V. Pancaldi

  NetB(IO)² : Network Biology for Immuno-oncology

 

 

 

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.

 

Fig.1: differential variability of gene expression and DNA methylation across three immune cell types (From Ecker et al. Genome Biology 2017). Neutrophils were found to display higher variability, especially in genes related to their immune function

 

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.

Fig. 2: Cell type supervised deconvolution: Deconvolution is the set of tools that allow the unmixing of signals specific to different cell types present in a mixed population (blood, tumour sample). In an analogy with music, deconvolution methods could isolate the sound of different instruments from a mixed recording of an orchestra. In biology one of the main uses of these approaches is the deconvolution of gene expression or DNA methylation signals, to estimate cell type abundances, using reference profiles corresponding to each cell type (signature matrix). This is known as supervised cell type deconvolution, and in our lab we work specifically in the generation of enhanced signature matrices combining information obtained from different omics data, such as transcription and methylation.

 

    • 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.

Fig 3 : Network inference from time-courses of RNAseq and integrated models of intra-cellular biological process (Marku et al. 2020) and intercellular interactions.


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.

 

Fig. 4: chromatin assortativity (ChAs) of polycomb features in a network of chromatin contacts (adapted from Pancaldi et al. Genome Biology 2016). A promoter-centred network of chromatin contacts inside the nucleus of mouse embryonic stem cells. Nodes are colored by presence of binding of a polycomb complex member protein (EZH2, shown in red), which shows clear clusters of genes that are repressed. The presence of these clusters can be inferred by measuring assortativity.

 

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.

Objectives

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.

Key words
  • Computational biology
  • Modelling
  • Networks
  • Tumor heterogeneity
  • Immune system
  • Epigenomics
  • Transcriptomics
  • Genome architecture
Labels and networks

Selected publications – All publications can be found here


2021

Stuani, L; Sabatier, M; Saland, E; Cognet, G; Poupin, N; Bosc, C; Castelli, FA. ; Gales, L; Turtoi, E; Montersino, C; Farge, T; Boet, E; Broin, N; Larrue, C; Baran, N; Cissé, MY. ; Conti, M; Loric, S; Kaoma, T; Hucteau, A; Zavoriti, A; Sahal, A; Mouchel, PL. ; Gotanègre, M; Cassan, C; Fernando, L; Wang, F; Hosseini, M; Chu-Van, E; Le-Cam, L; Carroll, M; Selak, MA. ; Vey, N; Castellano, R; Fenaille, F; Turtoi, A; Cazals, G; Bories, P; Gibon, Y; Nicolay, B; Ronseaux, S; Marszalek, JR. ; Takahashi, K; DiNardo, CD. ; Konopleva, M; Pancaldi, V; Collette, Y; Bellvert, F; Jourdan, F; Linares, LK. ; Récher, C; Portais, JC. ; Sarry, JE.

Mitochondrial metabolism supports resistance to IDH mutant inhibitors in acute myeloid leukemia. Journal Article

J Exp Med, 218 (5), pp. e20200924, 2021.

Abstract | Links | BibTeX

Ting, X; Pernet, J; Verstraete, N; Madrid-Mencia, M; Kuo, MS. ; Hucteau, A; Coullomb, A; Solorzano, J; Delfour, O; Cruzalegui, F; Pancaldi, V

GEM-DeCan: Improving tumor immune microenvironment profiling by the integration of novel gene expression and DNA methylation deconvolution signatures Journal Article

bioRxiv Pre-print, pp. 439207, 2021.

Abstract | Links | BibTeX

2020

Haynes, K; Yau, C; Bild, A; Laughney, A; Morsut, L; Yang, X; Zaugg, J; Hsu, P; Pancaldi, V; Iyer-Biswas, S

How Has the COVID-19 Pandemic Changed How You Will Approach Research and Lab Work in the Future? Journal Article

Cell Syst, 11 (6), pp. 550-554, 2020.

Links | BibTeX

Marku, M; Verstraete, N; Raynal, F; Madrid-Mencía, M; Domagala, M; Fournié, JJ. ; Ysebaert, L; Poupot, M; Pancaldi, V

Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies. Journal Article

Cancers (Basel), 12 (12), pp. 3664, 2020.

Abstract | Links | BibTeX

Verstraete, N; Jurman, G; Bertagnolli, G; Ghavasieh, A; Pancaldi, V; De Domenico, M

CovMulNet19, Integrating Proteins, Diseases, Drugs, and Symptoms: A Network Medicine Approach to COVID-19. Journal Article

Netw Syst Med, 3 (1), pp. 130-141, 2020.

Abstract | Links | BibTeX

Coulomb, A; Pancaldi, V

Tysserand - Fast reconstruction of spatial networks from bioimages Journal Article

BioRXiv - Pre-print, 2020.

Abstract | Links | BibTeX

Trama, A; Proto, C; Whisenant, JG. ; Torri, V; Cortellini, A; Michielin, O; Barlesi, F; Dingemans, AC. ; Van-Meerbeeck, J; Pancaldi, V; Mazieres, J; Soo, RA. ; Leighl, NB. ; Peters, S; Wakelee, H; Horn, L; M., Hellmann; Wong, SK. ; Garassino, MC. ; Baena, J

Supporting Clinical Decision-Making during the SARS-CoV-2 Pandemic through a Global Research Commitment: The TERAVOLT Experience. Journal Article

Cancer Cell, 38 (5), pp. 602-604, 2020.

Abstract | Links | BibTeX

Weulersse, M; Asrir, A; Pichler, AC. ; Lemaitre, L; Braun, M; Carrié, N; Joubert, MV. ; Le-Moine, M; Do-Souto, L; Gaud, G; Das, I; Brauns, E; Scarlata, CM. ; Morandi, E; Sundarrajan, A; Cuisinier, M; Buisson, L; Maheo, S; Kassem, S; Agesta, A; Pérès, M; Verhoeyen, E; Martinez, A; Mazieres, J; Dupré, L; Gossye, T; Pancaldi, V; Guillerey, C; Ayyoub, M; Dejean, AS. ; Saoudi, A; Goriely, S; Avet-Loiseau, H; Bald, T; Smyth, MJ. ; Martinet, L

Eomes-Dependent Loss of the Co-activating Receptor CD226 Restrains CD8+ T Cell Anti-tumor Functions and Limits the Efficacy of Cancer Immunotherapy. Journal Article

Immunity, 53 (4), pp. 824-839, 2020.

Abstract | Links | BibTeX

Malod-Dognin, N; Pancaldi, V; Valencia, A; Pržulj, N

Chromatin network markers of leukemia Journal Article

Bioinformatics, 36 (sup 1), pp. i445-i463, 2020.

Abstract | Links | BibTeX

Garassino, MC. ; Whisenant, JG. ; Huang, LC. ; Trama, A; Torri, V; Agustoni, F; Baena, J; Banna, G; Berardi, R; Bettini, AC. ; Bria, E; Brighenti, M; CAdranel J.and De-toma, A; Chini, A; Cortellini, A; Felip E.and Finocchiaro, G; Garrido, P; Genova, C; Giusti, R; Gregorc, V; Grossi, F; Grosso, F; Intagliata, S; La-Verde, N; Liu, SV. ; Mazieres, J; Mercadante, E; Michielin, O; Minuti, G; Mro-Sibilot, D; Pasello, G; Passatroi, A; Scotti, V; Solli, P; Stroppa, E; Tiseo, M; Viscardi, G; Voltolini, L; Wu, YL. ; Zai, S; Pancaldi, V; Dingemans, AM. ; Meerbeeck, JV. ; Barlesi, F; Wakelee, H; Peters, S; Horn, L

COVID-19 in patients with thoracic malignancies (TERAVOLT): first results of an international, registry-based, cohort study Journal Article

Lancet Oncology, 21 (7), pp. 914-922, 2020.

Abstract | Links | BibTeX

Whisenant, JG. ; Trama, AL. ; Torri, V; De-Toma, A; Viscardi, G; Cortellini, A; Michielin, O; Barlesi, F; Dingemans, AMC. ; Van-Meerbeeck, J; Pancaldi, V; Soo, RA. ; Leighl, NB. ; Peters, S; Wakelee, H; Garassino, MC. ; Horn, L

TERAVOLT: Thoracic Cancers International COVID-19 Collaboration Journal Article

Cancer Cell, 37 (6), pp. 742-745, 2020.

Abstract | Links | BibTeX

Valle, JS. ; Tejero, H; Fernández, JM. ; Juan, D; Urda-García, B; Capella-Gutiérrez, S; Al-Shahrour, F; Tabarés-Seisdedos, R; Baudot, A; Pancaldi, V; Valencia, A

Interpreting molecular similarity between patients as a determinant of disease comorbidity relationships Journal Article

Nature communication, 11 (1), pp. 2854, 2020.

Abstract | Links | BibTeX

Madrid-Mencía, M; Raineri, E; Cao, TBN. ; Pancaldi, V

GARDEN-NET and ChAseR: a suite of tools for the analysis of chromatin networks Journal Article

Nucleic Acids Res, 2020.

Abstract | Links | BibTeX

2019

Greco, A; Sanchez Valle, J; Pancaldi, V; Baudot, A; Barillot, E; Caselle, M; Valencia, A; Zinovyev, A; Cantini, L

Molecular Inverse Comorbidity between Alzheimer's Disease and Lung Cancer: New Insights from Matrix Factorization. Journal Article

Int J Mol Sci, 20 (13), pp. E3114, 2019.

Abstract | Links | BibTeX

Karolina Jodkowska, K; Pancaldi, V; Almeida, R; Rigau, M; Graña-Castro, O; Fernández-Justel, JM. ; Rodríguez-Acebes, S; Rubio-Camarillo, M; Carrillo-de Santa Pau, E; Pisano, D; Al-Shahrour, F; Valencia, A; Gómez, M; Méndez, J

Three-dimensional connectivity and chromatin environment mediate the activation efficiency of mammalian DNA replication origins Journal Article

2019.

Abstract | Links | BibTeX

Ben Zouari, Y; Molitor, AM. ; Sikorska, N; Pancaldi, V; Sexton, T

ChiCMaxima: a robust and simple pipeline for detection and visualization of chromatin looping in Capture Hi-C. Journal Article

Genome Biol, 20 (1), pp. 102, 2019.

Abstract | Links | BibTeX

Forés-Martos, J; Catalá-López, F; Sánchez-Valle, J; Ibáñez, K; Tejero, H; Palma-Gudiel, H; Climent, J; Pancaldi, V; Fañanás, L; Arango, C; Parellada, M; Baudot, A; Vogt, D; Rubenstein, JL. ; Valencia, A; Tabarés-Seisdedos, R

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer. Journal Article

Mol Autism, 10 , pp. 17, 2019.

Abstract | Links | BibTeX

2018

Sánchez-Valle, J; Tejero, H; Fernández, JM. ; Juan, D; Capella-Gutiérrez, S; Al-Shahrour, F; Tabarés-Seisdedos, R; Pancaldi, V; Valencia, A

Unveiling the molecular basis of disease co-occurrence: towards personalized comorbidity profiles Journal Article

2018.

Abstract | Links | BibTeX

Ecker, S; Pancaldi, V; Valencia, A; Beck, S; Paul, D S

Epigenetic and Transcriptional Variability Shape Phenotypic Plasticity Journal Article

Bioessays, 40 (2), 2018, ISSN: 1521-1878 (Electronic) 0265-9247 (Linking).

Links | BibTeX

2017

Carrillo-de-Santa-Pau, E; Juan, D; Pancaldi, V; Were, F; Martin-Subero, I; Rico, D; Valencia, A; Consortium, Blueprint

Automatic identification of informative regions with epigenomic changes associated to hematopoiesis Journal Article

Nucleic Acids Res, 45 (16), pp. 9244-9259, 2017, ISSN: 1362-4962 (Electronic) 0305-1048 (Linking).

Links | BibTeX

Ecker, S; Chen, L; Pancaldi, V; Bagger, F O; Fernandez, J M; Carrillo de Santa Pau, E; Juan, D; Mann, A L; Watt, S; Casale, F P; Sidiropoulos, N; Rapin, N; Merkel, A; Consortium, Blueprint ; Stunnenberg, H G; Stegle, O; Frontini, M; Downes, K; Pastinen, T; Kuijpers, T W; Rico, D; Valencia, A; Beck, S; Soranzo, N; Paul, D S

Genome-wide analysis of differential transcriptional and epigenetic variability across human immune cell types Journal Article

Genome Biol, 18 (1), pp. 18, 2017, ISSN: 1474-760X (Electronic) 1474-7596 (Linking).

Links | BibTeX

2016

Chen, L; Ge, B; Casale, F P; Vasquez, L; Kwan, T; Garrido-Martin, D; Watt, S; Yan, Y; Kundu, K; Ecker, S; Datta, A; Richardson, D; Burden, F; Mead, D; Mann, A L; Fernandez, J M; Rowlston, S; Wilder, S P; Farrow, S; Shao, X; Lambourne, J J; Redensek, A; Albers, C A; Amstislavskiy, V; Ashford, S; Berentsen, K; Bomba, L; Bourque, G; Bujold, D; Busche, S; Caron, M; Chen, S H; Cheung, W; Delaneau, O; Dermitzakis, E T; Elding, H; Colgiu, I; Bagger, F O; Flicek, P; Habibi, E; Iotchkova, V; Janssen-Megens, E; Kim, B; Lehrach, H; Lowy, E; Mandoli, A; Matarese, F; Maurano, M T; Morris, J A; Pancaldi, V; Pourfarzad, F; Rehnstrom, K; Rendon, A; Risch, T; Sharifi, N; Simon, M M; Sultan, M; Valencia, A; Walter, K; Wang, S Y; Frontini, M; Antonarakis, S E; Clarke, L; Yaspo, M L; Beck, S; Guigo, R; Rico, D; Martens, J H A; Ouwehand, W H; Kuijpers, T W; Paul, D S; Stunnenberg, H G; Stegle, O; Downes, K; Pastinen, T; Soranzo, N

Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells Journal Article

Cell, 167 (5), pp. 1398-1414 e24, 2016, ISSN: 1097-4172 (Electronic) 0092-8674 (Linking).

Links | BibTeX

Gurard-Levin, Z A; Wilson, L O; Pancaldi, V; Postel-Vinay, S; Sousa, F G; Reyes, C; Marangoni, E; Gentien, D; Valencia, A; Pommier, Y; Cottu, P; Almouzni, G

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).

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Pancaldi, V; Carrillo-de-Santa-Pau, E; Javierre, B M; Juan, D; Fraser, P; Spivakov, M; Valencia, A; Rico, D

Integrating epigenomic data and 3D genomic structure with a new measure of chromatin assortativity Journal Article

Genome Biol, 17 (1), pp. 152, 2016, ISSN: 1474-760X (Electronic) 1474-7596 (Linking).

Links | BibTeX

2015

Ecker, S; Pancaldi, V; Rico, D; Valencia, A

Higher gene expression variability in the more aggressive subtype of chronic lymphocytic leukemia Journal Article

Genome Med, 7 (1), pp. 8, 2015, ISSN: 1756-994X (Print) 1756-994X (Linking).

Links | BibTeX

Emmrich, P M; Roberts, H E; Pancaldi, V

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).

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Kulis, M; Merkel, A; Heath, S; Queiros, A C; Schuyler, R P; Castellano, G; Beekman, R; Raineri, E; Esteve, A; Clot, G; Verdaguer-Dot, N; Duran-Ferrer, M; Russinol, N; Vilarrasa-Blasi, R; Ecker, S; Pancaldi, V; Rico, D; Agueda, L; Blanc, J; Richardson, D; Clarke, L; Datta, A; Pascual, M; Agirre, X; Prosper, F; Alignani, D; Paiva, B; Caron, G; Fest, T; Muench, M O; Fomin, M E; Lee, S T; Wiemels, J L; Valencia, A; Gut, M; Flicek, P; Stunnenberg, H G; Siebert, R; Kuppers, R; Gut, I G; Campo, E; Martin-Subero, J I

Whole-genome fingerprint of the DNA methylome during human B cell differentiation Journal Article

Nat Genet, 47 (7), pp. 746-56, 2015, ISSN: 1546-1718 (Electronic) 1061-4036 (Linking).

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2014

Pancaldi, V

Biological noise to get a sense of direction: an analogy between chemotaxis and stress response Journal Article

Front Genet, 5 , pp. 52, 2014, ISSN: 1664-8021 (Print) 1664-8021 (Linking).

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