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PRBB-CRG Sessions Valentina Boeva

PRBB-CRG Sessions Valentina Boeva

26/04/2019
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PRBB-CRG Sessions Valentina Boeva

CHARLES DARWIN

26/04/201912:00CHARLES DARWINPRBB-CRG SessionsValentina BoevaInstitut Cochin, Biomedical research institute, Paris, FR"Computational strategies to analyze cancer ChIP-seq data: application to neuroblastoma"Host: Julia Ponomarenko (CRG)Abstract:The ChIP-seq technique is used by thousands of research studies to profile histone modifications in cancer. However, methods developed for normal diploid genomes, when applied to cancer samples, can result in false discoveries due to the presence of copy number aberrations distorting the ChIP-seq signal. In order to circumvent this issue, our group has developed a set of ChIP-seq data analysis methods for cancer studies (http://boevalab.com/tools.html). I will present some of these approaches including the HMCan method to call ChIP-seq peaks and normalize read density profiles for copy number bias [1], and the LILY method to identify super-enhancer regions based on the HMCan output [2].
We applied HMCan and LILY to detect super-enhancer regions in 25 neuroblastoma cell lines and 6 patient derived mouse xenografts. Analysis of super-enhancer landscape in these samples suggested that neuroblastoma cells can be in two different epigenetic and transcriptional states. Single cell analysis showed that both states can co-exist in the same cell line or tumor. One state, which we call noradrenergic, was associated with amplification or high expression of the MYCN oncogene and high activity of noradrenergic transcription factors: PHOX2B, GATA3 and HAND2. The second state, which we call neural crest-like was characterized by the high activity of the AP-1 transcription complex and transcription factors like PRRX1 and RUNX1. Furthermore, we demonstrated that cells in the neural crest-like state were less sensitive to chemotherapy independently of their genetic background [2].