Klinghammer Konrad, Keller James, George Jonathan, Hoffmann Jens, Chan Edward L, Hayman Michael J
Department of Hematology and Oncology, Charite University Medicine, Berlin, Germany.
Department of Microbiology and Molecular Genetics, Stony Brook University, Stony Brook, NY, 11794.
Int J Cancer. 2018 Jan 1;142(1):156-164. doi: 10.1002/ijc.31045. Epub 2017 Oct 4.
Tyrosine kinase inhibitors are effective treatments for cancers. Knowing the specific kinase mutants that drive the underlying cancers predict therapeutic response to these inhibitors. Thus, the current protocol for personalized cancer therapy involves genotyping tumors in search of various driver mutations and subsequently individualizing the tyrosine kinase inhibitor to the patients whose tumors express the corresponding driver mutant. While this approach works when known driver mutations are found, its limitation is the dependence on driver mutations as predictors for response. To complement the genotype approach, we hypothesize that a phosphoarray platform is equally capable of personalizing kinase inhibitor therapy. We selected head and neck squamous cell carcinoma as the cancer model to test our hypothesis. Using the receptor tyrosine kinase phosphoarray, we identified the phosphorylation profiles of 49 different tyrosine kinase receptors in five different head and neck cancer cell lines. Based on these results, we tested the cell line response to the corresponding kinase inhibitor therapy. We found that this phosphoarray accurately informed the kinase inhibitor response profile of the cell lines. Next, we determined the phosphorylation profiles of 39 head and neck cancer patient derived xenografts. We found that absent phosphorylated EGFR signal predicted primary resistance to cetuximab treatment in the xenografts without phosphorylated ErbB2. Meanwhile, absent ErbB2 signaling in the xenografts with phosphorylated EGFR is associated with a higher likelihood of response to cetuximab. In summary, the phosphoarray technology has the potential to become a new diagnostic platform for personalized cancer therapy.
酪氨酸激酶抑制剂是治疗癌症的有效方法。了解驱动潜在癌症的特定激酶突变可预测对这些抑制剂的治疗反应。因此，当前的个性化癌症治疗方案包括对肿瘤进行基因分型以寻找各种驱动突变，随后为肿瘤表达相应驱动突变的患者个体化使用酪氨酸激酶抑制剂。虽然当发现已知的驱动突变时这种方法有效，但其局限性在于依赖驱动突变作为反应的预测指标。为了补充基因分型方法，我们假设磷酸化蛋白质阵列平台同样能够实现激酶抑制剂治疗的个性化。我们选择头颈部鳞状细胞癌作为癌症模型来检验我们的假设。使用受体酪氨酸激酶磷酸化蛋白质阵列，我们确定了五种不同头颈部癌细胞系中49种不同酪氨酸激酶受体的磷酸化谱。基于这些结果，我们测试了细胞系对相应激酶抑制剂治疗的反应。我们发现这种磷酸化蛋白质阵列准确地反映了细胞系的激酶抑制剂反应谱。接下来，我们确定了39个源自头颈部癌患者的异种移植瘤的磷酸化谱。我们发现，在没有磷酸化ErbB2的异种移植瘤中，缺乏磷酸化的EGFR信号预示着对西妥昔单抗治疗的原发性耐药。同时，在具有磷酸化EGFR的异种移植瘤中缺乏ErbB2信号与对西妥昔单抗反应的可能性较高有关。总之，磷酸化蛋白质阵列技术有潜力成为个性化癌症治疗的新诊断平台。
