Our research project at a glance
Our lab studies the relationship between patients and cancer, focusing on how the immune system and the tumour interact. We aim to improve therapies that exploit this interaction. Currently such therapies only work on some patients and, often, for a limited time before patients develop resistance against the therapy.
Our goal is to understand how variability in the patients, in the tumours, and in their interactions affects the efficacy of these therapies. We use computational approaches including data science, simulations, modeling and especially network theory.
Networks are a useful framework to represent systems in which relationships such as interactions or similarity between objects are important. All networks share some common properties and a vast literature exists on how to measure them. The beauty is that our understanding of one type of networks can help us better understand a completely different one.
We apply network models to study such diverse systems as networks of patient-patient similarity, networks of interactions between different cell types in a tumour, and 3D interactions of genes in the nucleus. The important common factor characterising these systems is variability.
Variability pervades biological systems. On one side, the difference between patients is thwarting our successes in medicine and calls for personalised medicine approaches, especially in oncology. On the other side, there are many contexts within the body characterised by a heterogeneous mix of cell types; for example the gut, the bone marrow or the tissue surrounding a tumour. Complex relationships between cell types establish ‘ecological networks’ whose properties reflect the equilibria that define health against disease. Finally, even when looking at cells of the same type and with identical DNA sequence – for example from the same healthy individual – there is still some variability due to how two metres of DNA are organized in a network inside a tiny nucleus.
All three types of variability just mentioned can be of medical relevance. We are interested in studying how the properties of these different networks relate to variability at the different levels.
This research plan will build on the following previous results:
- 1) We have adapted methods inspired by social network theory to study the organization of different types of chromatin in 3D inside the nucleus. Just like similar people tend to connect to each other on social media, genomic regions with specific chromatin properties tend to interact with each other in the nucleus. We were able to identify proteins and histone modifications that are important for to 3D genome architecture and gene regulation.
- We have identified high levels of inter-individual variability in specific immune cell types (neutrophils, monocytes and T-cells) in healthy individuals, beyond what could be explained by differences in their genomes. Variability of gene expression across patients was also found to be an important factor in differentiating two different types of Chronic Lymphocytic Leukemia.
Vera Pancaldi is laureate of the “Chair of bioinformatics in Oncology of the CRCT” and benefits the scientific and financial support from the Fondation Toulouse Cancer Santé and the Pierre Fabre Research Institute.
To access Vera Pancaldi’s full list of publication, click here.
Last update december 2019
Various projects will be articulated along two main lines of research with the following objectives:
- The quantitative characterization of the different cell populations in the tumour microenvironment, through (epi)genomics data mining and network modelling, leading to computational simulations of interactions between the different cell types present. We will integrate epigenomic marks and gene expression in the model to explore their impact on the behaviour of the different cells and the overall state of the tumour microenvironment. Observing the properties of the tumours simulated in the computer will give us clues on how to improve therapies in real life.
- The investigation of the epigenome of immune cells in different individuals and at single-cell resolution within the same individual. We will characterize the properties of 3D gene interaction networks in these cells to identify the connection between topology of the network, cellular differentiation state and variability of the phenotype.
Last update december 2019
- Computational biology
- Logic modelling
- Tumor heterogeneity
- Immune system
- Genome architecture
Last update december 2019
Labels and networks
Grants and funders
Selected publications – All publications can be found here
Chromatin network markers of leukemia Journal Article
Bioinformatics, 36 (sup 1), pp. i445-i463, 2020.
Lancet Oncology, 21 (7), pp. 914-922, 2020.
TERAVOLT: Thoracic Cancers International COVID-19 Collaboration Journal Article
Cancer Cell, 37 (6), pp. 742-745, 2020.
Nature communication, 11 (1), pp. 2854, 2020.
Nucleic Acids Res, 2020.
Int J Mol Sci, 20 (13), pp. E3114, 2019.
Genome Biol, 20 (1), pp. 102, 2019.
Mol Autism, 10 , pp. 17, 2019.
Bioessays, 40 (2), 2018, ISSN: 1521-1878 (Electronic) 0265-9247 (Linking).
Nucleic Acids Res, 45 (16), pp. 9244-9259, 2017, ISSN: 1362-4962 (Electronic) 0305-1048 (Linking).
Genome Biol, 18 (1), pp. 18, 2017, ISSN: 1474-760X (Electronic) 1474-7596 (Linking).
Cell, 167 (5), pp. 1398-1414 e24, 2016, ISSN: 1097-4172 (Electronic) 0092-8674 (Linking).
Chromatin Regulators as a Guide for Cancer Treatment Choice Journal Article
Mol Cancer Ther, 15 (7), pp. 1768-77, 2016, ISSN: 1538-8514 (Electronic) 1535-7163 (Linking).
Genome Biol, 17 (1), pp. 152, 2016, ISSN: 1474-760X (Electronic) 1474-7596 (Linking).
Genome Med, 7 (1), pp. 8, 2015, ISSN: 1756-994X (Print) 1756-994X (Linking).
A Boolean gene regulatory model of heterosis and speciation Journal Article
BMC Evol Biol, 15 , pp. 24, 2015, ISSN: 1471-2148 (Electronic) 1471-2148 (Linking).
Nat Genet, 47 (7), pp. 746-56, 2015, ISSN: 1546-1718 (Electronic) 1061-4036 (Linking).
Front Genet, 5 , pp. 52, 2014, ISSN: 1664-8021 (Print) 1664-8021 (Linking).