Characterizing treatment responses for common lung cancer (LC) subtypes in Latinos and Asians

具体目标 UCATS项目2的目的是针对对EGFR早期耐药的临床重要的基因组标记 酪氨酸激酶抑制剂(EGFR-TKIs)在EGFR突变的非小细胞肺癌(NSCLC)中的研究进展 受抑制的受体酪氨酸激酶(RTK)驱动的信号通路的定量模型 探讨种族、民族和遗传祖先在预测基线EGFR信号和EGFR-TKI中的作用 治疗反应。EGFR突变的非小细胞肺癌包括肺腺癌的一个重要亚群(15%在 西部/白人人群)，在亚裔美国人、夏威夷原住民、太平洋地区出现频率较高 岛民(AANHPI~50%)和拉丁裔(~38%)。EGFR突变与吸烟状况有很强的相关性， 性别、种族/民族和遗传血统。AANHPI和拉丁裔种族/族裔的妇女 非拉丁裔白人(NLW)，吸烟率较低，EGFR突变肿瘤的频率较高。有趣的是，一个 最近的研究表明，高美洲原住民血统(与亚洲血统密切相关) 在拉丁裔人群中患上EGFR突变肺癌的风险更高。这表明体细胞表皮生长因子受体 突变与种族或民族有关的生物学和非生物学方面密切相关 血统。通过这项研究，我们将开发新的治疗策略，FDA批准的组合 和NCI-CTEP药物转化为NCI赞助的克服EGFR-TKI耐药性的临床试验 EGFR突变的非小细胞肺癌的相关机制。我们还将评估种族/民族和基因 祖先影响EGFR信号转导，并影响对EGFR抑制的反应。 患者来源的异种移植物(PDX)已广泛应用于肺癌研究和药物开发。 我们在建立PDX和开展基于PDX的研究方面拥有丰富的经验。通过杰克逊家族 实验室(JAX)、加州大学旧金山分校和德克萨斯大学西南分校的合作，我们建立了200多个肺的特征 癌症PDX中有25个具有EGFR激活突变，我们已经鉴定出另外10个EGFR突变 PDXnet中的肺癌模型。我们预计UCATS将产生至少25个额外的EGFR突变 PDX。我们已经对这些PDX中的许多进行了详细的组织病理学和基因组表征 聚焦于癌基因驱动的非小细胞肺癌，我们发现了多种可能的耐药机制 在这些模型中，对当前EGFR靶向治疗方法的早期抵抗。我们还表明， EGFR突变的PDX可能被用于优化治疗组合，以克服EGFR-TKI 并确定最有效的药物或药物组合，包括FDA批准的药物和NCI- CTEP代理。我们将使用高含量的活细胞成像平台来分析细胞内的EGFR信号，这 提供细胞对抑制的适应的高分辨率评估。这个项目的工作源于 我们目前的研究将用于解决以下具体目标： 目标1：开发与FDA批准的药物和/或NCI-CTEP药物的靶向治疗组合，以 克服对EGFR-TKIs耐药的机制，这些机制预示着肿瘤的早期进展。我们计划进行a) 在携带MET的EGFR突变型PDX模型中确定最有效的双重MET/EGFR靶向策略 介导EGFR-TKI耐药的旁路机制；b)确定最有效的双重EGFR- 对携带不常见的EGFR突变的EGFR突变的PDX的阻断方法；以及c)克服限制 BCL-2/BCL-XL抑制剂和NCI-CTEP联合应用对RBM10缺陷型EGFR突变型非小细胞肺癌细胞的凋亡反应 佩尔西托克拉克斯探员。 目的2：利用活细胞中的信号动力学来模拟EGFR抑制剂的作用。前提是有针对性的 通过表征RTK驱动的细胞内信号的数量变化，可以优化抑制剂的治疗 跨越不同的EGFR突变体、遗传背景和抑制剂类别。我们将使用激酶活性 用于跟踪AKT和ERK活动模式的生物传感器，具有单细胞分辨率 目的1.这种高含量的分析，结合不同的EGFR突变和基因 以PDX面板为代表的背景，将为量化模型提供前所未有的数据 作用域。利用机器学习方法，我们的模型将允许我们识别主要信令 确定对多种类别(和组合)的EGFR抑制剂、MET抑制剂、 和Bcl2/Bclxl家族抑制物。重要的是，我们的模型将有助于回答一个长期存在的问题： EGFR抑制剂抗性涉及一组共享的跨个体和背景的信号决定因素， 或者取而代之的是取决于患者或遗传背景特定的因素。此外，当我们的结果是 与AIM 1整合后，我们将有独特的机会根据体内数据验证我们的细胞模型。 目标3：研究种族/民族和遗传血统在EGFR信号转导和反应中的作用 EGFR疗法。由于我们的研究将在AANHPI的大量EGFR突变模型中测试EGFR治疗， ，我们将评估基线信号和对第三代EGFR-TKI的响应 Osimertinib受到种族/民族的影响。在拉丁裔中，我们还将估计土著居民的个人水平 美国血统，并将评估其与基线信号和治疗反应的关联。 影响：该项目将促进EGFR治疗和我们对肺癌中EGFR信号的理解 关注少数群体，包括将导致更公平地转化为临床试验的信息。

SPECIFIC AIMS The purpose of UCaTS Project 2 is to target clinically important genomic markers of early resistance to EGFR tyrosine kinase inhibitors (EGFR-TKIs) in EGFR-mutant non-small cell lung cancer (NSCLC), to develop quantitative models of Receptor Tyrosine Kinase (RTK)-driven signaling pathways under inhibition, and to explore the role race, ethnicity and genetic ancestry has in predicting baseline EGFR signaling and EGFR-TKI treatment response. EGFR-mutant NSCLC comprises a substantial subset of lung adenocarcinoma (15% in Western/White populations) and occurs with a higher frequency in Asian Americans, Native Hawaiians, Pacific Islanders (AANHPI ~50%), and Latinos (~38%). EGFR mutations show strong associations with smoking status, gender, race/ethnicity, and genetic ancestry. Women of AANHPI and Latino race/ethnicity have, compared to non-Latino Whites (NLW), lower smoking rates and higher frequency of EGFR mutant tumors. Interestingly, a recent study showed that high Indigenous American Ancestry (which is closely associated with Asian ancestry) confers a higher risk of developing EGFR-mutant lung cancer in Latinos. This suggests that somatic EGFR mutations have close associations of biological and non-biological aspects associated with race/ethnicity or ancestry. Through this research, we will develop novel therapeutic strategies with combinations of FDA approved and NCI-CTEP drugs to translate into NCI-sponsored clinical trials overcoming EGFR-TKI resistance mechanisms associated in EGFR-mutant NSCLC. We will also assess whether race/ethnicity and genetic ancestry impacts EGFR signaling and influences responses to EGFR inhibition. Patient-derived xenografts (PDXs) have been broadly used in lung cancer research and drug development. We have extensive experience in establishing PDXs and conducting PDX-based research. Through the Jackson Laboratory (JAX), UCSF and UT Southwestern collaborations, we established and characterized over 200 lung cancer PDXs in which 25 have EGFR-activating mutations and we have identified an additional 10 EGFR-mutant lung cancer models in PDXnet. We anticipate that UCaTS will generate at least 25 additional EGFR-mutant PDXs. We have performed detailed histopathological and genomic characterization on many of these PDXs focused on oncogene driven NSCLC where we identified multiple putative resistance mechanisms that mediate early resistance to current EGFR-targeted therapeutic approaches in these models. We have also shown that EGFR-mutant PDXs can potentially be used to optimize treatment combinations to overcome EGFR-TKI resistance and to identify the most efficacious drugs or drug combinations including FDA approved and NCI- CTEP agents. We will use a high-content live-cell imaging platform to analyze intracellular EGFR signaling, which provides a high-resolution assessment of cellular adaptation to inhibition. The work for this project arises out of our current research, which will be used to address the following specific aims.: Aim 1: Develop targeted treatment combinations with FDA approved drugs and/or NCI-CTEP agents to overcome mechanisms of resistance to EGFR-TKIs that herald early tumor progression. We plan to a) determine the most effective dual MET/EGFR targeting strategy in EGFR-mutant PDX models harboring MET- mediated bypass tract mechanisms of EGFR-TKI resistance; b) determine the most effective dual EGFR- blockade approach in EGFR-mutant PDXs harboring uncommon EGFR-mutations; and c) overcome the limited apoptotic response in RBM10 deficient EGFR-mutant NSCLC with the BCL-2/BCL-xL inhibitor and NCI-CTEP agent pelcitoclax. Aim 2: Modeling EGFR inhibitor effects using signaling dynamics in live cells. The premise is that targeted inhibitor therapy can be optimized by characterizing quantitative variation in RTK-driven intracellular signaling across different EGFR mutants, genetic backgrounds, and inhibitor classes. We will use kinase activity biosensors to track AKT and ERK activity patterns with single-cell resolution in the set of PDX models used in Aim 1. This high-content analysis, in combination with the diverse set of EGFR mutations and genetic backgrounds represented by the PDX panel, will provide the data for a quantitative model with an unprecedented scope. Leveraging machine learning methods, our model will allow us to identify the primary signaling parameters determining response to multiple classes (and combinations) of EGFR inhibitors, MET inhibitors, and Bcl-2/Bcl-xL family inhibitors. Importantly, our model will help to answer a long-standing question of whether EGFR inhibitor resistance involves a shared set of signaling determinants across individuals and backgrounds, or instead depends on patient or genetic background-specific factors. Furthermore, when our results are integrated with Aim 1, we will have the unique opportunity to validate our cellular model against in vivo data. Aim 3: Examining the role of race/ethnicity and genetic ancestry in EGFR signaling and responses to EGFR therapies. As our study will test EGFR therapies in large numbers of EGFR-mutant models from AANHPI, Latinos, and NLW, we will evaluate whether baseline signaling and responses to the third generation EGFR-TKI osimertinib are influenced by race/ethnicity. In Latinos, we will also estimate individual levels of Indigenous American ancestry and will evaluate its associations with baseline signaling and treatment response. IMPACT: This project will advance EGFR therapy and our understanding of EGFR signaling in lung cancer with a focus on minorities, including information that will lead to a more equitable translation into clinical trials.