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

脑转移导致生存率下降、发病率增加、认知功能受损和生活质量下降 治疗肺癌的方法缺乏治疗脑转移的有效疗法，部分原因是 对调节脑实质定殖的分子机制的了解有限。肺癌 是美国癌症死亡率的主要原因，总体五年生存率约为16%。据估计 每年约有234，030例新发肺癌病例和约154，050例死亡。值得注意的是，约40%的肺癌患者 在诊断时有转移，并且表现出脑转移的最高患病率（40- 60%） 在所有癌症类型中。目前治疗肺癌脑转移的疗法已被证明无效， 可变的、短暂的和不完全的反应。我们最近报道ABL激酶的变构抑制剂 通过血脑屏障（BBB）并显著损害脑转移瘤的生长。增强表达 ABL和下游靶基因的突变与肺腺癌患者生存期缩短有关。我们有 确定了介导肺癌脑转移潜能的特定ABL调节转录因子 通过调节脑转移瘤和神经细胞之间的相互串扰来控制神经细胞。一个意想不到的角色， ABL调节的转录网络是它促进脑转移瘤中神经元标签的表达 肺癌细胞肺肿瘤获得“神经元拟态”是一种潜在的机制适应， 通过吸收神经元细胞、星形胶质细胞和神经胶质细胞的功能， 和小胶质细胞。在这方面，我们发现抑制肺癌细胞中的ABL激酶会损害有害的 脑小胶质细胞的炎症反应。总的假设是ABL调控的转录 网络促进脑转移，并且ABL信号传导的抑制通过以下方式增强肿瘤细胞的脆弱性： 破坏脑肿瘤微环境中脑转移细胞和驻留神经细胞之间的串扰。 为了验证这一假设，我们提出了三个具体的目标：1）定义ABL调控的转录网络 促进肺癌脑转移的机制以及ABL激酶失活损害 脑转移2)评估ABL激酶的失活是否损害脑转移性定植 通过破坏肿瘤-神经细胞串扰，同时伴随着钝化肿瘤诱导的神经毒性， 3)定义肺癌脑转移的转录景观，并评估ABL调节的 转录组标记识别转移到脑的肺癌类型。为此，我们将采用 最先进的转录组学技术来评估转录变化，不仅在脑转移中， 也存在于周围脑实质的神经细胞类型中。这些实验的结果将揭示 肺癌脑转移的转录景观，对于识别生物标志物和可操作性至关重要 目标的生成的数据将识别促进脑转移的新型信号网络，并揭示新的 治疗这种疾病和减少肿瘤诱导的神经毒性的目标。

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.