Visualisation de l'hétérogénéité au niveau des réseaux d'interactions moléculaires des tumeurs malignes par imagerie par spectrométrie de masse

Cancer remains the second leading cause of death globally, with approximately 10 million cancer-related deaths and 19 million new cases reported in 2020. Breast cancer is the most prevalent type among women, accounting for 2.26 million cases and nearly 685,000 deaths in 2020, highlighting the urgent need for focused research on this disease. Diagnosis typically relies on biopsy samples examined through histopathological analysis, identifying the molecular and morphological characteristics of cancer cells. Based on the expression of hormone receptors, oncoproteins, growth factor receptors, and other biomarkers, breast cancer subtypes are classified to guide treatment. However, despite current therapies, approximately 30% of patients experience recurrence or metastasis, largely due to tumor heterogeneity. Genomic studies have revealed that breast cancer tumors are composed of genetically distinct subpopulations of cells, each with unique molecular profiles and varied responses to treatment. Understanding and addressing this complexity is critical to improving therapeutic outcomes.The study's primary objective is to refine tumor characterization by assessing the proteomic heterogeneity of breast cancer. Through the use of mass spectrometry, specifically MALDI MSI, the research aims to identify and analyze the distinct molecular subpopulations within tumors. By understanding this heterogeneity at a proteomic level, potential druggable targets can be identified, facilitating the development of more personalized treatments tailored to individual tumor profiles. Initial results from patient-derived tumor samples showed that proteomics-guided treatments outperformed conventional therapies, offering better anti-tumor efficacy. This approach also identified biomarkers related to drug resistance, helping to guide future treatment strategies. However, this method requires large amounts of biological material and can be time-consuming, posing challenges for widespread clinical application.The second objective focused on developing a machine learning model to predict biological pathways and protein information from lipid analysis via MALDI MSI, thus bypassing the need for separate spatial proteomics experiments. This "dry proteomics" method aims to link lipid clusters to protein pathways, enhancing tumor characterization without lengthy proteomic workflows. The approach was first validated using rat brain tissues and then applied to glioblastoma, confirming its potential in analyzing complex, heterogeneous tumors. By enabling faster predictions of protein data from lipid images, this method could significantly reduce the time required for breast cancer tumor analysis, making it more feasible for clinical use.The third objective involves applying the dry proteomics workflow to better understand the spatial and temporal heterogeneity of breast cancer. By accounting for clonal expansion, tumor subtypes, and treatment history, this approach seeks to identify actionable targets at various stages of the disease.The final objective is the development of a novel MALDI IHC multiplex technique, using tag mass technology. This approach will allow for rapid, sensitive identification of key protein targets for therapeutic intervention and provide spatial mapping of these targets. Additionally, this method enables detailed analysis of the interactions between tumor and immune cells, enhancing the precision of treatments, especially in immunotherapy contexts.Together, these research objectives aim to improve the characterization of breast cancer tumors, enhancing the precision of therapeutic strategies by addressing the molecular heterogeneity that contributes to treatment resistance and disease recurrence. By integrating proteomic data, machine learning predictions, and advanced imaging techniques, this study seeks to revolutionize personalized breast cancer treatment and improve patient outcomes through a more tailored approach to therapy.