Illuminating molecular targetable pathways in HNSCC

阐明 HNSCC 的分子靶向途径

• 批准号: 9116154
• 负责人: MOLLY F. KULESZ-MARTIN
• 金额: $ 50.45万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 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/9116154
• 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; Health; Housing; Human; 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; Tissues; Treatment Efficacy; Xenograft Model; base; chemotherapy; computerized tools; design; drug development; drug efficacy; drug testing; effective therapy; epigenomics; follow-up; genetic approach; genomic data; human disease; humanized antibody; improved; in vivo; individualized medicine; inhibitor/antagonist; innovation; killings; knock-down; leukemia; molecular targeted therapies; neoplastic cell; novel strategies; pre-clinical; precision medicine; reconstitution; repository; research study; response; small molecule inhibitor; success; targeted agent; targeted treatment; tool; tumor

 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（由申请人提供）：我们建议更好地利用现有药物来定义治疗HNSCC的新药物和药物组合，HNSCC是一种四十年来生存率不变的疾病，需要新的方法，工具和观点。为此，我们将联合收割机结合癌症基因组图谱（TCGA）中大型数据集的优势，该数据集包含基因组学、表观基因组学和基本结果数据，但几乎不包含支持疾病因果关系或其治疗疗效的功能信息，具有良好注释的临床数据集，该数据集唯一地包括关于HNSCC患者肿瘤细胞对一组已批准或正在开发的用于人类的药物的敏感性的功能信息。疾病，但尚未应用于HNSCC。使用尖端的计算方法，我们将在异常基因通路网络方面挖掘TCGA数据集，优先考虑它们与HNSCC的相关性。我们正在利用最初在白血病中显示成功的高通量抑制剂测定和计算工具来设计和采用HNSCC特异性抑制剂组，其捕获TCGA中异常途径的多样性，以测试来自患者的HNSCC肿瘤的活细胞，从而预测有效的靶向治疗剂，并确定差异反应或特异性抑制剂的原因。 患者对某些药物的耐药性。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准医学（McWeeney）以及存储库和专业细胞培养设计，生化和分子分析以及原位异种移植模型（Kulesz-Martin）的工具。我们的初步结果显示，我们的OHSU数据集的功能分析的途径和靶点与TCGA数据中高度显著的途径和靶点的收敛，其中73条途径在初始~120种药物面板上表示，另外121条途径，我们称之为“暗”，没有表示。暗通路为创建HNSCC特异性抑制剂组提供了创新靶点的来源。我们的初步数据显示，15种药物在7个测试病例中达到了疗效阈值。我们对TCGA与我们的HNSCC患者数据集的初步分析通过RNASeq注释，并通过抑制剂测定在功能上鉴定了对EGFR抑制剂、PI 3 K抑制剂和其他靶向药物的差异反应。单一药物无效的病例对EGFR抑制剂联合用药敏感。我们建议对HNSCC进行更有效的治疗，具体如下：1）对TCGA数据与OHSU HNSCC患者细胞的组学和功能数据进行互补分析，以开发HNSCC特异性抑制剂组，并更有效地将可用药物应用于HNSCC治疗，并阐明“黑暗通路”作为新靶点的来源; 2）利用最先进的生物信息学工具对TCGA和OHSU数据进行跨平台分析，在计算上对靶点进行优先级排序，并在体外验证这些靶点对患者肿瘤细胞特异性药物敏感性的影响;和3）使用原位异种移植物模型在原始专利肿瘤中原位和体内测试预测，将结果与临床结果相关联以定义HNSCC的子集用于未来的分子预测测试和针对患者肿瘤特征定制治疗。