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

描述拉丁裔和亚洲人常见肺癌 (LC) 亚型的治疗反应

• 批准号: 10733396
• 负责人: Jonathan W Riess
• 金额: $ 28.83万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10733396
• 关键词: Address; Apoptotic; Asian Americans; Asian ancestry; Asian population; Automobile Driving; BCL1 Oncogene; BCL2 gene; BCL2L1 gene; Biological; Biosensor; Bypass; California; Cancer Model; Cancer Patient; Cancer Therapy Evaluation Program; Cell model; Cells; Clinical; Clinical Trials; Collaborations; Combined Modality Therapy; Comprehensive Cancer Center; Data; Development; Drug Combinations; Epidermal Growth Factor Receptor; Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor; Equity; Ethnic Origin; FDA approved; Family; Frequencies; Gender; Generations; Genes; Genetic; Genomics; Indigenous American; Individual; Latino; Latino Population; Lung Adenocarcinoma; Lung Neoplasms; Malignant neoplasm of lung; Mediating; Minority; Modeling; Mutation; Native Hawaiian; Non-Small-Cell Lung Carcinoma; Oncogenes; Pacific Islander; Patients; Pattern; Pharmaceutical Preparations; Phosphotransferases; Population; Proto-Oncogene Proteins c-akt; Race; Receptor Inhibition; Receptor Protein-Tyrosine Kinases; Receptor Signaling; Research; Resistance; Resolution; Role; Signal Pathway; Signal Transduction; Smoking; Smoking Status; Testing; Texas; The Jackson Laboratory; Translating; Translations; Universities; Variant; Woman; Work; cancer subtypes; drug development; ethnic minority; experience; genomic biomarker; high risk; imaging platform; in vivo; inhibitor; inhibitor therapy; live cell imaging; machine learning method; minority patient; mutant; novel therapeutic intervention; novel therapeutics; patient derived xenograft model; racial minority; repository; research and development; resistance mechanism; response; targeted treatment; treatment optimization; treatment response; tumor; tumor progression

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.

具体目标 UCATS项目2的目的是针对早期耐药性临床上重要的基因组标记 EGFR突变非小细胞肺癌（NSCLC）中的酪氨酸激酶抑制剂（EGFR-TKI） 受体酪氨酸激酶（RTK）驱动的信号通路的定量模型，在抑制作用下 探索种族，种族和遗传血统在预测基线EGFR信号和EGFR-TKI方面具有的角色 治疗反应。 EGFR突变的NSCLC包括大量的肺腺癌（15％ 西方/白人人口），在亚裔美国人，夏威夷原住民，太平洋地区发生较高的频率 岛民（Aanhpi〜50％）和拉丁裔（约38％）。 EGFR突变表现出与吸烟状况的密切关联， 性别，种族/种族和遗传血统。与 非latino白色（NLW），较低的吸烟率和EGFR突变肿瘤的频率较高。有趣的是， 最近的研究表明，美国的原住民血统（与亚洲血统密切相关） 在拉丁美洲裔中发展了EGFR突变肺癌的较高风险。这表明躯体EGFR 突变与种族/种族相关的生物学和非生物学方面有着密切的关联 祖先。通过这项研究，我们将通过FDA批准的组合制定新颖的治疗策略 和NCI-CTEP药物转化为NCI赞助的临床试验克服EGFR-TKI抗性 EGFR突变NSCLC相关的机制。我们还将评估种族/种族和遗传 祖先会影响EGFR信号传导，并影响对EGFR抑制的反应。 患者衍生的异种移植物（PDX）已广泛用于肺癌研究和药物开发。 我们在建立PDX和进行基于PDX的研究方面拥有丰富的经验。通过杰克逊 实验室（JAX），UCSF和UT西南合作，我们建立了200多个肺 癌症PDX在其中25个具有EGFR激活突变，我们已经确定了10个EGFR突变。 PDXNET中的肺癌模型。我们预计UCAT将至少产生25种额外的EGFR突变。 PDXS。我们对许多PDX进行了详细的组织病理学和基因组表征 专注于癌基因驱动的NSCLC，我们确定了多种推定的抗性机制 在这些模型中，对当前以EGFR为目标的治疗方法的早期抵抗力。我们还表明 EGFR突变的PDX可以可能用于优化治疗组合以克服EGFR-TKI 抗性并确定最有效的药物或药物组合，包括批准的FDA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EGFR信号，该信号传导 提供了对细胞适应抑制作用的高分辨率评估。该项目的工作是由 我们目前的研究将用于解决以下特定目标。 目标1：与FDA批准的药物和/或NCI-ctep剂一起开发有针对性的治疗组合 克服对EGFR-TKIS的抗性机制，即先驱早期肿瘤进展。我们计划A） 确定具有MET- EGFR-TKI耐药性的绕道旁路机制； b）确定最有效的双重EGFR- EGFR突变的PDX中具有罕见EGFR突变的封锁方法； c）克服有限 rbm10缺乏EGFR突变的NSCLC中的凋亡反应与Bcl-2/bcl-XL抑制剂和NCI-CTEP 代理pelcitoclax。 AIM 2：使用活细胞中的信号动力学对EGFR抑制剂效应进行建模。前提是针对性的 可以通过表征RTK驱动的细胞内信号传导的定量变化来优化抑制剂疗法 在不同的EGFR突变体，遗传背景和抑制剂类别中。我们将使用激酶活动 在使用的PDX模型中，以单细胞分辨率跟踪Akt和ERK活动模式的生物传感器 目的1。这种高含量分析，结合了多样的EGFR突变和遗传 PDX面板代表的背景将为定量模型提供数据的数据 范围。利用机器学习方法，我们的模型将使我们能够识别主要信号 确定对EGFR抑制剂多种类（和组合）响应的参数，MET抑制剂， 和Bcl-2/bcl-XL家族抑制剂。重要的是，我们的模型将有助于回答一个长期以来的问题 EGFR抑制剂耐药性涉及一个跨个体和背景的共享信号决定因素， 或者取决于患者或遗传背景特异性因素。此外，当我们的结果是 与AIM 1集成在一起，我们将有独特的机会来验证我们的细胞模型，以防止体内数据。 目标3：检查种族/民族和遗传血统在EGFR信号传导中的作用以及对 EGFR疗法。由于我们的研究将测试来自AANHPI的大量EGFR突变模型的EGFR疗法，因此 Latinos和NLW，我们将评估基线信号和对第三代EGFR-TKI的响应是否 Osimertinib受种族/种族的影响。在拉丁美洲人中，我们还将估计本地的个人水平 美国血统，并将评估其与基线信号传导和治疗反应的关联。 影响：该项目将推进EGFR疗法以及我们对肺癌中EGFR信号传导的理解 关注少数民族的重点，包括将导致更公平地转化为临床试验的信息。