Targeting tumor-neural cell interactions to inhibit lung cancer brain metastasis

脑转移导致存活率下降、发病率增加、认知受损和生活质量下降 适用于肺癌患者。治疗脑转移瘤缺乏有效的治疗方法，部分原因是 对调节脑实质定植的分子机制的了解有限。肺癌 是美国癌症死亡的主要原因，总的五年存活率约为16%。据估计， 每年约234,030例新发肺癌病例和约154,050例死亡病例。值得注意的是，约40%的肺癌患者 确诊时有转移者，脑转移率最高(40-60%) 在所有癌症类型中。目前治疗肺癌脑转移的方法已被证明无效，原因是 可变的、瞬时的和不完整的反应。我们最近报道了ABL激酶的变构抑制剂 穿过血脑屏障(BBB)，显著损害脑转移瘤的生长。增强的表达能力 ABL及其下游靶基因的表达与肺腺癌患者的生存期缩短有关。我们有 鉴定介导肺癌脑转移潜能的ABL调节的特异性转录因子 通过调节脑转移瘤和神经细胞之间的相互串扰。一个意想不到的角色 ABL调控的转录网络在脑转移瘤中促进神经元信号的表达 肺癌细胞。肺肿瘤获得“神经元拟态”是一种潜在的机制适应 通过利用神经细胞、星形胶质细胞的功能在大脑中实现有效的定居和生长 和小胶质细胞。在这一点上，我们发现抑制肺癌细胞中的ABL激酶是有害的 脑小胶质细胞的炎症反应。总体假设是ABL调节的转录 网络促进脑转移，ABL信号的抑制通过以下方式增强肿瘤细胞的脆弱性 在脑肿瘤微环境中破坏脑转移细胞和驻留神经细胞之间的串扰。 为了评估这一假设，我们提出了三个具体的目标：1)定义ABL调控的转录网络 促进肺癌脑转移及ABL激酶失活损伤的机制 脑部转移。2)评估ABL激酶的失活是否会损害脑转移定植 通过破坏肿瘤-神经细胞的串扰，同时钝化肿瘤诱导的神经毒性，从而减少实质内的损伤。 3)明确肺癌脑转移的转录图谱，并评估ABL是否调节 转录特征识别转移到大脑的肺癌类型。为此，我们将聘请 最先进的转录切割技术不仅可以评估脑转移瘤中的转录变化，还可以评估 在周围脑实质的神经细胞类型中也是如此。这些实验的结果将揭示 肺癌脑转移的转录图谱，对识别生物标记物和可操作至关重要 目标。产生的数据将识别促进脑转移的新信号网络并发现新的 目标是治疗这种疾病，减少肿瘤引起的神经毒性。

Brain metastases account for decreased survival, increased morbidity, impaired cognition and poor quality of life for patients with lung cancer. Effective therapies for the treatment of brain metastases are lacking due in part to limited knowledge of the molecular mechanisms that regulate colonization of the brain parenchyma. Lung cancer is the leading cause of cancer mortality in the U.S., with an overall five-year survival rate of ~16%. It is estimated that ~ 234,030 new cases and ~ 154,050 deaths of lung cancer per year. Notably, ~40% of lung cancer patients have metastases at the time of diagnosis, and exhibit the highest prevalence of brain metastasis (40-60 %) among all cancer types. Current therapies to treat lung cancer brain metastases have proven ineffective due to variable, transient and incomplete responses. We recently reported that allosteric inhibitors of the ABL kinases cross the blood-brain barrier (BBB) and markedly impair the growth of brain metastases. Enhanced expression of ABL and downstream target genes is linked to shortened survival of lung adenocarcinoma patients. We have identified specific ABL-regulated transcription factors that mediate the brain metastatic potential of lung cancer cells by regulating reciprocal crosstalk between brain metastases and neural cells. An unexpected role of this ABL-regulated transcription network is that it promotes expression of neuronal signatures in brain metastatic lung cancer cells. Acquisition of “neuronal mimicry” by lung tumors is a potential mechanistic adaptation to achieve effective colonization and outgrowth in the brain by co-opting the function of neuronal cells, astrocytes and microglia. In this regard, we found that inhibition of ABL kinases in lung cancer cells impairs deleterious inflammatory responses of brain microglia cells. The overall hypothesis is that ABL-regulated transcription networks promote brain metastasis, and that inhibition of ABL signaling enhances tumor cell vulnerabilities by disrupting crosstalk between brain metastatic cells and resident neural cells in the brain tumor microenvironment. To evaluate this hypothesis, we propose three specific aims: 1) Define the ABL-regulated transcription networks that promote lung cancer brain metastasis and the mechanisms whereby inactivation of ABL kinases impairs brain metastasis. 2) Evaluate whether inactivation of ABL kinases impairs metastatic colonization of the brain parenchyma by disrupting tumor-neural cell crosstalk, while concomitantly blunting tumor-induced neurotoxicity. 3) Define the transcriptional landscape of lung cancer brain metastases and assess whether ABL-regulated transcriptomic signatures identify lung cancer types that metastasize to the brain. To this end we will employ state-of-the-art transcriptomic technologies to evaluate transcriptional changes, not only in brain metastasis, but also among neural cell types in the surrounding brain parenchyma. Results from these experiments will reveal the transcriptional landscapes of lung cancer brain metastases, critical to identify biomarkers and actionable targets. Data generated will identity novel signaling networks that promote brain metastases and uncover novel targets to treat this disease and decrease tumor-induced neurotoxicity.