A Novel Therapeutic Strategy for Ovarian Cancer

摘要 高级别浆液性卵巢癌(HGSOC)占所有卵巢癌死亡的70%-80%。这个 雌激素受体α(ERα)在约80%的肝细胞癌中过表达，但尽管ERα 成为乳腺癌内分泌治疗的靶点(使用芳香酶抑制剂和选择性雌激素 受体调节剂和降解物)，数十项卵巢癌内分泌治疗的临床试验 令人失望的是，没有内分泌疗法被批准用于治疗HGSOC。我们一直在采取一种不同的 治疗ERα表达肿瘤的方法，开发一套通过机制选择性杀伤的化合物 与内分泌治疗不同，通过过度激活预期的未折叠蛋白反应(a-UPR) 以ERα依赖的方式。我们在ERα阳性乳腺癌的这一策略上取得了显著的成功； 如背景所述，我们合成了具有显著选择性的新型化合物ERSO 杀伤ERα阳性癌细胞(IC50值~30 nM)与ERα阴性细胞(IC50值~12)的比较 µM)，并在多种ERα阳性乳腺癌小鼠模型中诱导完全消退。而ERSO 对ERα阳性的卵巢癌也有活性，它不适合在这种情况下发展，因为它的 效力降低，再加上它在体内的毒性，使其治疗指数不足。我们现在寻求建立 关于ERSO的有希望的数据，以开发手术治疗ERα阳性卵巢癌的新疗法 通过a-UPR的过度激活。在目标1中，我们将使用ERSO的结构-活性关系和 物理化学预报器，用于构建将反复评估其针对 HGSOC及其在小鼠中的耐受性，前提是LIPE值越高的化合物越好 在体内耐受。事实上，使用这一指导原则，并只构建了几个衍生品，我们已经 已经确定了一种有前景的化合物(称为ERSO-DFP)，它可以保持效力并选择性地对抗ERα+ 卵巢癌细胞，但在体内的耐受性明显好于ERSO，提示有很大的潜力 我们的迭代综合/评估计划。顶级化合物将进入目标2，在那里它们将在 挑战HGSOC小鼠模型，包括原位、耐药模型和患者来源的异种移植 (PDX)型号。在目标3中，我们将利用炔化衍生物和蛋白质组学来鉴定卵巢癌细胞中的 这些极具希望的抗癌药物和有效的a-UPR超活化剂的精确结合伙伴(S)。 我们的目标是在3至5年内确定一种适合转化为人类临床试验的化合物 资助期。这一由一组专家提出的高度关注、假设驱动的提案 曾将抗癌药物带入人体临床试验，并在所有 必要的学科可以为HGSOC提供有效的靶向治疗；这将是一个主要的 对这一严重缺乏服务的患者群体的突破。

Abstract High-grade serous ovarian cancer (HGSOC) causes 70-80% of all deaths from ovarian cancer. The overexpression of estrogen receptor α (ERα) has been observed in ~80% of HGSOC tumors, but despite ERα being targeted by endocrine therapies in breast cancer (using aromatase inhibitors and selective estrogen receptor modulators and degraders), dozens of clinical trials with endocrine therapies for ovarian cancer have been disappointing, and no endocrine therapy is approved for treating HGSOC. We have been taking a different approach to ERα-expressing cancers, developing a suite of compounds that selectively kill via a mechanism distinct from endocrine therapies, through hyperactivation of the anticipatory unfolded protein response (a-UPR) in an ERα-dependent fashion. We have had notable success with this strategy for ERα-positive breast cancer; as detailed in the Background, we synthesized the novel compound ErSO, which has remarkable selectivity for killing ERα-positive cancer cells (IC50 values of ~30 nM) compared to ERα-negative cells (IC50 values of ~12 µM), and induces complete regression in multiple mouse models of ERα-positive breast cancer. While ErSO also has activity against ERα-positive ovarian cancer, it is not suitable for advancement in this setting as its reduced potency combined with its toxicity in vivo give it an insufficient Therapeutic Index. We now seek to build off the promising data on ErSO to develop novel therapeutics for ERα-positive ovarian cancer that operate through hyperactivation of the a-UPR. In Aim 1 we will use the structure-activity relationship for ErSO and physiochemical predictors to construct derivatives that will be iteratively evaluated for their potency against HGSOC and their tolerability in mice, based on the premise that compounds with better LipE values will be better tolerated in vivo. Indeed, using this guiding principle and constructing just a handful of derivatives, we have already identified a promising compound (called ErSO-DFP) that retains potency and selectively against ERα+ ovarian cancer cells but is markedly better tolerated in vivo as compared to ErSO, suggesting great potential for our iterative synthesis/evaluation plan. Top compounds will advance to Aim 2 where they will be assessed in challenging mouse models of HGSOC, including orthotopic, drug-resistant models, and patient-derived xenograft (PDX) models. In Aim 3 we will utilize alkynlyated derivatives and proteomics to identify, in ovarian cancer cells, the precise binding partner(s) of these highly promising anticancer agents and potent a-UPR hyperactivators. Our goal is to have identified a compound suitable for translation to human clinical trials between years 3 and 5 of the funding period. This tightly-focused, hypothesis-driven proposal from a team of experts who have previously brought anticancer drugs to human clinical trials and who have decades of experience in all the necessary disciplines could provide an impactful targeted therapy for HGSOC; this would be a major breakthrough for this vastly underserved patient population.