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

项目摘要/摘要 小细胞肺癌(SCLC)是一个重大的健康问题，预计将困扰超过35,000名新患者 2021年在美国。新诊断的患者中，只有不到20%的人存活超过两年。新颖而有效 迫切需要治疗来改善与这种疾病相关的不良结局。SCLC是 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没有建立起来 治疗这种疾病的策略。这在一定程度上是因为小细胞肺癌的大多数改变涉及到肿瘤抑制因子。 不能直接靶向的基因，如TP53和RB1。我们认为，虽然肿瘤抑制因子 基因改变不是药物开发努力的理想靶点，由 这些改变可用于小细胞肺癌的治疗。我们雇佣了一位不偏不倚的不可知论者 临床前筛选，在一组小细胞肺癌细胞系中测试几类靶向药物。我们发现 四种不同的Polo样激酶1(PLK1)酶抑制剂rigosertib，volasertib， CyC140和Onvansertib。我们随后证实了使用传统的异种移植和 小细胞肺癌患者来源的异种移植模型。我们做了一个有趣的观察，发现小细胞肺癌细胞系 失活的TP53基因突变对PLK1抑制剂更敏感。我们复制了这个有趣的东西 野生型TP53基因缺失和P53活性突变型过表达的观察。这些 因此，数据表明，特定类型的TP53突变可以作为潜在的预测生物标志物 指导PLK1抑制剂治疗小细胞肺癌的研究进展。我们建议检验假设(i.) Onvansertib是一种有效的PLK1抑制剂，将对复发的SCLC患者和 (Ii)破坏性的、失活的TP53基因突变将预测PLK1抑制剂在患者中的疗效。还有，就是 YAP1阳性亚型小细胞肺癌对PLK1抑制剂及其联合免疫治疗易感 检查站封锁将进一步提高小细胞肺癌的这一子集的效力。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，以系统地评估 昂凡瑟替布治疗复发性小细胞肺癌的疗效观察一项两阶段的II期临床试验将评估 昂凡瑟替布治疗复发性小细胞肺癌的疗效。目标2：询问推测的预测生物标志物和 阐明失活的TP53基因突变是否以及如何在小细胞肺癌中对PLK1抑制剂产生易感性。 PLK1抑制剂的临床前疗效将在一大组具有基因组特征的小细胞肺癌细胞中进行评估 台词。活性将与特定类型的TP53突变和其他生物相关 小细胞肺癌的基因改变。目的3：探讨白血病细胞对PLK1抑制剂获得性耐药机制(S) 小细胞肺癌和联合策略，以克服耐药性，提高疗效。使用实验室和患者派生的 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 询问可能的耐药机制，并确定其他提高疗效的策略。

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.