Soizic Garaud

Dr. Soizic Garaud joined the LBAI-UMR1227 as a Postdoctoral Fellow in January 2022 after her project "Immunological variables associated to immune checkpoint inhibitor toxicity in cancer patients" was selected in the framework of the BIENVENÜE programme. This programme funded by the MSCA-COFUND and the Région Bretagne aims at welcoming talented researchers to help supporting the implementation of Breton RIS3 – Regional Research and Innovation Strategy – and therefore contributing to the territorial development.

Soizic Garaud joined the group a first time in 2006 as a PhD Student to carry out her thesis project called "Contribution of CD5 variant, CD5-E1B, to B cells autoreactivity ". In 2010, she joined the Molecular Immunology Laboratory of the Jules Bordet Institute (integrated centre for cancer) in Brussels as a Postdoctoral Researcher to conduct research on immune response. More specifically, her research aimed at reaching a better understanding of the FOXP1 function, a transcription factor, in normal and pathologic CD4+ T cells, and of the breast cancer-infiltrating B cells contribution in antitumor response. She carried out various research projects in immunology at the Institute until 2021.

Recently, the use of immunotherapies in the medical oncology field has profoundly changed the management of solid cancer patients. She naturally started to focus her work on a better understanding of immune mechanisms associated to response, as well as immunotherapy induced-toxicities.

She started to lead this BIENVENÜE project in 2022 which aims at deciphering the peripheral and tissular immune response during ICI treatment, and at identifying immune biomarkers that predict irAE onset and severity in treated patients. The project plans for a collaboration with the Jules Bordet Institute to establish two cohorts of cancer patients to obtain data, together with the University Hospital of Brest (CHRU de Brest). The LBAI's Hyperion platform, thanks to its state-of-the-art Imaging Mass Cytometry technology, will be a major asset for the identification of predictive biomarkers.

 

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