bioRxivCancer BiologyPreprint
Cell type-centric interaction networks define spatial architecture of intrahepatic cholangiocarcinoma
Hsin-Pei Lee, Meng LiuiD, Wenqi WuiD, Ngoc Thao Lam Nguyen, Jittiporn ChaisaingmongkoliD, Darko CastveniD, Ethan Levy, Noémi KedeiiD, Maria Olga HernandeziD, Manjari KunduiD, Marshonna ForguesiD, Man‐Hsin HungiD, Anuradha BudhuiD, Nada AlaniiD, Stephen M. HewittiD, Ross G. LakeiD, Eytan RuppiniD, Stanley LipkowitziD, Xin Wei WangiD, Jens Uwe MarquardtiD, Mathuros RuchirawatiD, Lichun MaiD
Abstract
Tumor spatial organization critically shapes disease progression and therapeutic response, yet remains poorly defined. Intrahepatic cholangiocarcinoma (iCCA), a rare and aggressive liver malignancy with extensive stromal and immune remodeling, provides a compelling model to study tumor architecture. We generated a single-cell spatial atlas of 1 million cells from 131 iCCA patients using 53-plex spatial proteomics. To systemically characterize tumor spatial organization, we developed a graph-based deep learning framework to define cell type-centric interaction networks, identifying 41 distinct multicellular spatial patterns. Integration of these networks revealed higher-order tumor- and immune-enriched microenvironments associated with patient outcomes. Notably, neutrophil-associated tumor-enriched and tumor-desert microenvironments delineated patient groups with opposing clinical outcomes and distinct neutrophil states. These findings were validated by single-cell spatial transcriptomic profiling of 6 million cells from 162 iCCA patients. Together, this study defines the spatial architecture of iCCA and provides a comprehensive resource for exploring tumor spatial organization.
- organism: human
- assay: spatial transcriptomics
- assay: proteomics
- research focus: cell-cell communication
- research focus: spatial domain detection
- approach: deep learning
- approach: graph neural network
- Citations
- 0
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