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.
Thus, in our lab we use two approaches targeting cell type composition and relationship:
- Determining the cellular composition of a sample is still one of the main priorities of cancer research. Taking advantage of the vast amount of public data our team uses innovative approaches with different omics to generate enhanced signatures for supervised cell type deconvolution, allowing an increasingly accurate estimation of the different immune and cancer cell types and states.
- Simulation of these complex interactions, both at the molecular and population dynamics level through mathematical modelling offer the opportunity of studying these systems computationally, under many different scenarios. In the computer we can reproduce experimental conditions and predict system behaviour, a great way of generating new hypotheses and optimising experimental resources.
Another main application of network theory is in uncovering the principles of chromatin organization in the nucleus. In the last years we have applied network theory to understanding patterns of genomic and epigenomic features to study the impact they have on phenotype (gene expression and functional characteristics) and their role in expression and replication.
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.
Our projects are articulated along two main lines of research with the following objectives:
1. The description of the different cell populations in the tumour microenvironment, through omics data mining and network modelling, leading to computational simulations of interactions between the different cell types present. We will integrate different sources of omics data, such as epigenomic marks and gene expression, for the generation of new and enhanced gene signatures for specific cell types and states.
2. Develop mathematical models to explore the impact of the different layers of regulation of gene expression on cellular behaviour 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.
3. 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.
- Computational biology
- Tumor heterogeneity
- Immune system
- Genome architecture
Labels and networks
Grants and funders
Selected publications – All publications can be found here
J Exp Med, 218 (5), pp. e20200924, 2021.
bioRxiv Pre-print, pp. 439207, 2021.
Cell Syst, 11 (6), pp. 550-554, 2020.
Cancers (Basel), 12 (12), pp. 3664, 2020.
Netw Syst Med, 3 (1), pp. 130-141, 2020.
BioRXiv - Pre-print, 2020.
Cancer Cell, 38 (5), pp. 602-604, 2020.
Immunity, 53 (4), pp. 824-839, 2020.
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).