PO.CL01.14 · 临床研究
胰腺腺癌的空间蛋白-转录组学分析揭示不同恶性亚型相关的免疫和基质功能状态
Spatial proteo-transcriptomic profiling of pancreatic adenocarcinoma unveils distinct malignant subtype-associated immune and stromal functional states
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
胰腺腺癌（PDAC）仍是癌症死亡的主要原因之一。越来越明确的是，肿瘤的空间结构可显著影响治疗反应和预后。大规模空间分析技术的进步实现了对细胞类型和状态的详细原位定位，揭示了与不同临床病理特征相关的多细胞邻域和相互作用。例如，我们此前将全转录组空间分子成像（WT-SMI；约19,000个蛋白编码基因）应用于一个由匹配的化放疗前后PDAC标本组成的前瞻性队列（DF/HCC 18-469），并识别出肿瘤微环境（TME）中一致的治疗诱导性转录改变，这些改变与不同多细胞邻域内特定的配体-受体相互作用相关联。然而，许多免疫细胞群在转录水平上表征不足，采用蛋白质组学能更好地捕捉。为弥补这一空白，我们利用了Bruker Spatial/NanoString Technologies近期商业化的一种多组学SMI平台，该平台能够对单个组织切片同时进行WT和64重蛋白分析，以对一个人类PDAC组织微阵列（TMA）进行分析。转录组水平数据用于分配宽泛的细胞类型标签，蛋白染色则通过应用HieraType指导免疫细胞的嵌套亚型分型。该方法能够在每个细胞覆盖>1000条转录本和>750个独特基因的水平上注释>450,000个细胞，此外还对每个细胞的64个蛋白靶点进行定量染色。利用空间非负矩阵分解，我们为由恶性细胞、基质细胞和免疫细胞组成的不同邻域建立了细胞特征。初步分析提示，不同的转录邻域与不同的生存结局相关。随后，我们利用空间蛋白质组学数据指导对细胞邻域内免疫细胞的功能注释。这实现了对免疫亚型群及其与TME中其他细胞相互作用的详细区分，可能揭示免疫逃逸和治疗耐药的新机制。本研究突显了一种新型空间多组学方法，能够更准确地表征与治疗反应和临床预后相关的关键多细胞邻域。
查看英文原文 English abstract
Pancreatic adenocarcinoma (PDAC) remains one of the leading causes of cancer mortality. It has become increasingly clear that the spatial architecture of tumors can drastically influence treatment response and prognosis. Advances in large-scale spatial profiling have enabled detailed in situ mapping of cell types and states, which has unveiled multicellular neighborhoods and interactions associated with distinct clinicopathologic features. For example, we previously applied whole-transcriptome spatial molecular imaging (WT-SMI; ~19,000 protein-coding genes) to a prospective cohort of matched pre- and post-chemoradiation PDAC specimens (DF/HCC 18-469) and identified consistent treatment-induced transcriptional shifts in the tumor microenvironment (TME) that were linked to specific ligand-receptor interactions within distinct multicellular neighborhoods. However, many immune cell populations are poorly characterized at the transcriptional level and are better captured using proteomics. To address this gap, we leveraged a recently commercialized multi-omic SMI platform developed by Bruker Spatial/NanoString Technologies that enables concurrent WT and 64-plex protein analysis of a single tissue section to profile a human PDAC tissue microarray (TMA). Transcriptome-level data was used to assign broad cell type labels, and protein stains guided nested subtyping of immune cells by applying HieraType. This approach enabled the annotation of >450,000 cells at a coverage of >1000 transcripts and >750 unique genes per cell, in addition to quantitative staining of 64 protein targets per cell. Using spatial non-negative matrix factorization, we established cellular signatures for distinct neighborhoods composed of malignant cells, stromal cells and immune cells. Preliminary analyses suggest that different transcriptional neighborhoods correlate with distinct survival outcomes. We then used spatial proteomics data to guide functional annotation of immune cells within cellular neighborhoods. This enabled detailed differentiation of immune subtype populations and their interactions with other cells in the TME, which may uncover new mechanisms of immune evasion and therapeutic resistance. This study highlights a novel spatial multi-omics approach that enables more accurate characterization of key multicellular neighborhoods associated with treatment response and clinical prognosis.
利益披露 Disclosure
B. W. Awasthi, None..
G. Pozo Mattos Pereira, None..
N. Caldwell, None..
M. Madhavan, None..
J. Bae, None.