Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.
Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.
Genome Med. 2018 May 23;10(1):37. doi: 10.1186/s13073-018-0545-2.
Targeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients' treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors.
To determine the dynamics of these molecular changes, we obtained high throughput omics data (RNA-sequencing and DNA methylation) weekly during the development of cetuximab resistance in a head and neck cancer in vitro model. The CoGAPS unsupervised algorithm was used to determine the dynamics of the molecular changes associated with resistance during the time course of resistance development.
CoGAPS was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically.
Genes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize the resistant phenotype. These genes include FGFR1, which was associated with EGFR inhibitors resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. This understanding of the time course progression of molecular changes in acquired resistance is important for the development of alternative treatment strategies that would introduce appropriate selection of new drugs to treat cancer before the resistant phenotype develops.
靶向治疗通过特异性阻断肿瘤发生相关基因编码的蛋白活性来发挥作用。然而，大多数癌症会产生耐药性，且很少能观察到长期疾病缓解。了解导致获得性耐药的分子变化的时间进程，可以优化患者的治疗选择。临床上，只能在耐药肿瘤的单个时间点研究获得性治疗耐药。
为了确定这些分子变化的动态，我们在头颈部癌症的体外模型中每周获取高通量组学数据（RNA 测序和 DNA 甲基化），以确定在获得性西妥昔单抗耐药过程中与耐药相关的分子变化的动态。使用 CoGAPS 无监督算法来确定耐药发展过程中耐药相关分子变化的动态。
CoGAPS 用于量化不断变化的转录和表观遗传变化。将 PatternMarker 统计应用于 CoGAPS 的结果，使我们能够对这些时间序列组学数据的动态进行基于热图的新可视化。我们证明转录变化源于即时治疗反应或耐药性，而表观遗传改变仅发生在耐药时。综合分析表明，DNA 甲基化的变化相对于转录发生延迟，表明耐药性在表观遗传上得到稳定。
与耐药相关的表观遗传改变的基因，如果与表达变化一致，则假设可以稳定耐药表型。这些基因包括之前与 EGFR 抑制剂耐药相关的 FGFR1。因此，综合组学分析可以区分耐药的分子驱动因素的时间。了解获得性耐药中分子变化的时间进程对于开发替代治疗策略非常重要，这些策略将在耐药表型出现之前引入适当的新药选择来治疗癌症。
