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CISNET Multiscale Meeting Presentation: Drift CpGs in Normal and Progressed Esophagus
- 1. Spatial and temporal epigenetic pattern gradients differentiate normal and progressed tissues in esophagus Sean Maden Data Analyst Assistant Grady Lab Clinical Research, Fred Hutch
- 2. Epigenetics Overview • Epigenetics - a host of biological features and mechanisms that affect gene expression independent of sequence modifications: • Genome-wide CpG methylation • Chromatin remodeling • Nucleosome positioning • lnc- and microRNA activity, etc. • Trend of the field: Work towards integrative and multiscale analysis incorporating CpG methylation and other genomic, molecular and clinical data
- 3. Aberrant Methylation in Cancer • Promoter CpG methylation (or “hypermethylation”) can reduce or knock out a gene’s expression. • In cancer: repressed tumor suppressors, upreg. oncogenes.. • Methylation linked to changes in cell potency during normal development, which may be reversible • Cancer Stem Cell (CSC) model • Clonal grown pattern • Etc. Image modified from: Toh et al. “Epigenetics in cancer stem cells” (2017) Molecular Cancer 16:29.
- 4. CpG Methylation in Esophageal Cancers • Extensive integrative analysis showed location-specific molecular heterogeneity • CpG Island Methylator Phenotype (CIMP) in lower- esophagus (where BE and EAC form) • Molecular evidence distinguishing EAC from ESCC and non-CIN GEAs Image modified from: The cancer Genome Atlas Research Network. “Integrated genomic characterization of oesophageal carcinoma” (2017) Nature 541, 169-175.
- 5. Measuring Methylation: Infinium Array Technology Images from: Illumina Infinium Array documentation, Georg Leubeck • Illumina Infinium HM450 BeadChip array • Assays genome-wide CpG methylation, with enriched mapping of CpG island, gene promoters and bodies, and enhancer regions • Uses Infinium assay with bisulphite-converted DNA • Methylation is read as either: Beta-value (M/[M+U]) or M-value (logit2-Beta val) bj = Im, j (Im, j + Iu, j ) ; M -value = logit2 (bj ) = log2 bj 1- bj æ è çç ö ø ÷÷
- 6. Chronological and Biological Age • Chronological age: age of patient • Biological age: age of tissue • Correlated with chronological age • Potentially stratifies patients on risk than chronological age • Ways of calculating tissue age • Elastic net, lasso penalized regression modeling • Correlating on Chron. Age and comparing to normal tissue
- 7. Methylation Drift in Aging • Methylomic drift: age-dependent, progressive methylation change distinct from normal tissue • Most drift CpGs start as hypomethylated, and become methylated with age in premalignant and diseased tissue • Biological basis in DNMT chemistry (accumulation and propagation of errors, de novo methylation) • Consistent with a non-uniform maintenance (eg. hotspots or mosaicism in tissues and cells) and stochastic process • Potentially linked to mitotic age • Utility: determine BE dwell time based on population-based Bayesian analyses
- 8. Drift in Nondysplastic BE • Curtius et al 2016 described 67 drifting CpGs in BE • Methylation data from longitudinal BE, matched BE-SQ, and sporadic BE used to inform a Bayesian model for estimation of BE dwell time • Next: explore methylation patterns at these 67 CpGs and other correlated CpGs to better understand drift dynamics and how to differentiate progressed and diseased tissues Curtius et al. “A Molecular Clock Infers Heterogeneous Tissue Age Among Patients with Barrett’s Esophagus” Image courtesy of Georg Leubeck
- 9. Longitudinal BE Sample Methylation At Drift CpGs Most CpGs increase in methylation with age, some dependence of increase on starting point. Heterogeneity in drift rate may be an independent risk factor for progression to EAC BETRNet longitudinal samples (youngest and oldest available), methylation shown at each drift CpG individually
- 10. Methylation correlates with Age at Drift CpGs • Cross-sectional BE samples: population-wise positive corr. with chron. age Methylation (Beta-value) Age (years)
- 11. Heterogeneous Drift Within Patients 1 2 3 45 6
- 12. Correlating Drift Across the Methylome • CpG islands are concentrated regions of CG dinucleotides • 40% of islands normally hypomethylated • ~60% of gene promoters overlap CpG Islands • 67 drift CpGs map to 51 unique islands, with 50 in island regions Images courtesy of Georg Leubeck Island-assoc. N_Shore OpenSea S_Shore
- 13. Functional Units of Drift: Promoter Island Drift Images courtesy of Georg Leubeck Drift is highly correlated among CpGs at 1,317 Islands that drift (contain at least 5 drift CpGs) in NDBE
- 14. Transition of drift patterns from BE to EAC Image courtesy of Georg Leubeck • Subtypes in BE and EAC visible in density plots of drift CpG methylation. • Going from low to high average methylation, there are distinct unimodal-to-bimodal density pattern transitions in BE and EAC • Could be leveraged as a discrete criterion for drift subtyping
- 15. Transition of drift patterns from BE to EAC Image courtesy of Georg Leubeck • Methylation density pattern evolution suggests a threshold effect: 1. Gradual drift changes accumulate, with maintenance of low- level mode 2. Changes become selective and drive cancer formation unimodal low (L), bimodal intermediate (I), and bimodal high (H).
- 16. Functional Consequences of Drift • Evidence of functional ramifications of drift at promoter drift islands with increased methylation • Agreement/validation with two datasets: TCGA (N=87 EACs,, methylation arrays and RNAseq) and Krause et al (N=51 EACs, Queensland Australia, methylation arrays and Agilent expr. arrays). • Hypermethylation at Island/promoter drift CpGs results in silencing of disease-assoc. genes • 20/35 sig. repressed genes in both datasets, • CHFR (checkpoint with forkhead associated and ring finger domains) repressed in both datasets, and enriched in EAC comparison to GEA in TCGA Nature paper. • In network analysis of repressed genes, 3-fold enrichment of DNA binding transcription factor activity term, and the Krueppel-assoc. box domain zinc finger (KRAB, ZNF) TFs.
- 17. Conclusions • Spatial distribution of CpGs contributes to an understanding of their functional importance. CpG methylation can be correlated at functional units such as islands • Progressive age-dependent methylation changes in BE and EAC show a threshold effect and affect • In EAC, functional consequences of promoter drift island hypermethylation on gene expression • Functional consequences of drift in BE necessary to better understand mechanisms of progression. We have a hypothesis in drift patterns and dwell time. Now need more expression data
- 18. Thanks! This work was supported by: • Dr.’s Bill Grady, Ming Yu, members of Grady Lab, and the Genomics Core at Fred Hutch • Collaborators at Case Western and UW, including Dr.’s Joe Willis, Andrew Kaz, and Amitabh Chak • Dr.’s Georg Leubeck, Bill Hazelton, and Kit Curtius • BETRNet and MEMO consortium facilitators, doctors, and patients • Organizers and facilitators at CISNET 2017 GRADY LAB Original analyses run in R with modules from Bioconductor and CRAN
- 19. Supplemental Slide 1 • Methylation-based subtypes identified in EAC identified using most variant CpG probes in model-based clustering. • EAC subtype signature apparent in BE, suggesting they arise early in the progression sequence • No apparent clinical basis for subtypes (ie. correlation with age, gender, smoking, survival). • Molecular features distinguish the EAC subtypes: ERBB2 expr./CN gain; mRNA and microRNA expr.; response of cell cultures to drug treatments BETRNet Cohort
- 20. Supplemental Slide 2 HM = high methylator; IM = intermediate methylator; LM = low methylator; MM = minimal methylator
- 21. Supplemental Slide 3: Towards developing screening panels - Discovery
- 22. Supplemental Slide 4: Towards developing screening panels - Validation Color = Val1 Sens. Krause et al. 2016TCGA EACs
Editor's Notes
- Thanks to Dr. Georg Leubeck inviting me to present at CISNET. My name is Sean, I am a Data Analyst Asst at Dr. Bill Grady’s lab in Clinical Research at Fred Hutch. We focus on epigenetics of esophageal and colorectal cancers, and a primary part of my work is handling qc pipelines for arrays run at the Hutch, managing data storage and sharing, and encouraging collaborations by sharing code and collaborating on experimental design with other labs.
- Note the interconnectedness of certain features. microRNA loci can be regulated by methylation, CpG methylation near histones can affect enhancer binding, while methylation or lack thereof at histones themselves can mark a region as active or inactive (H3K27ac, H3K4me/me3) etc. We are perpetually interested in collaborations!
- Image is regulation of key cancer stem cell signaling pathways by epigenetic mechanisms. Methylation-induced decreased expr of DKK1 enhances wnt/beta-catenin signaling; Hedgehog signaling pathway can be activated by Shh promoter hypomethylation coupled with increased HDAC1 expression. Degregulation of these pathways enables CSCs to acquire self-renewal abilities ands drug resistance properties.
- TCGA paper largely compared EACs to gastric cancers, finding that gastroesophageal adenocarcinoma with chromosomal instability were similar according to microRNA, mRNA, protein, and chromosome platform data, but that clustering on methylation differentiated the two into clusters. Hypermethylated clusters were enriched for EACs, and samples in the highest cluster showed silencing of MGMT and Checkpoint with forkhead associated and ring finger domain (CHFR) genes.
- Primary assay is Infinium 2, which involves single-bp extension at the end of a 50bp probe. A fluorescent marker on the added base is read in a single channel, resulting in a red (unmethylated) or green (methylated) signal. Another Inf 1 assay covers a small subset of about 5% of CpGs, and is a variation on this tech with a single color channel instead of two. 420k CpGs detected after quality control measures.
- Curtius et al 2016 describe and validated 67 drift CpGs undergoing age-related drift. These were then used with longitudinal BE samples, paired BE-NSQ samples, and sporadic samples to derive a Bayesian model for calculation of BE dwell time.
- This is a selection of 9 BETRNet BE patients and their longitudinally collected samples. Note a general tendency for increased methylation over time. It is clear there is heterogeneity in drift rates between patients, and this my contribute to risk of EAC independent of BE dwell time. Ideally we would want to compare matched SQ tissues with each BE sample.
- 69 nondysplastic BE samples are taken from among several datasets, including BETRNet and MEMO cohorts through University of Washington. Shown are methylation density and mean methylation at 67 drifting CpGs in BE. Note evidence of a unimodal, low methylation distribution transitioning to a bimodal, high methylation distribution as chron. age increases. These trends are noted later in the context of drift at functional units.
- Drift within a patient at cross-sectional samples from throughout their esophagus can vary considerably. That is to say, there is variance in the deviance from normal tissue methylation for samples from that same patient at the same chronological age. This could be important for advancing theories about the significance of BE and how to screen for BE. For instance, if BE advances up the esophagus over time, this could be consistent with patient 1, where old BE is detected at the top, and younger BE is detected at the bottom, and could suggest greater risk of progression if lower BE is younger due to continued damage response. Old BE near the GEJ, by contrast, may suggest BE formed but does not elicit the same inflammatory response. These insights could change whether and what intervention is deemed necessary.
- Image shows pairwise correlation of island CpGs and others in island, shelf and shore as a function of distance from island proper. Results shown for islands containing CpGs that are correlated with the former 67 CpGs. 64 NDBE patients samples were used.
- Image is density of methylation at 11,425 island-associated drift CpGs (min, 5 drift-CpGs per island). Three drift groups reflect discrete density patterns: unimodal low (L), bimodal intermediate (I), and bimodal high (H). For EACs, TCGA and Krause et al cohorts were used, and 200 repressed genes found from expression array and sequencing data, whose promoters overlapped one of 1240 of drift islands. Checkpoint with forkhead associated and ring finger domain (CHFR) gene a mitotic stress checkpoint gene significantly repressed in both Krause and TCGA cohorts.
- Image is density of methylation at 11,425 island-associated drift CpGs (min, 5 drift-CpGs per island). Three drift groups reflect discrete density patterns: unimodal low (L), bimodal intermediate (I), and bimodal high (H). For EACs, TCGA and Krause et al cohorts were used, and 200 repressed genes found from expression array and sequencing data, whose promoters overlapped one of 1240 of drift islands. Checkpoint with forkhead associated and ring finger domain (CHFR) gene a mitotic stress checkpoint gene significantly repressed in both Krause and TCGA cohorts.
- Image is density of methylation at 11,425 island-associated drift CpGs (min, 5 drift-CpGs per island). Three drift groups reflect discrete density patterns: unimodal low (L), bimodal intermediate (I), and bimodal high (H). For EACs, TCGA and Krause et al cohorts were used, and 200 repressed genes found from expression array and sequencing data, whose promoters overlapped one of 1240 of drift islands. Checkpoint with forkhead associated and ring finger domain (CHFR) gene a mitotic stress checkpoint gene significantly repressed in both Krause and TCGA cohorts.
- A panel search could recapitulate the observed threshold effect in drift. Here is shown a separate but similar workflow used for a related project in EAC. Samples are from TCGA. RPMM groups correspond to hyper and hypo methylated subtypes, roughly related to the EAC-H and EAC-I/L groups from prior slide. Conceivably this approach could be adopted for drift leveraging the threshold effect, where the cutoff is informed by the likelihood a CpG will advance or retard from one mode to the other.
- In this example, DMPs are differentially methylated probes from normal vs. tumor comparison in BETRNet EACs. Validation 1 is N=87 TCGA EACs and 2 is N=125 Krause et al EACs for which clusters HM and NHM are defined. In this context, panels have a single set of coordinates for spec/sens due to discrete criteria. Panels are designed from among DMPs with a predefined set of parameters (ie. number of CpGs that must be above a given threshold to classify a sample as HM). Panels can then be compared along four axes (Val1 Sens and Spec, and Val2 Sens and Spec), or two coordinates from each validation, to determine the best overall performing panel.