Our group aims to understand the role of epigenetic changes in cancer initiation and progression and decipher the link between genetic and epigenetic modifications. We obtained insights about the role of genetic and epigenetic changes in specific cancer types: neuroblastoma, Ewing sarcoma and erythroleukemia. We also developed pioneer methodology to analyze high-throughput data from DNA sequencing and ChIP-seq experiments generated for cancer samples.

Selected publications

  • Heterogeneity of neuroblastoma cell identity defined by transcriptional circuitries. V. Boeva, C. Louis-Brennetot, A. Peltier, S. Durand, C. Pierre-Eugene, V. Raynal, H. Etchevers, S. Thomas, A. Lermine, E. Daudigeos-Dubus, B.Geoerger, M.F. Orth, T.G.P. Grunewald, E. Diaz, B. Ducos, D. Surdez, A.M. Carcaboso, I. Medvedeva, T. Deller, V. Combaret, E. Lapouble, G. Pierron, S. Grossetete-Lalami, S. Baulande, G. Schleiermacher, E. Barillot, H. Rohrer, O. Delattre, and I. Janoueix-Lerosey. Nature Genetics. 2017 Sep;49(9):1408-1413. PMID: 28740262. Link to the paper Using the analysis of super-enhancer landscape in neuroblastoma cell lines and PDXs we show that neuroblastoma cells can be in two different epigenetic and transcriptional states. These two states can co-exist in the same cell line. Cells of the less differentiated state are less sensitive to chemotherapy.
  • HMCan-diff: a method to detect changes in histone modifications in cells with different genetic characteristics. H. Ashoor, C. Louis-Brennetot, I. Janoueix-Lerosey, V.B. Bajic, and V. Boeva. Nucleic Acids Research. 2017. 45(8):e58. doi: 10.1093/nar/gkw1319, PMID: 28053124. HMCan-diff is the first computational method for detection of differential ChIP-seq or ATAC-seq signal in cancer cells. The main improvement of HMCan-diff over methods developed for normal cells is its ability to correct the signal for the GC-content and copy number bias.
  • HMCan: a method for detecting chromatin modifications in cancer samples using ChIP-seq data. H. Ashoor, A. Herault, A. Kamoun, F. Radvanyi, V.B. Bajic, E. Barillot and V. Boeva. Bioinformatics, 2013, 29 (23): 2979-2986. PMID: 24021381. Here we propose the first method to detect regions of enrichment in histone modifications in cancer ChIP-seq data. We show that existing methods, such as MACS or SICER, suffer from significant copy number bias.
  • Control-FREEC: a tool for assessing copy number and allelic content using next generation sequencing data. V. Boeva, T. Popova, K. Bleakley, P. Chiche, I. Janoueix-Lerosey, O. Delattre and E. Barillot. Bioinformatics, 2012, 28(3):423-5. PMID: 22155870. In this paper, we present one of the first methods to assess copy number and genotype information in whole genome and exome sequencing data. When applied to cancer data, our method allows correcting for contamination by normal cells and variable sample ploidy.

    Publications 2021

    Publications 2020

    Publications 2018

    Publications 2017

    Publications 2016

    PI's publications before the official creation of the lab

  Book chapters: