Research
Signaling of tumor invasion : P. Coopman

Understanding of the signaling pathways that govern tumor formation and progression is challenged by the complexity and crosstalk between pathways. The major aim of our team is to unravel the signaling pathways involved in the positive (oncogenic) or negative regulation (tumor suppressor genes) of tumor formation and progression (invasion & metastasis) and to identify novel biomarkers and therapeutic targets. Our studies initially focused on the signaling pathways controlled by the Syk protein tyrosine kinase and the PTPN13 protein tyrosine phosphatase in breast cancer. Following recent observations, our studies have been extended to ovarian and lung cancers, as well as melanoma. Our team uses proteomic approaches by mass spectrometry or single-cell mass cytometry to (i) identify partners involved in Syk and PTPN13-mediated signaling pathways in order to elucidate the mechanisms responsible for their tumor suppressor activity in breast cancer, (ii) decipher the signaling networks linking KRAS, Syk and PTPN13 in lung cancer to discover new therapeutic targets in mutated KRAS tumors, and to better understand the resistance to targeted therapies, and improve the treatment efficacy, and finally (iii) investigate the resistance of melanoma to kinase inhibitor therapies.

Theme 1: Tumor heterogeneity during cancer formation and progression

Axis 1: Role of Syk/PTPN13-mediated signaling pathways in breast cancers

Breast cancer is the most common invasive cancer in women and metastatic spread is the major cause of treatment failure and mortality. In breast cancer, we were the first to demonstrate the negative effect of Syk and PTPN13 on the formation and invasion of mammary tumors (tumor suppressor role). Our team and others have found a positive correlation between decreased expression and increased metastatic capacity, as well as decreased survival in breast cancer patients. We have developed several complementary approaches to identify substrates (quantitative phospho-proteomics) and partner proteins using catalytically active and inactive forms of Syk and PTPN13.

Axis 2: Interconnection between KRAS, Syk and PTPN13 signaling networks in lung cancer

Our team also focusses on lung cancer, which is the leading cause of cancer deaths worldwide. Our research concentrates on the KRAS oncogene, which is known to have a positive effect on lung cancer development, progression and chemoresistance, and its possible connections with the pathways controlled by Syk and PTPN13. We identified that Syk and PTPN13 are involved in the tumorigenesis of lung adenocarcinomas. Our preliminary results on the KRAS-G12C interactome and in genetically-engineered cell and mouse models, as well as patient survival data suggest that there is an overlap and cross-talk between their signaling pathways. We aim to identify the signaling networks linking KRAS, Syk and PTPN13 using interactome and (phospho)proteome analyses, coupled with bio-informatic and mathematical modeling, to integrate their signaling pathways with the aim to identify novel therapeutic targets.

Axis 3: Role of newly identified Syk and PTPN13 effectors in tumor development and progression

Using proteomics, we have identified several novel Syk and PTPN13 downstream effectors and direct substrates involved in cell-cell adhesion or cell-ECM adhesion. We are currently investigating the consequences of their Syk-mediated phosphorylation and PTPN13-catalyzed dephosphorylation and on intercellular adhesion, cell proliferation, motility, invasion and drug resistance using in vitro and in cellulo assays, as well as in genetically-modified preclinical models. A understanding of these consequences could lead to the discovery of novel biomarkers of tumor invasion and malignant progression.

Theme 2: Tumor heterogeneity and plasticity in therapy resistance

Axis 1: MAPK networks in melanoma

We are studying the resistance of melanoma to MAPK inhibitor-based therapies. On the one hand, we analyze cellular heterogeneity and plasticity using single-cell quantitative phospho-proteomics with biological models of patient-derived cell lines and tumor organoids. On the other hand, we exploit our experimental results to build mechanistic models that reproduce cellular behavior. Finally, we use artificial intelligence approaches to predict patient response to cancer treatment based on the initial tumor characteristics. 

Axis 2: PTPN13 network in HGSOC platinum sensitivity

Our team is also interested in High-Grade Serous Ovarian Carcinoma (HGSOC), which is the most common and aggressive subtype of ovarian cancer. We have shown that an increased PTPN13 expression is associated with a longer survival in HGSOC patients. Correspondingly, PTPN13 expression makes tumor cells more sensitive to platinum-based drugs. These observations indicate that the expression level of PTPN13 may be a way of predicting how HGSOC patients will respond to platinum salts (predictive factor?). To gain further insights into this process, we are investigating the role of recently identified novel PTPN13 substrates involved in chemotherapy resistance in HGSOC.

Theme 3: Integrated approaches to the molecular and therapeutic analysis of cancer: From proteomics to systems biology

Axis 1: Global proteomic analysis, biochemistry, cell biology and imaging methodologies

Our research projects are based on the use of a wide range of complementary approaches to (i) identify substrates (quantitative phospho-proteomics) and partner proteins (interactomics using BioID or GFP/RFP nano-trap), (ii) analyze cellular heterogeneity and plasticity (single-cell quantitative phospho-mass cytometry), (iii) determine the functions of target proteins (in vitro and in cellulo assays related to proliferation, motility, invasion, drug resistance, protein-protein interactions (FRET-FLIM) and protein dynamics (FRAP), and (iv) assess the clinical relevance of crucial and targetable network components (small-scale clinical studies using immunocytochemistry on annotated tumor collections).

Axis 2: Computational Systems Biology

To analyze and exploit the large amount of proteomic data obtained, we recently developed and published a computational pipeline (Phos2Net) that bootstraps the reconstruction of comprehensive networks integrating the different signaling pathways involved (collaboration with Pr. Ovidiu Radulescu, Computational Systems Biology Team, LPHI, CNRS, Montpellier). Algorithms are being developed to compare multiple proteomic datasets to identify common, complementary or opposing signaling pathways involving our preferred oncogenes and tumor suppressors, to reduce the complexity of the most relevant interactors and substrates and reveal targetable nodes. We also use artificial intelligence approaches to predict patient’s response to cancer treatment based on the initial tumor characteristics in melanoma.