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（由申请人提供）：我们建议更好地利用现有药物来定义新的药物和药物组合来治疗 HNSCC，这种疾病的生存率在 40 年来没有变化，需要新的方法、工具和观点。结合了癌症基因组图谱 (TCGA) 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支持疾病因果关系或其治疗功效的功能信息的大型数据集的优点，以及注释良好的临床数据集，该数据集独特地包括功能关于 HNSCC 患者肿瘤细胞对一组已批准或正在开发的用于人类疾病但尚未应用于 HNSCC 的药物的敏感性信息，我们将使用尖端计算方法挖掘 TCGA 数据集的异常基因通路网络，并优先考虑。我们正在利用最初在白血病中取得成功的高通量抑制剂测定和计算工具来设计和使用 HNSCC 特异性抑制剂组合，捕获 TCGA 中异常通路的多样性来测试活细胞。从患者的 HNSCC 肿瘤中预测有效的靶向治疗药物并确定差异反应的原因或 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) 工具以及存储库和专门的细胞培养设计、生化和分子分析以及原位异种移植模型 (Kulesz-Martin)。我们的初步结果显示，我们的 OHSU 数据集功能分析得出的途径和靶标与 TCGA 数据中的显着性途径和靶标高度一致，其中 73 条途径代表了最初的约 120 种药物我们的初步数据显示，有 15 种药物在 7 种药物中达到了疗效阈值。我们对 HNSCC 患者数据集进行了初步分析，并通过 RNASeq 进行了注释，并通过抑制剂测定进行了功能分析，确定了对 EGFR 抑制剂、PI3K 抑制剂和其他靶向药物的不同反应。无效的患者对与 EGFR 抑制剂的药物组合敏感，我们建议按如下方式寻求更有效的 HNSCC 疗法：1) 对 OHSU HNSCC 患者细胞的组学和功能数据进行 TCGA 数据的补充分析，以开发 HNSCC 特异性抑制剂组和更多地将现有药物有效地应用于 HNSCC 治疗，并阐明“暗通路”作为新靶标的来源 2) 利用最先进的生物信息学对靶标进行计算优先级排序；用于 TCGA 和 OHSU 数据跨平台分析的工具，并在体外验证这些工具对患者肿瘤细胞特异性药物的敏感性；以及 3) 使用原位异种移植模型在原始专利肿瘤中和体内测试预测，将结果与临床结果联系起来，以确定 HNSCC 的子集，用于未来的分子预测测试，并根据患者肿瘤特征定制治疗。

项目成果

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

IL-10 和整合素信号通路与头颈癌的进展相关。

• 发表时间: 2016-01-08
• 影响因子: 4.4
• 作者: Bornstein, Sophia;Schmidt, Mark;Choonoo, Gabrielle;Levin, Trevor;Gray, Joe;Thomas Jr, Charles R;Wong, Melissa;McWeeney, Shannon
• 通讯作者: McWeeney, Shannon