A Comparison of Methods for Studying the Tumor Microenvironment's Spatial Heterogeneity in Digital Pathology
Ines Panicou Nearchou1, Daniel Alexander Soutar2, Hideki Ueno3
1School of Medicine, University of St Andrews, St Andrews, Scotland, UK.
Journal of Pathology Informatics
|May 20, 2021
Summary
Spatial statistics reveal intratumor heterogeneity in colorectal cancer (CRC), identifying hotspots of lymphocytes and tumor buds. This analysis aids in understanding tumor progression and developing prognostic risk models for better patient outcomes.
Area of Science:
- Oncology
- Spatial Statistics
- Cancer Research
Background:
- The tumor microenvironment's heterogeneity influences colorectal cancer (CRC) progression and patient outcomes.
- Tumor-infiltrating lymphocytes and tumor budding are known prognostic factors in CRC.
- The spatial distribution of these features within the tumor immune microenvironment (TIME) and its prognostic significance remain underexplored.
Purpose of the Study:
- To evaluate intratumor heterogeneity in the spatial distribution of CD3+, CD8+ lymphocytes, and tumor buds in Stage II CRC.
- To develop novel spatial statistical methodologies for quantifying intratumor heterogeneity.
- To create prognostic risk models based on spatial patterns within the TIME.
Main Methods:
- Applied Getis-Ord hotspot analysis to identify regions of high and low feature density.
- Developed a novel spatial heatmap methodology accounting for interpatient and intratumor heterogeneity.
- Analyzed 232 cases of Stage II colorectal cancer.
Main Results:
- Characterized spatial intratumor heterogeneity of lymphocytes and tumor buds.
- Developed two new highly prognostic risk models utilizing spatial analysis data.
- Identified significant spatial patterns correlating with patient prognosis.
Conclusions:
- Spatial statistics are valuable for assessing intratumor heterogeneity in CRC.
- Getis-Ord hotspot analysis and spatial heatmap methods are broadly applicable to other tissues and features.
- Understanding spatial heterogeneity can reveal novel aggressive phenotypes and potential therapeutic targets.
