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: 10315207
• 负责人: Aaron Timothy Griffin
• 金额: $ 4.61万
• 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/10315207
• 关键词: A549; Algorithms; Antineoplastic Agents; Biological; Biological Markers; Cancer Etiology; Cancer cell line; Cell Line; Cells; Certification; Cessation of life; Chest; Clinical; 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; Investigation; 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; Pharmacology; Phenotype; Physicians; Presbyterian Church; Protein-Serine-Threonine Kinases; Proteins; Quality of life; Research Project Grants; Residencies; Resistance; Scientist; 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; base; cancer gene expression; cancer subtypes; career; clinically actionable; cohort; college; computer studies; driver mutation; drug sensitivity; genomic biomarker; high throughput analysis; immunohistochemical markers; improved; in silico; in vivo; knock-down; learning classifier; mortality; mouse model; mutant; next generation sequencing; novel; patient derived xenograft model; pre-doctoral; precision oncology; prognostic value; programs; reconstruction; response; single-cell RNA sequencing; standard of care; success; targeted treatment; 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.

项目总结/摘要 肺癌是美国癌症相关死亡率的主要原因， 每年有10万人死亡。转移性肺腺癌（LUAD）的治疗，最常见的 肺癌的组织学亚型，近几十年来通过靶向治疗的出现， 治疗具有致癌驱动突变的肿瘤，免疫检查点抑制剂用于没有的肿瘤。然而，在这方面， 高达50%的转移性LUAD肿瘤将对标准护理化疗没有反应。先前 精确的肿瘤学工作，以发现LUAD肿瘤药物的基因组或免疫组织化学生物标志物 敏感性取得了有限的成功。为了弥补这些缺点，我们建议利用翻译 系统生物学方法，以确定和靶向LUAD耐药性的生物决定因素， 肿瘤转录组学数据的网络分析。由于计算生物学的进步和下一代 通过测序技术，每个患者的LUAD肿瘤内的基因的动态表达可以准确地 测量，提供了一个新的窗口，用于识别关键的转录调控蛋白， 启动和维持耐药肿瘤表型（即主调节因子）。系统识别 主调节蛋白可以通过非参数分析基于秩的富集分析来获得 （NaRnEA），一种新开发的能够利用上下文特异性转录调控的统计方法， 网络从LUAD肿瘤转录组数据中提取高度机械信息，以实现计算机精确度 肿瘤学，从而克服了以前的基因组和免疫组织化学方法的局限性。NaRnEA- 将利用协调对靶向治疗的抗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。此外，一个- LUAD中>400种FDA批准的和研究性化合物的类扰动基因表达谱 将询问细胞系NCIH 1793以鉴定能够靶向这些药物的主调节因子的药物。 使用OncoTreat算法，一种新的系统生物学精确肿瘤学方法， 获得NYS CLIA认证，目前正在哥伦比亚大学欧文进行多项临床试验 医学中心这一转化研究项目将与同时进行的科学和临床培训相吻合 由于申请人学习计算生物学并与CUIMC的胸部肿瘤学家密切合作， 分别完成本研究项目后，申请人将在 纽约长老会医院通过哥伦比亚大学瓦盖洛斯内外科医学院。 这种结合科学和医学博士前奖学金将准备申请人的内科 住院医师和血液学/肿瘤学临床奖学金，最终成为一名独立医生- 精准肿瘤医学领域的科学家。