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表皮生长因子受体激活调控的基因表达谱及其与头颈部鳞状细胞癌西妥昔单抗耐药的关系。

Gene expression signatures modulated by epidermal growth factor receptor activation and their relationship to cetuximab resistance in head and neck squamous cell carcinoma.

机构信息

Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.

出版信息

BMC Genomics. 2012 May 1;13:160. doi: 10.1186/1471-2164-13-160.

Abstract

BACKGROUND

Aberrant activation of signaling pathways downstream of epidermal growth factor receptor (EGFR) has been hypothesized to be one of the mechanisms of cetuximab (a monoclonal antibody against EGFR) resistance in head and neck squamous cell carcinoma (HNSCC). To infer relevant and specific pathway activation downstream of EGFR from gene expression in HNSCC, we generated gene expression signatures using immortalized keratinocytes (HaCaT) subjected to ligand stimulation and transfected with EGFR, RELA/p65, or HRASVal12D.

RESULTS

The gene expression patterns that distinguished the HaCaT variants and conditions were inferred using the Markov chain Monte Carlo (MCMC) matrix factorization algorithm Coordinated Gene Activity in Pattern Sets (CoGAPS). This approach inferred gene expression signatures with greater relevance to cell signaling pathway activation than the expression signatures inferred with standard linear models. Furthermore, the pathway signature generated using HaCaT-HRASVal12D further associated with the cetuximab treatment response in isogenic cetuximab-sensitive (UMSCC1) and -resistant (1CC8) cell lines.

CONCLUSIONS

Our data suggest that the CoGAPS algorithm can generate gene expression signatures that are pertinent to downstream effects of receptor signaling pathway activation and potentially be useful in modeling resistance mechanisms to targeted therapies.

摘要

背景

表皮生长因子受体(EGFR)下游信号通路的异常激活被认为是头颈部鳞状细胞癌(HNSCC)中西妥昔单抗(一种针对 EGFR 的单克隆抗体)耐药的机制之一。为了从 HNSCC 的基因表达推断 EGFR 下游相关和特定的信号通路激活,我们使用经配体刺激和转染 EGFR、RELA/p65 或 HRASVal12D 的永生化角质形成细胞(HaCaT)生成基因表达特征。

结果

使用马尔可夫链蒙特卡罗(MCMC)矩阵分解算法 Coordinated Gene Activity in Pattern Sets(CoGAPS)推断了区分 HaCaT 变体和条件的基因表达模式。与使用标准线性模型推断的表达特征相比,这种方法推断的基因表达特征与细胞信号通路激活的相关性更高。此外,使用 HaCaT-HRASVal12D 生成的途径特征与同种型西妥昔单抗敏感(UMSCC1)和耐药(1CC8)细胞系中的西妥昔单抗治疗反应进一步相关。

结论

我们的数据表明,CoGAPS 算法可以生成与受体信号通路激活的下游效应相关的基因表达特征,并且可能有助于模拟针对靶向治疗的耐药机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25f1/3460736/f7faecd1828b/1471-2164-13-160-1.jpg

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