H. Ashoor, C. Louis-Brennetot, I. Janoueix-Lerosey, V.B. Bajic, V. Boeva
Nucl Acids Res, gkw1319, (2017)
Gene expression, Cancer, Genome, Histones, Genetics, Neoplasms,
Copy number polymorphism, Tumor cells, Malignant, Datasets
Comparing histone modification profiles between cancer and normal
states, or across different tumor samples, can provide insights into
understanding cancer initiation, progression and response to therapy.
ChIP-seq histone modification data of cancer samples are distorted by
copy number variation innate to any cancer cell. We present HMCan-diff,
the first method designed to analyze ChIP-seq data to detect changes in
histone modifications between two cancer samples of different genetic
backgrounds, or between a cancer sample and a normal control. HMCan-diff
explicitly corrects for copy number bias, and for other biases in the
ChIP-seq data, which significantly improves prediction accuracy compared
to methods that do not consider such corrections. On in silico
simulated ChIP-seq data generated using genomes with differences in
copy number profiles, HMCan-diff shows a much better performance
compared to other methods that have no correction for copy number bias.
Additionally, we benchmarked HMCan-diff on four experimental datasets,
characterizing two histone marks in two different scenarios. We
correlated changes in histone modifications between a cancer and a
normal control sample with changes in gene expression. On all
experimental datasets, HMCan-diff demonstrated better performance
compared to the other methods.