Application of Evolutionary Principles to Maintain Cancer Control (PQ21)

描述（由申请人提供）：在这个项目中，我们探索适应性治疗（AT）概念的临床前应用，这是一个由本资助的提案人创造的术语。AT将目前无法治愈的癌症的治疗模式从最大限度地杀死肿瘤细胞（“全面攻击”）的方法转变为使用“进化开明”的药物组合来稳定肿瘤负担。我们的目标是尽量减少出现多药耐药克隆（MDR）的可能性，这是目前癌症治疗失败和患者死亡的最终原因。除了促进化疗耐药和化疗敏感肿瘤细胞竞争的原始AT想法外，作者建议探索基于进化的方法，增加化疗耐药的成本，使这些细胞比它们的化疗敏感细胞更不适合，从而降低出现耐药肿瘤的可能性。最后，作者建议使用临床可用的非侵入性代谢成像技术（FdG PET -正电子发射断层扫描）来评估肿瘤化疗耐药，并以此告知临床医生肿瘤内耐药细胞的存在和数量。我们的肿瘤模型将在乳腺癌中进行概念验证。我们将使用体外和小鼠模型，肿瘤负荷和代谢的无创成像，以及肿瘤进展和药物反应的计算进化模型。这些方法将用于检测和测量肿瘤耐药性，并提出最佳治疗策略，最大限度地延长对传统乳腺癌治疗的反应时间。作者建议结合目前临床上使用的三种化疗策略：(a)激素治疗（他莫昔芬和芳香化酶抑制剂），(b)靶向治疗（赫赛汀）和(c)标准化疗（阿霉素和紫杉醇）。在初步实验中，我们将把AT扩展到所有形式的乳腺癌治疗中。强化成像和治疗前后的分子研究将有助于评估肿瘤血管和细胞功能对进化疗法的反应。为了改进AT，我们将研究利用耐药性成本来检测耐药种群并抑制其增殖的机制。初步数据表明，表达P-gp泵的乳腺癌细胞系代谢加速，对葡萄糖剥夺比亲本细胞系更敏感。这些细胞在P-gp底物（如维拉帕米、环孢素A、奎尼丁和克拉霉素）的存在下也表现出能量消耗增加，这些底物处于亚毒性水平，使这些候选药物成为“假药”，用于增加P-gp细胞在化疗期间的耐药性成本。表达P-gp的细胞在“假药”存在时也表现出葡萄糖摄取增加。最后，我们还将研究如何在治疗间隔期间添加雌激素来增加ER+细胞的适应度，从而维持激素治疗敏感细胞的数量。

DESCRIPTION (provided by applicant): In this project we explore the pre-clinical application of the concept of Adaptive Therapy (AT), a term coined by the proposers of this grant. AT shifts the treatment paradigm of currently incurable cancers from the maximum tumor cell killing ("all-out attack") approach to the use of "evolutionarily enlightened" drug combinations to stabilize the tumor burden. Our goal is to minimize the probability of emergence of multi drug resistant clones (MDR), which are the ultimate cause of current cancer treatment failure and patient death. In addition to the original AT idea of promoting competition of chemoresistant and chemosensitive tumor cells, the authors propose to explore evolutionary-based approaches that increase the cost of chemoresistance, making these cells less fit than their chemosensitive counterpart, and thus reducing the likeliness of emergence of a resistant tumor. Finally, the authors propose the use of clinically available non-invasive metabolic imaging techniques (FdG PET -Positron Emission Tomography) to assess tumor chemoresistance, and use this to inform the clinicians about the presence and number of resistant cells within the tumor. Our proof-of-concept tumor model will be in breast cancer. We will use in vitro and mouse models, non- invasive imaging of tumor burden and metabolism, and computational evolutionary models of tumor progression and drug response. These approaches will be used to detect and measure tumor resistance and suggest the optimal treatment strategy that maximally prolongs the duration of response to conventional breast cancer therapy. The authors propose to combine three chemotherapeutic strategies currently used in the clinic: (a) hormonal therapy (tamoxifen and aromatase inhibitors), (b) targeted therapy (herceptin), and (c) standard chemotherapy (doxorubicin and taxol). In preliminary experiments we will extend AT to all forms of breast cancer therapy. Intensive imaging and pre and post therapy molecular studies will allow assessment of tumor vascularity and cell function in response to evolutionary therapy. To improve AT we will examine mechanisms to exploit the cost of resistance to both detect resistant populations and suppress their proliferation. Preliminary data has shown that breast cancer cell lines expressing P-gp pumps have accelerated metabolism and are more sensitive to glucose deprivation than their parental cell line. These cells also show increased energy depletion in presence of P-gp substrates, such as Verapamil, Cyclosporin A, Quinidine and Clarithromycin at sub-toxic levels, making these candidates for "fake drugs", to be used to increase the cost of resistance of P-gp cells between periods of chemotherapy. P-gp expressing cells also show increased glucose uptake in presence of "fake drugs". Finally, we will also study how the addition of estrogen during therapy intervals can be used to increase the fitness of ER+ cells and thus maintain the number of hormonal therapy- sensitive cells.