Clinical responses to ERK inhibition in BRAF(V600E)-mutant colorectal cancer predicted using a computational model.
Clinical responses to ERK inhibition in BRAF(V600E)-mutant colorectal cancer predicted using a computational model.
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DOI：
10.1038/s41540-017-0016-1
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发表时间：
2017
影响因子：
4
通讯作者：
Ramanujan S
中科院分区：
文献类型：
--
作者：
Kirouac DC;Schaefer G;Chan J;Merchant M;Orr C;Huang SA;Moffat J;Liu L;Gadkar K;Ramanujan S
大约10%的结直肠癌携带BRAF V600 E突变，其组成性激活MAPK信号通路。我们试图根据西妥昔单抗（EGFR）、维罗非尼（BRAF）、考比替尼（MEK）和GDC-0994（ERK）联合治疗的体外（细胞系）和体内（细胞和患者来源的异种移植物）研究数据，确定含ERK抑制剂（GDC-0994）的方案是否对这些患者有临床益处。临床前数据用于开发基于机制的计算模型，该模型将细胞表面受体（EGFR）激活、MAPK信号通路和肿瘤生长联系起来。通过使用来自三项I期临床试验的肿瘤缓解数据，对EGFR、BRAF和MEK抑制剂的组合进行临床预测。对GDC-0994单一疗法的模拟响应（总响应率= 17%）准确地预测了来自I期临床试验的关于响应患者的数量（2/18）和肿瘤大小变化的分布的结果（“瀑布图”）。然后使用前瞻性模拟来评估潜在的药物组合和预测生物标志物，以增加这些患者对MEK/ERK抑制剂的反应性。虽然癌症药物的开发依赖于实验肿瘤模型进行测试，但在这些系统中观察到的结果往往无法转化为临床结果。Kirouac等人展示了计算系统建模如何帮助弥合这一鸿沟。他们专注于一类预后不良的结直肠癌（具有BRAF癌基因突变形式的结直肠癌），开发了一种通过细胞信号转导将药物暴露与肿瘤生长联系起来的数学模型。通过对来自多个细胞系和小鼠模型的实验数据进行三角测量，以及相关药物的三项临床试验结果，该模型准确地预测了在ERK抑制剂GDC-0994的首次人体研究中观察到的肿瘤缩小。然后使用模拟来探索通过组合、替代给药方案和预测性生物标志物来增加这类药物（MAPK通路抑制剂）活性的策略，以指导未来的临床研究。扩展到其他癌症类型和药物，该方法可以简化早期临床开发。
Approximately 10% of colorectal cancers harbor BRAF V600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes (“waterfall plot”). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients. While cancer drug development relies on experimental tumor models for testing, results observed in these systems often fail to translate clinically. Kirouac et al. demonstrate how computational systems modelling can help bridge this divide. Focusing on a class of colorectal cancers with poor prognosis (those with a mutant form of the BRAF oncogene) they develop a mathematical model linking drug exposure, via cellular signal transduction, to tumor growth. By triangulating experimental data from multiple cell lines and mouse models, with results from three clinical trials of related drugs, the model accurately predicted tumor shrinkage observed in a first-in-human study of GDC-0994, an ERK inhibitor. Simulations were then used to explore strategies for increasing the activity of this class of drugs (MAPK pathway inhibitors) via combinations, alternate dosing regimens, and predictive biomarkers to guide future clinical studies. Extended to other cancer types and drugs, the approach could streamline early clinical development.