High-grade serous ovarian cancer (HGSOC) remains the most lethal gynecologic malignancy. Nearly 80% of patients are diagnosed at an advanced stage, and most experience recurrence despite achieving an initial clinical response to platinum-based chemotherapy. Over the past four decades, the introduction of platinum drugs and cytoreductive surgery yielded significant survival gains, but progress has since plateaued. This stagnation reflects a central paradox: while our treatments are delivered in static, uniform schedules, cancer behaves as a dynamic, evolving ecosystem. Each cycle of therapy reshapes the tumor’s fitness landscape—selecting for resistant clones, inducing cellular dormancy, and remodeling the immune and stromal microenvironment. These eco-evolutionary processes unfold across multiple timescales that remain invisible to standard clinical endpoints. Addressing this mismatch requires a shift from treating cancer as a fixed target to treating it as an adaptive system. My clinical and translational work focuses on bridging this gap through close integration of patient data, preclinical modeling, targeted therapeutics, and mathematical oncology. We aim to leverage robust clinical data through prospective collection protocols as well as patient-derived 3D microtumors and patient-derived xenografts to map how tumors evolve under treatment, while collaborating with Moffitt’s Integrated Mathematical Oncology program to translate these findings into quantitative frameworks. By parameterizing models with longitudinal measurements—tumor volume, circulating tumor DNA (ctDNA), and molecular profiles—we can simulate tumor evolution, identify critical transitions, and explore when therapy should be switched or sequenced to prevent the emergence of resistance. This approach provides a foundation for designing adaptive and evolution-informed treatment strategies in ovarian cancer. By uniting clinical observations with mathematical modeling, we can begin to predict—not just measure—how tumors change in response to therapy. In doing so, we aim to rewrite the rules of recurrence: shifting ovarian cancer management from reactive treatment of resistance to proactive steering of tumor evolution.