Mathematical Modeling of Drug Resistance Evolution and The Optimal Treatment Strategy in EGFR Mutated Lung Cancer

2022年，我按照自己的研究计划，取得了一些进展。我的研究是使用数学模型来模拟不同疗法中的肿瘤进展，并预测患者的最佳治疗策略。首先，为了获得模型模拟所需的数据，我建立了两种EGFR突变细胞，并将它们共培养。在这个实验中，我观察到即使只有10%的耐药细胞存在，药物敏感细胞也能在药物中存活。而且，这种共培养反应因药物不同而不同，我们将来可能会探索这种现象的机制。其次，模拟了不同治疗策略下肿瘤进展的时间进程，并通过计算机模拟分析了耐药细胞对药物敏感细胞的影响。在这一理论分析中，结果表明，尽管全耐药细胞被认为是帮助全药敏感细胞存活的最强有力的支持，因为它们对所有治疗药物都具有耐药性，但实际上并非如此。出乎意料的是，二次突变细胞，即一种耐药细胞，比所有耐药细胞更有助于所有药物敏感细胞的幸存者。这可能是因为在相应的药物中，全耐药细胞的生长速度比单耐药细胞慢，这意味着全耐药细胞可能会在“自我生存”中花费更多的“努力”。我们也希望在未来研究其机制。

In 2022, I followed my research plan and made some progress. My research is about using a mathematical model to simulate tumor progression in different therapies and predict the optimal treatment strategies for patients. Firstly, in order to get the data for model simulation, I established two types of EGFR mutation cells and co-culture them. In this experiment, I observed that drug-sensitive cells survived in drugs even with only 10% existence of drug-resistant cells. Also, this kind of co-culture response is different from different drugs, we may explore the mechanism of this phenomenon in the future. Secondly, I simulated the time course of tumor progression in different therapy strategies and analyzed the influence effect of drug-resistance cells on drug-sensitive cells through computational simulation. In this theoretical analysis, results indicated that although all-drug-resistant cells are expected to be the strongest support for helping all-drug-sensitive cells to survive because they resistant all therapy drugs, but actually it does not. Out of expectation, secondary mutation cells, which are one-drug-resistant cells, help the survivor of all-drug-sensitive cells more than all-drug-resistant cells. This might because the growth of all-drug-resistant cells is slower compared with one-drug-resistant cells in the corresponding drugs, which means all-drug-resistant cells may spend more “effort” in “self-survivor”. We would like to research its mechanism in the future, too.