⚡ Quick Summary
This study presents a comprehensive spatial atlas of intrahepatic cholangiocarcinoma (iCCA), utilizing advanced artificial intelligence-assisted spatial multiomics to decode the tumor microenvironment (TME). The findings reveal critical spatial features that correlate with patient prognosis and potential immunotherapy responses.
🔍 Key Details
- 📊 Dataset: 155 samples analyzed using various spatial multiomics techniques
- 🧩 Techniques used: Imaging mass cytometry, spatial proteomics, spatial transcriptomics, multiplex immunofluorescence, single-cell RNA sequencing, bulk RNA-seq, and bulk proteomics
- 🔬 Total cells resolved: Over 1.06 million
- 🏆 Key findings: Identification of five spatial subtypes with distinct prognoses
- 🤖 Technology: Deep learning system for prognosis prediction from a single 1-mm² tumor sample
🔑 Key Takeaways
- 🌟 Spatial topology of the TME significantly correlates with iCCA patient prognosis.
- 🦠 CD163hi M2-like macrophages suppress anti-tumor immunity, leading to poorer survival outcomes.
- 🔍 Five distinct spatial subtypes of iCCA were identified, each with unique prognostic implications.
- 💡 Potential therapeutic options were generated for the identified spatial subtypes.
- 📈 Deep learning system developed to predict prognosis with high accuracy from minimal tumor sample size.
- 🌍 Study conducted by a collaborative team of researchers, enhancing our understanding of iCCA.
📚 Background
Intrahepatic cholangiocarcinoma (iCCA) is a challenging malignancy characterized by poor prognosis and limited treatment options. Understanding the tumor microenvironment (TME) is crucial, as it plays a significant role in tumor progression and response to therapy. However, the spatial characteristics of the TME in iCCA have remained largely unexplored, necessitating innovative approaches to decode this complex ecosystem.
🗒️ Study
This study aimed to create a detailed spatial atlas of iCCA using a combination of artificial intelligence and various spatial multiomics techniques. The researchers analyzed a diverse dataset, including imaging mass cytometry, spatial proteomics, and single-cell RNA sequencing, to elucidate the spatial interactions within the TME and their implications for patient outcomes.
📈 Results
The analysis revealed that the spatial arrangement of cells within the TME is intricately linked to patient prognosis. Specifically, the presence of CD163hi M2-like macrophages was found to inhibit anti-tumor immune responses, correlating with decreased survival rates. Additionally, the identification of five spatial subtypes of iCCA, each associated with distinct prognostic outcomes, highlights the potential for tailored therapeutic strategies.
🌍 Impact and Implications
The insights gained from this study could significantly enhance our understanding of iCCA and its TME. By leveraging advanced technologies such as deep learning, clinicians may soon be able to predict patient outcomes more accurately and develop personalized treatment plans. This research paves the way for future studies aimed at improving therapeutic interventions and patient care in iCCA.
🔮 Conclusion
This study underscores the importance of spatial characteristics in the TME of iCCA, revealing critical insights that could lead to improved prognostic assessments and personalized treatment strategies. The integration of AI and multiomics represents a promising frontier in cancer research, with the potential to transform how we approach treatment for complex malignancies like iCCA.
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Spatial single-cell proteomics landscape decodes the tumor microenvironmental ecosystem of intrahepatic cholangiocarcinoma.
Abstract
BACKGROUND AIMS: The prognoses and therapeutic responses of patients with intrahepatic cholangiocarcinoma (iCCA) depend on spatial interactions among tumor microenvironment (TME) components. However, the spatial TME characteristics of iCCA remain poorly understood. The aim of this study was to generate a comprehensive spatial atlas of iCCA using artificial intelligence-assisted spatial multiomics patterns and to identify spatial features associated with prognosis and immunotherapy.
APPROACH RESULTS: Spatial multiomics, including imaging mass cytometry (IMC, n=155 in-house), spatial proteomics (n=155 in-house), spatial transcriptomics (n=4 in-house), multiplex immunofluorescence (mIF, n=20 in-house), single-cell RNA sequencing (scRNA-seq, n=9 in-house and n=34 public), bulk RNA-seq (n=244 public), and bulk proteomics (n=110 in-house and n=214 public), were employed to elucidate the spatial TME of iCCA. More than 1.06 million cells were resolved, and the findings revealed that spatial topology, including cellular deposition patterns, cellular communities, and intercellular communications, profoundly correlates with the prognosis of iCCA patients. Specifically, CD163hi M2-like resident-tissue macrophages suppress anti-tumor immunity by directly interacting with CD8+ T cells, resulting in poorer patient survival. Additionally, five spatial subtypes with distinct prognoses were identified, and potential therapeutic options were generated for these subtypes. Furthermore, a spatial TME deep learning system was developed to predict the prognosis of iCCA patients with high accuracy from a single 1-mm2 tumor sample.
CONCLUSIONS: This study offers preliminary insights into the spatial TME ecosystem of iCCA, providing valuable foundations for precise patient classification and the development of personalized treatment strategies.
Author: [‘Hong L’, ‘Mei J’, ‘Sun X’, ‘Wu Y’, ‘Dong Z’, ‘Jin Y’, ‘Gao L’, ‘Cheng J’, ‘Tian W’, ‘Liu C’, ‘Li B’, ‘Hu P’, ‘Liu L’, ‘Xin S’, ‘Dai X’, ‘Zhao P’, ‘Guo R’, ‘Chen M’, ‘Yun J’, ‘Lin B’, ‘Wei W’, ‘Fang W’, ‘Bao X’]
Journal: Hepatology
Citation: Hong L, et al. Spatial single-cell proteomics landscape decodes the tumor microenvironmental ecosystem of intrahepatic cholangiocarcinoma. Spatial single-cell proteomics landscape decodes the tumor microenvironmental ecosystem of intrahepatic cholangiocarcinoma. 2025; (unknown volume):(unknown pages). doi: 10.1097/HEP.0000000000001283