Illuminating molecular targetable pathways in HNSCC

阐明 HNSCC 的分子靶向途径

• 批准号: 9753710
• 负责人: Shannon K. McWeeney
• 金额: $ 48.94万
• 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 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9753710
• 关键词: Affect; Algorithms; Antibodies; Biochemical; Biochemical Genetics; Bioinformatics; Biological Assay; Candidate Disease Gene; Cell Culture Techniques; Cell Survival; Cells; Cetuximab; Characteristics; Clinical; Clinical Trials; Computer Simulation; Computing Methodologies; 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 Abnormality; Gene Targeting; Genes; Genetic; Genomics; Goals; Head and Neck Cancer; Head and Neck Squamous Cell Carcinoma; Human; In Situ; In Situ Hybridization; In Vitro; Larynx; Legal patent; Light; Malignant Epithelial Cell; Malignant Neoplasms; Mathematics; 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; bioinformatics tool; chemotherapy; computerized tools; design; drug development; drug efficacy; drug testing; effective therapy; epigenomics; experimental study; follow-up; genetic approach; genomic data; human disease; humanized antibody; improved; in vivo; individualized medicine; inhibitor/antagonist; innovation; knock-down; leukemia; molecular targeted therapies; neoplastic cell; novel strategies; off-patent; pre-clinical; precision medicine; predictive test; preservation; public health relevance; reconstitution; repository; response; small molecule inhibitor; success; targeted agent; targeted treatment; tool; transcriptome sequencing; 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(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40年来存活率保持不变的疾病，需要新的方法、工具和视角。为此，我们将结合癌症基因组图谱(TCGA)中的大型数据集的优势，该数据集包含基因组学、表观基因组学和基本结果数据，但包括很少的功能信息来支持疾病的因果关系或其治疗效果，以及一个注释良好的临床数据集，其中唯一包括HNSCC患者肿瘤细胞对一组已批准或正在开发但尚未应用于HNSCC的药物的敏感性的功能信息。使用尖端计算方法，我们将挖掘异常基因途径网络方面的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成功展示的高通量抑制物分析和计算工具来设计和使用HNSCC特异性抑制物面板，捕捉TCGA中异常通路的多样性，以测试患者HNSCC肿瘤中的活细胞，以预测有效的靶向治疗药物，并确定差异反应或 患者对某些药物的抗药性。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确医学工具(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)。我们的初步结果显示，来自OHSU数据集功能分析的通路和靶点与来自TCGA数据的高度有意义的通路和靶点收敛，其中73条通路在最初的~120药物面板上被代表，另有121条通路，我们称之为“暗”，没有被代表。暗通道为创建HNSCC特异性抑制物小组提供了创新靶点。我们的初步数据显示，在7个测试病例中，有15种药物达到了疗效阈值。我们用RNAseq注释的HNSCC患者数据集和通过抑制剂试验对TCGA进行的初步分析发现，TCGA对EGFR抑制剂、PI3K抑制剂和其他靶向药物的反应存在差异。单一药物无效的病例对与EGFR抑制剂联合用药敏感。我们建议寻求更有效的治疗HNSCC的方法如下：1)将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特异性抑制剂小组，并更有效地将现有药物应用于HNSCC治疗，并阐明作为新靶点来源的“暗通路”；2)利用最先进的生物信息学工具对TCGA和HSU数据进行跨平台分析，并在体外验证这些药物对患者肿瘤细胞特异性药物(IN)的敏感性；以及3)使用原位异种移植模型在原始专利肿瘤和体内进行原位预测，将结果与临床结果联系起来，为未来的分子预测测试确定HNSCC亚型，并根据患者的肿瘤特征进行量身定制的治疗。

项目成果

IL-10 and integrin signaling pathways are associated with head and neck cancer progression.

• DOI: 10.1186/s12864-015-2359-6
• 发表时间: 2016-01-08
• 期刊: BMC genomics
• 影响因子: 4.4
• 作者: Bornstein S;Schmidt M;Choonoo G;Levin T;Gray J;Thomas CR Jr;Wong M;McWeeney S
• 通讯作者: McWeeney S