Dynamical Models of Cetuximab Resistance in HNSCC Based on Serial Genomics Data

基于系列基因组数据的 HNSCC 西妥昔单抗耐药动态模型

• 批准号: 9325461
• 负责人: Elana Judith Fertig
• 金额: $ 33.62万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-16 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9325461
• 关键词: Algorithms; Apoptosis; Apoptotic; Award; Binding; Biological Models; Cell Death; Cell Line; Cell Proliferation; Cell Signaling Process; Cell Survival; Cetuximab; Chronic; Cisplatin; Clinical; Clinical Trials; Combined Modality Therapy; Complex; Computational algorithm; Computer Simulation; DNA Methylation; Data; Development; Disease; Dose; Epidermal Growth Factor Receptor; Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor; Epigenetic Process; Exposure to; Gene Expression; Generations; Genetic Transcription; Genomics; Head and Neck Squamous Cell Carcinoma; Heterogeneity; Human papilloma virus infection; In Vitro; Individual; Link; Malignant Epithelial Cell; Malignant Neoplasms; Measurement; Measures; MicroRNAs; Modeling; Molecular; Molecular Profiling; Molecular Target; Oncogenic; Pathway interactions; Patient Selection; Patients; Pattern; Pharmacology; Process; Radiation; Resistance; Resistance development; Risk Factors; Sampling; Series; Signal Pathway; Signal Transduction; Silicon; Staining method; Stains; Techniques; Therapeutic; Therapeutic Uses; Time; Treatment Efficacy; Western Blotting; Xenograft Model; Xenograft procedure; alcohol exposure; base; biomarker development; chemotherapy; epigenomics; flexibility; genomic data; improved; in vivo; in vivo Model; molecular dynamics; novel; patient subsets; pressure; public health relevance; resistance mechanism; response; targeted agent; targeted treatment; therapeutic target; therapy resistant; tobacco exposure; treatment response; tumor

DESCRIPTION (provided by applicant): Head and neck squamous cell carcinoma (HNSCC) is the sixth most frequent cancer worldwide, with only a 50% cure rate in spite of combined treatment modalities. Therapeutic targeting of the epidermal growth factor receptor (EGFR) improves the survival in a subset of patients, although molecular predictors of sensitivity are currently elusive. Moreover, responsive patients often acquire resistance and ultimately succumb to their disease. Distinguishing the specific molecular processes that drive such therapeutic resistance amid complex cross-talk in cell signaling processes and stochastic evolutionary pressures requires dynamical models built from serial data. Therefore, in this application, we develop novel computational algorithms to infer the molecular mechanisms underlying cetuximab resistance from in vitro and in vivo model of cetuximab resistant HNSCC. Specifically, we will investigate the hypotheses that: (1) short-term time course data improve the ability of in silicon modeling techniques to infer both on- and off-target signaling responses to cetuximab; (2) combined epigenetic, post-transcriptional, and genomic changes in HNSCC cells upon chronic exposure to cetuximab result in acquired resistance; and (3) modeling inter and intra-individual heterogeneity will discern the specific cellular signaling processes that are activated to drive in vivo acquired cetuximab resistance in cell- line xenograft models of HNSCC. The results from this project will ultimately contribute to the selection of patients for cetuximab treatment and alternative molecular targets to overcome acquired cetuximab resistance. The algorithms developed will also be directly applicable to inference of molecular drivers of therapeutic resistance in additional cancers.

描述(申请人提供)：头颈部鳞状细胞癌(HNSCC)是全球第六大最常见的癌症，尽管进行了综合治疗，但治愈率仅为50%。针对表皮生长因子受体(EGFR)的治疗靶向改善了部分患者的存活率，尽管目前对敏感性的分子预测尚不明确。此外，有反应的患者往往会产生抵抗力，并最终屈服于他们的疾病。在细胞信号传递过程中的复杂串扰和随机进化压力中，区分驱动这种治疗耐药的特定分子过程需要从系列数据建立动力学模型。因此，在这一应用中，我们开发了新的计算算法来从西妥昔单抗耐药的HNSCC的体外和体内模型中推断西妥昔单抗耐药的分子机制。具体地说，我们将调查如下假设：(1)短期时间进程数据提高了电子计算机模拟技术中推断西妥昔单抗靶上和靶外信号反应的能力；(2)HNSCC细胞在长期暴露于西妥昔单抗后的表观遗传、转录后和基因组变化导致获得性耐药性；以及(3)对个体间和个体内的异质性进行建模，将在HNSCC细胞系异种移植模型中识别被激活以驱动体内获得的西妥昔单抗耐药性的特定细胞信号过程。该项目的结果最终将有助于西妥昔单抗治疗患者的选择和克服获得性西妥昔单抗耐药性的替代分子靶点。开发的算法也将直接适用于推断其他癌症治疗耐药的分子驱动因素。