Nature:癌症免疫疗法靶向实体瘤新突破

2017-05-04 生物谷 生物谷

基于T细胞的免疫疗法对于癌症的治疗提供了巨大的希望:在针对血液癌症的初期试验中已经取得了初步成功。然而,对于实体瘤的治疗来说目前仍然十分困难。最近发表在《Nature》杂志上的一项研究指出,IFN-gamma-T细胞分泌的一类信号分子能够切断肿瘤组织的血液供应,因而对于实体瘤治疗效果具有重要的影响。

基于T细胞的免疫疗法对于癌症的治疗提供了巨大的希望:在针对血液癌症的初期试验中已经取得了初步成功。然而,对于实体瘤的治疗来说目前仍然十分困难。最近发表在《Nature》杂志上的一项研究指出,IFN-gamma-T细胞分泌的一类信号分子能够切断肿瘤组织的血液供应,因而对于实体瘤治疗效果具有重要的影响。

免疫系统是机体抵抗疾病的有力武器,因此科学家们一直在寻找方法利用免疫系统攻击癌症。如今,研究者们找到了合适的方法。举例来说,通过将T细胞从患者体内分离出来,进行一系列的"训练",再导入患者体内,能够有效地杀伤肿瘤细胞。这一技术在初期临床试验中已经取得了成功,但仅仅局限于无法形成肿瘤块的癌症类型,例如血液癌症。

善于抵抗血液癌症,但对抗实体瘤效果不佳

对于体积较大的实体瘤来说,T细胞的靶向治疗效果则有限。尽管将接受训练的T细胞导入血液系统中,但它们难以进入紧密的肿瘤组织间隙。此外,肿瘤细胞通过分泌一系列的抑制因子能够削弱T细胞的效应。

在最近发表于《nature》杂志的一篇文章中,作者研究了T细胞分泌的细胞因子对于肿瘤微环境的影响,包括肿瘤间隙的连接组织以及血管等等。

T细胞不仅能够分泌TNF,而且还能够分泌IFNgamma。直到目前为止,我们对IFNgamma的工作机制还不是很清楚。"我们的确知道TNFgamma能够经过肿瘤的微环境对肿瘤细胞进行杀伤,但我们希望了解究竟哪类细胞是IFNgamma的靶标"。

引发血管收缩

研究者们培育了转基因小鼠,并将其作为肿瘤的研究模型。这类小鼠的血管细胞对于信号分子的敏感度极高。在这一小鼠模型中,IFNgamma能够造成肿瘤组织内部血管的收缩,进而降低其对肿瘤组织的氧气及营养物质的供应,最终杀死肿瘤。因此,研究者们利用这一模型进行了精细的活体观察。他们发现只有血管细胞能够识别这一信号分子。

这一发现解释了该信号分子的强大功能。作者认为:"TNF与IFNgamma结合起来具有巨大的威力。TNF能够使得血管扩张,进而扩大肿瘤组织的体积,而IFNgamma则能够阻断血液的供应,因此延缓肿瘤的增长"。

优化T细胞疗法

这一发现使得研究者们考虑如何提高T细胞免疫疗法在治疗实体瘤上的效果。作者们认为:"我们希望了解T细胞究竟是如何靶向肿瘤细胞的,毕竟摧毁肿瘤组织内部的结构相比杀伤单个的肿瘤细胞可能更为有效"。 

原始出处
Thomas Kammertoens et al, Tumour ischaemia by interferon-γ resembles physiological blood vessel regression, Nature (2017). DOI: 10.1038/nature22311 

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    2017-10-29 liye789132251
  2. [GetPortalCommentsPageByObjectIdResponse(id=1881994, encodeId=e2d61881994a4, content=<a href='/topic/show?id=3b2112532d8' target=_blank style='color:#2F92EE;'>#Nat#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=32, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=12532, encryptionId=3b2112532d8, topicName=Nat)], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=2e6f107, createdName=liye789132251, createdTime=Sun Oct 29 15:16:00 CST 2017, time=2017-10-29, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=197247, encodeId=8fdb19e24720, content=涨见识了,多谢分享, beContent=null, objectType=article, channel=null, level=null, likeNumber=49, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=84e21956153, createdName=1e1263e3m20(暂无匿称), createdTime=Fri May 12 23:28:01 CST 2017, time=2017-05-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=196907, encodeId=2f1c19690e69, content=r干扰素,学习了,谢谢分享, beContent=null, objectType=article, channel=null, level=null, likeNumber=54, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=, createdBy=550d2008262, createdName=luominglian113, createdTime=Fri May 12 07:18:52 CST 2017, time=2017-05-12, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=1724927, encodeId=83a61e249274d, content=<a href='/topic/show?id=9ee758586bb' target=_blank style='color:#2F92EE;'>#新突破#</a>, beContent=null, objectType=article, channel=null, level=null, likeNumber=39, replyNumber=0, topicName=null, topicId=null, topicList=[TopicDto(id=58586, encryptionId=9ee758586bb, topicName=新突破)], attachment=null, authenticateStatus=null, createdAvatar=null, createdBy=792a33728807, createdName=zxxiang, createdTime=Mon May 22 16:16:00 CST 2017, time=2017-05-22, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=196500, encodeId=285219650023, content=学习了,谢谢, beContent=null, objectType=article, channel=null, level=null, likeNumber=2, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/NUyjXTCJjo6KcicAVC1MwuOWcPEsEP4FrvDcicue5V6rMkmXmY73KjYP59vQrEdjU89iaN4ZkOhsItlHkmGTjhV3b5S3zOQA4E0/0, createdBy=cc261682151, createdName=卡圣, createdTime=Thu May 11 07:30:05 CST 2017, time=2017-05-11, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=196412, encodeId=d4bc19641286, content=继续突破的研究, beContent=null, objectType=article, channel=null, level=null, likeNumber=47, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/jW482SpianMayicTRbRZ5RzfPkmqRTH5rHib5mcib8Wb3QdXjhfOK1UubQL2lictjJFdFJ6Lc3cnnCOSWqxXZ4Kk6y5GdzDoy764R/0, createdBy=156e1644785, createdName=doctorJiangchao, createdTime=Thu May 11 06:10:53 CST 2017, time=2017-05-11, status=1, ipAttribution=), GetPortalCommentsPageByObjectIdResponse(id=196298, encodeId=759219629834, content=厉害厉害……学习了, beContent=null, objectType=article, channel=null, level=null, likeNumber=1, replyNumber=0, topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://wx.qlogo.cn/mmopen/ajNVdqHZLLCSvgHPvuQp0g9YLQuk8Dv2tO2J8cA1nBOPWOR46Rv1IMw309iaVI4zibSE52MibbXtA54h46besGYv6vpQfsUHpY9TaCKOVRmm2A/0, createdBy=14511949364, createdName=尹玉伟, createdTime=Wed May 10 17:37:05 CST 2017, time=2017-05-10, status=1, ipAttribution=)]
    2017-05-12 1e1263e3m20(暂无匿称)

    涨见识了,多谢分享

    0

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    2017-05-12 luominglian113

    r干扰素,学习了,谢谢分享

    0

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    2017-05-22 zxxiang
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    2017-05-11 卡圣

    学习了,谢谢

    0

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    2017-05-11 doctorJiangchao

    继续突破的研究

    0

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    2017-05-10 尹玉伟

    厉害厉害……学习了

    0

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