Illuminating molecular targetable pathways in HNSCC

阐明 HNSCC 的分子靶向途径

基本信息

批准号：
8987478
负责人：
MOLLY F. KULESZ-MARTIN
金额：
$ 50.58万
依托单位：
OREGON HEALTH & SCIENCE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-08-01 至 2020-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8987478
关键词：
Affect Algorithms Antibodies Biochemical Biochemical Genetics Bioinformatics Biological Assay Candidate Disease Gene Cell Culture Techniques Cells Cetuximab Characteristics Clinical Clinical Trials Computer Simulation Computing Methodologies Cultured Tumor Cells Data Data Analyses Data Set Development Disease Dose Drug Combinations Drug Targeting Epidermal Growth Factor Receptor Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor Etiology Evaluation Experimental Models FDA approved Failure Funding Future Gene Targeting Genes Genetic Genomics Goals Head and Neck Cancer Head and Neck Squamous Cell Carcinoma Housing Human Development In Situ In Situ Hybridization In Vitro Larynx Legal patent Life Light Malignant Epithelial Cell Malignant Neoplasms Mining Molecular Molecular Analysis Molecular Genetics Molecular Target Mus Mutagenesis Mutation National Cancer Institute New Agents Operative Surgical Procedures Oral cavity Outcome Pathway interactions Patients Pharmaceutical Preparations Pharyngeal structure Prevalence Proteins Quality of life RNA Radiation Resistance Sampling Site Source Survival Rate Testing The Cancer Genome Atlas Therapeutic Therapeutic Agents Tissues Treatment Efficacy Xenograft Model base chemotherapy computerized tools design drug development drug efficacy drug testing effective therapy epigenomics follow-up genetic approach human disease humanized antibody improved in vivo individualized medicine inhibitor/antagonist innovation killings knock-down leukemia neoplastic cell novel strategies pre-clinical precision medicine public health relevance reconstitution repository research study response small molecule success targeted treatment tool tumor

项目摘要

DESCRIPTION (provided by applicant): We propose to better employ existing drugs to define new agents and combinations of agents to treat HNSCC, a disease with unchanged survival rates for four decades in need of new approaches, tools and perspectives. To do so we will combine the advantages of the large dataset in The Cancer Genome Atlas (TCGA) containing genomics, epigenomics and basic outcomes data but including little functional information to support causality in the disease or its treatment efficacy, with a well annotated clinical dataset that uniquely includes functional information on the sensitivity of HNSCC patient tumor cells to a panel of drugs approved or under development for human diseases but not yet applied to HNSCC. Using cutting-edge computational methods, we will mine the TCGA dataset in terms of aberrational gene pathway networks, prioritizing their relevance to HNSCC. We are taking advantage of a high throughput inhibitor assay and computational tools originally showing success in leukemia to design and employ HNSCC-specific inhibitor panels that capture the diversity of aberrational pathways in TCGA to test viable cells from patients' HNSCC tumors to predict effective targeted therapeutic agents and identify the reasons for differential response or resistance to certain drugs among patients. Our dual-PI expertise encompasses creating and using tools of cross-platform data analysis and precision medicine (McWeeney) and repository and specialized cell culture design, biochemical and molecular analysis and orthotopic xenograft models (Kulesz-Martin). Our preliminary results show convergence of pathways and targets from functional analysis of our OHSU dataset with those highly significant from TCGA data, in which 73 pathways are represented on the initial ~120 drug panel, and another 121 pathways, which we call "dark", are not represented. The dark pathways offer a source of innovative targets for creation of a HNSCC-specific inhibitor panel. Our preliminary data show 15 drugs that reach thresholds of efficacy in 7 cases tested. Our preliminary analyses of TCGA with our HNSCC patient datasets annotated by RNASeq and functionally by inhibitor assay identified differential responses to EGFR inhibitors, PI3K inhibitors and other targeted agents. Cases where single agents were ineffective were sensitive to drug combinations with EGFR inhibitors. We propose to pursue more effective therapies for HNSCC as follows: 1) Perform complementary analysis of TCGA data with -omics and functional data on OHSU HNSCC patients' cells to develop HNSCC-specific inhibitor panels and more effectively apply available drugs to HNSCC treatment, and to illuminate "dark pathways" as a source of new targets; 2) Prioritize targets computationally, taking advantage of state-of-the-art bioinformatics tools for cross-platform analysis of TCGA and OHSU data, and validate these in vitro as responsible for patient tumor cell-specific drug (in)sensitivities; and 3) Test predictions in situ in original patent tumors and in vivo using orthotopic xenograft models, relating results to clinical outcomes to define subsets of HNSCC for future molecular predictive tests and tailoring treatment to patient tumor characteristics.

描述（由适用提供）：我们建议更好地使用现有的药物来定义新的药物和药物的组合来治疗HNSCC，这是一种需要新方法，工具和观点的疾病，该疾病具有不变的生存率。为此，我们将结合大型数据集在癌症基因组图集（TCGA）中的优势，其中包含基因组学，表观基因组学和基本结果数据，但几乎没有功能信息来支持疾病或其治疗效率的因果关系，以及良好注释的临床数据集中的临床数据，这些数据与HNSCC患者的敏感性有关，该数据具有良好的临床数据，这些信息包括HNSCC患者的敏感性，该数据均在HNSCC的敏感性中，该数据集中在HNSCC的敏感性中，该数据集中有效，并包括HNSCC的敏感性。尚未应用于HNSCC。使用尖端计算方法，我们将根据畸变基因途径网络挖掘TCGA数据集，并将其与HNSCC相关。我们正在利用高通量抑制剂测定和计算工具，最初在白血病中表现出成功和员工设计和雇员HNSCC特异性抑制剂板，该抑制剂捕获了TCGA中畸变途径的多样性，以测试患者HNSCC肿瘤的活细胞，以预测有效的靶向治疗剂或确定有效的差异反应或确定原因的原因患者中某些药物的抵抗力。我们的双PI专业知识包括创建和使用跨平台数据分析和精密医学（McWeeney）以及存储库以及专业的细胞培养设计，生化和分子分析以及原位异种移植模型（Kulesz-Martin）的工具。我们的初步结果表明，我们的OHSU数据集功能分析的途径和靶标的收敛性以及TCGA数据高度显着的途径，其中最初的〜120个药物面板上表示73个途径，而另外121个我们称为“ Dark”的途径并未表示。黑暗通道为创建HNSCC特异性抑制剂面板的创新目标提供了一种来源。我们的初步数据显示，在7例测试的7例中达到效率阈值的15种药物。我们对RNASEQ注释的HNSCC患者数据集的TCGA进行初步分析，并在功能上通过抑制剂对EGFR抑制剂，PI3K抑制剂和其他靶向药物的差异反应表示了差异反应。单个药物无效的情况对与EGFR抑制剂的药物组合敏感。我们建议对HNSCC寻求更有效的疗法：1）对OHSU HNSCC患者细胞的-OMICS和功能数据进行完整分析，以开发HNSCC特异性抑制剂面板，并更有效地将可用的药物应用于HNSCC治疗，并将“黑暗途径”作为新目标的源头源源； 2）在计算上优先考虑目标，利用最先进的生物信息学工具来对TCGA和OHSU数据进行跨平台分析，并在体外验证这些工具作为患者肿瘤细胞特异性药物（IN）敏感性负责； 3）使用原位异种移植模型在原始专利肿瘤和体内进行测试预测，将结果与临床结果相关，以定义HNSCC的亚群，以实现未来的分子预测测试，并将其定制为患者肿瘤特征。