Project 3

项目3概要 小细胞肺癌代谢脆弱性的作用和机制 小细胞肺癌（SCLC）缺乏靶向治疗，标准治疗的总生存率仅为7% 顺铂/依托泊苷化疗，只有10%的生存免疫检查点治疗，NCI已分类 SCLC是一种顽固性恶性肿瘤。因此，迫切需要确定新的和有效的靶向治疗 为SCLC。在非小细胞肺癌（NSCLC）基因组研究中， 驱动因子，如突变型EGFR。不幸的是，SCLC的驱动程序是不可篡改的，其中存在功能丢失 肿瘤抑制因子视网膜母细胞瘤蛋白（RB 1）、p53（TP 53）和p73的突变以及扩增 和/或MYC致癌转录因子的过表达。为了识别SCLC的漏洞，我们执行了 基于无偏质谱的筛选，通过 基于活性的蛋白质组分析（ABPP），并在代谢组学（代谢组学和脂质组学）中使用大的 SCLC和NSCLC细胞系库、患者来源的异种移植物（PDX）和原发性肿瘤组织。我们还 用抑制新陈代谢不同方面的化合物进行筛选。综合这些数据显示， SCLC具有高度升高的糖酵解、1-碳、嘌呤和脂质代谢水平， 用两种代谢调节剂- MCT乳酸转运蛋白（MCTi）和糖酵解酶的抑制剂治疗 PFKFB 3（PFKFB 3 i）-触发SCLC细胞系代谢崩溃、生长停滞和细胞死亡。遗传研究 验证了这些发现，并证实了自相矛盾的观察，即PFKFB 3抑制引起崩溃 氧化磷酸化（OxPhos）鉴于这些发现，我们将评估MCT和PFKFB 3在 使用已建立的（来自项目1）和新的（来自核心）SCLC的代谢、发育和维持 2)基因工程SCLC小鼠模型（GEMM）、SCLC PDX和循环SCLC异种移植物（CDX） (Aim 1）。这些SCLC模型还将用于测试MCTi/PFKB 3 i组合的安全性和有效性 作为SCLC的治疗策略，我们还将评估靶向MCT和/或PFKB 3对SCLC的影响。 使用SCLC GEMM的肿瘤内免疫细胞的库和活性（与项目4）。此外，我们将使用 ABPP、体内示踪（项目2）、代谢通量和代谢组学研究（核心3），以确定 靶向由MCT和/或PFKFB 3的丧失或抑制引起的适应性代谢变化。这些研究 将定义PFKFB 3抑制损害OxPhos的机制，以及MCTi/PFKFB 3 i组合如何影响OxPhos。 治疗引起SCLC代谢衰竭（目的2）。重要的是，我们的代谢组学研究配对敏感 顺铂/足叶乙甙耐药SCLC PDX支持MCTi/PFKB 3 i治疗代表一种新的治疗方法的假设。 治疗和预防化疗耐药疾病的机会，我们将使用这些SCLC进行测试。 PDX模型和SCLC GEMM（目标3）。最后，在ABPP、代谢组学和脂质组学分析的指导下， 我们将使用有针对性的CRISPR筛选来识别SCLC中新的代谢漏洞，我们将验证这些漏洞， 使用我们的SCLC模型和ABPP，代谢通量和代谢组学实验进行表征（目的4）。

PROJECT 3 SUMMARY ROLES AND MECHANISMS OF ACTION OF METABOLIC VULNERABILITIES OF SCLC Small cell lung cancer (SCLC) lacks targeted therapies, and with only 7% overall survival on standard-of-care cisplatin/etoposide chemotherapy, and only 10% survival on immune checkpoint therapy, NCI has classified SCLC as a recalcitrant malignancy. Thus, there is an urgent need to identify new and effective targeted therapies for SCLC. In non-small cell lung cancer (NSCLC) genomic studies led to effective targeted therapies directed at drivers such as mutant EGFR. Unfortunately, drivers of SCLC are undruggable, where there are loss-of-function mutations in the tumor suppressors retinoblastoma protein (RB1), p53 (TP53) and p73, as well as amplification and/or overexpression of MYC oncogenic transcription factors. To identify vulnerabilities for SCLC, we performed unbiased mass spectrometry-based screens for SCLC-specific changes in the ATP-binding proteome via activity-based proteome profiling (ABPP), and in the metabolome (metabolomics and lipidomics) using a large bank of SCLC and NSCLC cell lines, patient-derived xenografts (PDX) and primary tumor tissue. Further, we performed screens with compounds that inhibit different aspects of metabolism. Integrating these data revealed SCLC has highly elevated levels of glycolysis, 1-carbon and purine and lipid metabolism, and that combined treatment with inhibitors of two metabolic regulators – MCT lactate transporters (MCTi) and the glycolytic enzyme PFKFB3 (PFKFB3i) – triggers SCLC cell line metabolic collapse, growth arrest and cell death. Genetic studies validated these findings, and confirmed the paradoxical observation that PFKFB3 inhibition provokes a collapse in oxidative phosphorylation (OxPhos). Given these findings, we will assess the roles of MCTs and PFKFB3 in the metabolism, development and maintenance of SCLC using established (from Project 1) and new (from Core 2) genetically engineered SCLC mouse models (GEMM), SCLC PDX, and circulating SCLC xenografts (CDX) (Aim 1). These SCLC models will also be used to test the safety and efficacy of the MCTi/PFKB3i combination as a therapeutic strategy for SCLC, and we will also assess effects of targeting MCTs and/or PFKB3 on the repertoire and activity of intratumoral immune cells using SCLC GEMM (with Project 4). Further, we will use ABPP, in vivo tracing (with Project 2), metabolic flux, and metabolomics studies (with Core 3) to identify and then target adaptive metabolic changes provoked by the loss or inhibition of MCTs and/or PFKFB3. These studies will define the mechanism by which PFKFB3 inhibition impairs OxPhos, and how combined MCTi/PFKFB3i treatment provokes SCLC metabolic collapse (Aim 2). Importantly, our metabolomic studies of paired sensitive and cisplatin/etoposide-resistant SCLC PDX supports the hypothesis that MCTi/PFKB3i therapy represents an opportunity to treat and prevent emergence of chemoresistant disease, which we will test using these SCLC PDX models and SCLC GEMM (Aim 3). Finally, guided by our ABPP, metabolomic and lipidomic analyses, we will use targeted CRISPR screens to identify new metabolic vulnerabilities in SCLC, which we will validate and characterize using our SCLC models and ABPP, metabolic flux and metabolomics experiments (Aim 4).