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

描述（由申请人提供）：头颈部鳞状细胞癌（HNSCC）是全球第六大常见癌症，尽管采用了多种综合治疗方式，治愈率仅为50%。靶向治疗的表皮生长因子受体（EGFR）提高了一部分患者的生存率，尽管敏感性的分子预测因子目前还难以捉摸。此外，对疾病有反应的患者往往会产生耐药性，并最终死于疾病。在细胞信号传导过程的复杂串扰和随机进化压力中，区分驱动这种治疗耐药性的特定分子过程需要从序列数据中建立动态模型。因此，在本应用中，我们开发了新的计算算法，从西妥昔单抗耐药HNSCC的体内和体外模型中推断西妥昔单抗耐药的分子机制。具体来说，我们将研究以下假设：(1)短期时间过程数据提高了硅模型技术推断西妥昔单抗靶向和脱靶信号反应的能力；(2)慢性暴露于西妥昔单抗后，HNSCC细胞的表观遗传、转录后和基因组变化导致获得性耐药；(3)建立个体间和个体内异质性模型，将揭示在HNSCC细胞系异种移植模型中，激活驱动体内获得性西妥昔单抗耐药性的特定细胞信号传导过程。该项目的结果将最终有助于选择西妥昔单抗治疗的患者和替代分子靶点，以克服获得性西妥昔单抗耐药性。所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。

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.