Ite A. Laird-Offringa

DNA methylation analysis: a powerful new tool for lung cancer diagnosis

Carcinoma of the lung is the most common cause of cancer death worldwide. The estimated 5-year survival ranges from 6–16%, depending on the cell type. The best opportunity for improving survival of lung cancer patients is through early detection, when curative surgical resection is possible. Although the subjects at increased risk for developing carcinoma of the lung (long-term smokers) can be identified, only 10–20% of this group will ultimately develop the disease. Screening tests of long-term smokers employed to date (radiography and sputum cytology) have not been successful in reducing lung cancer mortality. The application of molecular markers specific for lung cancer offers new possibilities for early detection. Hypermethylation of CpG islands in the promoter regions of genes is a common phenomenon in lung cancer, as demonstrated by the analysis of the methylation status of over 40 genes from lung cancer tumors, cell lines, patient sputum and/or serum. Determination of the methylation patterns of multiple genes to obtain complex DNA methylation signatures promises to provide a highly sensitive and specific tool for lung cancer diagnosis. When combined with the development of non-invasive methods to detect such signatures, this may provide a viable method to screen subjects at risk for lung cancer.

Genome-scale analysis of DNA methylation in lung adenocarcinoma and integration with mRNA expression.

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma is its most common histological subtype. Clinical and molecular evidence indicates that lung adenocarcinoma is a heterogeneous disease, which has important implications for treatment. Here we performed genome-scale DNA methylation profiling using the Illumina Infinium HumanMethylation27 platform on 59 matched lung adenocarcinoma/non-tumor lung pairs, with genome-scale verification on an independent set of tissues. We identified 766 genes showing altered DNA methylation between tumors and non-tumor lung. By integrating DNA methylation and mRNA expression data, we identified 164 hypermethylated genes showing concurrent down-regulation, and 57 hypomethylated genes showing increased expression. Integrated pathways analysis indicates that these genes are involved in cell differentiation, epithelial to mesenchymal transition, RAS and WNT signaling pathways, and cell cycle regulation, among others. Comparison of DNA methylation profiles between lung adenocarcinomas of current and never-smokers showed modest differences, identifying only LGALS4 as significantly hypermethylated and down-regulated in smokers. LGALS4, encoding a galactoside-binding protein involved in cell-cell and cell-matrix interactions, was recently shown to be a tumor suppressor in colorectal cancer. Unsupervised analysis of the DNA methylation data identified two tumor subgroups, one of which showed increased DNA methylation and was significantly associated with KRAS mutation and to a lesser extent, with smoking. Our analysis lays the groundwork for further molecular studies of lung adenocarcinoma by identifying novel epigenetically deregulated genes potentially involved in lung adenocarcinoma development/progression, and by describing an epigenetic subgroup of lung adenocarcinoma associated with characteristic molecular alterations.

Kinetic studies of RNA-protein interactions using surface plasmon resonance.

Although structural, biochemical, and genetic studies have provided much insight into the determinants of specificity and affinity of proteins for RNA, little is currently known about the kinetics that underlie RNA-protein interactions. Protein-RNA complexes are dynamic, and the kinetics of binding and release could influence many processes, such as the ability of RNA-binding proteins to compete for binding sites, the sequential assembly of ribonucleoprotein complexes, and the ability of bound RNA to move between cellular compartments. Therefore, to attain a complete and biologically relevant understanding of RNA-protein interactions, complex formation must be studied not only in equilibrated reactions, but also as a dynamic process. BIACORE, a surface plasmon resonance-based biosensor technology, allows intermolecular interactions to be measured in real time, and can provide both equilibrium and kinetic information about complex formation. This technology is a powerful tool with which to study the dynamics of RNA-protein interactions. We have used BIACORE extensively to obtain detailed insight into the interaction between RNA and proteins carrying RNA recognition motif domains. Here we discuss the physical principles on which BIACORE is based, and the required instrumentation. We describe how to design well-controlled RNA-protein interaction experiments aimed at yielding high-quality data, and outline the steps required for data analysis. In addition, we present examples to illustrate how kinetic studies have provided us with unique insights into the interaction of the spliceosomal U1A protein and the neuronal HuD protein with their respective RNA targets.

Identification of a panel of sensitive and specific DNA methylation markers for squamous cell lung cancer

BackgroundLung cancer is the leading cause of cancer death in men and women in the United States and Western Europe. Over 160,000 Americans die of this disease every year. The five-year survival rate is 15% – significantly lower than that of other major cancers. Early detection is a key factor in increasing lung cancer patient survival. DNA hypermethylation is recognized as an important mechanism for tumor suppressor gene inactivation in cancer and could yield powerful biomarkers for early detection of lung cancer. Here we focused on developing DNA methylation markers for squamous cell carcinoma of the lung. Using the sensitive, high-throughput DNA methylation analysis technique MethyLight, we examined the methylation profile of 42 loci in a collection of 45 squamous cell lung cancer samples and adjacent non-tumor lung tissues from the same patients.ResultsWe identified 22 loci showing significantly higher DNA methylation levels in tumor tissue than adjacent non-tumor lung. Of these, eight showed highly significant hypermethylation in tumor tissue (p < 0.0001): GDNF, MTHFR, OPCML, TNFRSF25, TCF21, PAX8, PTPRN2 and PITX2. Used in combination on our specimen collection, this eight-locus panel showed 95.6% sensitivity and specificity.ConclusionWe have identified 22 DNA methylation markers for squamous cell lung cancer, several of which have not previously been reported to be methylated in any type of human cancer. The top eight markers show great promise as a sensitive and specific DNA methylation marker panel for squamous cell lung cancer.

Identification of a panel of sensitive and specific DNA methylation markers for lung adenocarcinoma

BackgroundLung cancer is the number one cancer killer of both men and women in the United States. Three quarters of lung cancer patients are diagnosed with regionally or distantly disseminated disease; their 5-year survival is only 15%. DNA hypermethylation at promoter CpG islands shows great promise as a cancer-specific marker that would complement visual lung cancer screening tools such as spiral CT, improving early detection. In lung cancer patients, such hypermethylation is detectable in a variety of samples ranging from tumor material to blood and sputum. To date the penetrance of DNA methylation at any single locus has been too low to provide great clinical sensitivity. We used the real-time PCR-based method MethyLight to examine DNA methylation quantitatively at twenty-eight loci in 51 primary human lung adenocarcinomas, 38 adjacent non-tumor lung samples, and 11 lung samples from non-lung cancer patients.ResultsWe identified thirteen loci showing significant differential DNA methylation levels between tumor and non-tumor lung; eight of these show highly significant hypermethylation in adenocarcinoma: CDH13, CDKN2A EX2, CDX2, HOXA1, OPCML, RASSF1, SFPR1, and TWIST1 (p-value << 0.0001). Using the current tissue collection and 5-fold cross validation, the four most significant loci (CDKN2A EX2, CDX2, HOXA1 and OPCML) individually distinguish lung adenocarcinoma from non-cancer lung with a sensitivity of 67–86% and specificity of 74–82%. DNA methylation of these loci did not differ significantly based on gender, race, age or tumor stage, indicating their wide applicability as potential lung adenocarcinoma markers. We applied random forests to determine a good classifier based on a subset of our loci and determined that combined use of the same four top markers allows identification of lung cancer tissue from non-lung cancer tissue with 94% sensitivity and 90% specificity.ConclusionThe identification of eight CpG island loci showing highly significant hypermethylation in lung adenocarcinoma provides strong candidates for evaluation in patient remote media such as plasma and sputum. The four most highly ranked loci, CDKN2A EX2, CDX2, HOXA1 and OPCML, which show significant DNA methylation even in stage IA tumor samples, merit further investigation as some of the most promising lung adenocarcinoma markers identified to date.

Characterizing the genetic basis of methylome diversity in histologically normal human lung tissue

The genetic regulation of the human epigenome is not fully appreciated. Here we describe the effects of genetic variants on the DNA methylome in human lung based on methylation-quantitative trait loci (meQTL) analyses. We report 34,304 cis- and 585 trans-meQTLs, a genetic-epigenetic interaction of surprising magnitude, including a regulatory hotspot. These findings are replicated in both breast and kidney tissues and show distinct patterns: cis-meQTLs mostly localize to CpG sites outside of genes, promoters, and CpG islands (CGIs), while trans-meQTLs are over-represented in promoter CGIs. meQTL SNPs are enriched in CTCF binding sites, DNaseI hypersensitivity regions and histone marks. Importantly, 4 of the 5 established lung cancer risk loci in European ancestry are cis-meQTLs and, in aggregate, cis-meQTLs are enriched for lung cancer risk in a genome-wide analysis of 11,587 subjects. Thus, inherited genetic variation may affect lung carcinogenesis by regulating the human methylome.

DNA methylation changes in atypical adenomatous hyperplasia, adenocarcinoma in situ, and lung adenocarcinoma.

Background Aberrant DNA methylation is common in lung adenocarcinoma, but its timing in the phases of tumor development is largely unknown. Delineating when abnormal DNA methylation arises may provide insight into the natural history of lung adenocarcinoma and the role that DNA methylation alterations play in tumor formation. Methodology/Principal Findings We used MethyLight, a sensitive real-time PCR-based quantitative method, to analyze DNA methylation levels at 15 CpG islands that are frequently methylated in lung adenocarcinoma and that we had flagged as potential markers for non-invasive detection. We also used two repeat probes as indicators of global DNA hypomethylation. We examined DNA methylation in 249 tissue samples from 93 subjects, spanning the putative spectrum of peripheral lung adenocarcinoma development: histologically normal adjacent non-tumor lung, atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS, formerly known as bronchioloalveolar carcinoma), and invasive lung adenocarcinoma. Comparison of DNA methylation levels between the lesion types suggests that DNA hypermethylation of distinct loci occurs at different time points during the development of lung adenocarcinoma. DNA methylation at CDKN2A ex2 and PTPRN2 is already significantly elevated in AAH, while CpG islands at 2C35, EYA4, HOXA1, HOXA11, NEUROD1, NEUROD2 and TMEFF2 are significantly hypermethylated in AIS. In contrast, hypermethylation at CDH13, CDX2, OPCML, RASSF1, SFRP1 and TWIST1 and global DNA hypomethylation appear to be present predominantly in invasive cancer. Conclusions/Significance The gradual increase in DNA methylation seen for numerous loci in progressively more transformed lesions supports the model in which AAH and AIS are sequential stages in the development of lung adenocarcinoma. The demarcation of DNA methylation changes characteristic for AAH, AIS and adenocarcinoma begins to lay out a possible roadmap for aberrant DNA methylation events in tumor development. In addition, it identifies which DNA methylation changes might be used as molecular markers for the detection of preinvasive lesions.

Molecular Portraits of Epithelial, Mesenchymal, and Hybrid States in Lung Adenocarcinoma and Their Relevance to Survival

Epithelial-to-mesenchymal transition (EMT) is a key process associated with tumor progression and metastasis. To define molecular features associated with EMT states, we undertook an integrative approach combining mRNA, miRNA, DNA methylation, and proteomic profiles of 38 cell populations representative of the genomic heterogeneity in lung adenocarcinoma. The resulting data were integrated with functional profiles consisting of cell invasiveness, adhesion, and motility. A subset of cell lines that were readily defined as epithelial or mesenchymal based on their morphology and E-cadherin and vimentin expression elicited distinctive molecular signatures. Other cell populations displayed intermediate/hybrid states of EMT, with mixed epithelial and mesenchymal characteristics. A dominant proteomic feature of aggressive hybrid cell lines was upregulation of cytoskeletal and actin-binding proteins, a signature shared with mesenchymal cell lines. Cytoskeletal reorganization preceded loss of E-cadherin in epithelial cells in which EMT was induced by TGFβ. A set of transcripts corresponding to the mesenchymal protein signature enriched in cytoskeletal proteins was found to be predictive of survival in independent datasets of lung adenocarcinomas. Our findings point to an association between cytoskeletal and actin-binding proteins, a mesenchymal or hybrid EMT phenotype and invasive properties of lung adenocarcinomas.

Classification of Individual Lung Cancer Cell Lines Based on DNA Methylation Markers Use of Linear Discriminant Analysis and Artificial Neural Networks

The classification of small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) can pose diagnostic problems due to inter-observer variability and other limitations of histopathology. There is an interest in developing classificatory models of lung neoplasms based on the analysis of multivariate molecular data with statistical methods and/or neural networks. DNA methylation levels at 20 loci were measured in 41 SCLC and 46 NSCLC cell lines with the quantitative real-time PCR method MethyLight. The data were analyzed with artificial neural networks (ANN) and linear discriminant analysis (LDA) to classify the cell lines into SCLC or into NSCLC. Models used either data from all 20 loci, or from five significant DNA methylation loci that were selected by a step-wise back-propagation procedure (PTGS2, CALCA, MTHFR, ESR1, and CDKN2A). The data were sorted randomly by cell line into 10 different data sets, each with training and testing subsets composed of 71 and 16 of the cases, respectively. Ten ANN models were trained using the 10 data sets: five using 20 variables, and five using the five variables selected by step-wise back-propagation. The ANN models with 20 input variables correctly classified 100% of the cell lines, while the models with only five variables correctly classified 87 to 100% of cases. For comparison, 10 different LDA models were trained and tested using the same data sets with either the original data or with logarithmically transformed data. Again, half of the models used all 20 variables while the others used only the five significant variables. LDA models provided correct classifications in 62.5% to 87.5% of cases. The classifications provided by all of the different models were compared with kappa statistics, yielding kappa values ranging from 0.25 to 1.0. We conclude that ANN models based on DNA methylation profiles can objectively classify SCLC and NSCLC cells lines with substantial to perfect concordance, while LDA models based on DNA methylation profiles provide poor to substantial concordance. Our work supports the promise of ANN analysis of DNA methylation data as a powerful approach for the development of automated methods for lung cancer classification.

The role of positively charged amino acids and electrostatic interactions in the complex of U1A protein and U1 hairpin II RNA

Previous kinetic investigations of the N-terminal RNA recognition motif (RRM) domain of spliceosomal protein U1A, interacting with its RNA target U1 hairpin II, provided experimental evidence for a ‘lure and lock’ model of binding in which electrostatic interactions first guide the RNA to the protein, and close range interactions then lock the two molecules together. To further investigate the ‘lure’ step, here we examined the electrostatic roles of two sets of positively charged amino acids in U1A that do not make hydrogen bonds to the RNA: Lys20, Lys22 and Lys23 close to the RNA-binding site, and Arg7, Lys60 and Arg70, located on ‘top’ of the RRM domain, away from the RNA. Surface plasmon resonance-based kinetic studies, supplemented with salt dependence experiments and molecular dynamics simulation, indicate that Lys20 predominantly plays a role in association, while nearby residues Lys22 and Lys23 appear to be at least as important for complex stability. In contrast, kinetic analyses of residues away from the RNA indicate that they have a minimal effect on association and stability. Thus, well-positioned positively charged residues can be important for both initial complex formation and complex maintenance, illustrating the multiple roles of electrostatic interactions in protein–RNA complexes.