模拟免疫治疗和化疗的最佳时机以预防三阴性乳腺癌的耐药和复发

One intrinsic characteristic of Triple Negative Breast Cancer (TNBC) is its high plasticity, resulting in heterogeneous cancer cell subpopulations with distinct interactions with the immune system.

To address TNBC plasticity, we set to model the dynamics of tumor cell subpopulations derived from the murine TNBC-like 4T1 cell line, by developing a system of ordinary differential equations (ODEs) based on experimental results, distinguishing between Sca1⁺ (Stem Cell Antigen 1) and Sca1⁻ cells and identifying chemotherapy-resistant populations. The model incorporates interactions with immune cells, including natural killer (NK) cells, T lymphocytes, and myeloid-derived suppressor cells (MDSCs).

We investigated the effects of chemotherapy and anti-MDSC immune-boosting agent—methotrexate (MTX) and Abequolixron, respectively—through various treatment regimens and combinations. Simulations were conducted to explore different treatment initiation times and variations in immune cell killing rates.

Our findings suggest treatment timing and administration order as key determinants of therapeutic outcome. Initiating chemotherapy in synchrony with immune-killer cell oscillations—near their local peak—promoted tumor elimination, whereas mistimed treatment led to tumor escape. An optimal chemotherapy exposure window was required for elimination; exposures that were too short or prolonged favored escape of MTX-sensitive and MTX-resistant cells, respectively.

Longer MTX-free intervals shifted tumors from dormancy toward elimination, suggesting reduced recurrence risk. Administering immune-boosting therapy before chemotherapy broadened the effective therapeutic window, and combination treatment with Abequolixron and MTX further improved outcomes. These results provide, for the first time a quantitative mathematical framework based on experimental data, leveraging TNBC cell plasticity for optimizing combined chemo-immunotherapy scheduling in TNBC.