Descodificar a complexidade do cancro: Estratégias integradas para simular e modelar o microambiente multicelular dos tumores in vitro

CAN-TARGET aims to decode the intricate tumor microenvironment (TME) to identify new biomarkers for cancer development, progression, and treatment response, ultimately leading to more effective targeted therapies. This will be achieved through innovative strategies and advanced models. The project comprises 6 Specific Objectives (SO): SO1. Development of refined microfluidics and 3D in vitro models SO1.1 Development of advanced microfluidic devices for TME modelling: Design and fabricate versatile microfluidic platforms that replicate the TME’s biochemical, structural, and dynamic features using biomimetic materials, patient-derived cells, and real-time optical nano-sensing. This system enables continuous monitoring of key physiological parameters for precise tumor behavior analysis and therapy response. SO1.2 Development of preclinical assembloid in vitro models: Establish assembloid models that incorporate multiple cell types and vascularization strategies, moving beyond 2D cultures. These models more accurately mimic tumor heterogeneity and the intricate interactions within the TME. (Research Vector 1) SO2. Unraveling microbiome-tumor interactions SO2.1 Unveiling microbiome-tumor interactions: Leverage advanced microfluidic devices from SO1 to determine how bacterial components affect cancer progression, invasion, and therapy resistance in colon, ovarian, and brain cancers, with a focus on vascular remodeling. SO2.2 Decipher microbiome-driven immune and metabolic reprogramming in tumor progression: Characterize microbial metabolites and immune signaling to understand their role in tumor heterogeneity, immune evasion, and resistance to treatment. (Res. Vector 2) SO3. Design of vascularization strategies towards modeling cancer progression and metastasis SO3.1 Engineer and recreate critical steps of tumor vascular network formation: Integrate sacrificial matrices and extracellular matrix analogs into the microfluidic models and assembloids (from SO1) to simulate blood vessel formation, invasion, extravasation, and metastasis, thereby replicating both vascular and lymphatic networks. (Res. Vector 3) SO4. Discovery of novel cancer biomarkers in the tumor ecosystem SO4.1 Identification of novel biomarkers for diagnostic, prognostic, or predictive clinical value: Utilize automated screening, NGS, mass spectrometry, and CRISPR-based methods alongside AI and ML to pinpoint molecular alterations driving cancer. SO4.2 Identification of biomarkers associated with actionable targets: Prioritize targets based on therapeutic potential, specificity, and druggability while ensuring minimal toxicity to normal tissues. (Res. Vector 4) SO5. Screening of cancer therapies in clinically relevant models SO5.1 Screening and testing of potential therapeutic agents: Conduct large-scale compound library screens to identify anticancer candidates, study their target interactions, cytotoxic effects, and resistance mechanisms, and optimize lead compounds. SO5.2 Validation of the most promising therapeutic candidates in in vitro dynamic models and in vivo: Test and validate promising therapies in dynamic 3D in vitro platforms (SO1) and refined animal models to assess efficacy, safety, and translational potential. (Research Vector 5) SO6. Maximizing the dissemination and exploitation of results: Implement an exploitation plan to maximize CAN-TARGET impact in cancer research, guided by Open Science, FAIR, Citizen Science, and EOSC. (Transv. Vector 1)