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评估联合药物癌症化学预防协同作用的新型统计模型的验证。

Validation of a novel statistical model for assessing the synergy of combined-agent cancer chemoprevention.

机构信息

Department of Thoracic/Head and Neck Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, 77030, USA.

出版信息

Cancer Prev Res (Phila). 2010 Aug;3(8):917-28. doi: 10.1158/1940-6207.CAPR-10-0129. Epub 2010 Jul 27.

Abstract

Lung cancer is the leading cause of cancer death, developing over prolonged periods through genetic and epigenetic changes induced and exacerbated by tobacco exposure. Many epigenetic changes, including DNA methylation and histone methylation and acetylation, are reversible. The use of agents that can modulate these aberrations are a potentially effective approach to cancer chemoprevention. Combined epigenetic-targeting agents have gained interest for their potential to increase efficacy and lower toxicity. The present study applied recently developed statistical methods to validate the combined effects of the demethylating agent 5-aza-2-deoxycytidine (5-AZA-CdR, or AZA, or decitabine) and the histone deacetylase inhibitor suberoylanilide hydroxamic acid (SAHA or vorinostat). This validation compared AZA alone with SAHA alone and with their combinations (at later or earlier time points and in varying doses) for inhibiting the growth of cell lines of an in vitro lung carcinogenesis system. This system comprises isogenic premalignant and malignant cells that are immortalized (earlier premalignant), transformed (later premalignant), and tumorigenic human bronchial epithelial cells [immortalized BEAS-2B and its derivatives 1799 (immortalized), 1198 (transformed), and 1170-I (tumorigenic)]. AZA alone and SAHA alone produced a limited (<50%) inhibition of cell growth, whereas combined AZA and SAHA inhibited cell growth more than either agent alone, reaching 90% inhibition under some conditions. Results of drug interaction analyses in the E(max) model and semiparametric model supported the conclusion that drug combinations exert synergistic effects (i.e., beyond additivity in the Loewe model). The present results show the applicability of our novel statistical methodology for quantitatively assessing drug synergy across a wide range of doses of agents with complex dose-response profiles, a methodology with great potential for advancing the development of chemopreventive combinations.

摘要

肺癌是癌症死亡的主要原因,它是通过遗传和表观遗传变化发展而来的,这些变化是由烟草暴露引起和加剧的。许多表观遗传变化,包括 DNA 甲基化和组蛋白甲基化和乙酰化,都是可逆的。使用能够调节这些异常的药物是癌症化学预防的一种潜在有效方法。联合使用表观遗传靶向药物因其提高疗效和降低毒性的潜力而受到关注。本研究应用最近开发的统计方法验证了去甲基化剂 5-氮杂-2-脱氧胞苷(5-AZA-CdR,或 AZA,或地西他滨)和组蛋白去乙酰化酶抑制剂丙戊酰基苯胺羟肟酸(SAHA 或伏立诺他)的联合作用。这种验证比较了 AZA 单独使用、SAHA 单独使用以及它们的组合(在后期或早期时间点,剂量不同)对抑制体外肺癌发生系统的细胞系生长的抑制作用。该系统包括永生(早期前癌)、转化(晚期前癌)和致瘤性人支气管上皮细胞[永生 BEAS-2B 及其衍生物 1799(永生)、1198(转化)和 1170-I(致瘤)]的同基因前癌和恶性细胞。AZA 单独使用和 SAHA 单独使用仅产生有限的(<50%)细胞生长抑制作用,而联合使用 AZA 和 SAHA 比单独使用任何一种药物都能更有效地抑制细胞生长,在某些条件下达到 90%的抑制率。E(max)模型和半参数模型中的药物相互作用分析结果支持药物组合产生协同作用(即在 Loewe 模型中具有加性作用之外)的结论。本研究结果表明,我们的新型统计方法适用于定量评估具有复杂剂量反应曲线的药物在广泛剂量范围内的协同作用,这种方法具有推进化学预防组合开发的巨大潜力。

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