic value of Beclin 1, EGFR and ALK for this cancer. Patients diagnosed with non-squamous NSCLC and admitted to our hospital from January 2011 to September 2016 were analyzed. Expression of Beclin 1 and mutation of EGFR and ALK were assessed using polymerase chain reaction (PCR) and fluorescent in situ hybridization (FISH) and analyzed for their relationship with demographic and clinical characteristics of the patients. Multivariate Cox regression models were applied to analyze the risk factors associated with survival and receiver response curves (ROC) were plotted to determine the prognostic value of Beclin 1, EGFR and ALK for patients with non-squamous NSCLC. Compared with adjacent normal tissue, Beclin 1 expression was elevated in the cancer tissue significantly; assessments of EGFR and ALK mutations showed that out of the 480 patients, 233 (48.5%) and 75 (12.6%) patients had EGFR and ALK mutations. Univariate analysis revealed that Beclin 1 level, EGFR and ALK mutations were associated with lymph node metastasis, TNM stage, tumor differentiation and prognosis, but not with gender, age and smoking status. The Kaplan-Meier survival analysis indicated that low Beclin 1 expression and positive EGFR and ALK rearrangements were associated with higher survival rate and longer progress-free survival (PFS). Multivariate Cox regression analysis showed that Beclin 1, EGFR, ALK mutations, tumor differentiation grade, TNM stage and lymph node metastasis were independently associated with PFS. ROC analysis showed that Beclin 1, EGFR and ALK were significant predictors for PFS; the areas under curve (AUC) for Beclin 1, EGFR and ALK were 0.812 (P = 0.018, cut-off value: 1.2), 0.781 (P = 0.011, cut-off value: 15%) and 0.722 (P = 0.010, cut-off value: 11%), respectively, suggesting that they have significant prognostic value for lung cancer patients. Our data indicate that Beclin 1, EGFR and ALK genes are associated with the prognosis of patients with non-squamous NSCLC. High Beclin 1 expression and negative EGFR and ALK mutations predict a poor prognosis with PFS.

Genetic and immunologic features of recurrent stage I lung adenocarcinoma

Although surgery for early-stage lung cancer offers the best chance of cure, recurrence still occurs between 30 and 50% of the time. Why patients frequently recur after complete resection of early-stage lung cancer remains unclear. Using a large cohort of stage I lung adenocarcinoma patients, distinct genetic, genomic, epigenetic, and immunologic profiles of recurrent tumors were analyzed using a novel recurrence classifier. To characterize the tumor immune microenvironment of recurrent stage I tumors, unique tumor-infiltrating immune population markers were identified using single cell RNA-seq on a separate cohort of patients undergoing stage I lung adenocarcinoma resection and applied to a large study cohort using digital cytometry. Recurrent stage I lung adenocarcinomas demonstrated higher mutation and lower methylation burden than non-recurrent tumors, as well as widespread activation of known cancer and cell cycle pathways. Simultaneously, recurrent tumors displayed downregulation of immune response pathways including antigen presentation and Th1/Th2 activation. Recurrent tumors were depleted in adaptive immune populations, and depletion of adaptive immune populations and low cytolytic activity were prognostic of stage I recurrence. Genomic instability and impaired adaptive immune responses are key features of stage I lung adenocarcinoma immunosurveillance escape and recurrence after surgery.

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.

Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer

Recent technological advances and the application of high-throughput mutation and transcriptome analyses have improved our understanding of cancer diseases, including non-small cell lung cancer. For instance, genomic profiling has allowed the identification of mutational events which can be treated with specific agents. However, detection of DNA alterations does not fully recapitulate the complexity of the disease and it does not allow selection of patients that benefit from chemo- or immunotherapy. In this context, transcriptional profiling has emerged as a promising tool for patient stratification and treatment guidance. For instance, transcriptional profiling has proven to be especially useful in the context of acquired resistance to targeted therapies and patients lacking targetable genomic alterations. Moreover, the comprehensive characterization of the expression level of the different pathways and genes involved in tumor progression is likely to better predict clinical benefit from different treatments than single biomarkers such as PD-L1 or tumor mutational burden in the case of immunotherapy. However, intrinsic technical and analytical limitations have hindered the use of these expression signatures in the clinical setting. In this review, we will focus on the data reported on molecular classification of non-small cell lung cancer and discuss the potential of transcriptional profiling as a predictor of survival and as a patient stratification tool to further personalize treatments.

Novel prognostic model for stratifying survival in stage I lung adenocarcinoma patients

PURPOSE

We combined conventional clinical and pathological characteristics and pathological architectural grading scores to develop a prognostic model to identify a specific group of patients with stage I lung adenocarcinomas with poor survival following surgery.

METHODS

This retrospective study included 198 patients with stage I lung adenocarcinomas recruited from 2004 to 2013. Multivariate analyses were used to confirm independent risk factors, which were checked for internal validity using the bootstrapping method. The prognostic scores, derived from β-coefficients using the Cox regression model, classified patients into high- and low-risk groups. The predictive performance and discriminative ability of the model were assessed by the area under the receiver operating characteristic curve (AUC), concordance index (C-index) and Kaplan-Meier survival analyses.

RESULTS

Three risk factors were identified: T2 (rounding of β-coefficients = 81), necrosis (rounding of β-coefficients = 67), and pathological architectural score of 5-6 (rounding of β-coefficients = 58). The final prognostic score was the sum of points. The derived prognostic scores stratified patients into low- (score ≤ 103) and high- (score > 103) risk groups, with significant differences in 5-year overall survival (high vs. low risk: 49.3% vs. 88.0%, respectively; hazard ratio: 4.55; p < 0.001). The AUC for the proposed model was 0.717. The C-index of the model was 0.693.

CONCLUSION

An integrated prognostic model was developed to discriminate resected stage I adenocarcinoma patients into low- and high-risk groups, which will help clinicians select individual treatment strategies.

Incorporation of a Molecular Prognostic Classifier Improves Conventional Non-Small Cell Lung Cancer Staging

INTRODUCTION

Despite adoption of molecular biomarkers in the management of NSCLC, the recently adopted eighth edition of the TNM staging system utilized only clinicopathologic characteristics and validated improvement in risk stratification of early-stage disease has remained elusive. We therefore evaluated the integration of a clinically validated molecular prognostic classifier into conventional staging.

METHODS

A novel staging system, the TNMB (with the B denoting biology) system, which integrates a 14-gene molecular prognostic classifier into the eighth edition of the TNM staging system, was developed by using data from 321 patients with NSCLC at the University of California, San Francisco. The TNMB staging system was subsequently validated in an independent, multicenter cohort of 1373 patients, and its implementation was compared with adoption of the seventh and eighth edition staging systems utilizing metrics of reclassification.

RESULTS

Compared with staging according to the eighth edition of the TNM system, the TNMB staging system enhanced the identification of high-risk patients, with a net reclassification improvement of 0.33 (95% confidence interval [CI]: 0.24-0.41). It better predicted differences in survival, with a relative integrated discrimination improvement of 22.1% (95% CI: 8.8%-35.3%), and it improved agreement between observed and predicted survival, with a decrease in the reclassification calibration statistic of from 39 to 21. The seventh and eighth editions failed to change the net reclassification improvement (0.01 [95% CI: -0.04 to 0.03] and 0.03 [95% CI: 0.00 to 0.06], respectively) or relative integrated discrimination improvement (2.1% [95% CI: -5.8 to 9.9] and -2.5% [95% CI: -17.6 to 12.4], respectively); in addition, the eighth edition worsened calibration, with an increase in the reclassification calibration statistic from 23 to 25.

CONCLUSIONS

Incorporation of a molecular prognostic classifier significantly improved identification of high-risk patients and survival predictions compared with when conventional staging is used. The TNMB staging system may lead to improved survival of early-stage disease through more effective application of adjuvant therapy.

Prediction of head and neck squamous cell carcinoma survival based on the expression of 15 lncRNAs

Recent evidence suggests that long noncoding RNAs (lncRNAs) are essential regulators of many cancer-related processes, including cancer cell proliferation, invasion, and migration. There is thus a reason to believe that the detection of lncRNAs may be useful as a diagnostic and prognostic strategy for cancer detection, however, at present no effective genome-wide tests are available for clinical use, constraining the use of such a strategy. In this study, we performed a comprehensive assessment of lncRNAs expressed in samples in the head and neck squamous cell carcinoma (HNSCC) cohort available in The Cancer Genome Atlas database. A risk score (RS) model was constructed based on the expression data of these 15 lncRNAs in the validation data set of HNSCC patients and was subsequently validated in validation data set and the entire data set. We were able to stratify patients into high- and low-risk categories, using our lncRNA expression panel to determine an RS, with significant differences in overall survival (OS) between these two groups in our test set (median survival, 1.863 vs. 5.484 years; log-rank test, p < 0.001). We were able to confirm the predictive value of our 15-lncRNA signature using both a validation data set and a full data set, finding our signature to be reproducible and effective as a means of predicting HNSCC patient OS. Through the multivariate Cox regression and stratified analyses, we were further able to confirm that the predictive value of this RS was independent of other predictive factors such as clinicopathological parameters. The Gene set enrichment analysis revealed potential functional roles for these 15 lncRNAs in tumor progression. Our findings indicate that an RS established based on a panel of lncRNA expression signatures can effectively predict OS and facilitate patient stratification in HNSCC.

Computational immune profiling in lung adenocarcinoma reveals reproducible prognostic associations with implications for immunotherapy

Non-small cell lung cancer is one of the leading causes of cancer-related death in the world. Lung adenocarcinoma, the most common type of non-small cell lung cancer, has been well characterized as having a dense lymphocytic infiltrate, suggesting that the immune system plays an active role in shaping this cancer's growth and development. Despite these findings, our understanding of how this infiltrate affects patient prognosis and its association with lung adenocarcinoma-specific clinical factors remains limited. To address these questions, we inferred the infiltration level of six distinct immune cell types from a series of four lung adenocarcinoma gene expression datasets. We found that naive B cell, CD8+ T cell, and myeloid cell-derived expression signals of immune infiltration were significantly predictive of patient survival in multiple independent datasets, with B cell and CD8+ T cell infiltration associated with prolonged prognosis and myeloid cell infiltration associated with shorter survival. These associations remained significant even after accounting for additional clinical variables. Patients stratified by smoking status exhibited decreased CD8+ T cell infiltration and altered prognostic associations, suggesting potential immunosuppressive mechanisms in smokers. Survival analyses accounting for immune checkpoint gene expression and cellular immune infiltrate indicated checkpoint protein-specific modulatory effects on CD8+ T cell and B cell function that may be associated with patient sensitivity to immunotherapy. Together, these analyses identified reproducible associations that can be used to better characterize the role of immune infiltration in lung adenocarcinoma and demonstrate the utility in using computational approaches to systematically characterize tissue-specific tumor-immune interactions.

A P53-Deficiency Gene Signature Predicts Recurrence Risk of Patients with Early-Stage Lung Adenocarcinoma

Background: Lung cancer is associated with the highest mortality rate of all cancer types, and the most common histologic subtype of lung cancer is adenocarcinoma. To apply more effective therapeutic treatment, molecular markers that are able to predict the recurrence risk of patients with adenocarcinoma are critically needed. Mutations in TP53 tumor suppressor gene have been found in approximately 50% of lung adenocarcinoma cases, but the presence of a TP53 mutation does not always associate with increased mortality.Methods: The Cancer Genome Atlas RNA sequencing data of lung adenocarcinoma were used to define a novel gene signature for P53 deficiency. This signature was then used to calculate a sample-specific P53 deficiency score based on a patient's transcriptomic profile and tested in four independent lung adenocarcinoma microarray datasets.Results: In all datasets, P53 deficiency score was a significant predictor for recurrence-free survival where high P53 deficiency score was associated with poor survival. The score was prognostic even after adjusting for several key clinical variables including age, tumor stage, smoking status, and P53 mutation status. Furthermore, the score was able to predict recurrence-free survival in patients with stage I adenocarcinoma and was also associated with smoking status.Conclusions: The P53 deficiency score was a better predictor of recurrence-free survival compared with P53 mutation status and provided additional prognostic values to established clinical factors.Impact: The P53 deficiency score can be used to stratify early-stage patients into subgroups based on their risk of recurrence for aiding physicians to decide personalized therapeutic treatment. Cancer Epidemiol Biomarkers Prev; 27(1); 86-95. ©2017 AACR.

A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer

Background

Accumulated evidence suggests that dysregulated expression of long non-coding RNAs (lncRNAs) may play a critical role in tumorigenesis and prognosis of cancer, indicating the potential utility of lncRNAs as cancer prognostic or diagnostic markers. However, the power of lncRNA signatures in predicting the survival of patients with non-small cell lung cancer (NSCLC) has not yet been investigated.

Methods

We performed an array-based transcriptional analysis of lncRNAs in large patient cohorts with NSCLC by repurposing microarray probes from the gene expression omnibus database. A risk score model was constructed based on the expression data of these eight lncRNAs in the training dataset of NSCLC patients and was subsequently validated in other two independent NSCLC datasets. The biological implications of prognostic lncRNAs were also analyzed using the functional enrichment analysis.

Results

An expression pattern of eight lncRNAs was found to be significantly associated with overall survival (OS) of NSCLC patients in the training dataset. With the eight-lncRNA signature, patients of the training dataset could be classified into high- and low-risk groups with significantly different OS (median survival 1.67 vs. 6.06 years, log-rank test p = 4.33E−09). The prognostic power of eight-lncRNA signature was further validated in other two non-overlapping independent NSCLC cohorts, demonstrating good reproducibility and robustness of this eight-lncRNA signature in predicting OS of NSCLC patients. Multivariate regression and stratified analysis suggested that the prognostic power of the eight-lncRNA signature was independent of clinical and pathological factors. Functional enrichment analyses revealed potential functional roles of the eight prognostic lncRNAs in tumorigenesis.

Conclusions

These findings indicate that the eight-lncRNA signature may be an effective independent prognostic molecular biomarker in the prediction of NSCLC patient survival.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0556-3) contains supplementary material, which is available to authorized users.

International trial of adjuvant therapy in high risk stage I non-squamous cell carcinoma identified by a 14-gene prognostic signature

There is widespread agreement amongst clinical oncologists that more refined risk-stratification in early-stage lung cancer patients beyond conventional TNM staging is needed. Over the past decade, a number of molecular prognostic signatures have been designed to meet this need by correlating patterns in the differences in gene expression or modification to patient prognosis. Unfortunately, the majority of proposed signatures are not amenable to practical widespread implementation or have not yet undergone large-scale, rigorous clinical validation. A practical 14-gene prognostic signature that has undergone large-scale blinded independent validation is now ready for widespread clinical use. An international clinical trial is underway that has been designed to document the precise degree of benefit derived from adjuvant therapy in high-risk stage I patients identified by the 14-gene prognostic assay.

Biological Marker Analysis as Part of the CIBERES-RTIC Cancer-SEPAR Strategic Project on Lung Cancer

The aim of the Clinical and Molecular Staging of Stage I-IIp Lung Cancer Project is to identify molecular variables that improve the prognostic and predictive accuracy of TMN classification in stage I/IIp non-small cell lung cancer (NSCLC). Clinical data and lung tissue, tumor and blood samples will be collected from 3 patient cohorts created for this purpose. The prognostic protein signature will be validated from these samples, and micro-RNA, ALK, Ros1, Pdl-1, and TKT, TKTL1 y G6PD expression will be analyzed. Tissue inflammatory markers and stromal cell markers will also be analyzed. Methylation of p16, DAPK, RASSF1a, APC and CDH13 genes in the tissue samples will be determined, and inflammatory markers in peripheral blood will also be analyzed. Variables that improve the prognostic and predictive accuracy of TNM in NSCLC by molecular staging may be identified from this extensive analytical panel.

Using logistic regression to improve the prognostic value of microarray gene expression data sets: application to early-stage squamous cell carcinoma of the lung and triple negative breast carcinoma

BACKGROUND

Numerous microarray-based prognostic gene expression signatures of primary neoplasms have been published but often with little concurrence between studies, thus limiting their clinical utility. We describe a methodology using logistic regression, which circumvents limitations of conventional Kaplan Meier analysis. We applied this approach to a thrice-analyzed and published squamous cell carcinoma (SQCC) of the lung data set, with the objective of identifying gene expressions predictive of early death versus long survival in early-stage disease. A similar analysis was applied to a data set of triple negative breast carcinoma cases, which present similar clinical challenges.

METHODS

Important to our approach is the selection of homogenous patient groups for comparison. In the lung study, we selected two groups (including only stages I and II), equal in size, of earliest deaths and longest survivors. Genes varying at least four-fold were tested by logistic regression for accuracy of prediction (area under a ROC plot). The gene list was refined by applying two sliding-window analyses and by validations using a leave-one-out approach and model building with validation subsets. In the breast study, a similar logistic regression analysis was used after selecting appropriate cases for comparison.

RESULTS

A total of 8594 variable genes were tested for accuracy in predicting earliest deaths versus longest survivors in SQCC. After applying the two sliding window and the leave-one-out analyses, 24 prognostic genes were identified; most of them were B-cell related. When the same data set of stage I and II cases was analyzed using a conventional Kaplan Meier (KM) approach, we identified fewer immune-related genes among the most statistically significant hits; when stage III cases were included, most of the prognostic genes were missed. Interestingly, logistic regression analysis of the breast cancer data set identified many immune-related genes predictive of clinical outcome.

CONCLUSIONS

Stratification of cases based on clinical data, careful selection of two groups for comparison, and the application of logistic regression analysis substantially improved predictive accuracy in comparison to conventional KM approaches. B cell-related genes dominated the list of prognostic genes in early stage SQCC of the lung and triple negative breast cancer.

Cost-effectiveness of a 14-gene risk score assay to target adjuvant chemotherapy in early stage non-squamous non-small cell lung cancer

Life Technologies has developed a 14-gene molecular assay that provides information about the risk of death in early stage non-squamous non-small cell lung cancer patients after surgery. The assay can be used to identify patients at highest risk of mortality, informing subsequent treatments. The objective of this study was to evaluate the cost-effectiveness of this novel assay. Patients and Methods. We developed a Markov model to estimate life expectancy, quality-adjusted life years (QALYs), and costs for testing versus standard care. Risk-group classification was based on assay-validation studies, and chemotherapy uptake was based on pre- and post-testing recommendations from a study of 58 physicians. We evaluated three chemotherapy-benefit scenarios: moderately predictive (base case), nonpredictive (i.e., the same benefit for each risk group), and strongly predictive. We calculated the incremental cost-effectiveness ratio (ICER) and performed one-way and probabilistic sensitivity analyses. Results. In the base case, testing and standard-care strategies resulted in 6.81 and 6.66 life years, 3.76 and 3.68 QALYs, and $122,400 and $118,800 in costs, respectively. The ICER was $23,200 per QALY (stage I: $29,200 per QALY; stage II: $12,200 per QALY). The ICER ranged from "dominant" to $92,100 per QALY in the strongly predictive and nonpredictive scenarios. The model was most sensitive to the proportion of high-risk patients receiving chemotherapy and the high-risk hazard ratio. The 14-gene risk score assay strategy was cost-effective in 68% of simulations. Conclusion. Our results suggest that the 14-gene risk score assay may be a cost-effective alternative to standard guideline-based adjuvant chemotherapy decision making in early stage non-small cell lung cancer.

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.

Validation of a proliferation-based expression signature as prognostic marker in early stage lung adenocarcinoma

PURPOSE

New prognostic markers to guide treatment decisions in early stage non-small cell lung cancer are necessary to improve patient outcomes. In this report, we assess the utility of a predefined mRNA expression signature of cell-cycle progression genes (CCP score) to define 5-year risk of lung cancer-related death in patients with early stage lung adenocarcinoma.

EXPERIMENTAL DESIGN

A CCP score was calculated from the mRNA expression levels of 31 proliferation genes in stage I and stage II tumor samples from two public microarray datasets [Director's Consortium (DC) and GSE31210]. The same gene set was tested by quantitative PCR in 381 formalin-fixed paraffin-embedded (FFPE) primary tumors. Association of the CCP score with outcome was assessed by Cox proportional hazards analysis.

RESULTS

In univariate analysis, the CCP score was a strong predictor of cancer-specific survival in both the Director's Consortium cohort (P = 0.00014; HR = 2.08; 95% CI, 1.43-3.02) and GSE31210 (P = 0.0010; HR = 2.25; 95% CI, 1.42-3.56). In multivariate analysis, the CCP score remained the dominant prognostic marker in the presence of clinical variables (P = 0.0022; HR = 2.02; 95% CI, 1.29-3.17 in Director's Consortium, P = 0.0026; HR = 2.16; 95% CI, 1.32-3.53 in GSE31210). On a quantitative PCR platform, the CCP score maintained highly significant prognostic value in FFPE-derived mRNA from clinical samples in both univariate (P = 0.00033; HR = 2.10; 95% CI, 1.39-3.17) and multivariate analyses (P = 0.0071; HR = 1.92; 95% CI, 1.18-3.10).

CONCLUSIONS

The CCP score is a significant predictor of lung cancer death in early stage lung adenocarcinoma treated with surgery and may be a valuable tool in selecting patients for adjuvant treatment.

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.

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.

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.

Prognostic and predictive value of a malignancy-risk gene signature in early-stage non-small cell lung cancer

BACKGROUND

The malignancy-risk gene signature is composed of numerous proliferative genes and has been applied to predict breast cancer risk. We hypothesized that the malignancy-risk gene signature has prognostic and predictive value for early-stage non-small cell lung cancer (NSCLC) patients.

METHODS

The ability of the malignancy-risk gene signature to predict overall survival (OS) of early-stage NSCLC patients was tested using a large NSCLC microarray dataset from the Director's Challenge Consortium (n = 442) and two independent NSCLC microarray datasets (n = 117 and 133, for the GSE13213 and GSE14814 datasets, respectively). An overall malignancy-risk score was generated by principal component analysis to determine the prognostic and predictive value of the signature. An interaction model was used to investigate a statistically significant interaction between adjuvant chemotherapy (ACT) and the gene signature. All statistical tests were two-sided.

RESULTS

The malignancy-risk gene signature was statistically significantly associated with OS (P < .001) of NSCLC patients. Validation with the two independent datasets demonstrated that the malignancy-risk score had prognostic and predictive values: Of patients who did not receive ACT, those with a low malignancy-risk score had increased OS compared with a high malignancy-risk score (P = .007 and .01 for the GSE13212 and GSE14814 datasets, respectively), indicating a prognostic value; and in the GSE14814 dataset, patients receiving ACT survived longer in the high malignancy-risk score group (P = .03), and a statistically significant interaction between ACT and the signature was observed (P = .02).

CONCLUSIONS

The malignancy-risk gene signature was associated with OS and was a prognostic and predictive indicator. The malignancy-risk gene signature could be useful to improve prediction of OS and to identify those NSCLC patients who will benefit from ACT.

TOPK/PBK promotes cell migration via modulation of the PI3K/PTEN/AKT pathway and is associated with poor prognosis in lung cancer

We integrated four gene expression profile data sets, namely two different pair-matched stage I lung adenocarcinoma data sets, secondary metastatic tumors vs benign tumors and lung tumor metastasizes to the brain, and we identified one kinase, T-LAK Cell-Originated Protein Kinase (TOPK), as a putative gene that promotes metastasis. To delineate the role of TOPK in lung cancer, we showed that overexpression of TOPK, but not a catalytically inactive form of TOPK, can enhance the migration and invasion of lung fibroblasts or cells with low TOPK expression. In addition, TOPK-induced cell migration was shown to be a PI3K/AKT-dependent event. TOPK concurrently promoted AKT phosphorylation at Ser(473) and decreased the phosphatase and tensin homolog (PTEN) levels, whereas TOPK knockdown had the reverse effects. LY294002, a PI3K inhibitor, did not inhibit the TOPK-induced decrease in PTEN, and co-expression of PTEN significantly reduced TOPK-induced AKT phosphorylation in a dose-dependent manner; these results indicate that the TOPK-mediated PTEN decrease has an upstream role in regulating PI3K/AKT-stimulated migration. Using immunohistochemical analysis of lung cancer tissue samples, we showed that a high TOPK expression level correlates strongly with reduced overall and disease-free survivals. Moreover, an inverse correlation between TOPK and PTEN expression was present and is consistent with the biochemical findings. Finally, a combination of high TOPK and low PTEN expression was inversely correlated with overall and disease-free survivals, independent of other pathologic staging factors. Our results suggest that TOPK is a potential therapeutic target in lung cancer that promotes cell migration by modulating a PI3K/PTEN/AKT-dependent signaling pathway; they also suggest that high TOPK expression, either alone or in combination with a low level of PTEN, may serve as a prognostic marker for lung cancer.

Varied pathways of stage IA lung adenocarcinomas discovered by integrated gene expression analysis

BACKGROUND

Discovery of the progression-associated genes and pathways in lung adenocarcinoma (LAD) has important implications in understanding the molecular mechanism of tumor development. However, few studies had been performed to focus on the changes of pathways in lung adenocarcinoma development using microarray expression profile.

RESULT

We performed a meta-analysis of 4 LAD microarray datasets encompassing 353 patients to reveal differentially expressed genes (DEGs) between normal lung tissues and LAD of different stages. Overall, 1 838 genes were found to be dys-regulated, and the adipogenesis, circadian rhythm, and Id pathways were significantly changed. Interestingly, most of the genes from the same gene family (such as Interleukin receptor, Matrix metallopeptidase, Histone cluster and Minichromosome maintenance complex component families) were found to be up-regulated (or down-regulated). Real-time PCR (qRT-PCR) was applied to validate the expression of randomly selected 18 DEGs in LAD cell lines. In the pathway analysis among stages, Oxidative stress, Glycolysis/Gluconeogenesis and Integrin-mediated cell adhesion pathways, which were involved in cancer cell proliferation and metastasis, were showed to be significantly regulated in stages other than IA.

CONCLUSION

Genes involved in adipogenesis and Id pathways might play important roles in development of LADs. The similar trend of expression of the gene family members suggested coordinate regulation in tumor progression. Three pathways (Oxidative stress, Glycolysis/Gluconeogenesis and Integrin-mediated cell adhesion pathways) significantly regulated in stages other than stage IA suggested that genes and pathways conferring invasive character might be activated in the preinvasive stage IB, while the Oxidative stress and the Glycolysis/Gluconeogenesis pathways might have strong connections to cisplatin-based chemotherapy. The insignificantly regulated three pathways in stage IA might be used in early-stage detection of LAD.

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.

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.

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.