Nat Commun:重磅!癌症患者的福音“细胞称重法”可以帮助医生选择癌症药物

2017-11-20 白木清水 来宝网

医生有很多治疗多发性骨髓瘤的药物,这是一种血癌。然而,没有办法通过基因标记或其他方法来预测病人对某一种药物的反应。由于这种药物不起作用,这可能会导致几个月的治疗不起作用。

医生有很多治疗多发性骨髓瘤的药物,这是一种血癌。然而,没有办法通过基因标记或其他方法来预测病人对某一种药物的反应。由于这种药物不起作用,这可能会导致几个月的治疗不起作用。

麻省理工学院的研究人员已经表明,他们可以使用一种新的测量方法来预测药物将如何影响从多发性骨髓瘤患者中取出的癌细胞。此外,他们还表明,他们的预测与那些患者在服用这些药物时的实际情况有关联。

麻省理工学院生物工程和机械工程系Andrew and Erna Viterbi教授,麻省理工学院Koch研究所的成员Scott Manalis在综合癌症研究说,这种类型的测试可以帮助医生根据药物暴露后癌细胞生长速率的测量值预测药物反应。

“对于传染病来说,基于细胞增殖的抗生素易感性检测已经非常有效了几十年了,”Manalis说。与细菌不同的是,对肿瘤细胞的类似测试具有挑战性,部分原因是细胞在从病人身上移除时并不总是增殖。我们开发的测量不需要增殖。

Manalis是这项研究的资深作者,该研究发表在11月20日的《自然通讯》杂志上。这篇论文的主要作者是马克·史蒂文斯(Mark Stevens),他是位于达纳-法伯癌症研究所(dana - farber Cancer Institute)的科赫研究所(Koch Institute)的访问科学家,以及前麻省理工学院(MIT)博士后的阿瑞斯·塞廷(Arif Cetin)。

预测响应

研究人员的新策略是基于Manalis和他实验室的其他人在过去几年里开发的技术来称重细胞。他们的设备,被称为悬浮微通道谐振器(SMR),可以比任何其他技术更精确地测量细胞质量,比其他任何技术都要精确10到100倍,这使得研究人员能够精确地计算单个细胞在短时间内的生长速率。

该设备的最新版本,每小时可以测量50到100个细胞,由一系列的SMR传感器组成,这些传感器通过微小的通道来称重细胞。在20分钟的时间里,每个细胞都有10倍的重量,这足以测量一个准确的MAR测量值。

几年前,Manalis和他的同事们开始采用这种技术来预测癌症药物如何影响肿瘤细胞的生长。他们去年表示,测量细胞获得质量的质量累积速率(MAR)可以揭示药物的易感性。药物治疗后的MAR下降意味着这些细胞对药物敏感,但如果它们具有耐药性,则在MAR中没有变化。

在这项新的研究中,研究人员与来自达纳-法伯癌症研究所的Nikhil Munshi合作,对多发性骨髓瘤患者的肿瘤细胞进行了多种药物测试。然后他们将结果与患者服用这些药物后发生的情况进行了比较。对于每个病人,他们追踪了细胞对三种不同药物的反应,以及几种药物的组合。他们发现,在所有的9个病例中,他们的数据与病人所见的结果相符,这是由血液中发现的临床蛋白生物标志物来衡量的,这些生物标志物是医生用来确定一种药物是否正在杀死肿瘤细胞的。

“当临床生物标志物显示病人应该对药物敏感时,我们也看到了我们的测量灵敏度。”然而,在病人有耐药性的情况下,我们在临床生物标志物以及我们的测量中都看到了这一点,”史蒂文斯说。

个性化医疗

治疗多发性骨髓瘤的困难之一是选择多种药物。患者通常对第一轮治疗反应良好,但最终会复发,因此医生必须选择另一种药物。然而,没有办法预测哪种药物对那个特定的病人最好。

在一种情况下,研究人员设想他们的传感器将在疾病复发时使用,当肿瘤可能对特定疗法产生耐药性时。

“在复发的时候,我们会从病人身上进行骨髓活组织检查,我们会分别测试每一种治疗方法,或者是在临床中使用的组合。”在这一点上,我们可以告诉临床医生,这个病人最敏感或最抗拒的治疗或组合疗法是什么,”史蒂文斯说。

骨髓活检通常会产生数量有限的肿瘤细胞,在这一研究中仅检测到5万个肿瘤细胞,但这一技术足以测试许多不同的药物和药物组合。麻省理工学院的研究人员已经成立了一家公司,开始了一项更大规模的临床研究,以验证这种方法,他们计划研究使用这种技术治疗其他类型的癌症的可能性。

原始出处:

Arif E. Cetin, Mark M. Stevens, Nicholas L. Calistri, et al. Determining therapeutic susceptibility in multiple myeloma by single-cell mass accumulation. Nature Communications 8, Article number: 1613 (2017), doi:10.1038/s41467-017-01593-2.

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    2018-07-25 liuli5079
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    2018-02-02 一叶知秋
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    2017-11-22 xxxx1054
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