Identifying and Targeting Master Regulators of Drug Resistance in Lung Adenocarcinoma through Network Analysis of Tumor Transcriptomic Data

通过肿瘤转录组数据的网络分析识别和靶向肺腺癌耐药性的主调节因子

• 批准号: 10676216
• 负责人: Aaron Timothy Griffin
• 金额: $ 5.27万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10676216
• 关键词: A549; Algorithms; Antineoplastic Agents; Biological; Biological Markers; CLIA certified; Cancer Etiology; Cancer cell line; Cell Line; Cells; Cessation of life; Chest; Clinical Oncology; Clinical Trials; Clustered Regularly Interspaced Short Palindromic Repeats; Computational Biology; DNA Sequence Alteration; Data; Development; Diagnosis; Disease; Drug resistance; Epidermal Growth Factor Receptor; Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor; FDA approved; Fellowship; Gene Expression; Genetic Transcription; Genomics; Hematology; Histologic; Hospitals; Immune checkpoint inhibitor; Immunocompetent; In Vitro; Internal Medicine; Laboratories; Lung Adenocarcinoma; Machine Learning; Malignant neoplasm of lung; Measures; Medical; Medical Oncology; Medical center; Methods; Modality; Mutate; New York; Oncogenic; Oncologist; Oncoproteins; Pathway Analysis; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Phenotype; Physicians; Presbyterian Church; Protein-Serine-Threonine Kinases; Proteins; Quality of life; Research Project Grants; Residencies; Resistance; Scientist; Serine; Statistical Methods; Surgeon; Systems Biology; Technology; Training; Transcription Regulatory Protein; Translational Research; Tumor Markers; Tumor Suppressor Proteins; Tyrosine Kinase Inhibitor; United States; Universities; Update; Work; cancer gene expression; cancer subtypes; career; clinical training; clinically actionable; cohort; college; computer studies; driver mutation; drug sensitivity; genomic biomarker; high throughput analysis; immunohistochemical markers; improved; in silico; in vivo; knock-down; machine learning classifier; machine learning prediction; mortality; mouse model; mutant; next generation sequencing; novel; patient derived xenograft model; pharmacologic; pre-doctoral; precision oncology; prognostic value; programs; reconstruction; response; single-cell RNA sequencing; standard of care; success; synergism; targeted treatment; transcription regulatory network; transcriptomics; treatment strategy; tumor

Project Summary/Abstract Lung cancer, the leading cause of cancer-related mortality in the United States, is responsible for more than 100,000 deaths each year. The treatment of metastatic lung adenocarcinoma (LUAD), the most common histological subtype of lung cancer, has improved substantially in recent decades through the advent of targeted therapy for tumors with oncogenic driver mutations and immune checkpoint inhibitors for those without. However, up to 50% of metastatic LUAD tumors will not respond to standard-of-care antineoplastic therapy. Previous precision oncology efforts to discover genomic or immunohistochemical biomarkers of LUAD tumor drug sensitivity have achieved limited success. To remedy these shortcomings, we propose to leverage a translational systems biology approach to identify and target the biological determinants of drug resistance in LUAD through network analysis of tumor transcriptomic data. Due to advances in computational biology and next-generation sequencing technologies, the dynamic expression of genes within each patient’s LUAD tumor may be accurately measured, providing a novel window for the identification of the key transcriptional regulatory proteins which initiate and maintain drug-resistant tumor phenotypes (i.e. Master Regulators). The systematic identification of Master Regulator proteins can be achieved with Non-parametric analytical Rank-based Enrichment Analysis (NaRnEA), a newly developed statistical method capable of leveraging context-specific transcriptional regulatory networks to extract highly mechanistic information from LUAD tumor transcriptomic data for in silico precision oncology, thus overcoming the limitations of previous genomic and immunohistochemical approaches. NaRnEA- inferred activity of Master Regulator proteins which coordinate resistance to targeted therapy will be leveraged for the development of a transcriptomic machine learning biomarker of drug-sensitivity. Additionally, one-of-a- kind perturbational gene expression profiles for >400 FDA-approved and investigational compounds in the LUAD cell line NCIH1793 will be interrogated to identify drugs capable of targeting these Master Regulators of drug- resistance using the OncoTreat algorithm, a novel systems biology precision oncology method which has received NYS CLIA certification and is currently in use for multiple clinical trials at the Columbia University Irving Medical Center. This translational research project will coincide with simultaneous scientific and clinical training as the applicant studies computational biology and works closely with thoracic oncologists at CUIMC, respectively. Following the completion of this research project the applicant will complete clinical training at the New York Presbyterian Hospital through the Columbia University Vagelos College of Physicians and Surgeons. This combined scientific and medical predoctoral fellowship will prepare the applicant for an Internal Medicine residency and a Hematology/Oncology clinical fellowship culminating in a career as an independent physician- scientist in the field of precision medical oncology.

项目概要/摘要 肺癌是美国癌症相关死亡的主要原因，造成超过 最常见的转移性肺腺癌 (LUAD) 的治疗每年导致 10 万人死亡。 近几十年来，通过靶向治疗的出现，肺癌的组织学亚型得到了显着改善。 对于具有致癌驱动突变的肿瘤和没有免疫检查点抑制剂的肿瘤进行治疗。 高达 50% 的转移性 LUAD 肿瘤对标准抗肿瘤治疗没有反应。 精准肿瘤学努力发现 LUAD 肿瘤药物的基因组或免疫组织化学生物标志物 为了弥补这些缺点，我们建议利用转化。 系统生物学方法通过以下方式识别和靶向 LUAD 耐药性的生物决定因素 由于计算生物学和下一代技术的进步，肿瘤转录组数据的网络分析。 通过测序技术，可以准确地了解每个患者 LUAD 肿瘤内基因的动态表达 测量，为鉴定关键转录调节蛋白提供了一个新的窗口 启动并维持耐药肿瘤表型（即主调节因子）。 主调节蛋白可以通过非参数分析基于等级的富集分析来实现 （NaRnEA），一种新开发的统计方法，能够利用上下文特定的转录调控 网络从 LUAD 肿瘤转录组数据中提取高度机械信息，以实现计算机精度 肿瘤学，从而克服了先前基因组和免疫组织化学方法的局限性。 将利用协调靶向治疗耐药性的主调节蛋白的推断活性 用于开发药物敏感性的转录组机器学习生物标志物。 为 LUAD 中超过 400 种经 FDA 批准和研究的化合物提供扰动基因表达谱 细胞系NCIH1793将被询问以确定能够靶向这些药物主调节因子的药物 使用 OncoTreat 算法产生耐药性，这是一种新颖的系统生物学精准肿瘤学方法， 获得纽约州 CLIA 认证，目前用于哥伦比亚大学欧文分校的多项临床试验 医疗中心。该转化研究项目将同时进行科学和临床培训。 由于申请人研究计算生物学并与 CUIMC 的胸部肿瘤学家密切合作， 完成该研究项目后，申请人将在该机构完成临床培训。 纽约长老会医院通过哥伦比亚大学瓦格洛斯内科医生和外科医生学院。 这种科学和医学博士前奖学金相结合，将为申请人的内科医学做好准备 住院医师实习和血液学/肿瘤学临床研究金最终成为一名独立医生- 精准医学肿瘤学领域的科学家。