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Spatial Distribution of Tumor Cells in Clear Cell Renal Cell Carcinoma Is Associated with Metastasis and a Matrisome Gene Expression Signature

  • 0Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 2130 W Holcombe Blvd., Houston, TX 77030, USA.
Cancers +

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January 25, 2025

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January 25, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Spatial analysis of clear cell renal cell carcinoma (ccRCC) tumor cell distribution in H&E images predicts metastasis. This approach, using matrisome genes, outperforms standard grading for predicting patient outcomes.

Area Of Science

  • Computational pathology
  • Cancer genomics
  • Renal cell carcinoma research

Background

  • Standard histopathologic examination of clear cell renal cell carcinoma (ccRCC) struggles to predict patient outcomes.
  • Current grading systems (e.g., WHO/ISUP) are insufficient for distinguishing aggressive ccRCC, particularly for grades 2 and 3 tumors.

Purpose Of The Study

  • To develop a novel method for predicting ccRCC behavior using spatial analysis of histopathologic images.
  • To identify spatial patterns and associated molecular signatures that correlate with metastasis and patient survival.

Main Methods

  • Spatial point process modeling was applied to H&E-stained images from 72 ccRCC patients (grades 2 and 3).
  • Differential gene expression analysis was performed between spatially defined patient groups.
  • Validation was conducted on a larger cohort (352 ccRCC patients) from The Cancer Genome Atlas (TCGA).

Main Results

  • Spatial analysis identified two distinct ccRCC groups, one associated with increased spatial randomness and clinical metastasis (p < 0.01).
  • Spatial analysis demonstrated superior predictive power for metastasis compared to standard pathologic grading.
  • A matrisome gene signature was identified in the metastasis-associated group, correlating with extracellular matrix involvement in tumor invasion.
  • Top differentially expressed genes stratified a larger cohort, showing significant differences in survival and TNM stage.

Conclusions

  • The spatial distribution of ccRCC cells in H&E images provides valuable prognostic information.
  • Spatial patterns are linked to metastasis and specific extracellular matrix genes, suggesting their role in aggressive tumor behavior.
  • This computational pathology approach offers a promising tool for improved ccRCC outcome prediction.

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