获得耐药性的患者衍生模型可以识别癌症治疗的有效药物组合
Adam S Crystal1, Alice T Shaw1, Lecia V Sequist1
1Massachusetts General Hospital Cancer Center, Department of Medicine and Harvard Medical School, Boston, MA 02114, USA.
概括
研究人员开发了一个药物遗传学平台,以发现克服癌症治疗抵抗力的药物组合. 这种方法确定了针对针对性治疗耐药的肺癌患者的有效组合.
科学领域:
- 在瘤学瘤学.
- 药物基因组学 药物基因组学
- 药物发现 药物发现 药物发现
背景情况:
- 向癌症疗法显示出临床反应,但经常面临瘤耐药性.
- 对表皮生长因子受体 (EGFR) 和形淋巴瘤激酶 (ALK) 抑制剂的耐药性是一个重要的临床挑战.
研究的目的:
- 建立一个药物基因组学平台,快速发现克服获得性耐药性的药物组合.
- 为了确定有效的药物组合患者衍生肺癌模型与获得的耐药性.
主要方法:
- 从耐受EGFR或ALK抑制剂的肺癌患者中建立的细胞培养模型.
- 在这些模型上进行了基因分析和广泛的药理查.
- 在患者衍生异种移植模型中验证的药物组合.
主要成果:
- 确定了多种有效的药物组合,包括ALK和MEK抑制剂用于MAP2K1-突变耐药瘤.
- 发现EGFR和FGFR抑制剂组合在FGFR3突变耐药肺癌中有效.
- 发现ALK和SRC联合抑制在ALK驱动模型中有效,独立于遗传预测.
结论:
- 药物基因组平台可以快速识别克服耐药性的药物组合.
- 这一策略有望指导个体癌症患者的治疗选择.
- 联合的抑制策略可能对克服复杂的抵抗机制至关重要.
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