This study proposes an advanced nonlinear control strategy for tumor growth management, utilizing the mathematical model framework established by [1]. While adopting the model structure of [1] to represent the dynamic interactions between tumor cells and treatment agents, this work diverges in its control synthesis by employing a two-step Backstepping methodology. The control objective is focused on driving the tumor cell density (x) toward a desired trajectory (xd) by treating the drug concentration (y) as a virtual control input. The proposed controller effectively manages the nonlinear interaction and inherent coupling within the system dynamics. A rigorous stability analysis using a composite Lyapunov function demonstrates that the tracking errors converge exponentially to zero, ensuring that all closed-loop signals remain bounded, [3] and [4]. This approach provides a robust and reliable alternative to existing protocols by guaranteeing precise tracking of tumor regression schedules. Finally, this work establishes a foundational control framework that is intended to be expanded in future studies by integrating the logistic growth dynamics characterized in [2], aiming to further enhance the biological fidelity and predictive power of the treatment model for breast cancer cells.