VERA PANCALDI

Multi-omics descriptions of the tumour microenvironment

The first step towards better understanding and one day controlling the TME involves describing it accurately. Our goal is to exploit clinically affordable datasets in large cohorts in combination with advanced deeper characterisation on smaller sample subsets to extract as much patient-specific information as possible and initialise TME models.

This involves 3 complementary approaches :

1- Quantification of cell type and state proportions by deconvolution of bulk RNAseq datasets
We have been developing tools to quantify and describe cellular populations in the TME using several kinds of deconvolution. In reference-based deconvolution we use reference molecular profiles for specific cell types and bulk data from the tumour samples combined using statistical approaches to estimate the proportion of cells from each type present in the mixture.

Selected papers

  • Xie, T., Solorzano, J. …, & Pancaldi, V. (2023). GEM-DeCan: Improving tumor immune microenvironment profiling by the integration of novel gene expression and DNA methylation deconvolution signatures. bioRxiv. https://doi.org/10.1101/2021.04.09.439207
  • Marcelo Hurtado, Leila Khajavi, Abdelmounim Essabbar, Michael Kammer, Ting Xie, Alexis Coullomb, Anne Pradines, Anne Casanova, Anna Kruczynski, Sandrine Gouin, Estelle Clermont, Léa Boutillet, Maria Fernanda Senosain, Yong Zou, Shillin Zhao, Prosper Burq, Abderrahim Mahfoudi, Jerome Besse, Pierre Launay, Alexandre Passioukov, Eric Chetaille, Gilles Favre, Fabien Maldonado, Francisco Cruzalegui, Olivier Delfour, Julien Mazières, Vera Pancaldi Front. Immunol., 15 – 2024 | https://doi.org/10.3389/fimmu.2024.1394965
  • Maria-Fernanda Senosain, Yong Zou , Dr. Khushbu Patel , Dr. Shilin Zhao , Dr. Alexis Coullomb , Ms. Dianna J. Rowe , Dr. Jonathan M. Lehman , Dr. Jonathan M. Irish , Dr. Fabien Maldonado , Dr. Michael N. Kammer , Dr. Vera Pancaldi , Dr. Carlos F. Lopez , Pierre P. Massion (2023), Multi-omics data analysis identifies correlations between tumor biology features and predicted behaviors in early lung adenocarcinoma, Cancer Research Communications, 2023 Jul 26;3(7):1350-1365. https://doi.org/10.1158/2767-9764.CRC-22-0373.

2- Quantification of spatial patterns of cells in tumoral tissues by analysing spatial omics datasets
Tysserand and MOSNA
Our team develops tools to analyze the highly complex data generated by these methods, and we think that networks provide a powerful framework to analyze spatial omics experiments. The tysserand library can reconstruct spatial networks from spatially resolved omics experiments, and the mosna library (Multi Omics Spatial Networks Analysis) provides methods to analyze these spatial networks to study cell-cell interactions and find cellular neighborhoods of potential clinical interest

Selected papers

  • Coullomb, A., Monsarrat, P. and Pancaldi, V. mosna reveals different types of cellular interactions predictive of response to immunotherapies in cancer, BioRxiv. https://www.biorxiv.org/content/10.1101/2023.03.16.532947v2
  • Coullomb, A., & Pancaldi, V. (2021). Tysserand—Fast and accurate reconstruction of spatial networks from bioimages. Bioinformatics (Oxford, England), btab490. https://doi.org/10.1093/bioinformatics/btab490
  • Lê Cao, K.-A., Abadi, A. J., Davis-Marcisak, E. F., Hsu, L., Arora, A., Coullomb, A., Deshpande, A., Feng, Y., Jeganathan, P., Loth, M., Meng, C., Mu, W., Pancaldi, V., Sankaran, K., Righelli, D., Singh, A., Sodicoff, J. S., Stein-O’Brien, G. L., Subramanian, A., … Fertig, E. (2021). Community-wide hackathons to identify central themes in single-cell multi-omics. Genome Biology, 22(1), 220. https://doi.org/10.1186/s13059-021-02433-9

3- Studying 3D epigenomes in cancer and other cells in the TME (immune cells, cancer associated fibroblasts) to capture coupling between tissue context, epigenomic landscapes and differentiation states.

We have recently developed 2 tools for the scientific community interested in integrating epigenomic data in a chromatin structure context: GARDEN-NET is a 3D genome browser accessible via a web interface, ChAseR is an R package facilitating the integration epigenomic datasets with chromatin contact networks and calculation of assortativity, which estimates an association between a given feature (also user defined) and 3D interactions.
We have applied these tools to study principles of genome organisation by analysing gene expression, DNA methylation, histone modifications, DNA replication origins and replication timing as well as, recently, gene ages.

Selected papers

  • Jean-Pascal Capp, Benoît Aliaga & Vera Pancaldi. Evidence of epigenetic oncogenesis: a turning point in cancer research, Bioessays, in press
  • Messina, O., Raynal, F., Gurgo, J., Fischer, J., Pancaldi, V. and Nollmann, M. ​​3D chromatin interactions involving Drosophila insulators are infrequent but preferential and arise before TADs and transcription Nat Commun 14, 6678 (2023). https://doi.org/10.1038/s41467-023-42485-y
  • V. Pancaldi (2023) Network models of chromatin structure, Curr. Op. Gen. Devel. 80:102051 https://doi.org/10.1016/j.gde.2023.102051
    Jodkowska K*, Pancaldi V* (co-first), Rigau M, Almeida R, Fernández-Justel JM, Graña-Castro O, Rodríguez-Acebes S, Rubio-Camarillo M, Carrillo-de Santa Pau E, Pisano D, Al-Shahrour F, Valencia A, Gómez M, Méndez J. (2022). 3D chromatin connectivity underlies replication origin efficiency in mouse embryonic stem cells. Nucleic Acids Res. https://doi: 10.1093/nar/gkac1111.
  • Madrid-Mencía, M., Raineri, E., Cao, T. B. N., & Pancaldi, V. (2020). Using GARDEN-NET and ChAseR to explore human haematopoietic 3D chromatin interaction networks. Nucleic Acids Research, 48(8), 4066–4080. https://doi.org/10.1093/nar/gkaa159
  • Pancaldi, V. (2021). Chromatin Network Analyses: Towards Structure-Function Relationships in Epigenomics. Frontiers in Bioinformatics, https://www.frontiersin.org/articles/10.3389/fbinf.2021.742216

Grants

ANR 3D_Spatial_Genomics

High-resolution mapping of cell type-specific 3D chromatin organization in a complex tissue – 3D_spatial_genomics
ttps://anr.fr/Project-ANR-23-CE12-0023

TRANSCAN  SCIE-PANC

Stromal Compartment and Immune Epigenome spatial networks to counter therapy evasion in PANcreatic Cancer
https://transcan.eu/output-results/funded-projects/scie-panc.kl

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