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: 10487448
• 负责人: Aaron Timothy Griffin
• 金额: $ 5.18万
• 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/10487448
• 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; machine 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; 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.

项目总结/文摘