UOSD PRECLINICAL MODELS AND NEW THERAPEUTIC AGENTS
1. Mission
Acquisition of drug resistance limits the efficacy of cancer treatments and accounts for therapeutic failure in most patients. Tumor evolution and the coexistence of multiple resistance mechanisms make these cases particularly difficult to treat. Instead of addressing resistance only after it emerges, a promising strategy is to prevent it by targeting the early adaptive processes that enable tumor cells to survive therapeutic stress. These non-genetic, reversible adaptations play a pivotal role in tumor relapse and treatment failure.
The Unit aims to identify and characterize the molecular determinants of these adaptive mechanisms in relevant preclinical models, including patient-derived xenografts and organoid/tumoroid systems of ovarian, lung, and colon cancers, melanoma, and mesothelioma. Within these models, the Unit investigates critical signaling pathways—such as endothelin-1, estrogen, and Bcl2/Bcl-xL—that regulate cell plasticity and the dynamic interactions between tumor cells and the tumor microenvironment (TME). Cellular plasticity enables tumor cells to activate finely tuned adaptive programs under therapeutic pressure, ensuring survival and evasion from treatment.
Our research seeks to decipher how cancer cells develop dependencies on essential survival signals, the disruption of which can block malignant interactions and open new therapeutic opportunities. These adaptive dependencies may occur within the same signaling cascade, among parallel compensatory pathways, or through functional cooperation across distinct networks. Recent advances in understanding these interdependencies—together with insights into tumor–stroma interactions and the broader tumor ecosystem—have laid the groundwork for the rational design of combined treatment regimens.
The Unit leverages advanced 3D technologies to generate patient-derived preclinical models—such as spheroids, organoids, and tumoroids—that self-organize into organotypic cultures embedded in tunable 3D matrices. These models faithfully retain the genetic landscape and drug responsiveness of the original tumors, offering a robust platform to explore actionable vulnerabilities and optimize combinatorial regimens.
Ultimately, the Unit’s goal is to characterize the molecular networks that govern drug response and resistance, enabling the discovery of pharmacologically actionable pathways and predictive models of treatment efficacy. Future directions include the integration of artificial intelligence and computational modeling to simulate cell–cell interactions driving cancer progression and therapeutic tolerance. The Unit is committed to advancing patient-centered translational research, fostering interdisciplinary collaborations, and engaging patient advocates to transform the clinical management of solid tumors.