Cancer remains one of the leading causes of mortality worldwide, motivating the development of predictive computational models to improve understanding of tumor growth and treatment response. Hybrid modeling approaches that combine agent-based descriptions of individual cells with continuum models for nutrient and drug transport have proven effective in capturing essential microscale biological behavior. However, such frameworks are limited in their ability to represent tissue-scale mechanical interactions and their feedback on cellular dynamics. In this work, we extend a previously developed hybrid agent-based tumor growth and drug response model [1] by coupling it to a continuum poro-elastic formulation of the surrounding tissue. The agent-based component describes individual cell states, proliferation, death, and drug response, while nutrient and drug concentrations are modeled via diffusion-reaction equations. To account for macroscopic mechanical effects, the tissue is modeled as a poro-elastic medium, enabling the representation of solid deformation, interstitial fluid flow, and pressure evolution. This multiscale coupling allows us to investigate the interplay between cellular processes and tissue-level mechanical forces, including pressure-induced cell responses and mechanically mediated transport phenomena. Numerical simulations demonstrate that the extended framework captures key features of tumor progression under therapy and reveals mechano-biological feedback mechanisms that are not accessible in purely agent-based or purely continuum models. The proposed hybrid poro-elastic framework provides a flexible tool for studying tumor growth, therapy response, and the role of mechanics in cancer progression. It highlights the importance of incorporating tissue-scale mechanics into hybrid tumor models and offers a foundation for future developments in patient-specific and treatment-optimization studies. References [1] T. Duswald, E. A. B. F. Lima, J. T. Oden, and B. Wohlmuth, Bridging scales: a hybrid model to simulate vascular tumor growth, treatment response, Comput. Meth. Appl. Mech. Engrg. 418, 2024, doi: 10.1016/j.cma.2023.116566