Journal of Pathology Informatics Journal of Pathology Informatics
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Year : 2021  |  Volume : 12  |  Issue : 1  |  Page : 6

A comparison of methods for studying the tumor microenvironment's spatial heterogeneity in digital pathology specimens

1 School of Medicine, University of St Andrews, St Andrews, Scotland, UK
2 School of Computer Science, University of St Andrews, St Andrews, Scotland, UK
3 Department of Surgery, National Defense Medical College, Tokorozawa, Saitama, Japan
4 School of Medicine, University of St Andrews, St Andrews; Lothian University Hospitals, Little France Crescent, Edinburgh, Scotland, UK

Correspondence Address:
Dr. Ines Panicou Nearchou
School of Medicine, University of St Andrews, St Andrews, KY16 9TF
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jpi.jpi_26_20

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Background: The tumor microenvironment is highly heterogeneous, and it is understood to affect tumor progression and patient outcome. A number of studies have reported the prognostic significance of tumor-infiltrating lymphocytes and tumor budding in colorectal cancer (CRC). However, the significance of the intratumoral heterogeneity present in the spatial distribution of these features within the tumor immune microenvironment (TIME) has not been previously reported. Evaluating this intratumoral heterogeneity may aid the understanding of the TIME's effect on patient prognosis as well as identify novel aggressive phenotypes which can be further investigated as potential targets for new treatment. Methods: In this study, we propose and apply two spatial statistical methodologies for the evaluation of the intratumor heterogeneity present in the distribution of CD3 + and CD8 + lymphocytes and tumor buds (TB) in 232 Stage II CRC cases. Getis-Ord hotspot analysis was applied to quantify the cold and hotspots, defined as regions with a significantly low or high number of each feature of interest, respectively. A novel spatial heatmap methodology for the quantification of the cold and hotspots of each feature of interest, which took into account both the interpatient heterogeneity and the intratumor heterogeneity, was further developed. Results: Resultant data from each analysis, characterizing the spatial intratumor heterogeneity of lymphocytes and TBs were used for the development of two new highly prognostic risk models. Conclusions: Our results highlight the value of applying spatial statistics for the assessment of the intratumor heterogeneity. Both Getis-Ord hotspot and our proposed spatial heatmap analysis are broadly applicable across other tissue types as well as other features of interest. Availability: The code underpinning this publication can be accessed at

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