Clinical impact of high-throughput gene expression studies in lung cancer

The impact of the lung EDRN-CVC on Phase 1, 2, & 3 biomarker validation studies

The Early Detection Research Network’s (EDRN) purpose is to discover, develop and validate biomarkers and imaging methods to detect early-stage cancers or at-risk individuals. The EDRN is composed of sites that fall into four categories: Biomarker Developmental Laboratories (BDL), Biomarker Reference Laboratories (BRL), Clinical Validation Centers (CVC) and Data Management and Coordinating Centers. Each component has a crucial role to play within the mission of the EDRN. The primary role of the CVCs is to support biomarker developers through validation trials on promising biomarkers discovered by both EDRN and non-EDRN investigators. The second round of funding for the EDRN Lung CVC at Vanderbilt University Medical Center (VUMC) was funded in October 2016 and we intended to accomplish the three missions of the CVCs: To conduct innovative research on the validation of candidate biomarkers for early cancer detection and risk assessment of lung cancer in an observational study; to compare biomarker performance; and to serve as a resource center for collaborative research within the Network and partner with established EDRN BDLs and BRLs, new laboratories and industry partners. This report outlines the impact of the VUMC EDRN Lung CVC and describes the role in promoting and validating biological and imaging biomarkers.

Molecular Patterns Based on Immunogenomic Signatures Stratify the Prognosis of Colon Cancer

Background: Colon cancer is an aggressive and heterogeneous disease associated with high morbidity and mortality. The immune system is intimately involved in tumorigenesis and can influence malignant properties at the protein, epigenetic, and even genomic levels by shaping the tumor immune microenvironment (TIM). However, immune-related molecules that can effectively predict the prognosis of colon cancer remain under exploration.

Methods: A total of 606 patients from TCGA and GEO databases were employed in our study, in which 429 cases were set as the training cohort and 177 were defined as the validation cohort. The immune infiltration was evaluated by ESTIMATE, TIMER, and CIBERSORT algorithms. The risk signature was constructed by LASSO Cox regression analysis. A nomogram model was generated subsequent to the multivariate Cox proportional hazards analysis to predict 1-, 3-, and 5-year survival of patients with colon cancer.

Results: Infiltrating immune cell profiling identified two colon cancer clusters (Immunity_L group and Immunity_H group). The abundances of immune cells were higher in the Immunity_H group, which indicated a better prognosis. Through further statistical analysis, we identified four genes which were highly correlated with prognosis and representative of this gene set, namely ARL4C, SERPINE1, BST2, and AXIN2. When the patients were divided into low- and high-risk groups based on their risk scores, we found that patients in the high-risk group had shorter overall survival time. Moreover, a nomogram including clinicopathologic features and the established risk signature could robustly predict 1-, 3-, and 5-year survival in patients with colon cancer.

Conclusion: We identified two distinct immune patterns by analyzing clinical and transcriptomic information from colon cancer patients. A subsequently constructed immune-related gene-based prognostic model as well as a nomogram model can be used to predict the prognosis of colon cancer, thereby guiding risk stratification and treatment regimen development for colon patients.

Multi-level multi-modality (PET and CT) fusion radiomics: prognostic modeling for non-small cell lung carcinoma

We developed multi-modality radiomic models by integrating information extracted from¹⁸F-FDG PET and CT images using feature- and image-level fusions, toward improved prognosis for non-small cell lung carcinoma (NSCLC) patients. Two independent cohorts of NSCLC patients from two institutions (87 and 95 patients) were cycled as training and testing datasets. Fusion approaches were applied at two levels, namely feature- and image-levels. For feature-level fusion, radiomic features were extracted individually from CT and PET images and concatenated. Alternatively, radiomic features extracted separately from CT and PET images were averaged. For image-level fusion, wavelet fusion was utilized and tuned with two parameters, namely CT weight and Wavelet Band Pass Filtering Ratio. Clinical and combined clinical + radiomic models were developed. Gray level discretization was performed at 3 different levels (16, 32 and 64) and 225 radiomics features were extracted. Overall survival (OS) was considered as the endpoint. For feature reduction, correlated (redundant) features were excluded using Spearman's correlation, and best combination of top ten features with highest concordance-indices (via univariate Cox model) were selected in each model for further multivariate Cox model. Moreover, prognostic score's median, obtained from the training cohort, was used intact in the testing cohort as a threshold to classify patients into low- versus high-risk groups, and log-rank test was applied to assess differences between the Kaplan-Meier curves. Overall, while models based on feature-level fusion strategy showed limited superiority over single-modalities, image-level fusion strategy significantly outperformed both single-modality and feature-level fusion strategies. As such, the clinical model (C-index = 0.656) outperformed all models from single-modality and feature-level strategies, but was outperformed by certain models from image-level fusion strategy. Our findings indicated that image-level fusion multi-modality radiomics models outperformed single-modality, feature-level fusion, and clinical models for OS prediction of NSCLC patients.

Transcriptomic Profiling Identifies DCBLD2 as a Diagnostic and Prognostic Biomarker in Pancreatic Ductal Adenocarcinoma

Background: Accumulating evidence shows that the elevated expression of DCBLD2 (discoidin, CUB and LCCL domain-containing protein 2) is associated with unfavorable prognosis of various cancers. However, the correlation of DCBLD2 expression value with the diagnosis and prognosis of pancreatic ductal adenocarcinoma (PDAC) has not yet been elucidated. Methods: Univariate Cox regression analysis was used to screen robust survival-related genes. Expression pattern of selected genes was investigated in PDAC tissues and normal tissues from multiple cohorts. Kaplan–Meier (K–M) survival curves, ROC curves and calibration curves were employed to assess prognostic performance. The relationship between DCBLD2 expression and immune cell infiltrates was conducted by CIBERSORT software. Biological processes and KEGG pathway enrichment analyses were adopted to clarify the potential function of DCBLD2 in PDAC. Results: Univariate analysis, K–M survival curves and calibration curves indicated that DCBLD2 was a robust prognostic factor for PDAC with cross-cohort compatibility. Upregulation of DCBLD2 was observed in dissected PDAC tissues as well as extracellular vesicles from both plasma and serum samples of PDAC patients. Both DCBLD2 expression in tissue and extracellular vesicles had significant diagnostic value. Besides, DCBLD2 expression was correlated with infiltrating level of CD8⁺ T cells and macrophage M2 cells. Functional enrichment revealed that DCBLD2 might be involved in cell motility, angiogenesis, and cancer-associated pathways. Conclusion: Our study systematically analyzed the potential diagnostic, prognostic and therapeutic value of DCBLD2 in PDAC. All the findings indicated that DCBLD2 might play a considerably oncogenic role in PDAC with diagnostic, prognostic and therapeutic potential. These preliminary results of bioinformatics analyses need to be further validated in more prospective studies.

The clinical relevance of gene expression based prognostic signatures in colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent cancers, with more than one million new cases every year. In the last few decades, several advancements in therapeutic and preventative levels have reduced the mortality rate, but new biomarkers are required for improved prognosis. The alterations at the genetic and epigenetic level have been recognized as major players in tumorigenesis. The products of gene expression in the form of mRNA, microRNA, and long-noncoding RNA, have started to emerge as important regulatory molecules, playing an important role in cancer. Gene-expression based prognostic risk scores, which quantify and compare their expression, have emerged as promising biomarkers with enormous clinical value. These composite multi-gene models in which more than one gene is used to predict prognosis have been shown to be significantly effective in identifying patients with multiple clinico-pathological risks like overall mortality, response to chemotherapy, risk of metastasis, etc. The advent of microarray and advanced sequencing technologies have led to the generation of large datasets like TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), which have fueled the search for new biomarkers. Continuous evaluation of these candidate biomarkers in clinical settings is promising to improve the management of CRC. These composite gene signatures provide potential in identifying high-risk patients, which might help clinicians to better manage these patients and design appropriate personalized therapeutic interventions. In this review, we emphasize on composite prognostic scores from diverse resources with clinical utility in CRC.

Identification of key genes and biological pathways in lung adenocarcinoma via bioinformatics analysis

Lung adenocarcinoma (LUAD) accounts for the majority of cancer-related deaths worldwide. Our study identified key LUAD genes and their potential mechanism via bioinformatics analysis of public datasets. GSE10799, GSE40791, and GSE27262 microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database. The RobustRankAggreg package was used to perform a meta-analysis, and 50 upregulated genes and 87 downregulated genes overlapped in three datasets. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Furthermore, protein-protein interaction (PPI) networks of the differentially expressed genes (DEGs) were built by the Search Tool for the Retrieval of Interacting Genes (STRING) and 22 core genes were identified by Molecular Complex Detection (MCODE) and visualized with Cytoscape. Subsequently, these core genes were analyzed by the Kaplan-Meier Plotter and Gene Expression Profiling Interactive Analysis (GEPIA). The results showed that all 22 genes were significantly associated with reduced survival rates. For GEPIA, the expression of only one gene was not significantly different between LUAD tissues and normal tissues. A KEGG pathway enrichment reanalysis of the 21 genes identified five key genes (CCNB1, BUB1B, CDC20, TTK, and MAD2L1) in the cell cycle pathway. Finally, the Comparative Toxicogenomics Database (CTD) website was used to explore the relationship between these key genes and certain drugs. Based on the bioinformatics analysis, five key genes were identified in LUAD, and drugs closely associated these genes can provide clues for the treatment and prognosis of LUAD.

Analyzing and validating the prognostic value and mechanism of colon cancer immune microenvironment

Background

Colon cancer is a disease with high malignancy and incidence in the world. Tumor immune microenvironment (TIM) and tumor mutational burden (TMB) have been proved to play crucial roles in predicting clinical outcomes and therapeutic efficacy, but the correlation between them and the underlying mechanism were not completely understood in colon cancer.

Methods

In this study, we used Single-Sample Gene Set Enrichment Analysis (ssGSEA) and unsupervised consensus clustering analysis to divide patients from the TCGA cohort into three immune subgroups. Then we validated their differences in immune cell infiltration, overall survival outcomes, clinical phenotypes and expression levels of HLA and checkpoint genes by Mann–Whitney tests. We performed weighted correlation network analysis (WGCNA) to obtain immunity-related module and hub genes. Then we explored the underlying mechanism of hub genes by gene set enrichment analysis (GSEA) and gene set evaluation analysis (GSVA). Finally, we gave an overall view of gene variants and verified the correlation between TIM and TMB by comparing microsatellite instability (MSI) and gene mutations among three immune subgroups.

Results

The colon cancer patients were clustered into low immunity, median immunity and high immunity groups. The median immunity group had a favorable survival probability compared with that of the low and high immunity groups. Three groups had significant differences in immune cell infiltration, tumor stage, living state and T classification. We got 8 hub genes (CCDC69, CLMP, FAM110B, FAM129A, GUCY1B3, PALLD, PLEKHO1 and STY11) and predicted that immunity may correlated with inflammatory response, KRAS signaling pathway and T cell infiltration. With higher immunity, the TMB was higher. The most frequent mutations in low and median immunity groups were APC, TP53 and KRAS, while TTN and MUC16 showed higher mutational frequency in high immunity group.

Conclusions

We performed a comprehensive evaluation of the immune microenvironment landscape of colon cancer and demonstrated the positive correlation between immunity and TMB. The hub genes and frequently mutated genes were strongly related to immunity and may give suggestion for immunotherapy in the future.

Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC

Advances in molecular analyses based on high-throughput technologies can contribute to a more accurate classification of non-small cell lung cancer (NSCLC), as well as a better prediction of both the disease course and the efficacy of targeted therapies. Here we set out to analyze whether global gene expression profiling performed in a group of early-stage NSCLC patients can contribute to classifying tumor subtypes and predicting the disease prognosis. Gene expression profiling was performed with the use of the microarray technology in a training set of 108 NSCLC samples. Subsequently, the recorded findings were validated further in an independent cohort of 44 samples. We demonstrated that the specific gene patterns differed significantly between lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC) samples. Furthermore, we developed and validated a novel 53-gene signature distinguishing SCC from AC with 93% accuracy. Evaluation of the classifier performance in the validation set showed that our predictor classified the AC patients with 100% sensitivity and 88% specificity. We revealed that gene expression patterns observed in the early stages of NSCLC may help elucidate the histological distinctions of tumors through identification of different gene-mediated biological processes involved in the pathogenesis of histologically distinct tumors. However, we showed here that the gene expression profiles did not provide additional value in predicting the progression status of the early-stage NSCLC. Nevertheless, the gene expression signature analysis enabled us to perform a reliable subclassification of NSCLC tumors, and it can therefore become a useful diagnostic tool for a more accurate selection of patients for targeted therapies.

AKAP4 is a circulating biomarker for non-small cell lung cancer

Cancer testis antigens (CTAs) are widely expressed in tumor tissues, circulating tumor cells (CTCs) and in cancer derived exosomes that are frequently engulfed by lymphoid cells. To determine whether tumor derived CTA mRNAs could be detected in RNA from purified peripheral blood mononuclear cells (PBMC) of non-small cell lung cancer (NSCLC) patients, we assayed for the expression of 116 CTAs in PBMC RNA in a discovery set and identified AKAP4 as a potential NSCLC biomarker. We validated AKAP4 as a highly accurate biomarker in a cohort of 264 NSCLCs and 135 controls from 2 different sites including a subset of controls with high risk lung nodules. When all (264) lung cancers were compared with all (135) controls the area under the ROC curve (AUC) was 0.9714. When 136 stage I NSCLC lung cancers are compared with all controls the AUC is 0.9795 and when all lung cancer patients were compared to 27 controls with histologically confirmed benign lung nodules, a comparison of significant clinical importance, the AUC was 0.9825. AKAP4 expression increases significantly with tumor stage, but independent of age, gender, smoking history or cancer subtype. Follow-up studies in a small number of resected NSCLC patients revealed a decrease of AKAP4 expression post-surgical resection that remained low in patients in remission and increased with tumor recurrence. AKAP4 is a highly accurate biomarker for the detection of early stage lung cancer.

The prognostic value of whole blood SOX2, NANOG and OCT4 mRNA expression in advanced small-cell lung cancer

BACKGROUND

The data on expression and clinical impact of cancer stem cell markers SOX2, NANOG and OCT4 in lung cancer is still lacking. The aim of our study was to compare SOX2, NANOG and OCT4 mRNA expression levels in whole blood between advanced small-cell lung cancer (SCLC) patients and healthy controls, and to correlate mRNA expression with progression-free survival (PFS) after first-line chemotherapy and overall survival (OS) in advanced SCLC patients.

PATIENTS AND METHODS

50 advanced SCLC patients treated with standard chemotherapy and followed at University Clinic Golnik, Slovenia, between 2009 and 2013 were prospectively included. SOX2, NANOG and OCT4 mRNA expression levels were determined using TaqMan qPCR in whole blood collected prior to chemotherapy. Whole blood of 34 matched healthy individuals with no cancerous disease was also tested.

RESULTS

SOX2 mRNA expression was significantly higher in whole blood of SCLC patients compared to healthy controls (p = 0.006). Significant correlation between SOX2 mRNA expression levels and the number of distant metastatic sites was established (p = 0.027). In survival analysis, patients with high SOX2 expression had shorter OS (p = 0.017) and PFS (p = 0.046). In multivariate Cox analysis, an independent value of high SOX2 expression for shorter OS (p = 0.002), but not PFS was confirmed. No significant differences were observed for NANOG or OCT4 expression levels when comparing SCLC patients and healthy controls neither when analysing survival outcomes in SCLC patients.

CONCLUSIONS

SOX2 mRNA expression in whole blood might be a promising non-invasive marker for molecular screening of SCLC and important prognostic marker in advanced chemotherapy-treated SCLC patients, altogether indicating important role of cancer stem-like cell (CSC) regulators in cancer spread. Further evaluation of SOX2 as a possible screening/prognostic marker and a therapeutic target of SCLC is warranted.

Thymidylate synthase polymorphisms are associated to therapeutic outcome of advanced non-small cell lung cancer patients treated with platinum-based chemotherapy

Thymidylate synthase (TYMS) has three polymorphisms that may modulate thymidylate synthase (TS) expression levels: (1) 28 base pairs (bp) variable number tandem repeat (VNTR) (rs34743033); (2) single nucleotide polymorphism (SNP) C>G at the twelfth nucleotide of the second repeat of 3R allele (rs2853542); and (3) 6 bp sequence deletion (1494del6, rs34489327). This study was conducted to evaluate the influence of TYMS polymorphisms on the survival of Portuguese patients with advanced non-small cell lung cancer (NSCLC) undergoing platinum-based chemotherapy. Our results showed no statistically significant differences between VNTR genotypes; although, considering the SNP C>G, homozygotes 3RG presented a better prognostic at 36 months (p=0.004) and overall survival (p=0.003) when compared to 2R3RG patients. Patients with "median/high expression genotypes" demonstrated a better survival at 12 months (p=0.041) when compared to "low expression genotypes". Furthermore, 6 bp- carriers (p=0.006) showed a better survival at 12 months when compared to 6 bp+ homozygotes patients. When analyzing TYMS haplotypes, better survival at 12 months was observed for patients carrying haplotypes with the 6 bp- allele (2R6 bp-; p=0.026 and 3RG6 bp-; p=0.045). This is the first report that evaluates the three major TYMS polymorphisms in the therapeutic outcome of NSCLC in Portugal. According to our results, the TYMS polymorphisms may be useful tools to predict which advanced NSCLC patients could benefit more from platinum-based chemotherapy regimens.

Gene copy number aberrations are associated with survival in histologic subgroups of non-small cell lung cancer

INTRODUCTION

Non-small cell lung cancer (NSCLC) is characterized by a multitude of genetic aberrations with unknown clinical impact. In this study, we aimed to identify gene copy number changes that correlate with clinical outcome in NSCLC. To maximize the chance to identify clinically relevant events, we applied a strategy involving two prognostically extreme patient groups.

METHODS

Short-term (<20 month; n = 53) and long-term survivors (>58 month; n = 47) were selected from a clinically well-characterized NSCLC patient cohort with available fresh frozen tumor specimens. The samples were analyzed using high-resolution single-nucleotide polymorphism array technology to assess gene copy number variations and array-based gene expression profiling. The molecular data were combined with information on clinical parameters.

RESULTS

Genetic aberrations were strongly associated with tumor histology. In adenocarcinoma (n = 50), gene copy number gains on chromosome 8q21-q24.3 (177 genes) were more frequent in long-term than in short-term survivors. In squamous cell carcinoma (n = 28), gains on chromosome 14q23.1-24.3 (133 genes) were associated with shorter survival, whereas losses in a neighboring region, 14q31.1-32.33 (110 genes), correlated with favorable outcome. In accordance with copy number gains and losses, messenger RNA expression levels of corresponding genes were increased or decreased, respectively.

CONCLUSION

Comprehensive tumor profiling permits the integration of genomic, histologic, and clinical data. We identified gene copy number gains and losses, with corresponding changes in messenger RNA levels that were associated with prognosis in adenocarcinoma and squamous cell carcinoma of the lung.

Discovery of lung cancer biomarkers by profiling the plasma proteome with monoclonal antibody libraries

A challenge in the treatment of lung cancer is the lack of early diagnostics. Here, we describe the application of monoclonal antibody proteomics for discovery of a panel of biomarkers for early detection (stage I) of non-small cell lung cancer (NSCLC). We produced large monoclonal antibody libraries directed against the natural form of protein antigens present in the plasma of NSCLC patients. Plasma biomarkers associated with the presence of lung cancer were detected via high throughput ELISA. Differential profiling of plasma proteomes of four clinical cohorts, totaling 301 patients with lung cancer and 235 healthy controls, identified 13 lung cancer-associated (p < 0.05) monoclonal antibodies. The monoclonal antibodies recognize five different cognate proteins identified using immunoprecipitation followed by mass spectrometry. Four of the five antigens were present in non-small cell lung cancer cells in situ. The approach is capable of generating independent antibodies against different epitopes of the same proteins, allowing fast translation to multiplexed sandwich assays. Based on these results, we have verified in two independent clinical collections a panel of five biomarkers for classifying patient disease status with a diagnostics performance of 77% sensitivity and 87% specificity. Combining CYFRA, an established cancer marker, with the panel resulted in a performance of 83% sensitivity at 95% specificity for stage I NSCLC.

Biomarkers: the useful and the not so useful--an assessment of molecular prognostic markers for cutaneous melanoma

Among individuals with localized (Stage I-II) melanoma, stratifying patients by a number of phenotypic variables (e.g., depth of invasion, ulceration) yields a wide range of 10-year melanoma-specific survival rates. With the possible exception of Ki-67, no molecular assessment is routinely used. However, there have been a tremendous number of studies assessing protein expression by immunohistochemistry toward the goal of better prediction of recurrence. In a previous systematic review, which required publication of multivariable prognostic models as a strict inclusion criterion, we identified 37 manuscripts that collectively reported on 62 proteins. Data for 324 proteins extracted from 418 manuscripts did not meet our inclusion criteria for that study, but are revisited here, emphasizing trends of protein expression across either melanocytic lesion progression or gradations of tumor thickness. These identified 101 additional proteins that stratify melanoma, organized according to the Hanahan and Weinberg functional capabilities of cancer.

The role of gene expression profiling in early-stage non-small cell lung cancer

For patients with identical clinical-pathological characteristics or the same stage of lung cancer, great uncertainties remain regarding how some patients will be cured while other patients will have cancer recurrence, metastasis, or death after surgical resection. Identification of patients at high risk of recurrence, those who are unlikely to respond to specific chemotherapeutic agents, is the rationale for measuring specific biochemical markers. Thus, main investigational studies nowadays are focused in identifying molecular markers of recurrence, beyond pathologic stage, after surgical treatment and factors that can predict a benefit from adjuvant chemotherapy in poor prognosis subgroups, to individualize treatments. Advances in genomics and proteomics have generated many candidate markers with potential clinical value. Gene expression profiling (GEP) by microarray or real-time quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) can be useful in the classification or prognosis of various types of cancer, including lung cancer. A number of prognostic gene expression signatures have been reported to predict survival in non-small cell lung cancer (NSCLC). In this review, we focus on the role of GEP in early-stage NSCLC as predictive and prognostic biomarker and its potential use for a 'personalized' medicine in the years to come.

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.

Smoking-Related Gene Expression in Laser Capture-Microdissected Human Lung

PURPOSE: Interindividual differences in quantitative expression could underlie a propensity for lung cancer. To determine precise individual gene expression signatures on a lung compartment-specific basis, we investigated the expression of carcinogen metabolism genes encoding cytochromes P450 (CYP) 1B1, 2A13, GSTP1, and a tumor suppressor gene p16 in laser capture-microdissected samples of human alveolar compartment (AC) and bronchial epithelial compartment (BEC) lung tissue from 62 smokers and nonsmokers. EXPERIMENTAL DESIGN: Tobacco exposure was determined by plasma nicotine, cotinine, and smoking history. Precise mRNA expression was determined using our RNA-specific qRT-PCR strategy, and correlated with detailed demographic and clinical characteristics. RESULTS: Several correlations of mRNA expression included (a) CYP1B1 in AC (positively with plasma nicotine level, P = 0.008; plasma cotinine level, P = 0.001), (b) GSTP1 in AC (positively with plasma cotinine level, P = 0.003), and (c) GSTP1 in BEC (negatively with smoke dose, P = 0.043; occupational risk, P = 0.019). CYP2A13 was rarely expressed in AC and not expressed in BEC. p16 expression was not correlated with any measured factor. For each gene, subjects showed expression that was individually concordant between these compartments. No clear association of mRNA expression with lung cancer risk was observed in this pilot analysis. CONCLUSIONS: The association between lung mRNA expression and tobacco exposure implies that gene-tobacco interaction is a measurable quantitative trait, albeit with wide interindividual variation. Gene expression tends to be concordant for alveolar and bronchial compartments for these genes in an individual, controlling for proximate tobacco exposure. (Clin Cancer Res 2009;15(24):7562-70).