Targeting polo-like kinase for therapy of small cell lung cancer

项目总结/摘要 小细胞肺癌（SCLC）是一个重大的健康问题，预计将困扰35，000多名新患者 2021年在美国。不到20%的新诊断患者存活超过2年。新的有效 迫切需要治疗来改善与这种疾病相关的不良结果。SCLC是一个 大多数基因组不稳定的肿瘤，但基因组信息的靶向治疗目前还不是一个既定的 在这种疾病的战略。这部分是因为SCLC中的大多数改变涉及肿瘤抑制基因， 例如TP 53和RB 1基因，它们不能被直接靶向。我们认为肿瘤抑制因子 基因改变不是药物开发工作的理想目标， 这些改变可用于小细胞肺癌的治疗增益。我们聘请了一位公正的不可知论者 临床前筛选以在一组SCLC细胞系中测试几类靶向剂。我们发现 四种不同的波罗样激酶1（PLK 1）酶抑制剂rigosertib、volasertib CYC 140和Onvansertib.我们随后使用传统的异种移植物证实了体内功效， 患者来源的SCLC异种移植模型。我们做了一个有趣的观察， 失活TP 53基因突变对PLK 1抑制剂更敏感。我们复制了这个有趣的 使用野生型TP 53的遗传缺失和p53的活性突变形式的过表达进行观察。这些 因此，数据表明，特定类型的TP 53突变可以作为潜在的预测生物标志物， 指导PLK 1抑制剂作为小细胞肺癌治疗药物的开发。我们建议测试的假设，（i.） Onvansertib是一种有效的PLK 1抑制剂，在复发性SCLC患者中具有显著的抗癌疗效， （二）申请人;破坏性的失活TP 53基因突变将预测PLK 1抑制剂在患者中的疗效。还有那个 YAP 1阳性亚型的SCLC将易受PLK 1抑制剂单独和与免疫抑制剂的组合的影响。 检查点阻断将进一步增强SCLC的这一亚组中的功效。我们将追求三个创新， 独立但综合的具体目标：目标1：进行II期联合临床试验，系统评估 onvansertib在复发性SCLC患者中的疗效。一项2阶段II期临床试验将评估 onvansertib在复发性SCLC患者中的疗效。目的2：询问推定的预测生物标志物， 阐明失活TP 53基因突变是否以及如何赋予SCLC对PLK 1抑制剂的脆弱性。 PLK 1抑制剂的临床前疗效将在一个基因组学表征的SCLC细胞的大面板中评估。 线活性将与TP 53突变的特定类型以及与其他生物学相关的突变相关。 小细胞肺癌的遗传变异目的3：表征在哺乳动物中对PLK 1抑制剂的获得性耐药性的机制。 SCLC和联合策略，以克服耐药性和提高疗效。使用实验室和患者来源的 从联合临床试验的临床前模型，我们将使用机器学习和人工智能方法 询问假定的耐药机制，并确定其他策略，以提高疗效。

PROJECT SUMMARY/ABSTRACT Small cell lung cancer (SCLC) is a significant health problem projected to afflict more than 35,000 new patients in the US in 2021. Less than 20% of newly diagnosed patients survive beyond 2 years. New and effective treatments are urgently needed to improve the poor outcome associated with this disease. SCLC is one of the most genomically unstable tumors but genomic-informed targeted therapy is currently not an established strategy in this disease. This is in part because most of the alterations in SCLC involve tumor suppressor genes such as TP53 and RB1, which cannot be targeted directly. We contend that while tumor suppressor gene alterations are not ideal targets for drug development efforts, the biological vulnerability conferred by these alterations can be exploited for therapeutic gains in SCLC. We employed an unbiased and agnostic preclinical screening to test several classes of targeted agents in a panel of SCLC cell lines. We discovered exquisite in vitro activity of four different polo like kinase 1 (PLK1) enzyme inhibitors, rigosertib, volasertib, CYC140 and Onvansertib. We subsequently confirmed the in vivo efficacy using traditional xenograft and patient-derived xenograft models of SCLC. We made the intriguing observation that SCLC cell lines harboring inactivating TP53 gene mutations are more sensitive to PLK1 inhibitors. We replicated this interesting observation using genetic depletion of wild type TP53 and overexpression of active mutant form of p53. These data therefore suggest that specific types of TP53 mutation could serve as potential predictive biomarkers to guide the development of PLK1 inhibitor as therapy of SCLC. We propose to test the hypotheses that (i.) Onvansertib, a potent PLK1 inhibitor, will have significant anticancer efficacy in relapsed SCLC patients and (ii.) Disruptive, inactivating TP53 gene mutation will predict efficacy of PLK1 inhibitors in patients. Also, that YAP1 positive subtype of SCLC will be vulnerable to PLK1 inhibitors alone and the combination with immune checkpoint blockade will further enhance efficacy in this subset of SCLC. We will pursue three innovative, independent but integrated specific aims: Aim 1: Conduct a phase II co-clinical trial to systematically assess the efficacy of onvansertib in patients with relapsed SCLC. A 2-stage phase II clinical trial will evaluate the efficacy of onvansertib in relapsed SCLC patients. Aim 2: Interrogate putative predictive biomarkers and elucidate whether and how inactivating TP53 gene mutations confer vulnerability to PLK1 inhibitors in SCLC. Preclinical efficacy of PLK1 inhibitors will be assessed in a large panel of genomically characterized SCLC cell lines. Activity will be correlated with specific types of TP53 mutations and with other biologically relevant genetic alterations in SCLC. Aim 3: Characterize mechanism(s) of acquired resistance to PLK1 inhibitor in SCLC and combination strategies to overcome resistance and enhance efficacy. Using lab and patient-derived preclinical models from the co-clinical trial, we will use machine learning and Artificial Intelligence approaches to interrogate putative resistance mechanisms and identify other strategies to enhance efficacy.