Nature解读 | 人类肿瘤图谱协作网络研究报告,探索乳腺癌转移的时间和空间演变

文摘   2024-11-21 15:18   上海  

  研究癌症恶性程度的空间和时间演变,可以为掌握肿瘤如何发展、如何逃避人体免疫系统以及如何耐药和复发提供至关重要的线索。2018年以来,由美国国家癌症研究所资助的抗癌登月计划项目之一人类肿瘤图谱协作网络一直致力于汇集全球科学家共同绘制整合癌症演变期间各种肿瘤细胞、分子和组织学特征的三维图谱。该协作网络由全球十大研究中心组成,共同致力于构建用于提取、分析和可视化癌症多维数据的工具,该综合方法旨在阐明导致癌症发生、发展和治疗耐药的潜在生物学过程。昨天,英国《自然》正刊联合旗下《自然医学》《自然癌症》《自然方法》《生物学通讯》发表十三篇研究报告,自豪地展示了这个非凡网络提供的工具、数据集和见解。

The Human Tumor Atlas Network (HTAN): exploring tumor evolution in time and space
  • Tumour evolution and microenvironment interactions in 2D and 3D space
  • Temporal recording of mammalian development and precancer
  • Progressive plasticity during colorectal cancer metastasis
  • A multi-modal single-cell and spatial expression map of metastatic breast cancer biopsies across clinicopathological features
  • Differential chromatin accessibility and transcriptional dynamics define breast cancer subtypes and their lineages
  • Defining the transcriptional lineages of breast cancer subtypes
  • Global loss of promoter-enhancer connectivity and rebalancing of gene expression during early colorectal cancer carcinogenesis
  • A 3D genome view of colon cancer initiation
  • Multiomic analysis of familial adenomatous polyposis reveals molecular pathways associated with early tumorigenesis
  • Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics
  • Quality control for single-cell analysis of high-plex tissue profiles using CyLinter
  • MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation
  • Consensus tissue domain detection in spatial omics data using multiplex image labeling with regional morphology (MILWRM)

  2024年10月30日,英国《自然》旗下《自然医学》在线发表美国哈佛大学、麻省理工学院、布罗德研究院、达纳法伯癌症研究院、斯坦福大学、阿斯利康研发中心、弗吉尼亚大学、霍华德休斯医学研究院、布莱根医院和波士顿妇女医院、基因泰克、德国慕尼黑大学、以色列巴尔伊兰大学、本古里安大学、耶路撒冷希伯来大学人类肿瘤图谱协作网络研究报告,利用转移性乳腺癌活检标本,采用多种方法绘制出不同临床病理特征的多状态单细胞和空间表达分布图谱。

  该研究首先对60例转移性乳腺癌患者67个活检标本的全部核糖核酸分子进行基因转录组分析,显示哪些基因在某个时间点处于活跃状态。随后,采用两种不同的方法,要么测量细胞的整个转录组,要么只测量细胞核的转录组。不过,这些方法可能导致有关细胞空间组织的信息丢失。为了获取这些信息,该研究还为其中15个标本绘制空间表达特征图谱,并用多达四种不同空间分辨方法分析肿瘤组织连续切片苏木精伊红染色标本。


  利用这个全面的数据集,能够比较各种方法,并且揭示共同点和不同点。该研究结果可以帮助科学家将来为研究课题选择最合适的方法。这些转移性乳腺癌来自全身九个不同部位,包括乳房、大脑、颈部、胸壁、肺部、腋窝、肝脏、皮肤和骨骼,并且代表不同的临床相关亚型。虽然该方法一定程度可能限制单个变量的统计学意义,但是仍然发现了有关转移细胞生物学的有趣线索,包括有关哪些类型细胞出现于转移癌、某些基因如何被激活于这些细胞以及转移癌细胞空间排列方式。例如,令人惊讶的是患者体内恶性细胞表现出非常稳定的表达特征,从一些患者进行不止一次活检的表达特征非常相似,即使转移灶位于身体不同部位或者标本采集时间相隔200天。相反,患者与患者之间存在极大差异。

  该研究还发现某些临床特征与不同恶性表达表型之间存在联系:大多数恶性细胞表现出上皮细胞的典型特征。然而,一些标本可见干细胞、神经元或软骨相关基因表达。其中,具有干细胞样行为的标本来自一例患者,虽然早期诊断并且合理治疗,但是生存时间最短。此外,具有软骨样行为的标本唯一具有罕见且通常难以治疗的乳腺癌典型组织学特征。

  转移通常由癌细胞(宿主组织天然细胞)以及可能对抗癌细胞的迁移免疫细胞组成。一个令人兴奋的发现是,肿瘤细胞的表达谱根据其附近是否有免疫细胞(例如巨噬细胞、T淋巴细胞、自然杀伤细胞)而不同。例如,与同一活检组织附近有免疫细胞的肿瘤细胞相比,似乎将免疫细胞排除于附近之外的肿瘤细胞更有可能表达SOX4基因,这进一步证明免疫逃逸过程也发生于局部邻近区域,而非仅仅肿瘤范围之内。

  因此,该研究为转移性乳腺癌的生物学特征提供了新视角,并展示了空间表达特征分析的巨大潜力。从长远来看,该研究结果可能有助于对乳腺癌患者进行更细致的分类,并制定更有针对性的治疗方法。

  对此,英国《自然医学》编辑部发表同期报道:通过单细胞和空间分析绘制转移性乳腺癌的复杂性和多样性


Nat Med. 2024 Oct 30. IF: 58.7

A multi-modal single-cell and spatial expression map of metastatic breast cancer biopsies across clinicopathological features.

Klughammer J, Abravanel DL, Segerstolpe A, Blosser TR, Goltsev Y, Cui Y, Goodwin DR, Sinha A, Ashenberg O, Slyper M, Vigneau S, Jané-Valbuena J, Alon S, Caraccio C, Chen J, Cohen O, Cullen N, DelloStritto LK, Dionne D, Files J, Frangieh A, Helvie K, Hughes ME, Inga S, Kanodia A, Lako A, MacKichan C, Mages S, Moriel N, Murray E, Napolitano S, Nguyen K, Nitzan M, Ortiz R, Patel M, Pfaff KL, Porter CBM, Rotem A, Strauss S, Strasser R, Thorner AR, Turner M, Wakiro I, Waldman J, Wu J, Gómez Tejeda Zanudo J, Zhang D, Lin NU, Tolaney SM, Winer EP, Boyden ES, Chen F, Nolan GP, Rodig SJ, Zhuang X, Rozenblatt-Rosen O, Johnson BE, Regev A, Wagle N.

Broad Institute of Harvard and MIT, Cambridge, MA, USA; Dana-Farber Cancer Institute, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard University, Cambridge, MA, USA; Stanford University School of Medicine, Stanford, CA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA; AstraZeneca R&D, Boston, MA, USA; University of Virginia, Charlottesville, VA, USA; Howard Hughes Medical Institute, Chevy Chase, MD, USA; Brigham and Women's Hospital, Boston, MA, USA; Genentech, Inc., South San Francisco, CA, USA; Ludwig Maximilians Universitat München, Munich, Germany; Bar-Ilan University, Ramat Gan, Israel; Ben-Guiron University, Beersheba, Israel; The Hebrew University of Jerusalem, Jerusalem, Israel.

Although metastatic disease is the leading cause of cancer-related deaths, its tumor microenvironment remains poorly characterized due to technical and biospecimen limitations. In this study, we assembled a multi-modal spatial and cellular map of 67 tumor biopsies from 60 patients with metastatic breast cancer across diverse clinicopathological features and nine anatomic sites with detailed clinical annotations. We combined single-cell or single-nucleus RNA sequencing for all biopsies with a panel of four spatial expression assays (Slide-seq, MERFISH, ExSeq and CODEX) and H&E staining of consecutive serial sections from up to 15 of these biopsies. We leveraged the coupled measurements to provide reference points for the utility and integration of different experimental techniques and used them to assess variability in cell type composition and expression as well as emerging spatial expression characteristics across clinicopathological and methodological diversity. Finally, we assessed spatial expression and co-localization features of macrophage populations, characterized three distinct spatial phenotypes of epithelial-to-mesenchymal transition and identified expression programs associated with local T cell infiltration versus exclusion, showcasing the potential of clinically relevant discovery in such maps.

PMID: 39478111

DOI: 10.1038/s41591-024-03215-z







Nat Med. 2024 Oct 30. IF: 58.7

Mapping metastatic breast cancer complexity through single-cell and spatial profiling.

PMID: 39478112

DOI: 10.1038/s41591-024-03308-9、























智惠学苑
医疗专业人士在线学术交流平台,传递最新ITP(原发免疫性血小板减少症)、血液肿瘤、实体瘤等医学研究进展,分享前沿资讯。
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