Understanding prognostic gene expression signatures in lung cancer

Pyroptosis in lung cancer: The emerging role of non-coding RNAs

Lung cancer remains an intractable malignancy worldwide, prompting novel therapeutic modalities. Pyroptosis, a lethal form of programmed cell death featured by inflammation, has been involved in cancer progression and treatment response. Simultaneously, non-coding RNA has been shown to have important roles in coordinating pattern formation and oncogenic pathways, including long non-coding RNA (lncRNAs), microRNA (miRNAs), circular RNA (circRNAs), and small interfering RNA (siRNAs). Recent studies have revealed that ncRNAs can promote or inhibit pyroptosis by interacting with key molecular players such as NLRP3, GSDMD, and various transcription factors. This dual role of ncRNAs offers a unique therapeutic potential to manipulate pyroptosis pathways, providing opportunities for innovative cancer treatments. In this review, we integrate current research findings to propose novel strategies for leveraging ncRNA-mediated pyroptosis as a therapeutic intervention in lung cancer. We explore the potential of ncRNAs as biomarkers for predicting patient response to treatment and as targets for overcoming resistance to conventional therapies.

Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene-A Paradigm Shift

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. The dataset used for the research is the Lung Harvard 2 Dataset (LH2) which consists of 150 Adenocarcinoma subjects and 31 Mesothelioma subjects. The paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Nonlinear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%.

A prognostic signature model for unveiling tumor progression in lung adenocarcinoma

A more accurate prognosis is important for clinical treatment of lung adenocarcinoma. However, due to the limitation of sample and technical bias, most prognostic signatures lacked reproducibility, and few were applied to clinical practice. In addition, understanding the molecular driving mechanism is indispensable for developing more promising therapies for lung adenocarcinoma. Here, we built an unbiased prognostic significance model to perform an integrative analysis, including differentially expressed genes and clinical data with lung adenocarcinoma patients from TCGA. Multivariable Cox proportional hazards model with the Lasso penalty and 10-fold cross-validate were used to identify the best gene signature. We generated a 17-gene signature for prognostic risk prediction based on the overall survival time of lung adenocarcinoma patients. To further test the model's predictive ability, we have applied an independent GEO database to verify the predictive ability of prognostic signature. The model can more objectively describe several biological processes related to tumors and reveal important molecular mechanisms in tumor development by GO and KEGG analysis. Furthermore, differential expression analysis by GSEA revealed that tumor microenvironments such as ER stress, exosome, and immune microenvironment were enriched. Using single-cell RNA sequence technology, we found that risk score was positively correlated with lung adenocarcinoma marker genes and copy number variation but negatively correlated with lung epithelial marker genes. High-risk cell populations with the model had stronger cancer stemness and tumor-related pathway activation. As we expected, the risk score was in accordance with the malignancy of each cluster from tumor progression. In conclusion, the risking model established in this study is more reliable than others in evaluating the prognosis of LUAD patients.

Improving Analysis and Annotation of Microarray Data with Protein Interactions

Gene expression microarrays are one of the most widely used high-throughput technologies in molecular biology, with applications such as identification of disease mechanisms and development of diagnostic and prognostic gene signatures. However, the success of these tasks is often limited because microarray analysis does not account for the complex relationships among genes, their products, and overall signaling and regulatory cascades. Incorporating protein-protein interaction data into microarray analysis can help address these challenges. This chapter reviews how protein-protein interactions can help with microarray analysis, leading to benefits such as better explanations of disease mechanisms, more complete gene annotations, improved prioritization of genes for future experiments, and gene signatures that generalize better to new data.

Prognostic value of ESR2 expression on adjuvant chemotherapy in completely resected NSCLC

Background

Prognostic biomarker, which can inform the treatment outcome of adjuvant chemotherapy (ACT) after complete resection of early-stage non-small cell lung cancer (NSCLC), is urgently needed for the personalized treatment of these patients.

Patients and methods

The prognostic value of gene expression of the estrogen receptor (ER) on the effect of ACT in completely resected NSCLC was investigated in the present study. Two independent datasets from Gene Expression Omnibus (GEO) with a total of 309 patients were included in this study. The prognostic value of ER gene expression on ACT’s efficacy was evaluated by survival analysis and Cox hazards models.

Results

We found a consistent and significant prognostic value of ERβ (ESR2) expression for ACT’s efficacy in completely resected NSCLC in both of the two independent cohorts. After multivariate adjustment, a significant survival benefit of ACT was observed in patients with low expression of ESR2, with a hazard ratio (HR) of 0.19 (95%CI 0.05–0.82, p = 0.026) in the discovery cohort and an HR of 0.27 (95%CI 0.10–0.76, p = 0.012) in the validation group. No significant benefit of ACT in the subgroup of patients with high expression of ESR2 was observed, with an HR of 0.80 (95%CI 0.31–2.09, p = 0.644) in the discovery cohort and an HR of 1.05 (95%CI 0.48–2.29, p = 0.896) in the validation group.

Conclusion

A significant survival benefit from ACT was observed in patients with low ESR2 expression. No significant survival benefit was observed in patients with high ESR2 expression. Detection of ESR2 expression in NSCLC may help personalize its treatment after complete resection.

High KIAA1522 expression predicts a poor prognosis in patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a highly malignant tumor associated with a poor prognosis, and the molecular mechanisms remain poorly understood. KIAA1522 expression is upregulated in various types of tumor tissue; however, its function remains unknown in HCC. Bioinformatics analysis was undertaken using Oncomine, OncoLnc and other databases, in order to determine KIAA1522 expression in HCC and to analyze its association with postoperative prognosis. Reverse transcription-quantitative PCR was performed to detect KIAA1522 mRNA expression in primary HCC and adjacent normal tissues, while KIAA1522 protein expression was assessed via immunohistochemical staining. KIAA1522 expression and clinicopathological characteristics of primary HCC were evaluated, and their association with patient prognosis was analyzed. The Oncomine database results indicated that KIAA1522 expression in HCC and normal liver tissues was significantly different. RT-qPCR analysis demonstrated that KIAA1522 mRNA expression was significantly higher in HCC tissues compared with that in adjacent normal tissues. Immunohistochemical analysis indicated that expression rate of KIAA1522 protein was significantly higher in primary HCC tissues compared with that in normal liver tissues. The OncoLnc database results demonstrated that KIAA1522 expression was significantly associated with short-term survival. Kaplan-Meier survival analysis indicated that high KIAA1522 protein expression was significantly associated with short-term survival for patients with HCC. Multivariate Cox regression analysis demonstrated that tumor size, Tumor-Node-Metastasis stage and high KIAA1522 protein expression were independent predictors of a poor prognosis in patients with primary HCC. Furthermore, high KIAA1522 expression was significantly associated with postoperative survival time in primary HCC, and thus may be a potential molecular marker for prognosis in patients with this cancer type.

Somatic Alteration Burden Involving Non-Cancer Genes Predicts Prognosis in Early-Stage Non-Small Cell Lung Cancer

The burden of somatic mutations and neoantigens has been associated with improved survival in cancer treated with immunotherapies, especially non-small cell lung cancer (NSCLC). However, there is uncertainty about their effect on outcome in early-stage untreated cases. We posited that the burden of mutations in a specific set of genes may also contribute to the prognosis of early NSCLC patients. From a small cohort of 36 NSCLC cases, we were able to identify somatic mutations and copy number alterations in 865 genes that contributed to patient overall survival. Simply, the number of altered genes (NAG) among these 865 genes was associated with longer disease-free survival (hazard ratio (HR) = 0.153, p = 1.48 × 10^-4). The gene expression signature distinguishing patients with high/low NAG was also prognostic in three independent datasets. Patients with a high NAG could be further stratified based on the presence of immunogenic mutations, revealing a further subgroup of stage I NSCLC with even better prognosis (85% with >5 years survival), and associated with cytotoxic T-cell expression. Importantly, 95% of the highly-altered genes lacked direct relation to cancer, but were implicated in pathways regulating cell proliferation, motility and immune response.

Combination of laser microdissection, 2D-DIGE and MALDI-TOF MS to identify protein biomarkers to predict colorectal cancer spread

Biomarkers are urgently required to support current histological staging to provide additional accuracy in stratifying colorectal cancer (CRC) patients according to risk of spread to properly assign adjuvant chemotherapy after surgery. Chemotherapy is given to patients with stage III to reduce the risk of recurrence but is controversial in stage II patients. Up to 25% of stage II patients will relapse within 5 years after tumor removal and when this occurs cure is seldom possible. The aim of this study was to identify protein biomarkers to stratify risk of spread of CRC patients. Laser micro-dissection was used to isolate cancer cells from primary colorectal tumors of stage II patients which did or did not metastasize within 5 years after surgical resection. Protein expression differences between two groups of tumors were profiled by 2D-DIGE with saturation CyDye labeling and identified using MALDI-TOF mass spectrometry. Evaluation of protein candidates was conducted using tissue micro array (TMA) immunohistochemistry on 125 colorectal tumor tissue samples of different stages. A total of 55 differentially expressed proteins were identified. Ten protein biomarkers were chosen based on p value and ratio between non metastasized and metastazised groups and evaluated on 125 tissues using TMA immunohistochemistry. Expression of HLAB, protein 14-3-3β, LTBP3, ADAMTS2, JAG2 and NME2 on tumour cells was significantly associated with clinical parameters related to tumour progression, invasion and metastasis. Kaplan–Meier survival curve showed strong expression of six proteins was associated with good CRC specific survival. Expression of HLAB, ADAMTS2, LTBP3, JAG2 and NME2 on tumour cells, was associated with tumour progression and invasion, metastasis and CRC specific survival may serve as potential biomarkers to stratify CRC patients into low and high risk of tumour metastasis. Combined methods of laser microdissection, 2D DIGE with saturation labelling and MALDI-TOF MS proved to be resourceful techniques capable of identifying protein biomarkers to predict risk of spread of CRC to liver.

Implication of Biomarker Mutations for Predicting Survival in Patients With Metastatic Lung Cancer to the Spine

STUDY DESIGN

A retrospective cohort study.

OBJECTIVE

We performed a retrospective study of patients treated at our institution over the last 7 years to ascertain whether gene expression signatures in patients with advanced metastatic disease are associated with survival, when the disease has progressed to the spine.

SUMMARY OF BACKGROUND DATA

Spinal metastases are a major cause of morbidity in patients with cancer. Molecular profiling strategies to characterize lung cancer have identified several genetic biomarkers that may lead to more effective prognostication.

METHODS

We queried our institutional database for patients with metastatic lung cancer who underwent treatment for spinal metastases between 2011 and 2017. Genetic mutations in ALK, MET, ROS1, EGFR, and KRAS were chosen a priori for study based on availability by standard SNaPshot Lung Tumor Genotyping Analysis. Survival time was the duration between treatment for spinal metastases and death. Kaplan-Meier methods and the log-rank test were applied to characterize survival data.

RESULTS

Twenty-six patients met criteria for inclusion. Median survival after surgery was 0.67 years. Median overall survival (OS) after diagnosis was 2.7 years. The presence of molecular abnormalities in patients with spinal metastases was significantly associated with increased OS (HR 0.38, 95% CI 0.12-1.22, P = 0.03).

CONCLUSION

Molecular phenotyping may provide prognostic insight in patients undergoing surgery for spinal metastases. This is the first study to demonstrate an association between genetic mutational data and OS in this patient population. It also represents the largest published series of such patients (n = 26) for which genetic mutational data are reported. Future models estimating survival for patients with spinal metastases may be enhanced by incorporation of molecular criteria.

LEVEL OF EVIDENCE

4.

Consensus of gene expression phenotypes and prognostic risk predictors in primary lung adenocarcinoma

Transcriptional profiling of lung adenocarcinomas has identified numerous gene expression phenotype (GEP) and risk prediction (RP) signatures associated with patient outcome. However, classification agreement between signatures, underlying transcriptional programs, and independent signature validation are less studied. We classified 2395 transcriptional adenocarcinoma profiles, assembled from 17 public cohorts, using 11 GEP and seven RP signatures, finding that 16 signatures were associated with patient survival in the total cohort and in multiple individual cohorts. For significant signatures, total cohort hazard ratios were ~2 in univariate analyses (mean=1.95, range=1.4-2.6). Strong classification agreement between signatures was observed, especially for predicted low-risk patients by adenocarcinoma-derived signatures. Expression of proliferation-related genes correlated strongly with GEP subtype classifications and RP scores, driving the gene signature association with prognosis. A three-group consensus definition of samples across 10 GEP classifiers demonstrated aggregation of samples with specific smoking patterns, gender, and EGFR/KRAS mutations, while survival differences were only significant when patients were divided into low- or high-risk. In summary, our study demonstrates a consensus between GEPs and RPs in lung adenocarcinoma through a common underlying transcriptional program. This consensus generalizes reported problems with current signatures in a clinical context, stressing development of new adenocarcinoma-specific single sample predictors for clinical use.

Comprehensive evaluation of published gene expression prognostic signatures for biomarker-based lung cancer clinical studies

Background

A more accurate prognosis for non-small-cell lung cancer (NSCLC) patients could aid in the identification of patients at high risk for recurrence. Many NSCLC mRNA expression signatures claiming to be prognostic have been reported in the literature. The goal of this study was to identify the most promising mRNA prognostic signatures in NSCLC for further prospective clinical validation.

Experimental design

We carried out a systematic review and meta-analysis of published mRNA prognostic signatures for resected NSCLC. The prognostic performance of each signature was evaluated via a meta-analysis of 1927 early stage NSCLC patients collected from 15 studies using three evaluation metrics (hazard ratios, concordance scores, and time-dependent receiver-operating characteristic curves). The performance of each signature was then evaluated against 100 random signatures. The prognostic power independent of clinical risk factors was assessed by multivariate Cox models.

Results

Through a literature search, we identified 42 lung cancer prognostic signatures derived from genome-wide expression profiling analysis. Based on meta-analysis, 25 signatures were prognostic for survival after adjusting for clinical risk factors and 18 signatures carried out significantly better than random signatures. When analyzing histology types separately, 17 signatures and 8 signatures are prognostic for adenocarcinoma and squamous cell lung cancer, respectively. Despite little overlap among published gene signatures, the top-performing signatures are highly concordant in predicted patient outcomes.

Conclusions

Based on this large-scale meta-analysis, we identified a set of mRNA expression prognostic signatures appropriate for further validation in prospective clinical studies.

Systematic Identification of Oncogenic EGFR Interaction Partners

The epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase (TK) that—once activated upon ligand binding—leads to receptor dimerization, recruitment of protein complexes, and activation of multiple signaling cascades. The EGFR is frequently overexpressed or mutated in various cancers leading to aberrant signaling and tumor growth. Hence, identification of interaction partners that bind to mutated EGFR can help identify novel targets for drug discovery.

Here, we used a systematic approach to identify novel proteins that are involved in cancerous EGFR signaling. Using a combination of high-content imaging and a mammalian membrane two-hybrid protein–protein interaction method, we identified eight novel interaction partners of EGFR, of which half strongly interacted with oncogenic, hyperactive EGFR variants. One of these, transforming acidic coiled-coil proteins (TACC) 3, stabilizes EGFR on the cell surface, which results in an increase in downstream signaling via the mitogen-activated protein kinase and AKT pathway. Depletion of TACC3 from cells using small hairpin RNA (shRNA) knockdown or small-molecule targeting reduced mitogenic signaling in non-small cell lung cancer cell lines, suggesting that targeting TACC3 has potential as a new therapeutic approach for non-small cell lung cancer.

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A combined screening approach involving an image-based green fluorescent protein-Grb2 translocation assay and a mammalian membrane two-hybrid protein–protein interaction assay identified 11 novel interactors of EGFR.

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Eight of those were further confirmed by co-immunoprecipitation.

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TACC3 was identified as a novel EGFR interactor, which specifically binds to oncogenic EGFR variants.

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TACC3 directly modulates EGFR stability at the cell surface and hence promotes mitogen-activated protein kinase signaling.

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Targeting TACC3 in non-small cell lung cancer cells partially resensitizes TK-resistant cells to TK inhibitors.

Circulating plant miRNAs can regulate human gene expression in vitro

While Brassica oleracea vegetables have been linked to cancer prevention, the exact mechanism remains unknown. Regulation of gene expression by cross-species microRNAs has been previously reported; however, its link to cancer suppression remains unexplored. In this study we address both issues. We confirm plant microRNAs in human blood in a large nutrigenomics study cohort and in a randomized dose-controlled trial, finding a significant positive correlation between the daily amount of broccoli consumed and the amount of microRNA in the blood. We also demonstrate that Brassica microRNAs regulate expression of human genes and proteins in vitro, and that microRNAs cooperate with other Brassica-specific compounds in a possible cancer-preventive mechanism. Combined, we provide strong evidence and a possible multimodal mechanism for broccoli in cancer prevention.

A Cost-Effectiveness Analysis of Using the JBR.10-Based 15-Gene Expression Signature to Guide Adjuvant Chemotherapy in Early Stage Non-Small-Cell Lung Cancer

BACKGROUND

Adjuvant chemotherapy (ACT) improved survival in the NCIC Clinical Trials Group JBR.10 trial of resected stage IB/II non-small-cell lung cancer. A prognostic 15-gene expression signature was developed, which may also predict for benefit from ACT. An exploratory economic analysis was conducted to assess the potential cost-effectiveness of using the 15-gene signature in guiding ACT decisions.

METHODS

A decision analytic model was populated by study patients with quantitative reverse transcription polymerase chain reaction tumor profiling, current costs, and quality-adjusted survival. Analysis was performed over the 6-year follow-up from the perspective of the Canadian public health care system in 2015 Canadian dollars (discounted 5%/year). Incremental cost-effectiveness and cost-utility ratios were determined for ACT versus observation using clinical stage, gene signature, or a combined approach to select treatment.

RESULTS

The mean survival gain of ACT versus observation was higher using the gene signature (1.86 years) compared with clinical stage (1.28 years). Although more costly, ACT guided by the gene signature remained cost-effective at $10,421/life-year gained (95% confidence interval [CI], $466-$19,568 Canadian), comparable to stage-directed selection ($7081/life-year gained; 95% CI, -$2370 to $14,721; P = .52). Incremental cost-utility ratios were $13,452/quality-adjusted life-year (95% CI, $373-$31,949) and $9194/quality-adjusted life-year (95% CI, -$4104 to $23,952), respectively (P = .53). Comparing the standard and test-and-treat approaches, use of the gene signature did not significantly alter survival compared with the standard strategy, but it reduced the ACT rate by 25%.

CONCLUSION

If validated, the use of the 15-gene expression signature to select patients for ACT may increase the survival gain of treatment in patients with high-risk stage IB/II non-small-cell lung cancer, while avoiding toxicities in low-risk patients.

KIAA1522 is a novel prognostic biomarker in patients with non-small cell lung cancer

Nowadays, no robust biomarkers have been applied to clinical practice to provide prognostic evaluation of non-small cell lung cancer (NSCLC). This study aims to identify new potential prognostic biomarkers for NSCLC. In the present work, KIAA1522 is screened out from two independent GEO datasets as aberrantly up-regulated gene in NSCLC tissues. We evaluate KIAA1522 expression immunohistochemically in 583 NSCLC tissue samples and paired non-tumor tissues. KIAA1522 displays stronger staining in NSCLC cases than in adjacent normal lung tissues. Importantly, patients with KIAA1522 overexpression had a significantly shorter overall survival compared to those with low expression (P < 0.00001). Multivariate Cox regression analyses show that KIAA1522 is an independent prognostic indicator, even for early-stage NSCLCs (P = 0.00025, HR = 2.317, 95%CI: 1.477–3.635). We also found that high expression of KIAA1522 is a significant risk factor for decreased overall survival of the patients who received platinum-based chemotherapy. Gene set enrichment analysis (GSEA) and functional studies reveal that KIAA1522 is associated with oncogenic KRAS pathways. Taken together, high expression of KIAA1522 can be used as an independent biomarker for predication of poor survival and platinum-resistance of NSCLC patients, and aberrant KIAA1522 might be a new target for the therapy of the disease.

Lung Cancer Staging and Prognosis

The seventh edition of the non-small cell lung cancer (NSCLC) TNM staging system was developed by the International Association for the Staging of Lung Cancer (IASLC) Lung Cancer Staging Project by a coordinated international effort to develop data-derived TNM classifications with significant survival differences. Based on these TNM groupings, current 5-year survival estimates in NSLCC range from 73 % in stage IA disease to 13 % in stage IV disease. TNM stage remains the most important prognostic factor in predicting recurrence rates and survival times, followed by tumor histologic grade, and patient sex, age, and performance status. Molecular prognostication in lung cancer is an exploding area of research where interest has moved beyond TNM stage and into individualized genetic tumor analysis with immunohistochemistry, microarray, and mutation profiles. However, despite intense research efforts and countless publications, no molecular prognostic marker has been adopted into clinical use since most fail in subsequent cross-validation with few exceptions. The recent interest in immunotherapy for NSCLC has identified new biomarkers with early evidence that suggests that PD-L1 is a predictive marker of a good response to new immunotherapy drugs but a poor prognostic indicator of overall survival. Future prognostication of outcomes in NSCLC will likely be based on a combination of TNM stage and molecular tumor profiling and yield more precise, individualized survival estimates and treatment algorithms.

Predicting Prognosis of Early-Stage Non-Small Cell Lung Cancer Using the GeneFx® Lung Signature

Use of adjuvant chemotherapy remains a complex decision in the treatment of early stage non-small cell lung cancer (NSCLC), with risk of recurrence being the primary indicator (i.e. adjuvant chemotherapy is considered for patients at high risk of recurrence but may not be beneficial for patients at low risk). However, although several clinical and pathological factors are typically considered when assessing the risk of recurrence, none are significantly associated with clinical outcome with the exception of tumor size. GeneFx® Lung (Helomics™ Corporation, Pittsburgh, PA) is a multi-gene RNA expression signature that classifies early stage NSCLC patients as high-risk or low-risk for disease recurrence. GeneFx Lung risk category has been shown to be significantly associated with overall survival in several independent clinical studies. The published literature regarding the analytical validity, clinical validity and clinical utility of GeneFx Lung is summarized herein.

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.

A 50-gene signature is a novel scoring system for tumor-infiltrating immune cells with strong correlation with clinical outcome of stage I/II non-small cell lung cancer

PURPOSE

To identify a novel system for scoring intratumoral immune response that can improve prognosis and therapy decisions in early stage non-small cell lung cancer (NSCLC).

METHODS/PATIENTS

Eighty-four completely resected stage I/II NSCLC without adjuvant therapy were classified by expression profiling using whole genome microarrays. An external cohort of 162 tumors was used to validate the results. Immune cells present in tumor microenvironment were evaluated semiquantitatively by CD20, CD79, CD3, CD8, CD4 and CD57 immunostaining. Univariate and multivariate analyses of variables associated with recurrence-free survival were performed.

RESULTS

Initial molecular classification identified three clusters, one with significantly better RFS. A reduced two-subgroup classification and a 50-gene predictor were built and validated in an external dataset: high and low risk of recurrence patients (HR = 3.44; p = 0.001). Analysis of the predictor´s genes showed that the vast majority were related to a B/plasma cell immune response overexpressed in the low-risk subgroup. The predictor includes genes coding for unique B lineage-specific genes, functional elements or other genes that, although non-restricted to this lineage, have strong influence on B-cell homeostasis. Immunostains confirmed increased B-cells in the low-risk subgroup. Gene signature (p < 0.0001) and CD20 (p < 0.05) were predictors for RFS, while CD79 and K-RAS mutations showed a tendency.

CONCLUSIONS

Favorable prognosis in completely resected NSCLC is determined by a B-cell-mediated immune response. It can be differently scored by a 50-gene expression profile or by CD20 immunostaining. That prognosis information not reflected by traditional classifications may become a new tool for determining individualized adjuvant therapies.

Prognostic markers in lung cancer: is it ready for prime time?

Non-small cell lung cancer (NSCLC) is a heterogeneity disease and to date, specific clinical factors and tumor stage are established as prognostic markers. Nevertheless, prognosis within stage may vary significantly. During the last 3 decades, genes/proteins that drive tumor initiation and progression, such as oncogenes and tumor suppressor genes have been studied as additional potential prognostic markers. The protein markers as evaluated by immunohistochemistry (IHC) have previously dominated these studies. However, with the development of high-throughput techniques to interrogate genome wide genetic or gene expression changes, DNA (copy number and mutation) and RNA (mRNA and microRNA) based markers have more recently been studied as prognostic markers. Largely due to the heterogeneity and complexity of NSCLC, single gene markers including KRAS mutation has not been validated as strong prognostic markers. In contrast, several gene expression signatures representing mRNA levels of multiple genes have been developed and validated in multiple microarray datasets of independent patient cohorts. The salient features of these gene signatures and their potential value to predict benefit from adjuvant chemotherapy is discussed.

Prognostic and predictive biomarkers in early stage non-small cell lung cancer: tumor based approaches including gene signatures

In early stage non-small cell lung cancer (NSCLC) large randomized trials have demonstrated that in patients with radically resected disease adjuvant chemotherapy improves 5-year survival rates. However, a customization of systemic treatment is needed to avoid treatments in patients cured by surgery alone or to justify the use of adjuvant chemotherapy in high risk patients, including those in stage IA. Recently, the possibility of identifying prognostic and predictive factors related to the genetic signatures of the tumor that could affect adjuvant and neo-adjuvant treatment choices for resectable non-small cell lung cancer (NSCLC) has been of interest. This review summarizes the current status and future opportunities for clinical application of genotyping and genomic tests in early NSCLC.

Smoking status impacts microRNA mediated prognosis and lung adenocarcinoma biology

Background

Cigarette smoke is associated with the majority of lung cancers: however, 25% of lung cancer patients are non-smokers, and half of all newly diagnosed lung cancer patients are former smokers. Lung tumors exhibit distinct epidemiological, clinical, pathological, and molecular features depending on smoking status, suggesting divergent mechanisms underlie tumorigenesis in smokers and non-smokers. MicroRNAs (miRNAs) are integral contributors to tumorigenesis and mediate biological responses to smoking. Based on the hypothesis that smoking-specific miRNA differences in lung adenocarcinomas reflect distinct tumorigenic processes selected by different smoking and non-smoking environments, we investigated the contribution of miRNA disruption to lung tumor biology and patient outcome in the context of smoking status.

Methods

We applied a whole transcriptome sequencing based approach to interrogate miRNA levels in 94 patient-matched lung adenocarcinoma and non-malignant lung parenchymal tissue pairs from current, former and never smokers.

Results

We discovered novel and distinct smoking status-specific patterns of miRNA and miRNA-mediated gene networks, and identified miRNAs that were prognostically significant in a smoking dependent manner.

Conclusions

We conclude that miRNAs disrupted in a smoking status-dependent manner affect distinct cellular pathways and differentially influence lung cancer patient prognosis in current, former and never smokers. Our findings may represent promising biologically relevant markers for lung cancer prognosis or therapeutic intervention.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2407-14-778) contains supplementary material, which is available to authorized users.

Validation of a histology-independent prognostic gene signature for early-stage, non-small-cell lung cancer including stage IA patients

BACKGROUND

Patients with early-stage non-small-cell lung carcinoma (NSCLC) may benefit from treatments based on more accurate prognosis. A 15-gene prognostic classifier for NSCLC was identified from mRNA expression profiling of tumor samples from the NCIC CTG JBR.10 trial. In this study, we assessed its value in an independent set of cases.

METHODS

Expression profiling was performed on RNA from frozen, resected tumor tissues corresponding to 181 stage I and II NSCLC cases collected at University Health Network (UHN181). Kaplan-Meier methodology was used to estimate 5-year overall survival probabilities, and the prognostic effect of the classifier was assessed using the log-rank test. A Cox proportional hazards model evaluated the signature's effect adjusting for clinical prognostic factors.

RESULTS

Expression data of the 15-gene classifier stratified UHN181 cases into high- and low-risk subgroups with significantly different overall survival (hazard ratio [HR] = 1.92; 95% confidence interval [CI], 1.15-3.23; p = 0.012). In a subgroup analysis, this classifier predicted survival in 127 stage I patients (HR = 2.17; 95% CI, 1.12-4.20; p = 0.018) and the smaller subgroup of 48 stage IA patients (HR = 5.61; 95% CI, 1.19-26.45; p = 0.014). The signature was prognostic for both adenocarcinoma and squamous cell carcinoma cases (HR = 1.76, p = 0.058; HR = 4.19, p = 0.045, respectively).

CONCLUSION

The prognostic accuracy of a 15-gene classifier was validated in an independent cohort of 181 early-stage NSCLC samples including stage IA cases and in different NSCLC histologic subtypes.

Recurrence after surgery in patients with NSCLC

Surgery remains the only potentially curative modality for early-stage non-small cell lung cancer (NSCLC) patients and tissue availability is made possible. However, a proportion of lung cancer patients develop recurrence, even after curative resection. This review discusses the superiority of surgery, the reasons for recurrence, the timing and pattern of recurrence, the identification of factors related to recurrence, current provisions for treatment and perspectives about surgery for patients with NSCLC.

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.

The mammalian-membrane two-hybrid assay (MaMTH) for probing membrane-protein interactions in human cells

Cell signaling, one of key processes in both normal cellular function and disease, is coordinated by numerous interactions between membrane proteins that change in response to stimuli. We present a split ubiquitin-based method for detection of integral membrane protein-protein interactions (PPIs) in human cells, termed mammalian-membrane two-hybrid assay (MaMTH). We show that this technology detects stimulus (hormone or agonist)-dependent and phosphorylation-dependent PPIs. MaMTH can detect changes in PPIs conferred by mutations such as those in oncogenic ErbB receptor variants or by treatment with drugs such as the tyrosine kinase inhibitor erlotinib. Using MaMTH as a screening assay, we identified CRKII as an interactor of oncogenic EGFR(L858R) and showed that CRKII promotes persistent activation of aberrant signaling in non-small cell lung cancer cells. MaMTH is a powerful tool for investigating the dynamic interactomes of human integral membrane proteins.

An omics strategy for discovering pulmonary biomarkers potentially relevant to the evaluation of tobacco products

Smoking is known to cause serious lung diseases including chronic bronchitis, chronic obstructive lung disease, obstruction of small airways, emphysema and cancer. Tobacco smoke is a complex chemical aerosol containing at least 8000 chemical constituents, either tobacco derived or added by tobacco product manufacturers. Identification of all of the toxic agents in tobacco smoke is challenging, and efforts to understand the mechanisms by which tobacco use causes disease will be informed by new biomarkers of exposure and harm. In 2009, President Obama signed into law the Family Smoking Prevention and Tobacco Control Act granting the US FDA the authority to regulate tobacco products to protect public health. This perspective article presents the background, rationale and strategy for using omics technologies to develop new biomarkers, which may be of interest to the FDA when implementing the Family Smoking Prevention and Tobacco Control Act.

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.

Pathology and personalized medicine in lung cancer

SUMMARY Personalized medicine for patients with non-small-cell lung cancer is a reality now and its use will only increase in the future. Pathology is key in supporting this approach to treatment decision-making, by performing the most complete and accurate histological subtyping of tumors possible, supported by predictive immunohistochemistry and the assessment of relevant biomarkers. The need for these extra diagnostic steps emphasizes the importance of maximizing tissue yields from biopsy procedures. Although multiplex approaches may allow simultaneous assessment of several biomarkers, there will remain a need for different types of test (e.g., immunohistochemistry, as well as mutation testing). Next-generation technologies for DNA sequencing are a great hope for extensive genetic analysis of single samples, provided various technical and logistical problems can be solved. All such laboratory activity must be supported by high-quality internal procedures and external quality-assurance schemes.

Tumor-microenvironment interactions studied by zonal transcriptional profiling of squamous cell lung carcinoma

Invasion is a critical step in lung tumor progression. The interaction between tumor cells and their surroundings may play an important role in tumor invasion and metastasis. To better understand the mechanisms of tumor invasion and tumor-microenvironment interactions in lung tumors, total RNA was isolated from the inner tumor, tumor invasion front, adjacent lung, and distant normal lung tissue from 17 patients with primary squamous cell lung carcinoma using punch-aided laser capture microdissection. Messenger RNA expression profiles were obtained by microarray analysis, and microRNA profiles were generated from eight of these samples using TaqMan Low Density Arrays. Statistical analysis of the expression data showed extensive changes in gene expression in the inner tumor and tumor front compared with the normal lung and adjacent lung tissue. Only a few genes were differentially expressed between tumor front and the inner tumor. Several genes were validated by immunohistochemistry. Evaluation of the microRNA data revealed zonal expression differences in nearly a fourth of the microRNAs analyzed. Validation of selected microRNAs by in situ hybridization demonstrated strong expression of hsa-miR-196a in the inner tumor; moderate expression of hsa-miR-224 in the inner tumor and tumor front, and strong expression of hsa-miR-650 in the adjacent lung tissue. Pathway analysis placed the majority of genes differentially expressed between tumor and nontumor cells in intrinsic processes associated with inflammation and extrinsic processes related to lymphocyte physiology. Genes differentially expressed between the inner tumor and the adjacent lung/normal lung tissue affected pathways of arachidonic acid metabolism and eicosanoid signaling.

[Establishment of Orthotopic Xuanwei lung cancer SCID mouse model and analysis of biological properties]

背景与目的

宣威女性肺癌发病率居全国首位，亟待深入探讨其发病机制。本研究拟建立SCID小鼠原位宣威肺癌模型，为该病的深入研究提供实验平台。

方法

将宣威肺癌细胞XWLC-05分别以高低剂量接种于SCID小鼠肺原位，并与皮下移植瘤比较，观察成瘤率、成瘤特性、自发性转移及生存情况。

结果

原位移植高低剂量组的成瘤率分别为81%和83%，其中高剂量组接种后13天小鼠出现恶液质、对侧肺及胸腔的广泛粘连，无远处转移；低剂量组接种后25天小鼠出现恶液质及远处转移。皮下移植高低剂量组成瘤率分别为100%及94.5%，无远处转移。原位移植组组内及皮下与原位移植组组间的转移率存在统计学差异（P < 0.05）。两组组内和组间的生存率比较有统计学差异（P < 0.001）。

结论

成功建立了宣威肺癌的SCID小鼠原位动物模型，为该病的深入研究奠定了实验基础。

Lung cancer staging: a physiological update

The tumour-node metastasis (TNM) classification system is anatomically based. We investigated whether the addition of simple physiological variables, age and body mass index (BMI), would affect survival curves, i.e. a composite anatomical and physiological staging system. We retrospectively analysed a prospectively validated thoracic surgery database (n = 1981). Cox multivariate analysis was performed to determine possible significant factors. Kaplan-Meier survival curves were constructed with combined anatomical and physiological factors. Cox multivariate analysis revealed age (P < 0.001) and BMI (P = 0.01) as significant factors affecting survival. Receiver operating curve analysis determined cut-off levels for age of 67 and BMI of 27.6. A composite anatomical and physiological survival curve based on TNM for BMI > 27.6 and age < 67 was produced. Age and BMI criteria resulted in significantly different survival curves, for stage I (P < 0.0001) and stage II (P = 0.0032), but not for stage III (P = 0.06). Neural network analysis confirmed the importance of BMI and age above cancer stage with regard to long-term survival. Combining age < 67, BMI > 27.6 and TNM anatomical classification results in very different estimated survival curves from the usual TNM system. Patients from stages I, II and III may have survival equivalent to a stage higher or lower depending on their age and BMI.

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.

5-alpha-reductase type I (SRD5A1) is up-regulated in non-small cell lung cancer but does not impact proliferation, cell cycle distribution or apoptosis

BACKGROUND

Non-small cell lung cancer (NSCLC) is one of the most frequent malignancies and has a high mortality rate due to late detection and lack of efficient treatments. Identifying novel drug targets for this indication may open the way for new treatment strategies. Comparison of gene expression profiles of NSCLC and normal adjacent tissue (NAT) allowed to determine that 5-alpha-reductase type I (SRD5A1) was up-regulated in NSCLC compared to NAT. This raised the question whether SRD5A1 was involved in sustained proliferation and survival of NSCLC.

METHODS

siRNA-mediated silencing of SRD5A1 was performed in A549 and NCI-H460 lung cancer cell lines in order to determine the impact on proliferation, on distribution during the different phases of the cell cycle, and on apoptosis/necrosis. In addition, lung cancer cell lines were treated with 4-azasteroids, which specifically inhibit SRD5A1 activity, and the effects on proliferation were measured. Statistical analyses using ANOVA and post-hoc Tamhane-T2-test were performed. In the case of non-parametric data, the Kruskal-Wallis test and the post-hoc Mann-Whitney-U-test were used.

RESULTS

The knock-down of SRDA51 expression was very efficient with the SRD5A1 transcripts being reduced to 10% of control levels. Knock-down efficiency was furthermore confirmed at the protein level. However, no effect of SRD5A1 silencing was observed in the proliferation assay, the cell cycle analysis, and the apoptosis/necrosis assay. Treatment of lung cancer cell lines with 4-azasteroids did not significantly inhibit proliferation.

CONCLUSIONS

In summary, the results suggest that SRD5A1 is not a crucial enzyme for the sustained proliferation of NSCLC cell lines.

Profiling of linker histone variants in ovarian cancer

H1 linker histones play a key role in facilitating higher order chromatin folding. Emerging evidence suggests that H1 and its multiple variants are important epigenetic factors in modulating chromatin function and gene expression. Ovarian cancer is a devastating disease, ranking the fifth leading cause of all women cancer death due to its poor prognosis and difficulty in early diagnosis. Although epigenetic alterations in ovarian cancers are being appreciated in general, the role of H1 has not been explored. Here, using quantitative RT-PCR assays, we systematically examined the expression of 7 H1 genes in 33 human epithelial ovarian tumors. Whereas the expression of H1.3 was markedly increased, the expression of H10, H1.1, H1.4 and H1x were significantly reduced in malignant adenocarcinomas compared with benign adenomas. Strikingly, ovarian adenocarcinomas and adenomas exhibited characteristic expression patterns, and expression profiling of 7 H1 genes in tumor samples discriminated adenocarcinomas vs. adenomas with high accuracy. These findings indicate that the expression of H1 variants is exquisitely regulated and may serve as potential epigenetic biomarkers for ovarian cancer.

Integrative computational biology for cancer research

Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed clinical cancer research. They have revealed novel molecular markers of cancer subtypes, metastasis, and drug sensitivity and resistance. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and great volumes of data are accumulating at a rapid pace. Here we discuss these challenges, and show how integrative computational biology can help diminish them by creating new software tools, analytical methods, and data standards.

[Long-term survival of personalized surgical treatment of locally advanced non-small cell lung cancer based on molecular staging]

BACKGROUND AND OBJECTIVE

Approximately 35%-40% of patients with newly diagnosed non-small cell Lung cancer have locally advanced disease. The average survival time of these patients only have 6-8 months with chemotherapy. The aim of this study is to explore and summarize the probability of detection of micrometastasis in peripheral blood for molecular staging, and for selection of indication of surgical treatment, and beneficiary of neoadjuvant chemotherapy and postoperative adjuvant therapy in locally advanced lung cancer; to summarize the long-time survival result of personalized surgical treatment of 516 patients with locally advanced non-small cell lung cancer based on molecular staging methods.

METHODS

CK19 mRNA expression of peripheral blood samples was detected in 516 lung cancer patients by RT-PCR before operation for molecular diagnosis of micrometastasis, personalized molecular staging, and for selection of indication of surgical treatment and the beneficiary of neoadjuvant chemotherapy and postoperative adjuvant therapy in patients with locally advanced nonsmall cell lung cancer invaded heart, great vessels or both. The long-term survival result of personalized surgical treatment was retrospectively analyzed in 516 patients with locally advanced non-small cell lung cancer based on molecular staging methods.

RESULTS

There were 322 patients with squamous cell carcinoma and 194 cases with adenocarcinoma in the series of 516 patients with locally advanced lung cancer involved heart, great vessels or both. There were 112 patients with IIIA disease and 404 cases with IIIB disease according to P-TNM staging. There were 97 patients with M-IIIA disease, 278 cases with M-IIIB disease and 141 cases with III disease according to our personalized molecular staging. Of the 516 patients, bronchoplastic procedures and pulmonary artery reconstruction was carried out in 256 cases; lobectomy combined with resection and reconstruction of partial left atrium was performed in 41 cases; Double sleeve lobectomy combined with resection and reconstruction of super vena cava was carried out in 90 cases; Lobectomy combined with resection and reconstruction of diaphragm was performed in 3 cases; Double sleeve lobectomy combined with resection and reconstruction of partial left atrium was performed in 30 cases; Bronchoplastic procedures and pulmonary artery reconstruction combined with reconstruction of aorta sheath was carried out in 10 cases; Right pneumonectomy combined with resection and reconstruction partial left atrium, total right diaphragm with Dacron, and post cava and right liver vein was performed in one case; Lobectomy combined with resection and reconstruction of carina was carried out in 10 cases; Bronchoplastic procedures and pulmonary artery reconstruction combined with resection and reconstruction of carina and superior vane cava, or combined with superior vena cava and left atrium, or with carina and left atrium was performed in 55 cases in this series. Five patients died of operative complications and the operative mortality was 0.97%. CK19 mRNA expression was found in 141 patients. The positive rate of CK19 mRNA expression was 27.3% in peripheral blood samples in the 516 cases. The positive rates of micrometastasis in peripheral blood was significantly related to histological classification, P-TNM staging and N staging of the cancer (P < 0.05), but not to age, sex, smoking status of the patients, and size of primary tumor, and locations of the tumor (P > 0.05). The median survival time was 43.74 months. The 1, 3, 5 and 10 year survival rates of the 516 cases was 89.1%, 39.3%, 19.8% and 10.4%, respectively. The postoperative survival rate was remarkably correlated with micrometastasis in peripheral blood, histological classification of the tumor, size of primary cancer and lymph mode involvement (P < 0.05). The results of multivariable Cox model analysis showed that "personalized molecular P-TNM staging", micrometastasis in peripheral blood, pathological types of the tumor and mediastinal lymph node metastasis of the cancer were the most significant factors for predicting prognosis in the patients with locally advanced nonsmall lung cancer.

CONCLUSIONS

(1) Micrometastasis was existed in peripheral blood of patients with lung cancer, which can not be detected with conventional methods. (2) Detecting of CK19 mRNA expression in peripheral blood in lung cancer patients can be used for diagnosis of micrometastasis of lung cancer and "molecular staging" and "molecular P-TNM staging" for lung cancer patients. It will be helpful for selection of surgical treatment indication, the beneficiary of neoadjuvant chemotherapy and postoperative adjuvant therapy in the patients with locally advanced non-small cell lung cancer. (3) Personalized surgical treatment can significantly improve prognosis and increase curative rate and long-term survival rate of locally advanced nonsmall cell lung cancer based on personalized molecular staging.

Novel biomarkers of metastatic cancer

Once metastatic disease is documented, cure is no longer the goal and the disease is generally associated with poor outcomes, with the majority of patients dying of their disease rather than other causes. The last three decades have seen significant advances in the genomics, proteomics and molecular pathology of biomarkers in cancer, allowing for individualization of therapy that has significantly and positively impacted survival outcomes. Genetic signatures have been identified that can predict not only the future development of metastases, but also the development of specific sites of metastases. Protein biomarkers have been identified that are in use clinically for the monitoring of both disease progression and therapeutic efficacy. DNA- and RNA-based biomarkers have also been identified. This review will focus on some of the novel biomarkers that have been developed over the last decade.

Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer

PURPOSE

The JBR.10 trial demonstrated benefit from adjuvant cisplatin/vinorelbine (ACT) in early-stage non-small-cell lung cancer (NSCLC). We hypothesized that expression profiling may identify stage-independent subgroups who might benefit from ACT.

PATIENTS AND METHODS

Gene expression profiling was conducted on mRNA from 133 frozen JBR.10 tumor samples (62 observation [OBS], 71 ACT). The minimum gene set that was selected for the greatest separation of good and poor prognosis patient subgroups in OBS patients was identified. The prognostic value of this gene signature was tested in four independent published microarray data sets and by quantitative reverse-transcriptase polymerase chain reaction (RT-qPCR).

RESULTS

A 15-gene signature separated OBS patients into high-risk and low-risk subgroups with significantly different survival (hazard ratio [HR], 15.02; 95% CI, 5.12 to 44.04; P < .001; stage I HR, 13.31; P < .001; stage II HR, 13.47; P < .001). The prognostic effect was verified in the same 62 OBS patients where gene expression was assessed by qPCR. Furthermore, it was validated consistently in four separate microarray data sets (total 356 stage IB to II patients without adjuvant treatment) and additional JBR.10 OBS patients by qPCR (n = 19). The signature was also predictive of improved survival after ACT in JBR.10 high-risk patients (HR, 0.33; 95% CI, 0.17 to 0.63; P = .0005), but not in low-risk patients (HR, 3.67; 95% CI, 1.22 to 11.06; P = .0133; interaction P < .001). Significant interaction between risk groups and ACT was verified by qPCR.

CONCLUSION

This 15-gene expression signature is an independent prognostic marker in early-stage, completely resected NSCLC, and to our knowledge, is the first signature that has demonstrated the potential to select patients with stage IB to II NSCLC most likely to benefit from adjuvant chemotherapy with cisplatin/vinorelbine.

New biomarkers for lung cancer

IMPORTANCE OF THE FIELD

Despite many efforts to improve early detection, lung cancer remains the leading cause of cancer deaths. Stage is the main determinant of prognosis and the basis for deciding treatment options. Screening tests for lung cancer have not been successful so far.

AREAS COVERED IN THE REVIEW

The article reviews the available literature related to biomarkers in use at present and those that could be used for early diagnosis, staging, prognosis, response to therapy and prediction of recurrence. The single biomarkers are analysed, divided according to the technological methods used and the locations of sampling.

WHAT THE READER WILL GAIN

The reader will gain knowledge on biomarkers in use and those now under study. The reader will also gain insights into the difficulties pertaining to the development of biomarkers, results reproducibility and clinical application.

TAKE HOME MESSAGE

Although some markers seem to be promising, at present there is no consensus on the proven value of their clinical use in lung cancer. The future lies probably in a panel of biomarkers instead of individual assays, or in predictive models derived from the integration of clinical variables and gene expression profiles.

Molecular predictive and prognostic markers in non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer death in the developed world. Platinum-based chemotherapy is the therapeutic foundation of treatment both in the metastatic and adjuvant setting and targeted therapies are entering standard treatment paradigms. However, many patients do not obtain benefit from cytotoxic agents or newer targeted therapies, but are still exposed to their toxic effects. Reliable biomarkers to select treatments for patients most likely to obtain benefit have, therefore, been an important focus for many research groups. In this paper, we review current predictive and prognostic biomarkers in NSCLC. We assess their potential clinical use and explore recent data pertaining to genome-wide approaches for treatment selection in NSCLC.