Srikant Ambatipudi

Epigenetic Signatures of Cigarette Smoking

Background—DNA methylation leaves a long-term signature of smoking exposure and is one potential mechanism by which tobacco exposure predisposes to adverse health outcomes, such as cancers, osteoporosis, lung, and cardiovascular disorders. Methods and Results—To comprehensively determine the association between cigarette smoking and DNA methylation, we conducted a meta-analysis of genome-wide DNA methylation assessed using the Illumina BeadChip 450K array on 15 907 blood-derived DNA samples from participants in 16 cohorts (including 2433 current, 6518 former, and 6956 never smokers). Comparing current versus never smokers, 2623 cytosine–phosphate–guanine sites (CpGs), annotated to 1405 genes, were statistically significantly differentially methylated at Bonferroni threshold of P<1×10−7 (18 760 CpGs at false discovery rate <0.05). Genes annotated to these CpGs were enriched for associations with several smoking-related traits in genome-wide studies including pulmonary function, cancers, inflammatory diseases, and heart disease. Comparing former versus never smokers, 185 of the CpGs that differed between current and never smokers were significant P<1×10−7 (2623 CpGs at false discovery rate <0.05), indicating a pattern of persistent altered methylation, with attenuation, after smoking cessation. Transcriptomic integration identified effects on gene expression at many differentially methylated CpGs. Conclusions—Cigarette smoking has a broad impact on genome-wide methylation that, at many loci, persists many years after smoking cessation. Many of the differentially methylated genes were novel genes with respect to biological effects of smoking and might represent therapeutic targets for prevention or treatment of tobacco-related diseases. Methylation at these sites could also serve as sensitive and stable biomarkers of lifetime exposure to tobacco smoke.

Tobacco smoking-associated genome-wide DNA methylation changes in the EPIC study

AIM Epigenetic changes may occur in response to environmental stressors, and an altered epigenome pattern may represent a stable signature of environmental exposure. MATERIALS & METHODS Here, we examined the potential of DNA methylation changes in 910 prediagnostic peripheral blood samples as a marker of exposure to tobacco smoke in a large multinational cohort. RESULTS We identified 748 CpG sites that were differentially methylated between smokers and nonsmokers, among which we identified novel regionally clustered CpGs associated with active smoking. Importantly, we found a marked reversibility of methylation changes after smoking cessation, although specific genes remained differentially methylated up to 22 years after cessation. CONCLUSION Our study has comprehensively cataloged the smoking-associated DNA methylation alterations and showed that these alterations are reversible after smoking cessation.

Genomic Profiling of Advanced-Stage Oral Cancers Reveals Chromosome 11q Alterations as Markers of Poor Clinical Outcome

Identifying oral cancer lesions associated with high risk of relapse and predicting clinical outcome remain challenging questions in clinical practice. Genomic alterations may add prognostic information and indicate biological aggressiveness thereby emphasizing the need for genome-wide profiling of oral cancers. High-resolution array comparative genomic hybridization was performed to delineate the genomic alterations in clinically annotated primary gingivo-buccal complex and tongue cancers (n = 60). The specific genomic alterations so identified were evaluated for their potential clinical relevance. Copy-number changes were observed on chromosomal arms with most frequent gains on 3q (60%), 5p (50%), 7p (50%), 8q (73%), 11q13 (47%), 14q11.2 (47%), and 19p13.3 (58%) and losses on 3p14.2 (55%) and 8p (83%). Univariate statistical analysis with correction for multiple testing revealed chromosomal gain of region 11q22.1–q22.2 and losses of 17p13.3 and 11q23–q25 to be associated with loco-regional recurrence (P = 0.004, P = 0.003, and P = 0.0003) and shorter survival (P = 0.009, P = 0.003, and P 0.0001) respectively. The gain of 11q22 and loss of 11q23-q25 were validated by interphase fluorescent in situ hybridization (I-FISH). This study identifies a tractable number of genomic alterations with few underlying genes that may potentially be utilized as biological markers for prognosis and treatment decisions in oral cancers.

Genome-wide expression and copy number analysis identifies driver genes in gingivobuccal cancers.

The molecular mechanisms contributing to the development and progression of gingivobuccal complex (GBC) cancers—a sub‐site of oral cancer, comprising the buccal mucosa, the gingivobuccal sulcus, the lower gingival region, and the retromolar trigone–remain poorly understood. Identifying the GBC cancer‐related gene expression signature and the driver genes residing on the altered chromosomal regions is critical for understanding the molecular basis of its pathogenesis. Genome‐wide expression profiling of 27 GBC cancers with known chromosomal alterations was performed to reveal differentially expressed genes. Putative driver genes were identified by integrating copy number and gene expression data. A total of 315 genes were found differentially expressed (P ≤ 0.05, logFC > 2.0) of which 11 genes were validated by real‐time quantitative reverse transcriptase‐PCR (qRT‐PCR) in tumors (n = 57) and normal GBC tissues (n = 18). Overexpression of LY6K, in chromosome band 8q24.3, was validated by immunohistochemical (IHC) analysis. We found that 78.5% (2,417/3,079) of the genes located in regions of recurrent chromosomal alterations show copy number dependent expression indicating that copy number alteration has a direct effect on global gene expression. The integrative analysis revealed BIRC3 in 11q22.2 as a candidate driver gene associated with poor clinical outcome. Our study identified previously unreported differentially expressed genes in a homogeneous subtype of oral cancer and the candidate driver genes that may contribute to the development and progression of the disease.

Roadmap for investigating epigenome deregulation and environmental origins of cancer.

The interaction between the (epi)genetic makeup of an individual and his/her environmental exposure record (exposome) is accepted as a determinant factor for a significant proportion of human malignancies. Recent evidence has highlighted the key role of epigenetic mechanisms in mediating gene–environment interactions and translating exposures into tumorigenesis. There is also growing evidence that epigenetic changes may be risk factor‐specific (“fingerprints”) that should prove instrumental in the discovery of new biomarkers in cancer. Here, we review the state of the science of epigenetics associated with environmental stimuli and cancer risk, highlighting key developments in the field. Critical knowledge gaps and research needs are discussed and advances in epigenomics that may help in understanding the functional relevance of epigenetic alterations. Key elements required for causality inferences linking epigenetic changes to exposure and cancer are discussed and how these alterations can be incorporated in carcinogen evaluation and in understanding mechanisms underlying epigenome deregulation by the environment.

Integrated data analysis reveals potential drivers and pathways disrupted by DNA methylation in papillary thyroid carcinomas

BackgroundPapillary thyroid carcinoma (PTC) is a common endocrine neoplasm with a recent increase in incidence in many countries. Although PTC has been explored by gene expression and DNA methylation studies, the regulatory mechanisms of the methylation on the gene expression was poorly clarified. In this study, DNA methylation profile (Illumina HumanMethylation 450K) of 41 PTC paired with non-neoplastic adjacent tissues (NT) was carried out to identify and contribute to the elucidation of the role of novel genic and intergenic regions beyond those described in the promoter and CpG islands (CGI). An integrative and cross-validation analysis were performed aiming to identify molecular drivers and pathways that are PTC-related.ResultsThe comparisons between PTC and NT revealed 4995 methylated probes (88% hypomethylated in PTC) and 1446 differentially expressed transcripts cross-validated by the The Cancer Genome Atlas data. The majority of these probes was found in non-promoters regions, distant from CGI and enriched by enhancers. The integrative analysis between gene expression and DNA methylation revealed 185 and 38 genes (mainly in the promoter and body regions, respectively) with negative and positive correlation, respectively. Genes showing negative correlation underlined FGF and retinoic acid signaling as critical canonical pathways disrupted by DNA methylation in PTC. BRAF mutation was detected in 68% (28 of 41) of the tumors, which presented a higher level of demethylation (95% hypomethylated probes) compared with BRAF wild-type tumors. A similar integrative analysis uncovered 40 of 254 differentially expressed genes, which are potentially regulated by DNA methylation in BRAFV600E-positive tumors. The methylation and expression pattern of six selected genes (ERBB3, FGF1, FGFR2, GABRB2, HMGA2, and RDH5) were confirmed as altered by pyrosequencing and RT-qPCR.ConclusionsDNA methylation loss in non-promoter, poor CGI and enhancer-enriched regions was a significant event in PTC, especially in tumors harboring BRAFV600E. In addition to the promoter region, gene body and 3’UTR methylation have also the potential to influence the gene expression levels (both, repressing and inducing). The integrative analysis revealed genes potentially regulated by DNA methylation pointing out potential drivers and biomarkers related to PTC development.

Downregulation of keratin 76 expression during oral carcinogenesis of human, hamster and mouse

Background Keratins are structural marker proteins with tissue specific expression; however, recent reports indicate their involvement in cancer progression. Previous study from our lab revealed deregulation of many genes related to structural molecular integrity including KRT76. Here we evaluate the role of KRT76 downregulation in oral precancer and cancer development. Methods We evaluated KRT76 expression by qRT-PCR in normal and tumor tissues of the oral cavity. We also analyzed K76 expression by immunohistochemistry in normal, oral precancerous lesion (OPL), oral squamous cell carcinoma (OSCC) and in hamster model of oral carcinogenesis. Further, functional implication of KRT76 loss was confirmed using KRT76-knockout (KO) mice. Results We observed a strong association of reduced K76 expression with increased risk of OPL and OSCC development. The buccal epithelium of DMBA treated hamsters showed a similar trend. Oral cavity of KRT76-KO mice showed preneoplastic changes in the gingivobuccal epithelium while no pathological changes were observed in KRT76 negative tissues such as tongue. Conclusion The present study demonstrates loss of KRT76 in oral carcinogenesis. The KRT76-KO mice data underlines the potential of KRT76 being an early event although this loss is not sufficient to drive the development of oral cancers. Thus, future studies to investigate the contributing role of KRT76 in light of other tumor driving events are warranted.

Chromosomal Alterations and Gene Expression Changes Associated with the Progression of Leukoplakia to Advanced Gingivobuccal Cancer

We present an integrative genome-wide analysis that can be used to predict the risk of progression from leukoplakia to oral squamous cell carcinoma (OSCC) arising in the gingivobuccal complex (GBC). We find that the genomic and transcriptomic profiles of leukoplakia resemble those observed in later stages of OSCC and that several changes are associated with this progression, including amplification of 8q24.3, deletion of 8p23.2, and dysregulation of DERL3, EIF5A2, ECT2, HOXC9, HOXC13, MAL, MFAP5 and NELL2. Comparing copy number profiles of primary tumors with and without lymph-node metastasis, we identify alterations associated with metastasis, including amplifications of 3p26.3, 8q24.21, 11q22.1, 11q22.3 and deletion of 8p23.2. Integrative analysis reveals several biomarkers that have never or rarely been reported in previous OSCC studies, including amplifications of 1p36.33 (attributable to MXRA8), 3q26.31 (EIF5A2), 9p24.1 (CD274), and 12q13.2 (HOXC9 and HOXC13). Additionally, we find that amplifications of 1p36.33 and 11q22.1 are strongly correlated with poor clinical outcome. Overall, our findings delineate genomic changes that can be used in treatment management for patients with potentially malignant leukoplakia and OSCC patients with higher risk of lymph-node metastasis.

Prognostic Classifier Based on Genome-Wide DNA Methylation Profiling in Well-Differentiated Thyroid Tumors

Context: Even though the majority of well‐differentiated thyroid carcinoma (WDTC) is indolent, a number of cases display an aggressive behavior. Cumulative evidence suggests that the deregulation of DNA methylation has the potential to point out molecular markers associated with worse prognosis. Objective: To identify a prognostic epigenetic signature in thyroid cancer. Design: Genome‐wide DNA methylation assays (450k platform, Illumina) were performed in a cohort of 50 nonneoplastic thyroid tissues (NTs), 17 benign thyroid lesions (BTLs), and 74 thyroid carcinomas (60 papillary, 8 follicular, 2 Hürthle cell, 1 poorly differentiated, and 3 anaplastic). A prognostic classifier for WDTC was developed via diagonal linear discriminant analysis. The results were compared with The Cancer Genome Atlas (TCGA) database. Results: A specific epigenetic profile was detected according to each histological subtype. BTLs and follicular carcinomas showed a greater number of methylated CpG in comparison with NTs, whereas hypomethylation was predominant in papillary and undifferentiated carcinomas. A prognostic classifier based on 21 DNA methylation probes was able to predict poor outcome in patients with WDTC (sensitivity 63%, specificity 92% for internal data; sensitivity 64%, specificity 88% for TCGA data). High‐risk score based on the classifier was considered an independent factor of poor outcome (Cox regression, P < 0.001). Conclusions: The methylation profile of thyroid lesions exhibited a specific signature according to the histological subtype. A meaningful algorithm composed of 21 probes was capable of predicting the recurrence in WDTC.

Identifying and correcting epigenetics measurements for systematic sources of variation.

BackgroundMethylation measures quantified by microarray techniques can be affected by systematic variation due to the technical processing of samples, which may compromise the accuracy of the measurement process and contribute to bias the estimate of the association under investigation. The quantification of the contribution of the systematic source of variation is challenging in datasets characterized by hundreds of thousands of features.In this study, we introduce a method previously developed for the analysis of metabolomics data to evaluate the performance of existing normalizing techniques to correct for unwanted variation. Illumina Infinium HumanMethylation450K was used to acquire methylation levels in over 421,000 CpG sites for 902 study participants of a case-control study on breast cancer nested within the EPIC cohort. The principal component partial R-square (PC-PR2) analysis was used to identify and quantify the variability attributable to potential systematic sources of variation. Three correcting techniques, namely ComBat, surrogate variables analysis (SVA) and a linear regression model to compute residuals were applied. The impact of each correcting method on the association between smoking status and DNA methylation levels was evaluated, and results were compared with findings from a large meta-analysis.ResultsA sizeable proportion of systematic variability due to variables expressing ‘batch’ and ‘sample position’ within ‘chip’ was identified, with values of the partial R2 statistics equal to 9.5 and 11.4% of total variation, respectively. After application of ComBat or the residuals’ methods, the contribution was 1.3 and 0.2%, respectively. The SVA technique resulted in a reduced variability due to ‘batch’ (1.3%) and ‘sample position’ (0.6%), and in a diminished variability attributable to ‘chip’ within a batch (0.9%). After ComBat or the residuals’ corrections, a larger number of significant sites (k = 600 and k = 427, respectively) were associated to smoking status than the SVA correction (k = 96).ConclusionsThe three correction methods removed systematic variation in DNA methylation data, as assessed by the PC-PR2, which lent itself as a useful tool to explore variability in large dimension data. SVA produced more conservative findings than ComBat in the association between smoking and DNA methylation.