Alterations in Bronchial Airway miRNA Expression for Lung Cancer Detection

We have previously shown that gene expression alterations in normal-appearing bronchial epithelial cells can serve as a lung cancer detection biomarker in smokers. Given that miRNAs regulate airway gene expression responses to smoking, we evaluated whether miRNA expression is also altered in the bronchial epithelium of smokers with lung cancer. Using epithelial brushings from the mainstem bronchus of patients undergoing bronchoscopy for suspected lung cancer (as part of the AEGIS-1/2 clinical trials), we profiled miRNA expression via small-RNA sequencing from 347 current and former smokers for which gene expression data were also available. Patients were followed for one year postbronchoscopy until a final diagnosis of lung cancer (n = 194) or benign disease (n = 153) was made. Following removal of 6 low-quality samples, we used 138 patients (AEGIS-1) as a discovery set to identify four miRNAs (miR-146a-5p, miR-324-5p, miR-223-3p, and miR-223-5p) that were downregulated in the bronchial airway of lung cancer patients (ANOVA P < 0.002, FDR < 0.2). The expression of these miRNAs is significantly more negatively correlated with the expression of their mRNA targets than with the expression of other nontarget genes (K-S P < 0.05). Furthermore, these mRNA targets are enriched among genes whose expression is elevated in cancer patients (GSEA FDR < 0.001). Finally, we found that the addition of miR-146a-5p to an existing mRNA biomarker for lung cancer significantly improves its performance (AUC) in the 203 samples (AEGIS-1/2) serving an independent test set (DeLong P < 0.05). Our findings suggest that there are miRNAs whose expression is altered in the cytologically normal bronchial epithelium of smokers with lung cancer, and that they may regulate cancer-associated gene expression differences. Cancer Prev Res; 10(11); 651-9. ©2017 AACR.

The cellular and molecular determinants of emphysematous destruction in COPD

The introduction of microCT has made it possible to show that the terminal bronchioles are narrowed and destroyed before the onset of emphysematous destruction in COPD. This report extends those observations to the cellular and molecular level in the centrilobular phenotype of emphysematous destruction in lungs donated by persons with very severe COPD (n = 4) treated by lung transplantation with unused donor lungs (n = 4) serving as controls. These lung specimens provided companion samples to those previously examined by microCT (n = 61) that we examined using quantitative histology (n = 61) and gene expression profiling (n = 48). The histological analysis showed that remodeling and destruction of the bronchiolar and alveolar tissue is associated with macrophage, CD4, CD8, and B cell infiltration with increased formation of tertiary lymphoid organs. Moreover, gene set enrichment analysis showed that genes known to be expressed by natural killer (NK), lymphoid tissue inducer (LTi), and innate lymphoid cell 1 (ILC1) cells, but not ILC2 or ILC3 cells, were enriched in the expression profiles associated with CD4, CD8, and B cell infiltration. Based on these findings, we postulate that the centrilobular phenotype of emphysematous destruction COPD is driven by a Th1 response activated by infiltrating ILC1, NK, and LTi cells.

Comparison of Prevalence and Types of Mutations in Lung Cancers Among Black and White Populations

Importance

Lung cancer is the leading cause of cancer death in the United States in all ethnic and racial groups. The overall death rate from lung cancer is higher in black patients than in white patients.

Objective

To compare the prevalence and types of somatic alterations between lung cancers from black patients and white patients. Differences in mutational frequencies could illuminate differences in prognosis and lead to the reduction of outcome disparities by more precisely targeting patients' treatment.

Design, Setting, and Participants

Tumor specimens were collected from Baptist Cancer Center (Memphis, Tennessee) over the course of 9 years (January 2004-December 2012). Genomic analysis by massively parallel sequencing of 504 cancer genes was performed at Dana-Farber Cancer Institute (Boston, Massachusetts). Overall, 509 lung cancer tumors specimens (319 adenocarcinomas; 142 squamous cell carcinomas) were profiled from 245 black patients and 264 white patients.

Main Outcomes and Measures

The frequencies of genomic alterations were compared between tumors from black and white populations.

Results

Overall, 509 lung cancers were collected and analyzed (273 women [129 black patients; 144 white patients] and 236 men [116 black patients; 120 white patients]). Using 313 adenocarcinomas and 138 squamous cell carcinomas with genetically supported ancestry, overall mutational frequencies and copy number changes were not significantly different between black and white populations in either tumor type after correcting for multiple hypothesis testing. Furthermore, specific activating alterations in members of the receptor tyrosine kinase/Ras/Raf pathway including EGFR and KRAS were not significantly different between populations in lung adenocarcinoma.

Conclusions and Relevance

These results demonstrate that lung cancers from black patients are similar to cancers from white patients with respect to clinically actionable genomic alterations and suggest that clinical trials of targeted therapies could significantly benefit patients in both groups.

Genomic approaches to accelerate cancer interception

Although major advances have been reported in the last decade in the treatment of late-stage cancer with targeted and immune-based therapies, there is a crucial unmet need to develop new approaches to improve the prevention and early detection of cancer. Advances in genomics and computational biology offer unprecedented opportunities to understand the earliest molecular events associated with carcinogenesis, enabling novel strategies to intercept the development of invasive cancers. This Series paper will highlight emerging big data genomic approaches with the potential to accelerate advances in cancer prevention, screening, and early detection across various tumour types, and the challenges inherent in the development of these tools for clinical use. Through coordinated multicentre consortia, these genomic approaches are likely to transform the landscape of cancer interception in the coming years.

Abstract 1433: Single cell RNA sequencing reveals smoking-associated alterations in bronchial airway epithelial subpopulations

Rationale: We have previously shown that bronchial airway epithelial gene expression reflects the physiologic response to cigarette smoke exposure. We have also shown that gene expression differences in cytologically normal airways cells can serve as a diagnostic biomarker for lung cancer. In this study, we use single cell RNA-seq to profile transcriptomes of individual bronchial epithelial cells from current and never smokers in order to detect smoking-associated alterations within specific epithelial cell types and to discover novel subpopulations that develop as a result of smoke exposure. This approach may be useful for identifying cell type-specific transcriptomic changes in the airways of cancer patients, which may lead to a better understanding of lung carcinogenesis and new approaches to early lung cancer detection.

Methods: We obtained bronchial brushings from current smokers (n=6) and never smokers (n=6) and isolated single cells by FACS. The CEL-Seq RNA library preparation protocol was used to sequence the transcriptomes of 1,140 cells (n=95/donor).

Results: Distinct populations of bronchial cells expressed known markers of basal (KRT5), ciliated (FOXJ1), secretory (SCGB1A1, MUC5AC) epithelial cells, as well as white blood cells (CD45). In the airways of smokers, we observed an increase in abundance of MUC5AC+ secretory cells as well as a decrease in abundance of KRT5+ basal cells and SCGB1A1+ secretory cells. A novel subset of KRT8+ cells that lacked expression of other known cell type markers was identified in the airways of smokers and may represent a population previously described as undifferentiated intermediate cells. Genes involved with metabolism of polycyclic aromatic hydrocarbons (CYP1B1) were detected in smoker secretory cells, whereas genes involved in the metabolic response to cigarette smoke toxins such as aldehydes (ALDH3A1) and quinones (NQO1), were most highly expressed by smoker ciliated cells. Interestingly, the novel KRT8+ cells identified in smokers expressed genes known to promote MUC5AC+ secretory cell differentiation (SPDEF), but did not express MUC5AC itself, suggesting that these may be pro-MUC5AC+ secretory intermediate cells. Furthermore, we found that genes previously associated with higher expression in the airways of lung cancer patients were enriched among genes most strongly associated with smoker ciliated and secretory cells, whereas genes with lower expression in lung cancer were enriched among genes most strongly associated with white blood cells.

Conclusion: We have identified cell type-specific transcriptomic alterations and shifts in epithelial cell population abundance in smoker airways. In future studies, profiling the transcriptomes of single cells from bronchial airways of smokers with and without lung cancer may lead to the identification of specific cellular subpopulations contributing to the airway field of lung-cancer associated injury.

Citation Format: Grant E. Duclos, Joshua D. Campbell, Yaron Gesthalter, Patrick Autissier, Yves M. Dumas, Robert Terrano, Gang Liu, Marc E. Lenburg, Avrum Spira, Jennifer Beane. Single cell RNA sequencing reveals smoking-associated alterations in bronchial airway epithelial subpopulations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1433. doi:10.1158/1538-7445.AM2017-1433

Abstract 5567: A scalable and integrated computational and experimental workflow to identify new driver genes in cancer genome data

High throughput sequencing has revolutionized the study of the cancer genome, enabling numerous discoveries in basic and clinical research. However, considerable sample sizes are required to find cancer driver genes with intermediate and low mutation frequencies, and for a large proportion of patients the molecular cause (e.g. driver gene(s)) of disease is unknown. Here, we describe an integrated computational and experimental workflow that combines cancer genome data, molecular network information, multiplexed in vivo tumorigenesis assays, and reanalysis of driver-gene-negative cancer patients to predict and validate new driver genes. We develop a statistic, network mutation burden, that combines molecular network information with data from 4,742 cancer genomes to accurately classify known driver genes across 21 tumor types and predict 62 driver gene candidates.Of these, 35 gene candidates were tested in multiplexed in vivo tumorigenesis cell assays using sensitized immortalized human embryonic kidney (HA1E-M) and immortalized human lung epithelial (SALE-Y) cell lines.Tumor formation in vivo was observed for 11 genes (2 in HA1E-M, 3 in SALE-Y, 6 in both). By reanalyzing 242 lung adenocarcinoma patients with an unknown molecular cause of disease we show that two of these candidates, TFDP2 and AKT2, are significantly amplified in multiple samples.Overall, we describe a scalable combined computational and experimental framework to predict and validate driver genes across many tumor types. Our proof-of-concept approach should become increasingly useful as the number of cancer genomes continues to grow.

Citation Format: Heiko Horn, Michael S. Lawrence, Candace R. Chouinard, Yashaswi Shrestha, Jessica Xin Hu, Elizabeth Worstell, Emily Shea, Nina Ilic, Eejung Kim, Atanas Kamburov, Alireza Kashani, William C. Hahn, Joshua D. Campbell, Jesse S. Boehm, Gad Getz, Kasper Lage. A scalable and integrated computational and experimental workflow to identify new driver genes in cancer genome data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5567. doi:10.1158/1538-7445.AM2017-5567

Institutional implementation of clinical tumor profiling on an unselected cancer population

BACKGROUND. Comprehensive genomic profiling of a patient's cancer can be used to diagnose, monitor, and recommend treatment. Clinical implementation of tumor profiling in an enterprise-wide, unselected cancer patient population has yet to be reported. METHODS. We deployed a hybrid-capture and massively parallel sequencing assay (OncoPanel) for all adult and pediatric patients at our combined cancer centers. Results were categorized by pathologists based on actionability. We report the results for the first 3,727 patients tested. RESULTS. Our cohort consists of cancer patients unrestricted by disease site or stage. Across all consented patients, half had sufficient and available (>20% tumor) material for profiling; once specimens were received in the laboratory for pathology review, 73% were scored as adequate for genomic testing. When sufficient DNA was obtained, OncoPanel yielded a result in 96% of cases. 73% of patients harbored an actionable or informative alteration; only 19% of these represented a current standard of care for therapeutic stratification. The findings recapitulate those of previous studies of common cancers but also identify alterations, including in AXL and EGFR, associated with response to targeted therapies. In rare cancers, potentially actionable alterations suggest the utility of a "cancer-agnostic" approach in genomic profiling. Retrospective analyses uncovered contextual genomic features that may inform therapeutic response and examples where diagnoses revised by genomic profiling markedly changed clinical management. CONCLUSIONS. Broad sequencing-based testing deployed across an unselected cancer cohort is feasible. Genomic results may alter management in diverse scenarios; however, additional barriers must be overcome to enable precision cancer medicine on a large scale. FUNDING. This work was supported by DFCI, BWH, and the National Cancer Institute (5R33CA155554 and 5K23CA157631).

High-throughput Phenotyping of Lung Cancer Somatic Mutations

Recent genome sequencing efforts have identified millions of somatic mutations in cancer. However, the functional impact of most variants is poorly understood. Here we characterize 194 somatic mutations identified in primary lung adenocarcinomas. We present an expression-based variant-impact phenotyping (eVIP) method that uses gene expression changes to distinguish impactful from neutral somatic mutations. eVIP identified 69% of mutations analyzed as impactful and 31% as functionally neutral. A subset of the impactful mutations induces xenograft tumor formation in mice and/or confers resistance to cellular EGFR inhibition. Among these impactful variants are rare somatic, clinically actionable variants including EGFR S645C, ARAF S214C and S214F, ERBB2 S418T, and multiple BRAF variants, demonstrating that rare mutations can be functionally important in cancer.

Abstract 895: Genomic characterization of premalignant lung squamous cell carcinoma lesions

Background: Lung squamous cell carcinoma (SqCC) arises in the epithelial layer of the bronchial airways and is often preceded by the development of premalignant lesions. However, not all premalignant lesions will progress to lung SqCC and many of these lesions will regress without therapeutic intervention. Understanding the molecular events that contribute to progression of premalignant lesions in the airway will allow us to identify biomarkers for early detection and develop therapeutic strategies for early intervention.

Methods: Bronchial brushings and biopsies were obtained from high-risk smokers undergoing lung cancer screening by auto-fluorescence bronchoscopy and CT at the Roswell Park Cancer Institute. For each subject (n = 30), both premalignant lesions (PMLs) and the cytologically normal mainstem bronchus were sampled repeatedly over time (n = 288 samples). DNA and RNA were isolated from a total of 197 bronchial biopsies of PML (average of 5 per subject) and 91 bronchial brushings. DNA was also isolated from the blood to serve as a matched normal. Exome capture was performed using the Agilent SureSelect Human All Exon+UTR 70MB kit and sequenced to a mean depth of coverage of 75x (n = 85 samples from 22 subjects). RNA libraries were prepared with Illumina TruSeq (mRNA-Seq: n = 288 samples from 30 subjects and miRNA-Seq: n = 183 samples from 26 subjects).

Results: We identified gene and miRNA expression changes associated with histological grade as well as progressive/stable disease. The Hippo pathway, Wnt signaling, p53 signaling, and immune-related pathways are modulated with histological grade and disease progression. Genes associated with histological grade in the cytologically normal airway and in the biopsies were significantly concordantly enriched (FDR&lt;0.05) demonstrating a strong relationship between the PMLs and the field of injury. The somatic mutation rate of PMLs displayed no significant association with histological grade (p = 0.65). Mutations in previously characterized lung cancer genes included TP53 (3%), CREBBP (3%), FAT1 (3%), and NOTCH1 (9%). Examining copy number alterations revealed a single metaplastic lesion with an arm-level amplification on chr5p containing TERT. The two lesions with the highest mutation rates (&gt;3/Mb) were taken from adjacent sites over two time points in the same individual with a history of lung squamous cell carcinoma. These lesions had a significantly overlapping set of mutations (p = 2.2 × 10−17) indicating a common evolutionary ancestor, and contained mutations in CREBBP and FAT1, suggesting they are at increased risk for progressing to frank malignancy.

Conclusions: We performed genomic profiling of PMLs in the airways of high-risk smokers. The gene expression and somatic alterations that were observed in known cancer genes may be among the earliest events in cancer development.

Citation Format: Joshua D. Campbell, Catalina Perdomo, Sarah Mazzilli, Yaron Geshalter, Samjot S. Dhillon, Gang Liu, Sherry Zhang, Hangqio Lin, Jessica Vick, Christopher Moy, Evan Johnson, Matthew Meyerson, Suso Platero, Marc Lenburg, Mary Reid, Avrum Spira, Jennifer Beane. Genomic characterization of premalignant lung squamous cell carcinoma lesions. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 895.

The Case for a Pre-Cancer Genome Atlas (PCGA)

Understanding the earliest molecular and cellular events associated with cancer initiation remains a key bottleneck to transforming our approach to cancer prevention and detection. While TCGA has provided unprecedented insights into the genomic events associated with advanced stage cancer, there have been few studies comprehensively profiling premalignant and early-stage disease or elucidating the role of the microenvironment in premalignancy and tumor initiation. In this article, we make a call for development of a "Pre-Cancer Genome Atlas (PCGA)," a concerted initiative to characterize the molecular alterations in premalignant lesions and the corresponding changes in the microenvironment associated with progression to invasive carcinoma. This initiative will require a multicenter coordinated effort to comprehensively profile (cellular and molecular) premalignant lesions and their corresponding "field of injury" collected longitudinally as the lesion progresses towards or regresses from frank malignancy across multiple tumor types. Genomic characterization of alterations in premalignant lesions and their microenvironment, for both bulk tissue and single cells, will enable development of biomarkers for early detection and risk stratification as well as allow for the development of novel targeted cancer interception strategies. The multi-institutional and multidisciplinary collaborative "big-data" effort underlying the PCGA will help usher in a new era of precision medicine for cancer detection and prevention.

Abstract A1-66: Leveraging Gene Expression in the Bronchial Airway to Develop a Nasal Biomarker for Early Detection of Lung Cancer

Rationale: Lung cancer results in five times more deaths per year than car accidents in the United States. Approximately 57% of lung cancers diagnosed this year will be diagnosed at a late stage and these patients will exhibit a 5-year survival rate of only 4%. Annual screening of high-risk current and former smokers by chest-CT can reduce cancer mortality, however this procedure has a 95% false positive rate. It is therefore critical to develop methods to rapidly and accurately determine which patients with nodules on chest CT have lung cancer and potentially spare those with benign disease an unnecessary invasive procedure. We have previously demonstrated that specific gene expression alterations in cytologically normal bronchial epithelial cells from patients with lung cancer can be leveraged to form a clinically informative lung cancer biomarker in the population of patients undergoing bronchoscopy for suspect lung cancer. We hypothesized that there might be similar expression differences in nasal epithelium and that these could form the basis of a less invasive test that could be applied more broadly to individuals with screen detected nodules on chest CT.

Methods: Bronchial (n=676) and nasal (n=280) epithelial brushings were collected from current and former smokers undergoing bronchoscopy for clinical suspicion of lung cancer within the AEGIS clinical trial. 271 subjects had matched bronchial and nasal samples. RNA was extracted and hybridized to Affymetrix Human Gene ST 1.0 Arrays. To establish a connection between bronchial and nasal epithelial gene expression signal for cancer, we first applied the bronchial gene expression-based diagnostic test, BronchoGen, directly to our nasal cohort. Gene Set Enrichment Analysis was then used to determine the concordance of cancer signal between the bronchial and nasal epithelium. To develop the nasal gene expression biomarker for lung cancer detection, we examined the correlation of each gene between the bronchial and nasal epithelium as well as the significance of each gene's association with cancer in each tissue. Genes passing our selection criteria were passed to a biomarker discovery pipeline in which we examined the performance of different biomarker algorithm configurations (e.g. feature-selection algorithms, classification algorithms, and other biomarker parameters) using cross-validation.

Results: Direct application of BronchoGen to our nasal cohort resulted in an AUC of 0.64 on a set of NE samples (n=110) with a matched bronchial sample in the training set used to develop the test. On an independent set of nasal samples (n=109), BronchoGen achieved an AUC of 0.67. Gene Set Enrichment Analysis revealed high levels of concordance between cancer-associated nasal and bronchial gene expression. Using a cross-validation approach, we found that nasal biomarkers built from sets of genes showing significant correlation (p&lt;0.05) between the bronchial and nasal epithelium as well as significance for cancer in both tissues (p&lt;0.05) perform better, on average, than biomarkers build from genes significant for cancer (p&lt;0.05) in the nasal epithelium alone.

Conclusions: Given the larger sample size, more isolated location in the airway, and higher RIN scores that characterize the bronchial cohort, we sought to leverage bronchial airway epithelial gene-expression to inform which genes in the nasal epithelium should be indicative of the presence of cancer. We have shown that gene expression in the nasal epithelium reflects the presence of lung cancer and can serve as a diagnostic biomarker. We have further demonstrated concordance between bronchial and nasal airway gene expression differences associated with lung cancer. These results suggest the potential to develop a robust nasal gene expression biomarker for lung cancer diagnosis that leverages cancer-associated gene expression differences occurring at other airway sites.

Citation Format: Joseph F. Perez-Rogers, Joseph Gerrein, Christina Anderlind, Rebecca L. Kusko, Joshua D. Campbell, Teresa W. Wang, Kate Porta, Duncan Whitney, Avrum Spira, Marc Lenburg. Leveraging Gene Expression in the Bronchial Airway to Develop a Nasal Biomarker for Early Detection of Lung Cancer. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-66.

Abstract B1-50: Leveraging gene expression in the bronchial airway to develop a nasal biomarker for early detection of lung cancer

Rationale: Lung cancer results in five times more deaths per year than car accidents in the United States. Approximately 57% of lung cancers diagnosed this year will be diagnosed at a late stage and these patients will exhibit a 5-year survival rate of only 4%. Annual screening of high-risk current and former smokers by chest-CT can reduce cancer mortality, however this procedure has a 95% false positive rate. It is therefore critical to develop methods to rapidly and accurately determine which patients with nodules on chest CT have lung cancer and potentially spare those with benign disease an unnecessary invasive procedure. We have previously demonstrated that specific gene expression alterations in cytologically normal bronchial epithelial cells from patients with lung cancer can be leveraged to form a clinically informative lung cancer biomarker in the population of patients undergoing bronchoscopy for suspect lung cancer. We hypothesized that there might be similar expression differences in nasal epithelium and that these could form the basis of a less invasive test that could be applied more broadly to individuals with screen detected nodules on chest CT.

Methods: Bronchial (n=676) and nasal (n=280) epithelial brushings were collected from current and former smokers undergoing bronchoscopy for clinical suspicion of lung cancer within the AEGIS clinical trial. 271 subjects had matched bronchial and nasal samples. RNA was extracted and hybridized to Affymetrix Human Gene ST 1.0 Arrays. To establish a connection between bronchial and nasal epithelial gene expression signal for cancer, we first applied the bronchial gene expression based diagnostic test, BronchoGen, directly to our nasal cohort. Gene Set Enrichment Analysis was then used to determine the concordance of cancer signal between the bronchial and nasal epithelium. To develop the nasal gene-expression biomarker for lung cancer detection, we examined the correlation of each gene between the bronchial and nasal epithelium as well as the significance of each gene's association with cancer in each tissue. Genes passing our selection criteria were passed to a biomarker discovery pipeline in which we examined the performance of different biomarker algorithm configurations (e.g. feature-selection algorithms, classification algorithms, and other biomarker parameters) using cross-validation.

Results: Direct application of BronchoGen to our nasal cohort resulted in an AUC of 0.64 on a set of NE samples (n=110) with a matched bronchial sample in the training set used to develop the test. On an independent set of nasal samples (n=109), BronchoGen achieved an AUC of 0.67. Gene Set Enrichment Analysis revealed high levels of concordance between cancer-associated nasal and bronchial gene expression. Using a cross-validation approach, we found that nasal biomarkers built from sets of genes showing significant correlation (p&lt;0.05) between the bronchial and nasal epithelium as well as significance for cancer in both tissues (p&lt;0.05) perform better, on average, than biomarkers build from genes significant for cancer (p&lt;0.05) in the nasal epithelium alone.

Conclusions: Given the larger sample size, more isolated location in the airway, and higher RIN scores that characterize the bronchial cohort, we sought to leverage bronchial airway epithelial gene expression to inform which genes in the nasal epithelium should be indicative of the presence of cancer. We have shown that gene expression in the nasal epithelium reflects the presence of lung cancer and can serve as a diagnostic biomarker. We have further demonstrated concordance between bronchial and nasal airway gene expression differences associated with lung cancer. These results suggest the potential to develop a robust nasal gene expression biomarker for lung cancer diagnosis that leverages cancer-associated gene expression differences occurring at other airway sites.

Citation Format: Joseph F. Perez-Rogers, Joseph Gerrein, Christina Anderlind, Rebecca L. Kusko, Joshua D. Campbell, Teresa Wang, Kate Porta, Duncan Whitney, Avrum Spira, Marc Lenburg. Leveraging gene expression in the bronchial airway to develop a nasal biomarker for early detection of lung cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-50.

Abstract 3077: Dysregulation of microRNA-mRNA regulatory networks in the bronchial airway epithelium of smokers with lung cancer

We have previously shown that gene expression alterations in cytologically normal epithelial cells from the bronchial airway can be used as an early detection biomarker for lung cancer in smokers. We hypothesize that bronchial epithelial expression of microRNAs, as regulators of gene expression, may also be affected by the presence of cancer and may regulate some of these gene expression differences.

We propose a novel method to identify microRNAs functionally associated with disease that leverages the relationship between microRNA and mRNA expression by determining the differential connectivity (DC) of microRNA-mRNA association networks between disease and normal states.

Bronchial epithelial brushes were collected from 220 former and current smokers who underwent bronchoscopy for suspicion of lung cancer (120 lung cancer patients and 100 healthy controls). For these subjects, we profiled microRNA expression via small RNA sequencing and gene expression via microarray.

Each microRNA node is assigned a DC score, which captures the overall difference in the pairwise microRNA-gene correlation strengths between lung cancer and control subjects. We quantify the change in both the directionality and strength of the correlations between a microRNA and the gene nodes. Then, the observed DC scores are compared to the DC scores obtained with permuted class labels to identify microRNAs with signinficant disease-specific differences in microRNA-mRNA connectivity.

The proposed DC method identifies 54 microRNAs which are significantly differentially connected in lung cancer cases compared to controls (FDR&lt;0.1). Among the 54 microRNAs we find those previously associated with lung cancer, such as known dysregulated oncomirs, regulators of drug sensitivity to anticancer agents or targets for DNA methylation in non-small cell lung cancers. In addition, the genes that are significantly correlated with the DC microRNAs are enriched in cancer and immune system related pathways, such as membrane trafficking, apoptosis, mitotic cell cycle, hemostasis, FC gamma R mediated phagocytosis and signaling by Rho GTPases (FDR&lt;0.1).

We propose a novel approach for integrating microRNA and gene expression data to identify disease-associated changes in gene regulation by microRNAs and show that the microRNA-mRNA networks are significantly different between disease and normal states. These data suggest that changes in microRNA expression may drive some of the gene expression alterations observed in the cytologically normal epithelium from the proximal airway of patients with lung cancer and that airway microRNA-mRNA expression changes may ultimately serve as a biomarker for lung cancer detection.

Citation Format: Ana Brandusa Pavel, Joshua D. Campbell, Gang Liu, Sherry Zhang, Hanqiao Liu, Lingqi Luo, Ji Xiao, Kate Porta, Duncan Whitney, Steven Dubinett, David Elashoff, Marc E. Lenburg, Avrum Spira. Dysregulation of microRNA-mRNA regulatory networks in the bronchial airway epithelium of smokers with lung cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3077. doi:10.1158/1538-7445.AM2015-3077

SubmiRine: assessing variants in microRNA targets using clinical genomic data sets

MicroRNAs (miRNAs) regulate gene expression by binding to partially complementary sequences on target mRNA transcripts, thereby causing their degradation, deadenylation, or inhibiting their translation. Genomic variants can alter miRNA regulation by modifying miRNA target sites, and multiple human disease phenotypes have been linked to such miRNA target site variants (miR-TSVs). However, systematic genome-wide identification of functional miR-TSVs is difficult due to high false positive rates; functional miRNA recognition sequences can be as short as six nucleotides, with the human genome encoding thousands of miRNAs. Furthermore, while large-scale clinical genomic data sets are becoming increasingly commonplace, existing miR-TSV prediction methods are not designed to analyze these data. Here, we present an open-source tool called SubmiRine that is designed to perform efficient miR-TSV prediction systematically on variants identified in novel clinical genomic data sets. Most importantly, SubmiRine allows for the prioritization of predicted miR-TSVs according to their relative probability of being functional. We present the results of SubmiRine using integrated clinical genomic data from a large-scale cohort study on chronic obstructive pulmonary disease (COPD), making a number of high-scoring, novel miR-TSV predictions. We also demonstrate SubmiRine's ability to predict and prioritize known miR-TSVs that have undergone experimental validation in previous studies.

Transcription factor and microRNA interactions in lung cells: an inhibitory link between NK2 homeobox 1, miR-200c and the developmental and oncogenic factors Nfib and Myb

BACKGROUND

The transcription factor NK2 homeobox 1 (Nkx2-1) plays essential roles in epithelial cell proliferation and differentiation in mouse and human lung development and tumorigenesis. A better understanding of genes and pathways downstream of Nkx2-1 will clarify the multiple roles of this critical lung factor. Nkx2-1 regulates directly or indirectly numerous protein-coding genes; however, there is a paucity of information about Nkx2-1-regulated microRNAs (miRNAs).

METHODS AND RESULTS

By miRNA array analyses of mouse epithelial cell lines in which endogenous Nkx2-1 was knocked-down, we revealed that 29 miRNAs were negatively regulated including miR-200c, and 39 miRNAs were positively regulated by Nkx2-1 including miR-1195. Mouse lungs lacking functional phosphorylated Nkx2-1 showed increased expression of miR-200c and alterations in the expression of other top regulated miRNAs. Moreover, chromatin immunoprecipitation assays showed binding of NKX2-1 protein to regulatory regions of these miRNAs. Promoter reporter assays indicated that 1kb of the miR-200c 5' flanking region was transcriptionally active but did not mediate Nkx2-1- repression of miR-200c expression. 3'UTR reporter assays support a direct regulation of the predicted targets Nfib and Myb by miR-200c.

CONCLUSIONS

These studies suggest that Nkx2-1 controls the expression of specific miRNAs in lung epithelial cells. In particular, we identified a regulatory link between Nkx2-1, the known tumor suppressor miR-200c, and the developmental and oncogenic transcription factors Nfib and Myb, adding new players to the regulatory mechanisms driven by Nkx2-1 in lung epithelial cells that may have implications in lung development and tumorigenesis.

Assessment of microRNA differential expression and detection in multiplexed small RNA sequencing data

Small RNA sequencing can be used to gain an unprecedented amount of detail into the microRNA transcriptome. The relatively high cost and low throughput of sequencing bases technologies can potentially be offset by the use of multiplexing. However, multiplexing involves a trade-off between increased number of sequenced samples and reduced number of reads per sample (i.e., lower depth of coverage). To assess the effect of different sequencing depths owing to multiplexing on microRNA differential expression and detection, we sequenced the small RNA of lung tissue samples collected in a clinical setting by multiplexing one, three, six, nine, or 12 samples per lane using the Illumina HiSeq 2000. As expected, the numbers of reads obtained per sample decreased as the number of samples in a multiplex increased. Furthermore, after normalization, replicate samples included in distinct multiplexes were highly correlated (R > 0.97). When detecting differential microRNA expression between groups of samples, microRNAs with average expression >1 reads per million (RPM) had reproducible fold change estimates (signal to noise) independent of the degree of multiplexing. The number of microRNAs detected was strongly correlated with the log2 number of reads aligning to microRNA loci (R = 0.96). However, most additional microRNAs detected in samples with greater sequencing depth were in the range of expression which had lower fold change reproducibility. These findings elucidate the trade-off between increasing the number of samples in a multiplex with decreasing sequencing depth and will aid in the design of large-scale clinical studies exploring microRNA expression and its role in disease.

MODMatcher: multi-omics data matcher for integrative genomic analysis

Errors in sample annotation or labeling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management. For integrative genomic studies, it is critical to identify and correct these errors. Different types of genetic and genomic data are inter-connected by cis-regulations. On that basis, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors in multiple types of molecular data, which can be used in further integrative analysis. Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step. Applied to a large lung genomic study, MODMatcher increased statistically significant genetic associations and genomic correlations by more than two-fold. In a simulation study, MODMatcher provided more robust results by using three types of omics data than two types of omics data. We further demonstrate that MODMatcher can be broadly applied to large genomic data sets containing multiple types of omics data, such as The Cancer Genome Atlas (TCGA) data sets.

Many human diseases are complex with multiple genetic and environmental causal factors interacting together to give rise to disease phenotypes. Such factors affect biological systems through many layers of regulations, including transcriptional and epigenetic regulation, and protein changes. To fully understand their molecular mechanisms, complex diseases are often studied in diverse dimensions including genetics (genotype variations by single nucleotide polymorphism (SNP) arrays or whole exome sequencing), transcriptomics, epigenetics, and proteomics. However, errors in sample annotation or labeling often occur in large-scale genetic and genomic studies and are difficult to avoid completely during data generation and management. Identifying and correcting these errors are critical for integrative genomic studies. In this study, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors based on multiple types of molecular data before further integrative analysis. Our results indicate that signals increased more than 100% after correction of sample labeling errors in a large lung genomic study. Our method can be broadly applied to large genomic data sets with multiple types of omics data, such as TCGA (The Cancer Genome Atlas) data sets.

miR-638 regulates gene expression networks associated with emphysematous lung destruction

Background

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterized by varying degrees of emphysematous lung destruction and small airway disease, each with distinct effects on clinical outcomes. There is little known about how microRNAs contribute specifically to the emphysema phenotype. We examined how genome-wide microRNA expression is altered with regional emphysema severity and how these microRNAs regulate disease-associated gene expression networks.

Methods

We profiled microRNAs in different regions of the lung with varying degrees of emphysema from 6 smokers with COPD and 2 controls (8 regions × 8 lungs = 64 samples). Regional emphysema severity was quantified by mean linear intercept. Whole genome microRNA and gene expression data were integrated in the same samples to build co-expression networks. Candidate microRNAs were perturbed in human lung fibroblasts in order to validate these networks.

Results

The expression levels of 63 microRNAs (P < 0.05) were altered with regional emphysema. A subset, including miR-638, miR-30c, and miR-181d, had expression levels that were associated with those of their predicted mRNA targets. Genes correlated with these microRNAs were enriched in pathways associated with emphysema pathophysiology (for example, oxidative stress and accelerated aging). Inhibition of miR-638 expression in lung fibroblasts led to modulation of these same emphysema-related pathways. Gene targets of miR-638 in these pathways were amongst those negatively correlated with miR-638 expression in emphysema.

Conclusions

Our findings demonstrate that microRNAs are altered with regional emphysema severity and modulate disease-associated gene expression networks. Furthermore, miR-638 may regulate gene expression pathways related to the oxidative stress response and aging in emphysematous lung tissue and lung fibroblasts.

A gene expression signature of emphysema-related lung destruction and its reversal by the tripeptide GHK

Background

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease consisting of emphysema, small airway obstruction, and/or chronic bronchitis that results in significant loss of lung function over time.

Methods

In order to gain insights into the molecular pathways underlying progression of emphysema and explore computational strategies for identifying COPD therapeutics, we profiled gene expression in lung tissue samples obtained from regions within the same lung with varying amounts of emphysematous destruction from smokers with COPD (8 regions × 8 lungs = 64 samples). Regional emphysema severity was quantified in each tissue sample using the mean linear intercept (Lm) between alveolar walls from micro-CT scans.

Results

We identified 127 genes whose expression levels were significantly associated with regional emphysema severity while controlling for gene expression differences between individuals. Genes increasing in expression with increasing emphysematous destruction included those involved in inflammation, such as the B-cell receptor signaling pathway, while genes decreasing in expression were enriched in tissue repair processes, including the transforming growth factor beta (TGFβ) pathway, actin organization, and integrin signaling. We found concordant differential expression of these emphysema severity-associated genes in four cross-sectional studies of COPD. Using the Connectivity Map, we identified GHK as a compound that can reverse the gene-expression signature associated with emphysematous destruction and induce expression patterns consistent with TGFβ pathway activation. Treatment of human fibroblasts with GHK recapitulated TGFβ-induced gene-expression patterns, led to the organization of the actin cytoskeleton, and elevated the expression of integrin β1. Furthermore, addition of GHK or TGFβ restored collagen I contraction and remodeling by fibroblasts derived from COPD lungs compared to fibroblasts from former smokers without COPD.

Conclusions

These results demonstrate that gene-expression changes associated with regional emphysema severity within an individual's lung can provide insights into emphysema pathogenesis and identify novel therapeutic opportunities for this deadly disease. They also suggest the need for additional studies to examine the mechanisms by which TGFβ and GHK each reverse the gene-expression signature of emphysematous destruction and the effects of this reversal on disease progression.

A single-sample microarray normalization method to facilitate personalized-medicine workflows

Gene-expression microarrays allow researchers to characterize biological phenomena in a high-throughput fashion but are subject to technological biases and inevitable variabilities that arise during sample collection and processing. Normalization techniques aim to correct such biases. Most existing methods require multiple samples to be processed in aggregate; consequently, each sample's output is influenced by other samples processed jointly. However, in personalized-medicine workflows, samples may arrive serially, so renormalizing all samples upon each new arrival would be impractical. We have developed Single Channel Array Normalization (SCAN), a single-sample technique that models the effects of probe-nucleotide composition on fluorescence intensity and corrects for such effects, dramatically increasing the signal-to-noise ratio within individual samples while decreasing variation across samples. In various benchmark comparisons, we show that SCAN performs as well as or better than competing methods yet has no dependence on external reference samples and can be applied to any single-channel microarray platform.

Abstract 3989: Discovery of a novel airway microRNA associated with lung cancer using next generation sequencing

The goal of this study was to characterize the small RNA transcriptome and discover novel microRNAs (miRNAs) associated with lung cancer in human bronchial epithelial cells using massively parallel sequencing. To this end, small RNA (10-40 nts) was isolated from bronchial brushings obtained via bronchoscopy. Samples pooled from healthy non-smokers, healthy smokers, and smokers with and without lung cancer (n=3 per pool) were sequenced with ABI SOLiD, using extremely deep coverage for small RNA in human tissue, 60 million 36nt-long reads/pool. Reads were aligned to the human genome with Bowtie and novel miRNAs were predicted using the miRDeep algorithm. One of the predicted novel miRNAs was selected for experimental validation. In vitro expression of the putative novel microRNA precursor was assayed with and without the siRNA knock-down of Dicer, an enzyme that processes miRNA precursors into mature miRNAs. Tissue specificity of the putative novel miRNA was profiled across 23 human tissue types using qRT-PCR. Potential microRNA targets were identified using transient overexpression of the miRNA in a cell line combined with in silico algorithms.

We identified 143 miRNA that are differentially expressed in the airways of smokers with lung cancer (FC &gt; 2) in our cohort. The miRDeep algorithm identified 131 putative novel miRNAs across all samples, including 68 differentially expressed putative miRNA in the airways of lung cancer subjects (FC &gt; 2). In vitro expression of the putative miRNA was reduced after siRNA knockdown of DICER, providing strong evidence that the novel transcript is truly a miRNA. Out of 23 human tissue types assayed, the microRNA is expressed almost exclusively in the respiratory tract, with highest expression in the bronchus and nose and moderate expression in lung. Importantly, the expression of the miRNA is decreased significantly in lung tumors compared to adjacent normal tissue. In addition, the expression of the novel miRNA is decreased in the bronchial epithelium of smokers with lung cancer as compared to smokers without lung cancer. Overexpression of the novel miRNA in lung cancer cell line has identified putative mRNA targets that are associated with apoptosis and cell proliferation.

In summary, using next generation sequencing, we have identified airway miRNA expression profiles associated with lung cancer, and we discovered a novel miRNA whose expression is reduced in the airway and lung tissue of smokers with lung cancer. This miRNA may serve as a novel biomarker to identify smokers at high risk of developing lung cancer and may also provide new insights into the genomic regulatory networks that underlie lung cancer pathogenesis.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3989. doi:10.1158/1538-7445.AM2011-3989

The costs and consequences of omalizumab in uncontrolled asthma from a USA payer perspective

BACKGROUND

Omalizumab, an anti-immunoglobulin E antibody, reduces exacerbations and symptoms in uncontrolled allergic asthma. The study objective was to estimate the costs and consequences of omalizumab compared to usual care from a US payer perspective.

METHODS

We estimated payer costs, quality-adjusted survival (QALYs), and the incremental cost-effectiveness ratio (ICER) of omalizumab compared to usual care using a state-transition simulation model that included sensitivity analyses. Every 2 weeks, patients could transition between chronic asthma and exacerbation health states. The best available evidence informed the clinical and cost input estimates. Five years of omalizumab treatment followed by usual care was assumed to estimate a lifetime horizon. Omalizumab responders (60.5% of treated) were modeled as a separate scenario where nonresponders reverted back to usual care after 16 weeks of active treatment.

RESULTS

The mean lifetime discounted costs and QALYs were $83,400 and 13.87 for usual care and $174,500 and 14.19 for omalizumab plus usual care resulting in $287 200/QALY (95% interval: $219,300, $557, 900). The ICER was $172 300/QALY when comparing omalizumab to usual care in the responder scenario. One-way sensitivity analyses indicated that the results were sensitive to the difference in treatment-specific utilities for the chronic state, exacerbation-associated mortality, omalizumab price, exacerbation rates, and response definition.

CONCLUSIONS

The results suggest that adding omalizumab to usual care improves QALYs at an increase in direct medical costs. The cost-effectiveness of omalizumab is similar to other chronic disease biologics. The value increases when omalizumab response is used to guide long-term treatment.

Health economics of asthma: assessing the value of asthma interventions

The aim of this systematic review was to summarize and assess the quality of asthma intervention health economic studies from 2002 to 2007, compare the study findings with clinical management guidelines, and suggest avenues for future improvement of asthma health economic studies. Forty of the 177 studies met our inclusion criteria. We assessed the quality of studies using The Quality of Health Economic Studies validated instrument (total score range: 0-100). Six studies (15%) had quality category 2, 26 studies (65%) achieved quality category 3, and the remaining eight (20%) studies were scored as the highest quality level, category 4. Overall, the findings from this review are in line with the Global Initiative for Asthma clinical guidelines. Many asthma health economic studies lacked appropriate long term time horizons to match the chronic nature of the disease and suffered from using effectiveness measures that did not capture all disease related risks and benefits. We recommend that new asthma simulation models: be flexible to allow for long term time horizons, focus on using levels of asthma control in their structure, and estimate both long term asthma specific outcomes like well-controlled time as well as generic outcomes such as quality adjusted survival.