Our team uses proteomic approaches by mass spectrometry (i) to identify partners involved in Syk and PTPN13-mediated signaling pathways to elucidate the mechanisms responsible for their tumor suppressor activity in breast cancer and (ii) To identify signaling networks linking KRAS, EGFR, Syk and PTPN13 in lung cancer to identify new therapeutic targets in mutated KRAS tumors, and to better understand the resistance to targeted therapies, as well as to improve the treatment efficacy of mutated EGFR tumors.
We have developed several complementary approaches to identify substrates (quantitative phospho-proteomics by SILAC) as well as partner proteins ("interactomics" by BioID or GFP/RFP nano-trap). For this, we use and compare in particular catalytically active and inactive forms of Syk and PTPN13, as well as wild-type and mutated KRAS and EGFR.
To analyze and exploit the substantial amount of proteomic data obtained, we recently developed and published a computational pipeline that bootstraps the reconstruction of comprehensive networks integrating the different signaling pathways involved (Collab Ovidiu Radulescu, Systems Biology Team, DIMNP, CNRS, Montpellier). Algorithms are being developed to compare multiple proteomic datasets to identify common, complementary or opposing signaling pathways in which these oncogenes and tumor suppressors are involved, to identify key nodes, and to reduce the complexity to the most relevant interactors and substrates.
The functions of appealing biochemically confirmed target proteins are consolidated by literature- and data-mining and experimentally explored. Their biological consequences on intercellular adhesion, cell proliferation, motility, invasion, drug resistance are studied using classical in vitro and in cellulo assays. Subsequently, their involvement in tumor formation and progression in vivo are investigated using genetically modified preclinical model models.
In order to assess the clinical relevance of the generated networks and their crucial and targetable components for the prognosis and treatment of cancer, small-scale clinical studies are conducted primarily using immunohistochemistry approaches on annotated tumor collections (TMAs), obtained via clinical collaborations with the two MD/PhD clinicians from our team.
In the end, our team has gained extensive expertise in the field of global proteomic analysis, as well as in the construction and validation of signal networks, in particular to reveal non-intuitive signaling pathways and to discover new valuable theranostic biomarkers. This expertise is based on our multidisciplinary collaborations between biologists, biochemists, systems biologists / bioinformaticians and clinicians who are clearly an asset.
It is now established that protein signaling networks represent a combination of biomarkers that are more robust than individual proteins. The failure of targeted therapies underlines the importance of linking genotype to phenotype via network modeling. With the advent of personalized medicine, drug development is moving from targeting individual genes to dynamic network targeting. The decryption of these networks and their cross-talk should help to better understand the events controlling cancer. This "bench-to-bedside" approach should allow to identify new proteins and signaling networks and integrate them into a broader picture with clinical relevance ranging from tumor progression to the acquisition of treatment resistance.
- Mueller S & Coopman P “Detecting and suppressing malignancy based on expression of spleen tyrosine kinase (Syk)”. WO2000063689/A9
- Freiss G., Puech C. & Vignon F. “PTPL1 as a biomarker of survival in breast cancer”. EP07301441.7 / WO2009047274/A3.
- Mangé A, Solassol J, Maudelonde T & Rouanet P. “Method and kit for the in vitro diagnosis of breast cancer”. FR2980579/WO2013045591
- Mangé A, Solassol J, Lacombe J & Azria D: “Method for determining radiosensitivity” EP2981331