MOSAIC Project

Cancer development is believed to be governed by appearance and accumulation of driving mutations that collectively reprogram molecular mechanisms at the level of global cell populations. However, these mutations may also have more general systemic effects: in particular, they may trigger diversification of cell properties leading to an increased cell-to-cell variability at the basis of the remarkable adaptation potential of cancer cells.

Single-cell RNA-seq profiling provides an exciting opportunity to investigate the heterogeneity of cancer cell populations directly by measuring individual cancer cell transcriptomes. Ewing sarcoma (ES), a pediatric cancer characterized by the expression of EWS-FLI1, an aberrant chimeric transcription factor, is characterized by very few somatic mutations and a very low sub-clonal genetic heterogeneity. Nevertheless, it exhibits all features of the remarkable adaptation potential of cancer cells suggesting that this potential is not a simple consequence of a mutation-based Darwinian selection process.

We aim at characterizing and mathematical modeling the effects of a EWS-FLI1 on the structure of the cell-to-cell transcriptome variability. The objectives of the project are: 1) characterize the dynamics of cell-to-cell variability induced by EWS-FLI1 activation and map the variance distribution onto various cellular functions; 2) explicitly model the process of adaptation in cancer cell populations mediated by biological networks; 3) validate the predictions in targeted experiments.

MOSAIC project is funded by ITMO Cancer BIOSYS program from December 2014 to December 2017. It continues previously funded ANR SITCON and INCa SybEwing projects and complements the efforts of ASSET EU FP7 project.

We are currently looking for candidates for post-doc positions (bioinformaticians and data analysts) for MOSAIC. Applications should be sent to the coordinators of the project (see below).


MOSAIC is a collaboration between INSERM U830 "Genetics and biology of the pediatric tumors and sporadic breast cancers" and INSERM U900 "Computational Systems Biology of Cancer" research groups co-localized at Institut Curie.

Researchers involved:

INSERM U830: Olivier Delattre (coordinator), Olivier Mirabeau, Marie Ming Aynaud, Nadege Gruel, Franck Tirode

INSERM U900: Andrei Zinovyev (co-coordinator), Emmanuel Barillot, Valentina Boeva, Laurence Calzone, Philippe Hupe


Key publications from previous collaboration:

  1. Stoll G, Surdez D, Tirode F, Laud K, Barillot E, Zinovyev A, Delattre O. Systems biology of Ewing sarcoma: a network model of EWS-FLI1 effect on proliferation and apoptosis. 2013. Nucleic Acids Res., 41(19):8853-71.
  2. Pauwels E, Surdez D, Stoll G, Lescure A, Del Nery E, Delattre O, Stoven V. A Probabilistic Model for Cell Population Phenotyping Using HCS Data. 2012. PLoS One 7(8):e42715.
  3. Martignetti L, Laud-Duval K, Tirode F, Pierron G, Reynaud S, Barillot E, Delattre O, Zinovyev A. Antagonism Pattern Detection between MicroRNA and Target Expression in Ewing's Sarcoma. 2012. PLoS One 7(7):e41770
  4. Boeva V., Popova T., Bleakley K., Chiche P., Cappo J., Schleiermacher G., Janoueix-Lerosey I., Delattre O., and Barillot E. Control-FREEC: a tool for assessing copy number and allelic content using next generation sequencing data. 2012. Bioinformatics 28:3, 423-425.
  5. Boeva V, Zinovyev A, Bleakley K, Vert JP, Janoueix-Lerosey I, Delattre O, Barillot E. Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization. 2011. Bioinformatics 27(2):268-269.
  6. Boeva V, Surdez D, Guillon N, Tirode F, Fejes AP, Delattre O, Barillot E. De novo motif identification improves the accuracy of predicting transcription factor binding sites in ChIP-Seq data analysis. 2010. Nucleic Acids Res. 38(11):e126
  7. Baumuratova T, Surdez D, Delyon B, Stoll G, Delattre O, Radulescu O, Siegel A. Localizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network. 2010. BMC Syst Biol 2;4(1):146
  8. Guillon N, Tirode F, Boeva V, Zinovyev A, Barillot E, Delattre O. The oncogenic EWS-FLI1 protein binds in vivo GGAA microsatellite sequences with potential transcriptional activation function. 2009. PLoS ONE 4(3):e4932.

Key single-cell transcriptomics publications:

  1. Patel AP et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014 Jun 20;344(6190):1396-401.
  2. Shalek AK et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature. 2014 Jun 19;509(7505):363-9.
  3. Kharchenko PV et al. Bayesian approach to single-cell differential expression analysis. Nat Methods. 2014 Jul;11(7):740-2.
  4. Brennecke P et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat Methods. 2013 Nov;10(11):1093-5.
  5. Shalek AK et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature. 2013 Jun 13;498(7453):236-40.

Data and Results

Links to some results of the project (some are password-protected)

  1. Data viewer for single-cell transcriptomes of inducible systems
  2. Link to the results of the analysis of data on inducible systems
  3. Presentations made during MOSAIC meetings

Usefull links

Web-page of SITCON project

Web-page of ASSET project

Complete project application text (password-protected)