bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: P
Poster
10th Benelux Bioinformatics Conference bbc 2015
P56. ESTIMATING THE IMPACT OF CIS-REGULATORY VARIATION IN
CANCER GENOMES USING ENHANCER PREDICTION MODELS AND
MATCHED GENOME-EPIGENOME-TRANSCRIPTOME DATA
Dmitry Svetlichnyy 1* , Hana Imrichova 1 , Zeynep Kalender Atak 1 & Stein Aerts 1 .
Laboratory of Computational Biology, University of Leuven 1 . *dmitry.svetlichnyy@med.kuleuven.be
The prioritization of candidate driver mutations in the non-coding part of the genome is a key challenge in cancer
genomics. Whereas driver mutations in protein-coding genes can be distinguished from passenger mutations based on
their recurrence, non-coding mutations are usually not recurrent at the same position. We aim to tackle this problem
using machine-learning methods to predict regulatory regions and cancer genome sequences in combination with samplespecific
chromatin profiles obtained using ChIP-seq against H3K27Ac.
INTRODUCTION
Perturbations of gene regulatory networks in cancer cells
can arise from mutations in transcription factors or cofactors,
but also from mutations in regulatory regions.
Prioritizing candidate driver mutations that have a
significant impact on the activity of a regulatory region is
a key challenge in cancer genomics.
METHODS
We have developed enhancer prediction methods using
Random Forest classifiers to estimate the Predicted
Regulatory Impact of a Mutation in an Enhancer
(PRIME). We find that the recently identified driver
mutation in the TAL1 enhancer has a high PRIME score,
representing a “gain-of-target” for the oncogenic
transcription factor MYB [1]. We trained enhancer models
for 45 cancer-related transcription factors, and used these
to score somatic mutations across more than five hundred
breast cancer genomes. Next, we re-sequenced the genome
of ten cancer cell lines representing six different cancer
types (breast, lung, melanoma, ovarian, and colon) and
profiled their active chromatin by ChIP-seq against
H3K27Ac.
RESULTS & DISCUSSION
Then we integrated these data with matched expression
data and with the Random Forest model predictions for
sets of oncogenic transcription factors per cancer type.
This resulted in surprisingly few high-impact mutations
that generate de novo regulatory (oncogenic) activity at
the chromatin and gene expression level. Our framework
can be applied to identify candidate cis-regulatory
mutations using sequence information alone, and to
samples with combined genome-epigenome-transcriptome
data. Our results suggest the presence of only few cisregulatory
driver mutations per genome in cancer genomes
that may alter the expression levels of specific oncogenes
and tumor suppressor genes.
REFERENCES
1. Mansour MR, Abraham BJ, Anders L, Berezovskaya A, Gutierrez A,
Durbin AD, et al. An oncogenic super-enhancer formed through somatic
mutation of a noncoding intergenic element. Science. 2014;346: 1373–
1377. doi:10.1126/science.1259037
100