This blog was originally posted on theSpringeropen博客。

乳腺癌每年都会诊断出数十万妇女和数百万新病例的生命。尽管在过去的50年中，乳腺癌死亡人数稳步下降，但肿瘤对药物治疗的抗性的出现是临床医生管理和控制乳腺癌的能力的主要障碍之一。换句话说，即使临床医生有最好的药物来治疗肿瘤并且肿瘤对药物治疗有反应，肿瘤通常也会对治疗有抵抗力并恢复治疗。

鉴于对药物治疗的抗性是不可避免的，因此问题是如何解决。Lessons from cancer types where curative drug treatments have been identified (e.g. certain leukemia subtypes) suggests that carefully designed drugs combinations are an important part of the solution. Unfortunately, designing drug combinations is challenging because of the large number of possible combinations and because of the complicated molecular network of genes and signaling molecules on which drugs act, particularly targeted drug treatments (drugs that, unlike chemotherapeutic drugs, target specific proteins or molecules, e.g. hormonal therapies like fulvestrant).

鉴于对药物治疗的抗性是不可避免的，因此问题是如何解决。

How do we then design drug combinations? In our work, we provide a methodology to tackle this problem and argue that it requires the combination of physical and mathematical modeling with the clinical and biological knowledge of the underlying molecular network. To be more precise, we do a comprehensive review of the literature of targeted therapies in breast cancer (with a focus on estrogen receptor positive, HER2 negative, PIK3CA-mutant breast cancer) to identify the key molecular players and the interactions among them, build a mathematical model that simulates how biological signals propagate throughout the molecular network, and use it to identify alterations that cause drug resistance and effective drug combinations.

我们使用的数学模型的类型称为离散动态模型，其主要特征是该模型中每个分子物种的状态由离散值（例如活动或无效）描述。对分子物种状态的这种简化（或粗粒）的描述使我们能够包括描述乳腺癌分子网络所需的大量信号分子和基因，而无需使其分析棘手。这种类型的模型还使我们能够关注网络的调节相互作用如何在存在变化（例如突变）的情况下导致有益或不必要的结果，而不必知道定量细节，例如生化参数和蛋白质丰度。

该模型能够概括某些蛋白质如何使乳腺癌细胞对PI3K抑制剂具有抗性或敏感的乳腺癌细胞（目前正在临床试验中的一种靶向疗法）来概括如何使乳腺癌细胞具有抗性或敏感性。

The breast cancer model is built based only on information and assumptions about the direct effect of interactions, yet it is able to reproduce network-level outcomes such as the effect of a drug or a mutation on cell death or proliferation. For example, the model is able to recapitulate how activating or inactivating certain proteins can make breast cancer cells resistant or sensitive to PI3K inhibitors, a type of targeted therapy currently in clinical trials. The model also predicts that drugs that downregulate directly or indirectly certain cell death proteins (namely, MCL1 or BCL2) can be combined with PI3K inhibitors to enhance their therapeutic effect.

与任何模型一样，我们提出的模型需要经过经验测试，这就是多机构翻译研究团队我们是正在做的一部分。通过测试实验室癌症模型中预测的药物组合或蛋白质改变，并探索用PI3K抑制剂治疗的患者的肿瘤活检的遗传信息，我们希望发现该模型预测的确认和矛盾。这些测试将导致我们重新评估模型并导致新的预测，从而完成任何模型所需的实验/模型周期。结果将是经过实验测试的乳腺癌分子网络关键成分的数学模型，可以对药物组合和激活/失活蛋白的作用做出强烈的预测。

将来，我们期望实验和临床验证的癌症亚型数学模型（类似于我们工作中的癌症）将成为鉴定有效的药物组合疗法的组成部分，这些疗法可以克服肿瘤的耐药性并导致提供耐用的治疗方法控制癌症。