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  • 《自然》子刊:新技术让癌症的治疗更具针对性

    发布时间:2021年09月14日 15:44:24 来源:振东健康网

    《自然》子刊:新技术让癌症的治疗更具针对性

    资讯作者:University College London

    编辑翻译:奇奇


    本文献于2021年9月8日发表在国际顶级生物医学期刊Nature Protocols上。文中伦敦大学学院的研究人员开发了一种新可以检测癌症治疗效果的新技术,该技术能帮助临床医生为癌症患者制定最佳治疗策略。

    伦敦大学学院的科学家们开发了一项新技术,该技术可以研究哪种疗法对实体癌患者有效。研究人员表示,该工具可以快速检测肿瘤组织对不同疗法(如化学疗法、免疫疗法或放射疗法)的反应,临床医生可以利用该工具为特定患者制定最佳疗法。

    与成纤维细胞(红色)和巨噬细胞免疫细胞(白色)共培养的类器官(绿色)的显微镜图像。

    目前,医生们很难知道肿瘤患者对哪种治疗会有反应,所以在找到一种治疗有效的方法之前,可能会尝试几种不同的治疗方法。

    发表在Nature Protocols上的这项技术突破是建立在该团队之前的工作基础上。2020年,他们开发了一种方法,可以同时测量实验室生长的肿瘤中数百万不同细胞的行为和相互作用。

    该研究为突变的癌细胞如何“模仿正常健康细胞表达的生长信号”提供了新的见解,这种模拟信号让癌细胞不受控制地生长。

    在这项新的研究中,他们利用患者的细胞培植了称为类器官的微型肿瘤,这些肿瘤是在实验室中通过将癌症干细胞嵌入胶原蛋白中生长的。每个微型肿瘤都像是一个病人的“化身”,可以在实验室里进行研究。伦敦大学学院的团队已经将他们的原始技术进行了改进,现在他们一次可以研究成百上千个病人的微型肿瘤。这使得研究人员可以试验许多不同的抗癌药物,以探索个体的肿瘤可能产生的反应。

    该研究的主要作者Chris Tape博士(伦敦大学学院癌症研究所)说:“筛查平台使我们能够观察癌细胞以及健康的免疫细胞、成纤维细胞和基质细胞,并观察它们如何相互反应——因此我们可以模拟单个患者的癌症表现。”

    “通过用不同种类的癌症治疗方法治疗微型肿瘤,筛查工具还允许我们观察癌症细胞和健康细胞如何反应(两者同等重要),并可以确定哪种治疗方法对患者最有效。” 

    虽然还需要通过临床试验进一步验证,但研究人员希望这一新工具能够改变实体癌症(结肠直肠癌、肝癌、乳腺癌和脑癌)患者的治疗选择。

    Tape博士补充说:“我们长期的愿景是将该工具用作实体癌治疗的临床决策的‘标准’。患者将在手术期间切除肿瘤,而肿瘤细胞将被送到实验室并长成类器官,然后用这种新技术对不同的治疗方案进行测试,并以单细胞分辨率进行分析。然后实验室会将研究结果反馈给治疗患者的临床医生,说明患者对哪种药物反应最好。”


    英语原文


    Predicting Whether Patients Will Respond To Cancer Treatment Is A Step Closer

    A new technology that can study which therapies will work on patients with solid cancerous tumors has been developed by scientists at UCL. Researchers say the tool, which can rapidly test tumorous tissue against different treatments, such as chemotherapy, immunotherapy or radiotherapy, could be used by clinicians to pinpoint the best therapy for a particular patient.

    Currently it is difficult for doctors to know which treatment a patient will respond to, so several different therapies may be tried before one works.

    The technological breakthrough, published in Nature Protocols, builds on the team's previous work. In 2020 they developed a method that can simultaneously measure the behaviors and interactions of millions of different cells, living inside lab-grown tumors.

    The research provided new insight into how mutated cancer cells “mimic the growth signals” normally expressed by healthy cells—which allows cancer cells to grow unchecked.

    In this new study they have taken patients' cells to develop mini-tumors, known as organoids, which are grown by embedding cancer stem cells in collagen in the lab. Each mini-tumor acts as an individual patient “avatar” that can be studied in the lab. The UCL team have advanced their original technology to now study hundreds and thousands of patient mini-tumors at a time. This allows researchers to trial lots of different anti-cancer drugs to explore how an individual's tumor might respond.

    Lead author Dr. Chris Tape (UCL Cancer Institute) said, “The screening platform enables us to observe cancer cells, alongside the healthy immune, fibroblast and stromal cells, and see how they respond to each other—so we can model how an individual patient's cancer behaves.

    “By treating the mini-tumors with different kinds of cancer treatments, the screening tool also allows us to observe how both the cancer and healthy cells respond—both equally as important—and determine which treatment could work best for a patient.”

    While further validation will be required via clinical trial, researchers are hopeful the new tool could transform therapy selection for people diagnosed with solid cancers including colorectal, liver, breast and brain cancers.

    Dr. Tape added, “Our long-term visionis for the tool to be used as ‘standard’ in clinical decision-making for solid cancer treatments. A patient would have a tumor removed during surgery, the tumor cells would be sent to a laboratory and grown into an organoid avatar, which would then be tested against different therapeutic options and analyzed with single-cell resolution using this new technology. The lab would then relay the findings back to the clinician treating the patient, saying this patient responds best to this drug.”


    参考文献

    Jahangir Sufi et al, Multiplexed single-cell analysis of organoid signaling networks, Nature Protocols(2021). DOI:10.1038/s41596-021-00603-4


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