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天文学与病理学相结合,确定癌症免疫治疗的预测性生物标志物 精选

已有 2865 次阅读 2021-6-12 15:38 |个人分类:新科技|系统分类:海外观察

天文学与病理学相结合,确定癌症免疫治疗的预测性生物标志物

诸平 

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Strong parallels between multispectral analyses in astronomy and emerging multiplexing platforms for pathology.

The next generation of tissue-based biomarkers are likely to be identified by use of large, well-curated datasets. To that end, image analysis approaches originally developed for astronomy were applied to pathology specimens to produce trillions of pixels of robust tissue imaging data and facilitate assay and atlas development.

IMAGES: (BENEATH “ASTRONOMY”) SLOAN DIGITAL SKY SURVEY; (MICROSCOPE) AKOYA BIOSCIENCE


据美国约翰·霍普金斯医学院Johns Hopkins Medicine2021610日提供的消息,约翰霍普金斯大学马克基金会高级基因组学和成像中心(The Mark Foundation Center for Advanced Genomics and Imaging at Johns Hopkins University )、布隆伯格-金梅尔癌症免疫治疗研究所(Bloomberg~Kimmel Institute for Cancer Immunotherapy)的研究人员将天空映射算法(sky-mapping algorithms)与癌症活检的高级免疫荧光成像相结合,开发了一个强大的平台——AstroPath,通过预测哪些癌症会产生反应来指导免疫治疗针对免疫系统的特定疗法。这个名为 AstroPath 的新平台将天文图像分析和映射与病理标本相结合,以分析肿瘤的显微图像。相关研究结果于2021611日已经在《科学》(Science)杂志网站发表——Sneha Berry, Nicolas A. Giraldo, Benjamin F. Green, Tricia R. Cottrell, Julie E. Stein, Elizabeth L. Engle, Haiying Xu, Aleksandra Ogurtsova, Charles Roberts, Daphne Wang, Peter Nguyen, Qingfeng Zhu, Sigfredo Soto-Diaz, Jose Loyola, Inbal B. Sander, Pok Fai Wong, Shlomit Jessel, Joshua Doyle, Danielle Signer, Richard Wilton, Jeffrey S. Roskes, Margaret Eminizer, Seyoun Park, Joel C. Sunshine, Elizabeth M. Jaffee, Alexander Baras, Angelo M. De Marzo, Suzanne L. Topalian, Harriet Kluger, Leslie Cope, Evan J. Lipson, Ludmila Danilova, Robert A. Anders, David L. Rimm, Drew M. Pardoll, Alexander S. Szalay, Janis M. Taube. Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade. Science, 11 Jun 2021; 372 (6547): eaba2609. DOI: 10.1126/science.aba2609

参与此项研究除了来自约翰霍普金斯大学马克基金会高级基因组学和成像中心的研究人员和来自约翰霍普金斯大学布隆伯格-金梅尔癌症免疫治疗研究所之外,还有来自约翰霍普金斯大学悉尼金梅尔综合癌症中心(Sidney Kimmel Comprehensive Cancer Center)、约翰霍普金斯大学医学院肿瘤学系、病理学系、皮肤科、放射科、外科学系;耶鲁大学医学院(Yale University School of Medicine)病理学系、医学系肿瘤内科以及约翰霍普金斯大学物理与天文学系的研究人员。

免疫荧光成像使用带有荧光标签的抗体,使研究人员能够同时观察多种细胞蛋白质并确定它们的表达模式和强度。研究人员应用AstroPath 研究了黑色素瘤,这是一种侵袭性皮肤癌。他们通过检查肿瘤块内癌细胞内部和周围的免疫细胞来表征黑色素瘤活检中的免疫微环境,然后确定了一种复合生物标志物,其中包括6个标志物,并且高度预测对特定类型的称为抗 PD-1 治疗(anti-PD-1 therapy)的免疫疗法的反应。

PD-1即程序性细胞死亡1(programmed cell death 1)是一种在免疫系统 T 细胞上发现的蛋白质,当它与另一种称为程序性死亡配体(programmed death ligand简称PD-L1)的蛋白质结合时,可帮助癌细胞逃避免疫系统的攻击。抗 PD-1 药物阻断 PD-1 蛋白,可以帮助免疫系统看到并杀死癌细胞。研究人员解释说,只有一些黑色素瘤患者对抗 PD-1 疗法有反应,预测反应或抵抗的能力对于为每个患者的癌症选择最佳治疗方法至关重要。AstroPath 平台也被应用于肺癌研究,并有可能为许多其他癌症提供治疗指导。该研究团队由彭博~金梅尔研究所皮肤病学教授兼肿瘤微环境实验室联合主任 Janis Taube 医学博士和约翰霍普金斯大学IDIESInstitute for Data Intensive Engineering and Science)所长Alexander Szalay博士领导。

彭博~金梅尔癌症免疫疗法研究所所长 Drew Pardoll 医学博士说:这个平台有可能改变肿瘤学家提供癌症免疫疗法的方式。在过去的40年里,癌症的病理学分析一次只检查一个标志物,提供的信息有限。比目前通过常规病理学可获得的单一活检相比,利用该新技术,包括同时对多达12个标志物进行成像的仪器,AstroPath成像算法提供了1000倍的信息内容。这有助于精确的癌症免疫疗法——识别每个患者癌症的独特特征,以预测谁会对给定的免疫疗法(例如抗 PD-1)产生反应,而谁又不会对免疫疗法(例如抗 PD-1)产生反应。该研究于611日已经在《科学》杂志网站上发表。

AstroPath平台的基础是图像分析技术,它为斯隆数字巡天(Sloan Digital Sky Survey)创建了数据库,斯隆数字巡天是由约翰霍普金斯大学布隆伯格物理学、天文学和计算机科学杰出教授、天体物理学家Alexander S. Szalay设计的大型宇宙数字地图。天空调查将数十亿天体的数百万张望远镜图像拼接在一起,每个图像都表达了不同的特征——就像用于染色肿瘤活检的抗体上的不同荧光标签一样。使用大型专用计算机处理数万亿像素的成像数据,这些对象的位置和特征存储在大型开放数据库中。该数据库用于量化恒星、类星体,正如斯隆调查在天文尺度上绘制宇宙图一样,约翰霍普金斯大学医学院皮肤病学系皮肤病理学主任Janis M. TaubeAlexander S. Szalay合作,在微观尺度上绘制肿瘤和免疫细胞图。

AstroPath使用来自Akoya Biosciences的多重免疫荧光(multiplex immunofluorescence简称mIF) 技术——用不同颜色的荧光分子标记每个感兴趣的蛋白质——来量化肿瘤微环境 ( tumor microenvironment 简称TME) 的许多细胞和分子特征。AstroPath的天体映射算法分析由mIF成像产生的数百万个细胞的庞大数据集,并将多个荧光图像”“缝合在一起。这在安装在具有单细胞分辨率的显微载玻片上的整个组织切片上创建了TME的二维多色视觉图,并使研究人员能够详细了解肿瘤细胞与周围组织相互作用的方式和位置,包括免疫系统。

Janis M. Taube 说:肿瘤内不同类型细胞的空间排列很重要。基于直接接触以及局部分泌的因素,细胞相互发出通过/不通过信号。量化表达特定蛋白质的细胞之间的接近程度有可能揭示这些位置相互作用是否可能发生,以及哪些相互作用可能导致抑制免疫细胞杀死肿瘤。

Alexander S. Szalay 说:在天文学中,我们经常问,'星系彼此靠近的概率是多少?我们对癌症应用相同的方法——观察肿瘤微环境中的空间关系,这是一个完全不同的问题。

在目前的研究中,研究人员使用AstroPath平台来表征晚期黑色素瘤患者肿瘤标本中癌细胞和免疫细胞上PD-1PD-L1的表达,这些患者随后接受了抗PD-1免疫治疗。他们还可视化了由不同类型免疫细胞表达的另外3种蛋白质(CD8CD163FOXP3),以及最终肿瘤细胞本身的标记物Sox10/S100

研究人员发现,这些标志物在肿瘤特定细胞上的特定表达模式和强度可以强烈预测哪些患者在抗PD-1治疗后会产生反应并存活下来。

Alexander S. Szalay 说:大数据正在改变科学。从天文学到基因组学再到海洋学可以说应用无处不在。数据密集型科学发现是一种新范式。我们面临的技术挑战是如何在大规模收集数据时获得一致、可重复的结果?AstroPath是朝着建立通用标准迈出的一步。

Janis M. Taube 说:接下来是重要的步骤。我们需要多机构研究表明这些测试可以标准化,然后进行前瞻性临床试验,将AstroPath的下一代诊断潜力带到患者护理中。除了开发新的伴随诊断外,该团队的长期目标还包括构建肿瘤免疫图谱的开源图谱open-source atlas),类似于美国国家癌症研究所的癌症基因组图谱。

马克癌症研究基金会(Mark Foundation for Cancer Research)首席执行官 Michele Cleary 说:天文学中先进绘图技术的应用有可能识别出预测性生物标志物,这些生物标志物将帮助医生为个体癌症患者设计精确的免疫疗法。这些早期结果令人兴奋并验证了该方法,我们马克癌症研究基金会很自豪能够支持这种开创性的科学研究。”上述介绍仅供参考,欲了解更多信息敬请注意浏览原文或者相关报道

Astronomy accelerates tumor imaging

Immunohistochemical stains for individual markers revolutionized diagnostic pathology decades ago but cannot capture enough information to accurately predict response to immunotherapy. Newer multiplex immunofluorescent technologies provide the potential to visualize the expression patterns of many functionally relevant molecules but present numerous challenges in accurate image analysis and data handling, particularly over large tumor areas. Drawing from the field of astronomy, in which petabytes of imaging data are routinely analyzed across a wide spectral range, Berry et al. developed a platform for multispectral imaging of whole-tumor sections with high-fidelity single-cell resolution. The resultant AstroPath platform was used to develop a multiplex immunofluorescent assay highly predictive of responses and outcomes for melanoma patients receiving immunotherapy.

Science, aba2609, this issue p. eaba2609

Structured Abstract

INTRODUCTION

New therapies have been designed to stimulate the host’s immune system to fight cancer. Despite these exciting, recent successes, a large proportion of patients still do not respond to anti–programmed cell death-1 (PD-1) or anti–programmed death ligand-1 (PD-L1) therapies, and thus, biomarkers for patient selection are highly desirable. The only U.S. Food and Drug Administration–approved histopathology biomarker tests for anti–PD-1 or anti–PD-L1 therapy is assessment of PD-L1 protein expression by means of immunohistochemistry. This approach is unidimensional and has limitations. Innovative characterization of the tumor microenvironment (TME) with a focus on multidimensional, spatially resolved interactions at the single-cell level will provide critical mechanistic insights into therapeutic responses and potentially identify improved biomarkers for patient selection. Using multispectral approaches to image the TME and substituting cells for stars and galaxies, we applied the methodology and infrastructure developed for astronomy to pathologic analysis of specimens from patients with melanoma.

RATIONALE

The next generation of pathologic analyses will require platforms that can characterize the coexpression of key molecules on specific cellular subsets in situ and spatial relationships between tumor cells and multiple immune elements. To that aim, we applied astronomical algorithms for high-quality imaging and the establishment of relational databases to multiplex immunofluorescence (mIF) labeling of pathology specimens, facilitating spatial analyses and immunoarchitectural characterization of the host-tumor interface. In all, we curated and coordinately mapped six markers, both individually and in combination in tumor tissue from 98 patients with melanoma receiving anti–PD-1 therapy. This dataset comprised ~127,400 image mosaics composed of more than 100 million single cells. The data outputs were linked to patient outcomes, informing in a clinically relevant way how cancer evades the immune system and potentiating biomarker assay development for precision immunotherapy.

RESULTS

The imaging protocols used in this study were used to address outstanding questions regarding the impact of high-power field sampling strategies on biomarker performance. This information was then used to develop an approach for operator-independent field selection. The image handling strategies also facilitated the robust assessment of the intensity of PD-1 and PD-L1 expression in situ (negative, low, mid, and high levels) on different cell types. Thus, with only six markers (PD-1, PD-L1, CD8, FoxP3, CD163, and Sox10/S100), we were able to develop 41 combinations of expression patterns for these molecules and map relatively rare cells such as CD8+FoxP3+ cells to the tumor stromal boundary. Moreover, a high density of CD8+FoxP3+PD-1low/mid cells was closely associated with response to PD-1 blockade. Cell types associated with a lack of response to therapy were also identified—for example, CD163+ macrophages that were PD-L1. This latter phenotype was also found to have a negative effect on long-term survival. When these and other key cell phenotype densities were combined, they were highly predictive of objective response and stratified long-term patient outcomes after anti–PD-1–based therapies in both a discovery cohort and an independent validation cohort.

CONCLUSION

Here, we present the AstroPath platform, an end-to-end pathology workflow with rigorous quality control for creating quantitative, spatially resolved mIF datasets. Although the current effort focused on a six-plex mIF assay, the principles described here provide a general framework for the development of any multiplex assay with single-cell image resolution. Such approaches will vastly improve the standardization and scalability of these technologies, enabling cross-site and cross-study comparisons. This will be essential for multiplex imaging technologies to realize their potential as biomarker discovery platforms and ultimately as standard diagnostic tests for clinical therapeutic decision-making.

 



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