Team
Translational Immunology & Immunotherapy in Chronic Diseases (emerging group)
Dpt: Environnement, Reproduction, Infections, Cancer
Our research activities
Our unifying theme is to understand disease development, and to identify novel biomarkers or dysfunctional pathways based on the in depth analysis of the immune landscape in different chronic diseases, in order to develop new therapeutic approaches. We will focus on different chronic inflammatory diseases for which we previously developed a clinical and scientific expertise, and for which patient cohorts and biological samples (i.e., blood, plasma, and tissue biopsies) are already available with ethical agreements. These diseases and altered conditions are cancers (hepatocellular carcinoma and melanoma), chronic infections (hepatitis B virus [HBV]- and HBV/hepatitis D virus [HDV]), and transplantation (liver and kidney), all of which share the common feature of non-resolving inflammation. Such inflammatory condition is characterized by the persistence of an inflammatory leukocyte infiltrate, with increased levels of pro-inflammatory factors (i.e., cytokines and chemokines), and dysregulated metabolism. In all these diseases, dendritic cells (DC) ‒at least one of their subsets (e.g., cDC1 or PDC) ‒ are altered and could be manipulated to restore homeostasis. These innate cells have the advantage to initiate and regulate immune responses. Their targeting may, in theory, restore the entire immune response. In addition of sharing non-resolving inflammation, studying immune dysregulations in cancer (or chronic viral diseases) and graft rejection mechanisms in transplantation is complementary and can be considered as a mirror image. We are convinced that synergy can be achieved by the parallel analysis of immune escape (cancer or chronic infections) and over-stimulation (graft rejection), since detrimental phenomena described on one side could be exploited for beneficial effect on the other side. Our guiding principle is that identification of dysregulated immune cellular, metabolic, or signaling pathways paves the way for developing novel therapeutic approaches to restore functional immune responses through DC targeting. Our ultimate goal is to patent our data and to conduct interventional clinical studies based on ‒or guided by‒ the identified biomarkers or immune signatures, in order to improve patient care.
Philippe SAAS
Team leader
Thomas DECAENS
Team leader, PU-PH CHUGA
Our research axes
The objective of this work package is to study how features associated with chronic diseases and non-resolving inflammation may modulate dendritic cell crosstalk with the whole immune landscape, modifying frequencies or functions of immune cell subsets in the blood or tissues, in link with dysregulated immunometabolism or altered expression of molecular patterns (e.g., immune checkpoints, C-type lectin receptors, SIGLECs…).
This work program represents a transdisciplinary initiative to integrate social science perspectives into our existing research. It explores an emerging research field focused on the interplay between social determinants and health trajectories, particularly in chronic liver diseases such as hepatocellular carcinoma.
Our major publications
See all publicationsOur collaborations
- • Academic
- ( Vall d’Hebron University Hospital, Barcelona, Spain; Icahn School of Medicine, Mount Sinai, New York, USA; German Cancer Research Center and University Hospital Tübingen, Germany; Ougadougou hospital, Burkina Faso; CRCL, Lyon,
- Institute of Hepatology of Lyon; Platform for Translational Oncometabolomics, CNRS, INSERM, Univ. Montpellier).
- • Private Companies
- Inovotion [Grenoble], Transgene [Strasbourg/Lyon], Nestris Pharma [Lyon], Genosciences Pharma [Marseille], ICOA [Orléans], GlycoDiag [Orléans], CEA-Leti [Grenoble]
Our technologies
- Multiparametric flow cytometry (supervised and unsupervised analysis)
- Immuno-metabolism (flow cytometry-based method SCENITH)
- Immune functional assays (proliferation, differentiation, cytotoxicity, cytokines…)
- Preclinical experimental models
- Bio-informatics