为克服耐药而进行的肿瘤治疗联合方案分析

Tumor heterogeneity, the variability among cancer cells within a tumor, is a major contributor to drug resistance and poses challenges to effective treatment. To address this, we developed a mathematical model that captures tumor-immune interactions and the combined effects of chemotherapy and immunotherapy, focusing on their potential synergy. The model includes two tumor cell populations: chemosensitive (responsive to chemotherapy) and chemoresistant (unresponsive).

We analyzed the stability of equilibrium states under three treatment scenarios: no treatment, chemotherapy alone, and combined therapy. To identify key parameters influencing the effectiveness of the combined treatment in reducing the overall tumor population, we performed a sensitivity analysis using Latin Hypercube Sampling (LHS) to calculate Partial Rank Correlation Coefficients (PRCCs) and their associated p-values.

The analysis revealed that the killing rate of chemosensitive tumor cells by chemotherapy, the killing rate of chemosensitive tumor cells by immune cells, and the external doses of tumor-infiltrating lymphocyte (TIL) therapy were critical parameters.

We formulated a quadratic optimal control problem to minimize tumor burden and treatment administration. Numerical simulations were conducted under two scenarios: with and without optimal control. Simulations without optimal control revealed that chemotherapy alone failed to eradicate tumors, while high-dose immunotherapy was more effective.

However, combining the two therapies resulted in greater tumor reduction than either therapy alone. Under optimal control, our findings suggest that the most effective strategy involves administering a full chemotherapy dose along with gradually decreasing doses of TILs and interleukin-2 (IL-2).