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患者来源的获得性耐药模型可识别出针对癌症的有效联合用药方案。

Patient-derived models of acquired resistance can identify effective drug combinations for cancer.

作者信息

Crystal Adam S, Shaw Alice T, Sequist Lecia V, Friboulet Luc, Niederst Matthew J, Lockerman Elizabeth L, Frias Rosa L, Gainor Justin F, Amzallag Arnaud, Greninger Patricia, Lee Dana, Kalsy Anuj, Gomez-Caraballo Maria, Elamine Leila, Howe Emily, Hur Wooyoung, Lifshits Eugene, Robinson Hayley E, Katayama Ryohei, Faber Anthony C, Awad Mark M, Ramaswamy Sridhar, Mino-Kenudson Mari, Iafrate A John, Benes Cyril H, Engelman Jeffrey A

机构信息

Massachusetts General Hospital Cancer Center, Department of Medicine and Harvard Medical School, Boston, MA 02114, USA.

Dana-Farber Cancer Institute, Department of Biological Chemistry and Molecular Pharmacology and Harvard Medical School, Boston, MA 02115, USA. Chemical Kinomics Research Center, Korea Institute of Science and Technology, Seoul, 136-791, South Korea.

出版信息

Science. 2014 Dec 19;346(6216):1480-6. doi: 10.1126/science.1254721. Epub 2014 Nov 13.

Abstract

Targeted cancer therapies have produced substantial clinical responses, but most tumors develop resistance to these drugs. Here, we describe a pharmacogenomic platform that facilitates rapid discovery of drug combinations that can overcome resistance. We established cell culture models derived from biopsy samples of lung cancer patients whose disease had progressed while on treatment with epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors and then subjected these cells to genetic analyses and a pharmacological screen. Multiple effective drug combinations were identified. For example, the combination of ALK and MAPK kinase (MEK) inhibitors was active in an ALK-positive resistant tumor that had developed a MAP2K1 activating mutation, and the combination of EGFR and fibroblast growth factor receptor (FGFR) inhibitors was active in an EGFR mutant resistant cancer with a mutation in FGFR3. Combined ALK and SRC (pp60c-src) inhibition was effective in several ALK-driven patient-derived models, a result not predicted by genetic analysis alone. With further refinements, this strategy could help direct therapeutic choices for individual patients.

摘要

靶向癌症疗法已产生了显著的临床反应,但大多数肿瘤会对这些药物产生耐药性。在此,我们描述了一个药物基因组学平台,该平台有助于快速发现能够克服耐药性的药物组合。我们建立了源自肺癌患者活检样本的细胞培养模型,这些患者在接受表皮生长因子受体(EGFR)或间变性淋巴瘤激酶(ALK)酪氨酸激酶抑制剂治疗时病情进展,然后对这些细胞进行基因分析和药理筛选。鉴定出了多种有效的药物组合。例如,ALK和丝裂原活化蛋白激酶(MEK)抑制剂的组合在一个发生了MAP2K1激活突变的ALK阳性耐药肿瘤中具有活性,而EGFR和成纤维细胞生长因子受体(FGFR)抑制剂的组合在一个具有FGFR3突变的EGFR突变耐药癌症中具有活性。联合抑制ALK和SRC(pp60c-src)在多个ALK驱动的患者来源模型中有效,这一结果仅靠基因分析无法预测。通过进一步完善,该策略可有助于指导针对个体患者的治疗选择。

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