Download 4436 Synergistic Effects of Promoter Associated DNA

#4436 Synergistic Effects of Promoter Associated DNA Methylation and Genetic Alterations to Better
Understand Oncogenic Gene Expression Profiles
Claire Olson1, Fang Yin Lo1, Kerry Deutsch1, Sharon Austin1, Kellie Howard1, Amanda Leonti1, Lindsey Maassel1, Christopher Subia1, Tuuli Saloranta1, Nicole Heying1, Kathryn Shiji1,
Shradha Patil1, Steven Anderson3 and Anup Madan1
1Covance, Seattle, WA; 2Laboratory Corporation of America® Holdings, Research Triangle Park, NC; 3Covance, Durham, NC
Introduction
The impacts of genetic and
epigenetic mechanisms on cancer
remain enigmatic, especially
regarding changes in gene
expression and methylation
differences between tumor and
normal samples. Tumorigenesis can
potentially alter DNA methylation,
however, the amount and location of
these DNA modifications are
unknown. The objective of our
research is to qualify differentially
methylated loci and identify roles of
differential methylation in regulating
gene expression between patients
with and without breast cancer.
Differences between normal human
peripheral blood and TNM stage IIB
grade 3 breast tissue were examined
on a genome-wide level to identify
differentially methylated CpG sites
and differential gene expression. A
comparative analysis of genetic and
epigenetic regulation of gene
expression will allow better
understanding of gene regulatory
networks in breast cancer.
Methods
Up-­regulated Genes
normal human peripheral blood tissues
+
TNM stage IIB, grade 3 primary ductal carcinoma in breast tissues
Methylome Analysis
Transcriptome Analysis
Promoter Associated CpG islands
Strand Specific RNA-­Seq
Differentially hypermethylated
CpG sites
13,126
654
1,199
Differentially expressed genes
Down-­regulated Genes
Differential Methylation
Differential Expression
▶ Sequencing reads mapped to
human genome
▶ Bisulfite treatment of lymphoblast cells
▶ Map to human genome using
Bisulfite Sequence Mapping Program
(BSMAP v2.74)
▶ Resulting alignment analyzed
with methratio
▶ HTSeq Python package to generate
gene count data and identification of
differential expressed genes using
Edge R package
▶ Examination of DNA methylation
differences using methylKit 0.9.2 in R
Figure 1. Patients with normal human peripheral blood and TNM stage IIB
grade 3 breast tissue were assayed across multiple platforms to provide a
better understanding of gene regulatory networks in breast cancer.
Genome-wide DNA methylation profiles were characterized in patients with and
without tumor cells. Differential gene expression analysis was also performed to
identify roles of differential methylation in regulating gene expression.
Table 1. Up-Regulated Genes Identified by Differential Expression and
Differential Methylation Analysis and Their Respective p-Values
Gene
Symbol
Gene Name
Core
Pathway
Process
RNASeq
p-value
Methylation
q-value
Notes
Cancer associated genes
CASP8
caspase 8, apoptosis-related Cell Cycle/
cysteine peptidase
Apoptosis
Cell Survival
0.0225
CSF1R
colony stimulating factor
1 receptor
PI3K; RAS
Cell Survival
0.0004
0.0017
Oncogene
ERBB2
v-erb-b2 erythroblastic
leukemia viral oncogene
homolog 2, neuro/
glioblastoma derived
oncogene homolog (avian)
PI3K; RAS
Cell Survival
0.0004
0.0017
Oncogene
GATA2
GATA binding protein 2
NOTCH,
TGF-b
Cell Fate
0.0038
0.0022
Oncogene
Tumor
Suppressor
Gene
DNA
Damage
Control
Genome
Maintenance
Fanconi anemia
complementation group E
6,285
219
988
Differentially expressed genes
Figure 2. Number of genes likely to be regulated by differential
methylation of CpG islands with TNM stage IIB, grade 3 primary
ductal carcinoma in breast tissues and those with normal blood
tissue. A total of 202,547 differentially methylated regions were identified
between patients with and without breast cancer. These loci were
identified using a qvalue cutoff of <0.01 and were within 1kb of TSS
of a gene indicating their potential role within a promoter region. A
total of 15,656 of the genes were unique, therefore many of the
genes potentially have multiple differentially methylated CpG sites
within their promoter regions. Using a p-value cutoff of 0.05 we
identified a total of 2,187 differentially expressed genes between
normal and breast cancer samples.
Figure 3. Pathway of differentially expressed genes between
samples with and without breast cancer. Biological processes that are
also differentially methylated between normal and tumor samples are
indicated by an additional purple circle. Pathways that involved
differentially expressed and differentially methylated genes between
normal and breast cancer patients included appendage development,
behavior, cell activation, chemotaxis, growth, locomotory behavior,
ossification, pattern specification process, skeletal system development
and positive regulation of molecular functions.
Summary
In an attempt to discover potential effects of methylation and gene expression on cancer mechanisms, differentially methylated
CpG sites and differentially expressed genes were identified between patients with TNM stage IIB, grade 3 primary ductal
carcinoma in breast tissues and patients without cancer. Interestingly, some of the genes associated with differentially
methylated CpG sites and differentially expressed genes are those that are involved in behavioral processes and immune
system response. Regulation of cell proliferation and cell-cell signaling were also common biological processes observed.
Further Analysis
Perform a comprehensive analysis incorporating Exome-Seq data and examination of promoter mutations to learn
contributions of genetic variation and mutations in patients with breast cancer.
▶ Identify potential gene fusion events and understand their role in tumorigenesis.
▶ Identify structural variants using whole genome sequencing and use these as potential biomarker for patient stratification.
▶
Cancer predisposition genes
FANCE
Differentially hypomethylated
CpG sites
▶ Assemble transcriptome using
cufflinks and RSEM to estimate gene
and isoform expression levels
0.0060
0.0028
Fanconi
anaemia E
Cancer
syndrome
Gene rearrangements
ARNT
aryl hydrocarbon receptor nuclear translocator
0.0170
0.0024
BCL3
B-cell CLL/lymphoma 3
0.0110
0.0000
fibroblast growth factor receptor 1
0.0186
0.0024
HOXA11 homeobox A11
0.0003
0.0018
HOXC11 homeobox C11
0.0003
0.0097
0.0002
0.0000
FGFR1
NIN
Presented at AACR 2016
ninein
Covance is the drug development business of Laboratory Corporation of America Holdings (LabCorp). Content of this material
was developed by scientists who at the time were affiliated with LabCorp Clinical Trials or Tandem Labs, now part of Covance.