Survival prediction of stage I lung adenocarcinomas by expression of 10 genes

Molecular Radiobiology in Non-Small Cell Lung Cancer: Prognostic and Predictive Response Factors

Simple Summary

The identification of prognostic and predictive gene signatures of response to cancer treatment (radiotherapy) could help in making therapeutic decisions in patients affected by NSCLC. There are multiple proposals for gene signatures that attempt to predict survival or predict response to treatment (not radiotherapy), but they mainly focus on early stages or metastasis at diagnosis. In contrast, there have been few studies that raise these predictive and/or prognostic elements in nonmetastatic locally advanced stages, where treatment with ionizing radiation plays an important role. In this work, we review in depth previous works discovering the prognostic and predictive response factors in non-small cell lung cancer, specially focused on non-deeply studied radiation-based therapy.

Abstract

Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide, generating huge economic and social impacts that have not slowed in recent years. Oncological treatment for this neoplasm usually includes surgery, chemotherapy, treatments on molecular targets and ionizing radiation. The prognosis in terms of overall survival (OS) and the different therapeutic responses between patients can be explained, to a large extent, by the existence of widely heterogeneous molecular profiles. The identification of prognostic and predictive gene signatures of response to cancer treatment, could help in making therapeutic decisions in patients affected by NSCLC. Given the published scientific evidence, we believe that the search for prognostic and/or predictive gene signatures of response to radiotherapy treatment can significantly help clinical decision-making. These signatures may condition the fractions, the total dose to be administered and/or the combination of systemic treatments in conjunction with radiation. The ultimate goal is to achieve better clinical results, minimizing the adverse effects associated with current cancer therapies.

A Six-Gene Prognostic and Predictive Radiotherapy-Based Signature for Early and Locally Advanced Stages in Non-Small-Cell Lung Cancer

Simple Summary

The search for prognostic and/or predictive gene signatures of the response to radiotherapy treatment can significantly aid clinical decision making. These signatures can condition the fractionation, the total dose to be administered, and/or the combination of systemic treatments and radiation. The ultimate goal is to achieve better clinical results, as well as to minimize the adverse effects associated with current cancer therapies. To this end, we analyzed the intrinsic radiosensitivity of 15 NSCLC lines and found the differences in gene expression levels between radiosensitive and radioresistant lines, resulting in a potentially applicable six-gene signature in NSCLC patients. The six-gene signature had the ability to predict overall survival and progression-free survival (PFS), which could translate into a prediction of the response to the cancer treatment received.

Abstract

Non-small-cell lung cancer (NSCLC) is the leading cause of cancer death worldwide, generating an enormous economic and social impact that has not stopped growing in recent years. Cancer treatment for this neoplasm usually includes surgery, chemotherapy, molecular targeted treatments, and ionizing radiation. The prognosis in terms of overall survival (OS) and the disparate therapeutic responses among patients can be explained, to a great extent, by the existence of widely heterogeneous molecular profiles. The main objective of this study was to identify prognostic and predictive gene signatures of response to cancer treatment involving radiotherapy, which could help in making therapeutic decisions in patients with NSCLC. To achieve this, we took as a reference the differential gene expression pattern among commercial cell lines, differentiated by their response profile to ionizing radiation (radiosensitive versus radioresistant lines), and extrapolated these results to a cohort of 107 patients with NSCLC who had received radiotherapy (among other therapies). We obtained a six-gene signature (APOBEC3B, GOLM1, FAM117A, KCNQ1OT1, PCDHB2, and USP43) with the ability to predict overall survival and progression-free survival (PFS), which could translate into a prediction of the response to the cancer treatment received. Patients who had an unfavorable prognostic signature had a median OS of 24.13 months versus 71.47 months for those with a favorable signature, and the median PFS was 12.65 months versus 47.11 months, respectively. We also carried out a univariate analysis of multiple clinical and pathological variables and a bivariate analysis by Cox regression without any factors that substantially modified the HR value of the proposed gene signature.

Aggressive early-stage lung adenocarcinoma is characterized by epithelial cell plasticity with acquirement of stem-like traits and immune evasion phenotype

Lung adenocarcinoma (LUAD) is the main non-small-cell lung cancer diagnosed in ~40-50% of all lung cancer cases. Despite the improvements in early detection and personalized medicine, even a sizable fraction of patients with early-stage LUAD would experience disease relapses and adverse prognosis. Previous reports indicated the existence of LUAD molecular subtypes characterized by specific gene expression and mutational profiles, and correlating with prognosis. However, the biological and molecular features of such subtypes have not been further explored. Consequently, the mechanisms driving the emergence of aggressive LUAD remained unclear. Here, we adopted a multi-tiered approach ranging from molecular to functional characterization of LUAD and used it on multiple cohorts of patients (for a total of 1227 patients) and LUAD cell lines. We investigated the tumor transcriptome and the mutational and immune gene expression profiles, and we used LUAD cell lines for cancer cell phenotypic screening. We found that loss of lung cell lineage and gain of stem cell-like characteristics, along with mutator and immune evasion phenotypes, explain the aggressive behavior of a specific subset of lung adenocarcinoma that we called C1-LUAD, including early-stage disease. This subset can be identified using a 10-gene prognostic signature. Poor prognosis patients appear to have this specific molecular lung adenocarcinoma subtype which is characterized by peculiar molecular and biological features. Our data support the hypothesis that transformed lung stem/progenitor cells and/or reprogrammed epithelial cells with CSC characteristics are hallmarks of this aggressive disease. Such discoveries suggest alternative, more aggressive, therapeutic strategies for early-stage C1-LUAD.

Establishment and validation of an eight-gene metabolic-related prognostic signature model for lung adenocarcinoma

In this study, we constructed an eight-gene metabolic related signature for LUAD. The eight-gene prognostic signature (including PLAUR, F2, UGT2B17, GNG7, IDO2, ST3GAL6, PIK3CG, and GLS2) exhibited a good prognostic value in the TCGA LUAD training dataset and testing dataset. In addition, the risk score based on the signature model was significantly correlated with immune cell infiltration and expression levels of immune markers in LUAD patients. LUAD cohorts from GEO were used to validate the model, indicating the usefulness of the model. In summary, we developed and validated an eight-gene signature model for LUAD, which can reflect the immune microenvironment characteristics and predict the prognostic outcomes for LUAD patients.

Denoising Autoencoder, A Deep Learning Algorithm, Aids the Identification of A Novel Molecular Signature of Lung Adenocarcinoma

Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise to leverage very large datasets for making better predictions of disease biomarkers. Denoising autoencoder (DA) is one of the unsupervised deep learning algorithms, which is a stochastic version of autoencoder techniques. The principle of DA is to force the hidden layer of autoencoder to capture more robust features by reconstructing a clean input from a corrupted one. Here, a DA model was applied to analyze integrated transcriptomic data from 13 published lung cancer studies, which consisted of 1916 human lung tissue samples. Using DA, we discovered a molecular signature composed of multiple genes for lung adenocarcinoma (ADC). In independent validation cohorts, the proposed molecular signature is proved to be an effective classifier for lung cancer histological subtypes. Also, this signature successfully predicts clinical outcome in lung ADC, which is independent of traditional prognostic factors. More importantly, this signature exhibits a superior prognostic power compared with the other published prognostic genes. Our study suggests that unsupervised learning is helpful for biomarker development in the era of precision medicine.

Non-Coding RNAs as Prognostic Biomarkers: A miRNA Signature Specific for Aggressive Early-Stage Lung Adenocarcinomas

Lung cancer burden can be reduced by adopting primary and secondary prevention strategies such as anti-smoking campaigns and low-dose CT screening for high risk subjects (aged >50 and smokers >30 packs/year). Recent CT screening trials demonstrated a stage-shift towards earlier stage lung cancer and reduction of mortality (~20%). However, a sizable fraction of patients (30–50%) with early stage disease still experience relapse and an adverse prognosis. Thus, the identification of effective prognostic biomarkers in stage I lung cancer is nowadays paramount. Here, we applied a multi-tiered approach relying on coupled RNA-seq and miRNA-seq data analysis of a large cohort of lung cancer patients (TCGA-LUAD, n = 510), which enabled us to identify prognostic miRNA signatures in stage I lung adenocarcinoma. Such signatures showed high accuracy (AUC ranging between 0.79 and 0.85) in scoring aggressive disease. Importantly, using a network-based approach we rewired miRNA-mRNA regulatory networks, identifying a minimal signature of 7 miRNAs, which was validated in a cohort of FFPE lung adenocarcinoma samples (CSS, n = 44) and controls a variety of genes overlapping with cancer relevant pathways. Our results further demonstrate the reliability of miRNA-based biomarkers for lung cancer prognostication and make a step forward to the application of miRNA biomarkers in the clinical routine.

A Recurrence-Specific Gene-Based Prognosis Prediction Model for Lung Adenocarcinoma through Machine Learning Algorithm

Background

After curative surgical resection, about 30-75% lung adenocarcinoma (LUAD) patients suffer from recurrence with dismal survival outcomes. Identification of patients with high risk of recurrence to impose intense therapy is urgently needed.

Materials and Methods

Gene expression data of LUAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were calculated by comparing the recurrent and primary tissues. Prognostic genes associated with the recurrence-free survival (RFS) of LUAD patients were identified using univariate analysis. LASSO Cox regression and multivariate Cox analysis were applied to extract key genes and establish the prediction model.

Results

We detected 37 DEGs between primary and recurrent LUAD tumors. Using univariate analysis, 31 DEGs were found to be significantly associated with RFS. We established the RFS prediction model including thirteen genes using the LASSO Cox regression. In the training cohort, we classified patients into high- and low-risk groups and found that patients in the high-risk group suffered from worse RFS compared to those in the low-risk group (P < 0.01). Concordant results were confirmed in the internal and external validation cohort. The efficiency of the prediction model was also confirmed under different clinical subgroups. The high-risk group was significantly identified as the risk factor of recurrence in LUAD by the multivariate Cox analysis (HR = 13.37, P = 0.01). Compared to clinicopathological features, our prediction model possessed higher accuracy to identify patients with high risk of recurrence (AUC = 96.3%). Finally, we found that the G2M checkpoint pathway was enriched both in recurrent tumors and primary tumors of high-risk patients.

Conclusions

Our recurrence-specific gene-based prognostic prediction model provides extra information about the risk of recurrence in LUAD, which is conducive for clinicians to conduct individualized therapy in clinic.

Reverse engineering a predictive signature characterized by proliferation, DNA damage, and immune escape from stage I lung adenocarcinoma recurrence

Identifying early-stage cancer patients at risk for progression is a major goal of biomarker research. This report describes a novel 19-gene signature (19-GCS) that predicts stage I lung adenocarcinoma (LAC) recurrence and response to therapy and performs comparably in pancreatic adenocarcinoma (PAC), which shares LAC molecular traits. Kaplan-Meier, Cox regression, and cross-validation analyses were used to build the signature from training, test, and validation sets comprising 831 stage I LAC transcriptomes from multiple independent data sets. A statistical analysis was performed using the R language. Pathway and gene set enrichment were used to identify underlying mechanisms. 19-GCS strongly predicts overall survival and recurrence-free survival in stage I LAC (P=0.002 and P<0.001, respectively) and in stage I-II PAC (P<0.0001 and P<0.0005, respectively). A multivariate cox regression analysis demonstrated the independence of 19-GCS from significant clinical factors. Pathway analyses revealed that 19-GCS high-risk LAC and PAC tumors are characterized by increased proliferation, enhanced stemness, DNA repair deficiency, and compromised MHC class I and II antigen presentation along with decreased immune infiltration. Importantly, high-risk LAC patients do not appear to benefit from adjuvant cisplatin while PAC patients derive additional benefit from FOLFIRINOX compared with gemcitabine-based regimens. When validated prospectively, this proof-of-concept biomarker may contribute to tailoring treatment, recurrence reduction, and survival improvements in early-stage lung and pancreatic cancers.

Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method

Objectives: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. The identification of prognostic biomarkers and prediction of prognosis for LUAD patients is necessary. Materials and Methods: In this study, LUAD RNA-Seq data and clinical data from the Cancer Genome Atlas (TCGA) were divided into TCGA cohort I (n = 338) and II (n = 168). The cohort I was used for model construction, and the cohort II and data from Gene Expression Omnibus (GSE72094 cohort, n = 393; GSE11969 cohort, n = 149) were utilized for validation. First, the survival-related seed genes were selected from the cohort I using the machine learning model (random survival forest, RSF), and then in order to improve prediction accuracy, the forward selection model was utilized to identify the prognosis-related key genes among the seed genes using the clinically-integrated RNA-Seq data. Second, the survival risk score system was constructed by using these key genes in the cohort II, the GSE72094 cohort and the GSE11969 cohort, and the evaluation metrics such as HR, p value and C-index were calculated to validate the proposed method. Third, the developed approach was compared with the previous five prediction models. Finally, bioinformatics analyses (pathway, heatmap, protein-gene interaction network) have been applied to the identified seed genes and key genes. Results and Conclusion: Based on the RSF model and clinically-integrated RNA-Seq data, we identified sixteen key genes that formed the prognostic gene expression signature. These sixteen key genes could achieve a strong power for prognostic prediction of LUAD patients in cohort II (HR = 3.80, p = 1.63e-06, C-index = 0.656), and were further validated in the GSE72094 cohort (HR = 4.12, p = 1.34e-10, C-index = 0.672) and GSE11969 cohort (HR = 3.87, p = 6.81e-07, C-index = 0.670). The experimental results of three independent validation cohorts showed that compared with the traditional Cox model and the use of standalone RNA-Seq data, the machine-learning-based method effectively improved the prediction accuracy of LUAD prognosis, and the derived model was also superior to the other five existing prediction models. KEGG pathway analysis found eleven of the sixteen genes were associated with Nicotine addiction. Thirteen of the sixteen genes were reported for the first time as the LUAD prognosis-related key genes. In conclusion, we developed a sixteen-gene prognostic marker for LUAD, which may provide a powerful prognostic tool for precision oncology.

A clonal expression biomarker associates with lung cancer mortality

An aim of molecular biomarkers is to stratify patients with cancer into disease subtypes predictive of outcome, improving diagnostic precision beyond clinical descriptors such as tumor stage1. Transcriptomic intratumor heterogeneity (RNA-ITH) has been shown to confound existing expression-based biomarkers across multiple cancer types2-6. Here, we analyze multi-region whole-exome and RNA sequencing data for 156 tumor regions from 48 patients enrolled in the TRACERx study to explore and control for RNA-ITH in non-small cell lung cancer. We find that chromosomal instability is a major driver of RNA-ITH, and existing prognostic gene expression signatures are vulnerable to tumor sampling bias. To address this, we identify genes expressed homogeneously within individual tumors that encode expression modules of cancer cell proliferation and are often driven by DNA copy-number gains selected early in tumor evolution. Clonal transcriptomic biomarkers overcome tumor sampling bias, associate with survival independent of clinicopathological risk factors, and may provide a general strategy to refine biomarker design across cancer types.

A novel LncRNA-based prognostic score reveals TP53-dependent subtype of lung adenocarcinoma with poor survival

The prognostic signatures play an essential role in the era of personalised therapy for cancer patients including lung adenocarcinoma (LUAD). Long noncoding RNA (LncRNA), a relatively novel class of RNA, has shown to play a crucial role in all the areas of cancer biology. Here, we developed and validated a robust LncRNA-based prognostic signature for LUAD patients using three different cohorts. In the discovery cohort, four LncRNAs were identified with 10% false discovery rate and a hazard ratio of >10 using univariate Cox regression analysis. A risk score, generated from the four LncRNAs' expression, was found to be a significant predictor of survival in the discovery and validation cohort (p = 9.97 × 10 ^-8 and 1.41 × 10 ^-3 , respectively). Further optimisation of four LncRNAs signature in the validation cohort, generated a three LncRNAs prognostic score (LPS), which was found to be an independent predictor of survival in both the cohorts ( p = 1.00 × 10 ^-6 and 7.27 × 10 ^-4 , respectively). The LPS also significantly divided survival in clinically important subsets, including Stage I ( p = 9.00 × 10 ^-4 and 4.40 × 10 ^-2 , respectively), KRAS wild-type (WT), KRAS mutant ( p = 4.00 × 10 ^-3 and 4.30 × 10 ^-2 , respectively) and EGFR WT ( p = 2.00 × 10 ^-4 ). In multivariate analysis LPS outperformed, eight previous prognosticators. Further, individual members of LPS showed a significant correlation with survival in microarray data sets. Mutation analysis showed that high-LPS patients have a higher mutation rate and inactivation of the TP53 pathway. In summary, we identified and validated a novel LncRNA signature LPS for LUAD.

Considering smoking status, coexpression network analysis of non-small cell lung cancer at different cancer stages, exhibits important genes and pathways

Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer among smokers, nonsmokers, women, and young individuals. Tobacco smoking and different stages of the NSCLC have important roles in cancer evolution and require different treatments. Existence of poorly effective therapeutic options for the NSCLC brings special attention to targeted therapies by considering genetic alterations. In this study, we used RNA-Seq data to compare expression levels of RefSeq genes and to find some genes with similar expression levels. We utilized the "Weighted Gene Co-expression Network Analysis" method for three different datasets to create coexpressed genetic modules having relations with the smoking status and different stages of the NSCLC. Our results indicate seven important genetic modules having important associations with the smoking status and cancer stages. Based on investigated genetic modules and their biological explanation, we then identified 13 newly candidate genes and 7 novel transcription factors in association with the NSCLC, the smoking status, and cancer stages. We then examined those results using other datasets and explained our results biologically to illustrate some important genes in relation with the smoking status and metastatic stage of the NSCLC that can bring some crucial information about cancer evolution. Our genetic findings also can be used as some therapeutic targets for different clinical conditions of the NSCLC.

Recent advances in the molecular landscape of lung neuroendocrine tumors

INTRODUCTION

Neuroendocrine tumors of the lung (Lung-NETs) make up a heterogenous family of neoplasms showing neuroendocrine differentiation and encompass carcinoids and neuroendocrine carcinomas. On molecular grounds, they considered two completely distinct and separate tumor groups with no overlap of molecular alterations nor common developmental mechanisms. Areas covered: Two perspectives were evaluated based on an extensive review and rethinking of literature: (1) the current classification as an instrument to obtaining clinical and molecular insights into the context of Lung-NETs; and (2) an alternative and innovative interpretation of these tumors, proposing a tripartite separation into early aggressive primary high-grade neuroendocrine tumors (HGNET), differentiating or secondary HGNET, and indolent NET. Expert opinion: We herein provide an alternative outlook on Lung-NETs, which is a paradigm shift to current pathogenesis models and expands the understanding of these tumors.

A Pathway-Based Strategy to Identify Biomarkers for Lung Cancer Diagnosis and Prognosis

Current research has identified several potential biomarkers for lung cancer diagnosis or prognosis. However, most of these biomarkers are derived from a relatively small number of samples using algorithms at the gene level. Hence, gene expression signatures discovered in these studies have little overlaps. In this study, we proposed a new strategy to identify biomarkers from multiple datasets at the pathway level. We integrated the genome-wide expression data of lung cancer tissues from 13 published studies and applied our strategy to identify lung cancer diagnostic and prognostic biomarkers. We identified a 32-gene signature that differentiates lung adenocarcinomas from other lung cancer subtypes. We also discovered a 43-gene signature that can predict the outcome of human lung cancers. We tested their performance in several independent cohorts, which confirmed their robust prognostic and diagnostic power. Furthermore, we showed that the proposed gene expression signatures were independent of several traditional clinical indicators in lung cancer management. Our results suggest that the pathway-based strategy is useful to identify transcriptomic biomarkers from large-scale gene expression datasets that were collected from multiple sources.

5 protein-based signature for resectable lung squamous cell carcinoma improves the prognostic performance of the TNM staging

INTRODUCTION

Prognostic biomarkers have been very elusive in the lung squamous cell carcinoma (SCC) and none is currently being used in the clinical setting. We aimed to identify and validate the clinical utility of a protein-based prognostic signature to stratify patients with early lung SCC according to their risk of recurrence or death.

METHODS

Patients were staged following the new International Association for the Study of Lung Cancer (IASLC) staging criteria (eighth edition, 2018). Three independent retrospective cohorts of 117, 96 and 105 patients with lung SCC were analysed to develop and validate a prognostic signature based on immunohistochemistry for five proteins.

RESULTS

We identified a five protein-based signature whose prognostic index (PI) was an independent and significant predictor of disease-free survival (DFS) (p<0.001; HR=4.06, 95% CI 2.18 to 7.56) and overall survival (OS) (p=0.004; HR=2.38, 95% CI 1.32 to 4.31). The prognostic capability of PI was confirmed in an external multi-institutional cohort for DFS (p=0.042; HR=2.01, 95% CI 1.03 to 3.94) and for OS (p=0.031; HR=2.29, 95% CI 1.08 to 4.86). Moreover, PI added complementary information to the newly established IASLC TNM 8th edition staging system. A combined prognostic model including both molecular and anatomical (TNM) criteria improved the risk stratification in both cohorts (p<0.05).

CONCLUSION

We have identified and validated a clinically feasible protein-based prognostic model that complements the updated TNM system allowing more accurate risk stratification. This signature may be used as an advantageous tool to improve the clinical management of the patients, allowing the reduction of lung SCC mortality through a more accurate knowledge of the patient's potential outcome.

MTBP promotes migration and invasion by regulation of ZEB2-mediated epithelial-mesenchymal transition in lung cancer cells

Background

It is clearly necessary to discover prognostic biomarkers to identify stage I patients at risk of recurrence and give them timely postoperative treatment.

Materials and methods

Data of stage I lung adenocarcinoma were retrieved from four gene series in Gene Expression Omnibus (GEO) database (GSE50081, GSE30219, GSE37745, and GSE13213). Partek Genomics Suite software was used to identify survival-related genes for finding candidate indicators for early-stage patients at risk of recurrence. Differential expression of MTBP (MDM₂ binding protein) in early-stage lung adenocarcinoma tissues was determined by immunohistochemical staining. The effects of MTBP interference expression and overexpression on viability, migration, and invasion capacity of lung cells were evaluated using Cell Counting Kit-8, wound healing, and Transwell assays. The tumor growth and lung metastasis in vivo were observed in chick embryo chorioallantoic membrane model. Human Exon 2.0 ST Array was used to analyze downstream regulation genes of MTBP in lung cancer cells. Involvement of ZEB2 and epithelial–mesenchymal transition (EMT) markers was investigated by Western blot.

Results

By mining GEO database, we identified MTBP as a poor prognostic indicator of stage I lung adenocarcinomas. In addition, increased expression of MTBP was also associated with poor survival in our early-stage lung adenocarcinoma cohort. Further experiment suggested that knockdown of MTBP suppressed the migration and invasion of A549 and H1975 cells in vitro and in vivo, whereas overexpression of MTBP in HCC827 and PC9 cells promoted the migration and invasion in vitro and in vivo. Furthermore, ZEB2 upregulation directly activated EMT to mediate the downstream effects of MTBP involved in lung cancer cells metastasis.

Conclusion

MTBP is an independent indicator for poor prognosis in stage I lung adenocarcinomas and might promote the aggressive phenotype of non-small-cell lung cancer by inducing the EMT process through upregulating ZEB2 expression.

A novel protein-based prognostic signature improves risk stratification to guide clinical management in early-stage lung adenocarcinoma patients

Each of the pathological stages (I-IIIa) of surgically resected non-small-cell lung cancer has hidden biological heterogeneity, manifested as heterogeneous outcomes within each stage. Thus, the finding of robust and precise molecular classifiers with which to assess individual patient risk is an unmet medical need. Here, we identified and validated the clinical utility of a new prognostic signature based on three proteins (BRCA1, QKI, and SLC2A1) to stratify early-stage lung adenocarcinoma patients according to their risk of recurrence or death. Patients were staged according to the new International Association for the Study of Lung Cancer (IASLC) staging criteria (8th edition, 2018). A test cohort (n = 239) was used to assess the value of this new prognostic index (PI) based on the three proteins. The prognostic signature was developed by Cox regression with the use of stringent statistical criteria (TRIPOD: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis). The model resulted in a highly significant predictor of 5-year outcome for disease-free survival (p < 0.001) and overall survival (p < 0.001). The prognostic ability of the model was externally validated in an independent multi-institutional cohort of patients (n = 114, p = 0.021). We also demonstrated that this molecular classifier adds relevant information to the gold standard TNM-based pathological staging, with a highly significant improvement of the likelihood ratio. We subsequently developed a combined PI including both the molecular and the pathological data that improved the risk stratification in both cohorts (p ≤ 0.001). Moreover, the signature may help to select stage I-IIA patients who might benefit from adjuvant chemotherapy. In summary, this protein-based signature accurately identifies those patients with a high risk of recurrence and death, and adds further prognostic information to the TNM-based clinical staging, even when the new IASLC 8th edition staging criteria are applied. More importantly, it may be a valuable tool for selecting patients for adjuvant therapy. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

HOXB7 overexpression in lung cancer is a hallmark of acquired stem-like phenotype

HOXB7 is a homeodomain (HOX) transcription factor involved in regional body patterning of invertebrates and vertebrates. We previously identified HOXB7 within a ten-gene prognostic signature for lung adenocarcinoma, where increased expression of HOXB7 was associated with poor prognosis. This raises the question of how HOXB7 overexpression can influence the metastatic behavior of lung adenocarcinoma. Here, we analyzed publicly available microarray and RNA-seq lung cancer expression datasets and found that HOXB7-overexpressing tumors are enriched in gene signatures characterizing adult and embryonic stem cells (SC), and induced pluripotent stem cells (iPSC). Experimentally, we found that HOXB7 upregulates several canonical SC/iPSC markers and sustains the expansion of a subpopulation of cells with SC characteristics, through modulation of LIN28B, an emerging cancer gene and pluripotency factor, which we discovered to be a direct target of HOXB7. We validated this new circuit by showing that HOXB7 enhances reprogramming to iPSC with comparable efficiency to LIN28B or its target c-MYC, which is a canonical reprogramming factor.

Molecular gene signature and prognosis of non-small cell lung cancer

The current staging system for non–small cell lung cancer (NSCLC) is inadequate for predicting outcome. Risk score, a linear combination of the values for the expression of each gene multiplied by a weighting value which was estimated from univariate Cox proportional hazard regression, can be useful. The aim of this study is to analyze survival-related genes with TaqMan Low-Density Array (TLDA) and risk score to explore gene-signature in lung cancer. A total of 96 NSCLC specimens were collected and randomly assigned to a training (n = 48) or a testing cohort (n = 48). A panel of 219 survival-associated genes from published studies were used to develop a 6-gene risk score. The risk score was used to classify patients into high or low-risk signature and survival analysis was performed. Cox models were used to evaluate independent prognostic factors. A 6-gene signature including ABCC4, ADRBK2, KLHL23, PDS5A, UHRF1 and ZNF551 was identified. The risk score in both training (HR = 3.14, 95% CI: 1.14–8.67, p = 0.03) and testing cohorts (HR = 5.42, 95% CI: 1.56–18.84, p = 0.01) was the independent prognostic factor. In merged public datasets including GSE50081, GSE30219, GSE31210, GSE19188, GSE37745, GSE3141 and GSE31908, the risk score (HR = 1.50, 95% CI: 1.25–1.80, p < 0.0001) was also the independent prognostic factor. The risk score generated from expression of a small number of genes did perform well in predicting overall survival and may be useful in routine clinical practice.

Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma

Adenocarcinoma accounts for more than 40% of lung malignancy, and microscopic pathology evaluation is indispensable for its diagnosis. However, how histopathology findings relate to molecular abnormalities remains largely unknown. Here, we obtained H&E-stained whole-slide histopathology images, pathology reports, RNA sequencing, and proteomics data of 538 lung adenocarcinoma patients from The Cancer Genome Atlas and used these to identify molecular pathways associated with histopathology patterns. We report cell-cycle regulation and nucleotide binding pathways underpinning tumor cell dedifferentiation, and we predicted histology grade using transcriptomics and proteomics signatures (area under curve >0.80). We built an integrative histopathology-transcriptomics model to generate better prognostic predictions for stage I patients (p = 0.0182 ± 0.0021) compared with gene expression or histopathology studies alone, and the results were replicated in an independent cohort (p = 0.0220 ± 0.0070). These results motivate the integration of histopathology and omics data to investigate molecular mechanisms of pathology findings and enhance clinical prognostic prediction.

Identification of prognostic genes through expression differentiation during metastatic process in lung adenocarcinoma

Cancer is a highly complicated biological process due to large scale heterogeneity. Identification of differentially expressed genes between normal and cancer samples is widely utilized in the discovery of prognostic factors. In this study, based on RNA sequencing data of lung adenocarcinoma, we focused on the expression differentiation during confined (with neither lymph node invasion nor distant metastasis) primary tumors and lymphnode (with only lymph node invasion but not distant metastasis) primary tumors. The result indicated that differentially expressed genes during confined-lymphnode transition were more closely related to patient’s overall survival comparing with those identified from normal-cancer transition. With the aid of public curated biological network, we successfully retrieved the biggest connected module composed of 135 genes, of which the expression was significantly associated with patient’s overall survival, confirmed by 9 independent microarray datasets.

Actinin-4 protein overexpression as a predictive biomarker in adjuvant chemotherapy for resected lung adenocarcinoma

Aim: Although several clinical trials demonstrated the benefits of platinum-combination adjuvant chemotherapy for stage II–IIIA lung adenocarcinoma, predictive biomarkers for the efficacy of such therapy have not yet been identified. We evaluated protein overexpression of actinin-4 as a predictive biomarker of the efficacy of adjuvant chemotherapy in resected lung adenocarcinoma. Materials & methods: We measured actinin-4 protein levels in patients with completely resected stage II–IIIA lung adenocarcinoma using immunohistochemistry and then retrospectively compared survival between adjuvant chemotherapy and observation groups. Results: A total of 148 eligible patients were classified into actinin-4 positive or negative cases by immunohistochemistry. In the former, patients with adjuvant chemotherapy survived significantly longer than those with observation (hazard ratio [HR]: 0.307; p = 0.028). But, no significant survival benefit was noted with adjuvant chemotherapy (HR: 0.926; p = 0.876) in the latter. Conclusion: This marker could predict the efficacy of adjuvant chemotherapy for resected lung adenocarcinoma patients.

Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma

Background

Precision therapy for lung cancer will require comprehensive genomic testing to identify actionable targets as well as ascertain disease prognosis. RNA-seq is a robust platform that meets these requirements, but microarray-derived prognostic signatures are not optimal for RNA-seq data. Thus, we undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature.

Methods

Lung adenocarcinoma RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were divided chronologically into training (n = 255) and validation (n = 157) cohorts. In the training cohort, prognostic association was assessed by univariate Cox analysis. A prognostic signature was built with stepwise multivariable Cox analysis. Outcomes by risk group, stage, and mutation status were analyzed with Kaplan-Meier and multivariable Cox analyses. All the statistical tests were two-sided.

Results

In the training cohort, 96 genes had prognostic association with P values of less than or equal to 1.00x10^-4, including five long noncoding RNAs (lncRNAs). Stepwise regression generated a four-gene signature, including one lncRNA. Signature high-risk cases had worse overall survival (OS) in the TCGA validation cohort (hazard ratio [HR] = 3.07, 95% confidence interval [CI] = 2.00 to 14.62) and a University of Michigan institutional cohort (n = 67; HR = 2.05, 95% CI = 1.18 to 4.55), and worse metastasis-free survival in the TCGA validation cohort (HR = 3.05, 95% CI = 2.31 to 13.37). The four-gene prognostic signature also statistically significantly stratified overall survival in important clinical subsets, including stage I (HR = 2.78, 95% CI = 1.91 to 11.13), EGFR wild-type (HR = 3.01, 95% CI = 1.73 to 14.98), and EGFR mutant (HR = 8.99, 95% CI = 62.23 to 141.44). The four-gene prognostic signature also stood out on top when compared with other prognostic signatures.

Conclusions

Here, we present the first RNA-seq prognostic signature for lung adenocarcinoma that can provide a powerful prognostic tool for precision oncology as part of an integrated RNA-seq clinical sequencing program.

Type I interferon related genes are common genes on the early stage after vaccination by meta-analysis of microarray data

The objective of this study was to find common immune mechanism across different kinds of vaccines. A meta-analysis of microarray datasets was performed using publicly available microarray Gene Expression Omnibus (GEO) and Array Express data sets of vaccination records. Seven studies (out of 35) were selected for this meta-analysis. A total of 447 chips (145 pre-vaccination and 302 post-vaccination) were included. Significance analysis of microarrays (SAM) program was used for screening differentially expressed genes (DEGs). Functional pathway enrichment for the DEGs was conducted in DAVID Gene Ontology (GO) database. Twenty DEGs were identified, of which 10 up-regulated genes involved immune response. Six of which were type I interferon (IFN) related genes, including LY6E, MX1, OAS3, IFI44L, IFI6 and IFITM3. Ten down-regulated genes mainly mediated negative regulation of cell proliferation and cell motion. Results of a subgroup analysis showed that although the kinds of genes varied widely between days 3 and 7 post vaccination, the pathways between them are basically the same, such as immune response and response to viruses, etc. For an independent verification of these 6 type I IFN related genes, peripheral blood mononuclear cells (PBMCs) were collected at baseline and day 3 after the vaccination from 8 Enterovirus 71(EV71) vaccinees and were assayed by RT-PCR. Results showed that the 6 DEGs were also upregulated in EV71 vaccinees. In summary, meta-analysis methods were used to explore the immune mechanism of vaccines and results indicated that the type I IFN related genes and corresponding pathways were common in early immune responses for different kinds of vaccines.

Integrating Tumor and Stromal Gene Expression Signatures With Clinical Indices for Survival Stratification of Early-Stage Non-Small Cell Lung Cancer

BACKGROUND

Accurate survival stratification in early-stage non-small cell lung cancer (NSCLC) could inform the use of adjuvant therapy. We developed a clinically implementable mortality risk score incorporating distinct tumor microenvironmental gene expression signatures and clinical variables.

METHODS

Gene expression profiles from 1106 nonsquamous NSCLCs were used for generation and internal validation of a nine-gene molecular prognostic index (MPI). A quantitative polymerase chain reaction (qPCR) assay was developed and validated on an independent cohort of formalin-fixed paraffin-embedded (FFPE) tissues (n = 98). A prognostic score using clinical variables was generated using Surveillance, Epidemiology, and End Results data and combined with the MPI. All statistical tests for survival were two-sided.

RESULTS

The MPI stratified stage I patients into prognostic categories in three microarray and one FFPE qPCR validation cohorts (HR = 2.99, 95% CI = 1.55 to 5.76, P < .001 in stage IA patients of the largest microarray validation cohort; HR = 3.95, 95% CI = 1.24 to 12.64, P = .01 in stage IA of the qPCR cohort). Prognostic genes were expressed in distinct tumor cell subpopulations, and genes implicated in proliferation and stem cells portended poor outcomes, while genes involved in normal lung differentiation and immune infiltration were associated with superior survival. Integrating the MPI with clinical variables conferred greatest prognostic power (HR = 3.43, 95% CI = 2.18 to 5.39, P < .001 in stage I patients of the largest microarray cohort; HR = 3.99, 95% CI = 1.67 to 9.56, P < .001 in stage I patients of the qPCR cohort). Finally, the MPI was prognostic irrespective of somatic alterations in EGFR, KRAS, TP53, and ALK.

CONCLUSION

The MPI incorporates genes expressed in the tumor and its microenvironment and can be implemented clinically using qPCR assays on FFPE tissues. A composite model integrating the MPI with clinical variables provides the most accurate risk stratification.

Identification of Subtype-Specific Prognostic Genes for Early-Stage Lung Adenocarcinoma and Squamous Cell Carcinoma Patients Using an Embedded Feature Selection Algorithm

The existence of fundamental differences between lung adenocarcinoma (AC) and squamous cell carcinoma (SCC) in their underlying mechanisms motivated us to postulate that specific genes might exist relevant to prognosis of each histology subtype. To test on this research hypothesis, we previously proposed a simple Cox-regression model based feature selection algorithm and identified successfully some subtype-specific prognostic genes when applying this method to real-world data. In this article, we continue our effort on identification of subtype-specific prognostic genes for AC and SCC, and propose a novel embedded feature selection method by extending Threshold Gradient Descent Regularization (TGDR) algorithm and minimizing on a corresponding negative partial likelihood function. Using real-world datasets and simulated ones, we show these two proposed methods have comparable performance whereas the new proposal is superior in terms of model parsimony. Our analysis provides some evidence on the existence of such subtype-specific prognostic genes, more investigation is warranted.

Are we ready to use biomarkers for staging, prognosis and treatment selection in early-stage non-small-cell lung cancer?

Lung cancer accounts for the majority of cancer-related deaths worldwide. At present, platinum-based therapy represents the standard of care in fit stage II and IIIA non-small cell lung cancer (NSCLC) patients following surgical resection. In advanced disease, personalized chemotherapy and targeted biologic therapy based on histological and molecular tumor profiling have already shown promise in terms of optimizing treatment efficacy. While disease stage is associated with outcome and is commonly used to determine adjuvant treatment eligibility, it is known that a subset of patients with early stage disease experience shorter survival than others with the same clinicopathological characteristics. Improved methods for identifying these individuals, at or near the time of initial diagnosis, may inform the decision to pursue adjuvant therapy options. Among the numerous candidate molecular biomarkers, only few gene-expression profiling signatures provide clinically relevant information, while real-time quantitative polymerase-chain reaction (RT-qPCR) strategy involving relatively small numbers of genes offers a practical alternative with high cross-platform performance. mRNA and/or protein expression levels of excision repair cross-complementation group 1 (ERCC1), ribonucleotide reductase M subunit 1 (RRM1) and breast cancer susceptibility gene 1 (BRCA1) are among the most promising potential biomarkers for early disease and their clinical utility is currently being evaluated in randomized phase II and III clinical trials. This review describes the most promising clinicopathological and molecular biomarkers with predictive and prognostic significance in lung cancer that have been identified through advanced research and which could influence adjuvant and neoadjuvant chemotherapy decisions for operable NSCLC in routine clinical practice.

The HBx oncoprotein of hepatitis B virus deregulates the cell cycle by promoting the intracellular accumulation and re-compartmentalization of the cellular deubiquitinase USP37

The HBx oncoprotein of hepatitis B Virus has been accredited as one of the protagonists in driving hepatocarcinogenesis. HBx exerts its influence over the cell cycle progression by potentiating the activity of cyclin A/E-CDK2 complex, the Cyclin A partner of which is a well-known target of cellular deubiquitinase USP37. In the present study, we observed the intracellular accumulation of cyclin A and USP37 proteins under the HBx microenvironment. Flow cytometry analysis of the HBx-expressing cells showed deregulation of cell cycle apparently due to the enhanced gene expression and stabilization of USP37 protein and deubiquitination of Cyclin A by USP37. Our co-immunoprecipitation and confocal microscopic studies suggested a direct interaction between USP37 and HBx. This interaction promoted the translocation of USP37 outside the nucleus and prevented its association and ubiquitination by E3 ubiquitin ligases - APC/CDH1 and SCF/β-TrCP. Thus, HBx seems to control the cell cycle progression via the cyclin A-CDK2 complex by regulating the intracellular distribution and stability of deubiquitinase USP37.

The potential of genome-wide analyses to improve non-small-cell lung cancer care

SUMMARY 

Genomic technologies have revolutionized the way we study and understand cancer. The advent of next-generation sequencing technology in particular is now starting to change the clinical management of non-small-cell lung cancer. These technologies have helped us to refine prognostication and identify new driver mutations that can allow subselection of patients for therapeutic intervention. However, several limitations and challenges must be overcome before these technologies are widely accepted in diagnostic laboratories. It will be important for clinicians and diagnostic laboratories to consider sample type, analytical platform, cost, data security and ethics, and the bioinformatics challenges associated with 'big data', before widespread integration to the clinic. If these challenges can be overcome, then genomics has the potential to change clinical management of lung cancer.

The expression of four genes as a prognostic classifier for stage I lung adenocarcinoma in 12 independent cohorts

BACKGROUND

We previously developed a prognostic classifier using the expression levels of BRCA1, HIF1A, DLC1, and XPO1 that identified stage I lung adenocarcinoma patients with a high risk of relapse. That study evaluated patients in five independent cohorts from various regions of the world. In an attempt to further validate the classifier, we have used a meta-analysis-based approach to study 12 cohorts consisting of 1,069 tumor-node-metastasis stage I lung adenocarcinoma patients from every suitable, publically available dataset.

METHODS

Cohorts were obtained through a systematic search of public gene expression datasets. These data were used to calculate the risk score using the previously published 4-gene risk model. A fixed effect meta-analysis model was used to generate a pooled estimate for all cohorts.

RESULTS

The classifier was associated with prognosis in 10 of the 12 cohorts (P < 0.05). This association was highly consistent regardless of the ethnic diversity or microarray platform. The pooled estimate demonstrated that patients classified as high risk had worse overall survival for all stage I [HR, 2.66; 95% confidence interval (CI), 1.93-3.67; P < 0.0001] patients and in stratified analyses of stage IA (HR, 2.69; 95% CI, 1.66-4.35; P < 0.0001) and stage IB (HR, 2.69; 95% CI, 1.74-4.16; P < 0.0001) patients.

CONCLUSIONS

The 4-gene classifier provides independent prognostic stratification of stage IA and stage IB patients beyond conventional clinical factors.

IMPACT

Our results suggest that the 4-gene classifier may assist clinicians in decisions about the postoperative management of early-stage lung adenocarcinoma patients.

Gene expression profiling reveals GC and CEACAM1 as new tools in the diagnosis of lung carcinoids

Background:

Classification of lung carcinoids into typical and atypical is a diagnostic challenge since no immunohistochemical tools are available to support pathologists in distinguishing between the two subtypes. A differential diagnosis is essential for clinicians to correctly discuss therapy, prognosis and follow-up with patients. Indeed, the distinction between the two typical and atypical subtypes on biopsies/cytological specimens is still unfeasible and sometimes limited also after radical surgeries. By comparing the gene expression profile of typical (TC) and atypical carcinoids (AC), we intended to find genes specifically expressed in one of the two subtypes that could be used as diagnostic markers.

Methods:

Expression profiling, with Affymetrix arrays, was performed on six typical and seven atypical samples. Data were validated on an independent cohort of 29 tumours, by means of quantitative PCR and immunohistochemistry (IHC).

Results:

High-throughput gene expression profiling was successfully used to identify a gene signature specific for atypical lung carcinoids. Among the 273 upregulated genes in the atypical vs typical subtype, GC (vitamin D-binding protein) and CEACAM1 (carcinoembryonic antigen family member) emerged as potent diagnostic markers. Quantitative PCR and IHC on a validation set of 17 ACs and 12 TCs confirmed their reproducibility and feasibility.

Conclusions:

GC and CEACAM1 can distinguish between TC and AC, defining an IHC assay potentially useful for routine cytological and histochemical diagnostic procedures. The high sensitivity and reproducibility of this new diagnostic algorithm strongly support a further validation on a wider sample size.

Lung Cancer Gene Signatures and Clinical Perspectives

Microarrays have been used for more than two decades in preclinical research. The tumor transcriptional profiles were analyzed to select cancer-associated genes for in-deep functional characterization, to stratify tumor subgroups according to the histopathology or diverse clinical courses, and to assess biological and cellular functions behind these gene sets. In lung cancer-the main type of cancer causing mortality worldwide-biomarker research focuses on different objectives: the early diagnosis of curable tumor diseases, the stratification of patients with prognostic unfavorable operable tumors to assess the need for further therapy regimens, or the selection of patients for the most efficient therapies at early and late stages. In non-small cell lung cancer, gene and miRNA signatures are valuable to differentiate between the two main subtypes' squamous and non-squamous tumors, a discrimination which has further implications for therapeutic schemes. Further subclassification within adenocarcinoma and squamous cell carcinoma has been done to correlate histopathological phenotype with disease outcome. Those tumor subgroups were assigned by diverse transcriptional patterns including potential biomarkers and therapy targets for future diagnostic and clinical applications. In lung cancer, none of these signatures have entered clinical routine for testing so far. In this review, the status quo of lung cancer gene signatures in preclinical and clinical research will be presented in the context of future clinical perspectives.

Down-regulation of SIX3 is associated with clinical outcome in lung adenocarcinoma

BACKGROUND

Lung cancer is a common cancer and the leading cause of cancer-related death worldwide. SIX3 is a human homologue of the highly conserved sine oculis gene family essential during embryonic development in vertebrates, and encodes a homeo-domain containing transcription factor. Little is known about the role of SIX3 in human tumorigenesis. This study is to assess the expression/function of SIX3 and the significance of SIX3 as a prognostic biomarker in lung adenocarcinoma.

METHODS

Quantitative real-time RT-PCR was used to analyze SIX3 mRNA expression and quantitative methylation specific PCR (MSP) was used to examine promoter methylation. MTS and colony formation assays were performed to examine cell proliferation. Wound healing assays were used to assess cell migration, and microarrays were utilized to examine genes regulated by SIX3 in lung cancer cells. Association of SIX3 expression levels with clinical outcomes of patients with lung adenocarcinoma was evaluated using the Kaplan-Meier method and a multivariate Cox proportional hazards regression model.

RESULTS

SIX3 was down-regulated in lung adenocarcinoma tissues compared to their matched adjacent normal tissues, and this down-regulation was associated with methylation of the SIX3 promoter. SIX3 was also methylation-silenced in lung cancer cell lines. Restoration of SIX3 in lung cancer cells lacking endogenous SIX3 suppressed cell proliferation and migration, and downregulated a number of genes involved in proliferation and metastasis such as S100P, TGFB3, GINS3 and BAG1. Moreover, SIX3 mRNA expression was associated with significantly improved overall survival (OS) and progression-free survival (PFS) in adenocarcinoma patients and patients with bronchioloalveolar carcinoma (BAC) features.

CONCLUSIONS

SIX3 may play an important role as a novel suppressor in human lung cancer. SIX3 has potential as a novel prognostic biomarker for patients with lung adenocarcinomas.

Combination of protein coding and noncoding gene expression as a robust prognostic classifier in stage I lung adenocarcinoma

Prognostic tests for patients with early-stage lung cancer may provide needed guidance on postoperative surveillance and therapeutic decisions. We used a novel strategy to develop and validate a prognostic classifier for early-stage lung cancer. Specifically, we focused on 42 genes with roles in lung cancer or cancer prognosis. Expression of these biologically relevant genes and their association with relapse-free survival (RFS) were evaluated using microarray data from 148 patients with stage I lung adenocarcinoma. Seven genes associated with RFS were further examined by quantitative reverse transcription PCR in 291 lung adenocarcinoma tissues from Japan, the United States, and Norway. Only BRCA1, HIF1A, DLC1, and XPO1 were each significantly associated with prognosis in the Japan and US/Norway cohorts. A Cox regression-based classifier was developed using these four genes on the Japan cohort and validated in stage I lung adenocarcinoma from the US/Norway cohort and three publicly available lung adenocarcinoma expression profiling datasets. The results suggest that the classifier is robust across ethnically and geographically diverse populations regardless of the technology used to measure gene expression. We evaluated the combination of the four-gene classifier with miRNA miR-21 (MIR21) expression and found that the combination improved associations with prognosis, which were significant in stratified analyses on stage IA and stage IB patients. Thus, the four coding gene classifier, alone or with miR-21 expression, may provide a clinically useful tool to identify high-risk patients and guide recommendations regarding adjuvant therapy and postoperative surveillance of patients with stage I lung adenocarcinoma.

Biomarkers and molecular testing for early detection, diagnosis, and therapeutic prediction of lung cancer

The search for biomarkers in the management of lung cancer involves the use of multiple platforms to examine changes in gene, protein, and microRNA expression. Multiple studies have been published in an attempt to describe early detection, diagnostic, prognostic, and predictive biomarkers using chiefly tissues and blood elements. Studies are characterized by a lack of commonality of specific biomarkers, and a lack of validated, clinically useful markers. The future of biomarker discovery as a means of tailoring therapy for patients with lung cancer will involve next-generation sequencing along with collaborative efforts to integrate and validate candidate markers.

Differentiation-associated microRNAs antagonize the Rb-E2F pathway to restrict proliferation

Transcriptional regulation by Rb–E2F and posttranscriptional regulation by microRNAs control the expression of cell cycle and DNA replication genes and restrict cellular proliferation.

The cancer-associated loss of microRNA (miRNA) expression leads to a proliferative advantage and aggressive behavior through largely unknown mechanisms. Here, we exploit a model system that recapitulates physiological terminal differentiation and its reversal upon oncogene expression to analyze coordinated mRNA/miRNA responses. The cell cycle reentry of myotubes, forced by the E1A oncogene, was associated with a pattern of mRNA/miRNA modulation that was largely reciprocal to that induced during the differentiation of myoblasts into myotubes. The E1A-induced mRNA response was preponderantly Retinoblastoma protein (Rb)-dependent. Conversely, the miRNA response was mostly Rb-independent and exerted through tissue-specific factors and Myc. A subset of these miRNAs (miR-1, miR-34, miR-22, miR-365, miR-29, miR-145, and Let-7) was shown to coordinately target Rb-dependent cell cycle and DNA replication mRNAs. Thus, a dual level of regulation—transcriptional regulation via Rb–E2F and posttranscriptional regulation via miRNAs—confers robustness to cell cycle control and provides a molecular basis to understand the role of miRNA subversion in cancer.

A 12-gene set predicts survival benefits from adjuvant chemotherapy in non-small cell lung cancer patients

PURPOSE

Prospectively identifying who will benefit from adjuvant chemotherapy (ACT) would improve clinical decisions for non-small cell lung cancer (NSCLC) patients. In this study, we aim to develop and validate a functional gene set that predicts the clinical benefits of ACT in NSCLC.

EXPERIMENTAL DESIGN

An 18-hub-gene prognosis signature was developed through a systems biology approach, and its prognostic value was evaluated in six independent cohorts. The 18-hub-gene set was then integrated with genome-wide functional (RNAi) data and genetic aberration data to derive a 12-gene predictive signature for ACT benefits in NSCLC.

RESULTS

Using a cohort of 442 stage I to III NSCLC patients who underwent surgical resection, we identified an 18-hub-gene set that robustly predicted the prognosis of patients with adenocarcinoma in all validation datasets across four microarray platforms. The hub genes, identified through a purely data-driven approach, have significant biological implications in tumor pathogenesis, including NKX2-1, Aurora Kinase A, PRC1, CDKN3, MBIP, and RRM2. The 12-gene predictive signature was successfully validated in two independent datasets (n = 90 and 176). The predicted benefit group showed significant improvement in survival after ACT (UT Lung SPORE data: HR = 0.34, P = 0.017; JBR.10 clinical trial data: HR = 0.36, P = 0.038), whereas the predicted nonbenefit group showed no survival benefit for 2 datasets (HR = 0.80, P = 0.70; HR = 0.91, P = 0.82).

CONCLUSIONS

This is the first study to integrate genetic aberration, genome-wide RNAi data, and mRNA expression data to identify a functional gene set that predicts which resectable patients with non-small cell lung cancer will have a survival benefit with ACT.

Skp1-Cul1-F-box ubiquitin ligase (SCF(βTrCP))-mediated destruction of the ubiquitin-specific protease USP37 during G2-phase promotes mitotic entry

Ubiquitin-mediated proteolysis is a key regulatory process in cell cycle progression. The Skp1-Cul1-F-box (SCF) and anaphase-promoting complex (APC) ubiquitin ligases target numerous components of the cell cycle machinery for destruction. Throughout the cell cycle, these ligases cooperate to maintain precise levels of key regulatory proteins, and indirectly, each other. Recently, we have identified the deubiquitinase USP37 as a regulator of the cell cycle. USP37 expression is cell cycle-regulated, being expressed in late G(1) and ubiquitinated by APC(Cdh1) in early G(1). Here we report that in addition to destruction at G(1), a major fraction of USP37 is degraded at the G(2)/M transition, prior to APC substrates and similar to SCF(βTrCP) substrates. Consistent with this hypothesis, USP37 interacts with components of the SCF in a βTrCP-dependent manner. Interaction with βTrCP and subsequent degradation is phosphorylation-dependent and is mediated by the Polo-like kinase (Plk1). USP37 is stabilized in G(2) by depletion of βTrCP as well as chemical or genetic manipulation of Plk1. Similarly, mutation of the phospho-sites abolishes βTrCP binding and renders USP37 resistant to Plk1 activity. Expression of this mutant hinders the G(2)/M transition. Our data demonstrate that tight regulation of USP37 levels is required for proper cell cycle progression.

Overexpression of T-LAK cell-originated protein kinase predicts poor prognosis in patients with stage I lung adenocarcinoma

Tumor recurrence is the most common cause of disease failure after surgical resection in early-stage lung adenocarcinoma. Identification of clinically relevant prognostic markers could help to predict patients with high risk of disease recurrence. A meta-analysis of available lung adenocarcinoma microarray datasets revealed that T-LAK cell-originated protein kinase (TOPK), a serine/threonine protein kinase, is overexpressed in lung cancer. Using stable cell lines with overexpression or knockdown of TOPK, we have shown that TOPK can promote cell migration, invasion, and clonogenic activity in lung cancer cells, suggesting its crucial role in lung tumorigenesis. To evaluate the prognostic value of TOPK expression in resected stage I lung adenocarcinoma, a retrospective analysis of 203 patients diagnosed with pathological stage I lung adenocarcinoma was carried out to examine the expression of TOPK by immunohistochemistry (IHC). The prognostic significance of TOPK overexpression was examined. Overexpression of TOPK (IHC score >3) was detected in 67.0% of patients, and these patients were more frequently characterized with disease recurrence and angiolymphatic invasion. Using multivariate analysis, patient age (>65 years old; P = 0.002) and TOPK overexpression (IHC score >3; P < 0.001) significantly predicted a shortened overall survival. Moreover, TOPK overexpression (IHC score >3; P = 0.005) also significantly predicted a reduced time to recurrence in the patients. Our results indicate that overexpression of TOPK could predetermine the metastatic capability of tumors and could serve as a significant prognostic predictor of shortened overall survival and time to recurrence.

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.

Prognostic role of Twist, Slug, and Foxc2 expression in stage I non-small-cell lung cancer after curative resection

By using immunohistochemistry in tissue microarrays of 137 cases, we evaluated the prognostic power of a 3-marker epithelial-mesenchymal transition–related model in patients with stage I non-small-cell lung cancer who underwent radical surgical resection. The Twist/Slug/Foxc2 coexpression model accurately prognosticated these patients and may be helpful in refining current treatment strategy for stage I non-small-cell lung cancer.

BACKGROUND

Lung cancer is the leading cause of cancer-related death in the world. Only about 60% of patients with stage I non-small-cell lung cancer (NSCLC) can be cured by surgery alone. Current clinical and molecular markers are inadequate prognosticators. We developed a 3-marker model that closely approximates survival probability of patients with stage I NSCLC.

METHODS

Expression of Twist, Slug, and Foxc2 was assessed by immunohistochemistry in tissue microarrays that contained paired tumor and peritumoral lung tissue from 137 patients who underwent surgical resection for stage I NSCLC. The prognostic value of Twist, Slug, and Foxc2, and the cumulative effects of the 3 markers on survival were evaluated.

RESULTS

Increased expression of Twist, Slug, and Foxc2 was observed in 38.0%, 18.2%, and 27.7% of primary tumors, respectively. Overexpression of Twist, Slug, and Foxc2 in stage I NSCLC was associated with a worse overall survival (P = .001, P = .002, P < .001, respectively) and correlated with a shorter recurrence-free survival (P < .001, P = .001, P < .001 respectively). The cumulative influence of these markers on outcome was analyzed; a combination of more than 2 positive markers was an independent predictor of recurrence-free and overall survival (P = .002 and P = .009, respectively).

CONCLUSIONS

The Twist/Slug/Foxc2 model is useful in predicting survival of stage I NSCLC and may be helpful in refining current treatment strategy.