PROJECT SUMMARY / ABSTRACT
Esophageal adenocarcinoma (EAC) is a rare yet lethal cancer with median survival <1 year. Genome-wide
association studies (GWAS) have estimated a substantial heritable component of risk (25-35%) for EAC and its
precursor, Barrett’s esophagus (BE). Nearly 20 novel genetic risk loci have been discovered, but most
heritability remains unexplained. ‘Missing heritability’ hinders the power of GWAS to illuminate molecular
pathways underlying disease risk and identify novel targets for intervention. In this proposal, we seek to
overcome inherent limits on sample sizes for BE/EAC and identify novel susceptibility loci by integrating
advanced network-based methods and tissue-specific regulome resources into a biologically-motivated
discovery framework. Several lines of evidence implicate transcriptional regulatory networks in BE/EAC biology
and motivate use of network-based approaches to probe undiscovered genetic underpinnings of this cancer.
These findings include reactivation of key embryonic transcriptional regulators in BE/EAC tissues; somatic
genomic alterations in transcription factor (TF) genes in EAC tumors; and genome-wide-significant GWAS
signals in close proximity to genes encoding esophageal/foregut TFs. Building on these observations, and the
prevailing view that disease-linked genetic variation functionally converges on a limited set of core biological
pathways, we hypothesize that genetic signals embedded in developmental transcriptional networks represent
an important source of ‘missing heritability’ for BE/EAC. Using customized disease-relevant reference networks
overlaid with GWAS-derived node weights, we will screen for gene-level and enhancer/promoter-level genetic
associations missed by prior genome-wide scans. Our multi-disciplinary MPI team draws on a strong track
record in BE/EAC genetics, leveraging access to the largest available GWAS datasets, and extensive omics
data from GTEx, RoadMap/ENCODE, and promoter-capture HiC. In Aim 1, we will identify co-expressed genes
enriched in risk-associated genetic variation, using transcriptional regulatory networks derived from RNA-seq
profiles. Co-expression networks assembled via mutual information and graphical lasso methods applied to
transcriptomes of 330 gastro-esophageal junction tissues will be populated with weights from gene-level
GWAS tests, and analyzed using Hierarchical Hotnet (HHN). In Aim 2, we will identify linked promoters and
enhancers with concentrated GWAS signal using regulatory maps from 3D chromatin interaction profiles.
Enhancer-target reference networks built using promoter-capture-HiC data in normal esophagus will be loaded
with weights from custom SNP-set-based tests and evaluated via HHN. Our proposed research will help
elucidate the genetic architecture of EAC and its only known precursor (BE). Candidate risk genes and
enhancers/promoters will be advanced to functional validation studies currently underway for known loci
through an ongoing collaboration, with the goal of defining new preventive/therapeutic targets for BE/EAC.
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