205 Development of an algorithm (‘LS Finder’) for identifying lymphoid structures, TLS and lymphoid aggregates, using immune cell spatial coordinates

<h3>Background</h3> Tertiary lymphoid structures (TLS) are ectopic lymphoid formations that develop in non-lymphoid tissues during chronic inflammation, autoimmune diseases, and cancer. Accurate identification of lymphoid structures (LS) such as lymphoid aggregates (LA) and TLS can provide crucial insights into the immune response within the tumor microenvironment as well as in infectious diseases. This study aims to develop a robust algorithm to recognize LS using the spatial coordinates of immune cells. <h3>Methods</h3> The ‘LS Finder’ algorithm was designed to identify lymphoid structures such as lymphoid aggregates and TLS by analyzing the X and Y coordinates of immune cells. For this study, CD20 and CD8 markers were used to define B cells and T cells, respectively. The cell size was standardized to 10 microns, and a query radius of 160 microns was selected to account for possible empty spaces between cells. A lymphoid aggregate was defined as an area containing 50 or more B and/or T cells within a 160-micron radius of a central B or T cell. Cells already counted in one LA were excluded from subsequent LA assessments. <h3>Results</h3> The algorithm successfully identified immune cells and determined their spatial distributions. In a test dataset, the algorithm counted 107,847 CD68+ cells, 39,746 CD8+ cells, and 29,775 CD20+ cells. When the search radius was adjusted to 160 microns, 84 aggregate centers were found, with the coordinates of each center provided. These centers matched the findings from the pathologist annotations. These findings demonstrate the algorithm’s capability to detect and locate LS within the tumor microenvironment. <h3>Conclusions</h3> The algorithm developed and tested in this study provides an effective tool for identifying lymphoid structures based on the spatial distribution of CD8+ and/or CD20+ immune cells. It is designed flexible to incorporate B cell, T cell and dendritic cells markers. This method enhances our understanding of the immune landscape in tumors and could be instrumental in studying the role of TLS in cancer progression and treatment response.

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Publication Details

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Regular and Young Investigator Award Abstracts
Published
2024-11-01
DOI
https://doi.org/10.1136/jitc-2024-sitc2024.0205
Primary Topic
Immunotherapy and Immune Responses
Type
article
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article

205 Development of an algorithm (‘LS Finder’) for identifying lymphoid structures, TLS and lymphoid aggregates, using immune cell spatial coordinates

Maria E. Salvatierra, Cara Haymaker, Luisa M. Solis Soto, Ayse A Koksoy
Regular and Young Investigator Award Abstracts
Immunotherapy and Immune Responses
article

205 Development of an algorithm (‘LS Finder’) for identifying lymphoid structures, TLS and lymphoid aggregates, using immune cell spatial coordinates

Maria E. Salvatierra, Cara Haymaker, Luisa M. Solis Soto, Ayse A Koksoy
article en

Abstract

<h3>Background</h3> Tertiary lymphoid structures (TLS) are ectopic lymphoid formations that develop in non-lymphoid tissues during chronic inflammation, autoimmune diseases, and cancer. Accurate identification of lymphoid structures (LS) such as lymphoid aggregates (LA) and TLS can provide crucial insights into the immune response within the tumor microenvironment as well as in infectious diseases. This study aims to develop a robust algorithm to recognize LS using the spatial coordinates of immune cells. <h3>Methods</h3> The ‘LS Finder’ algorithm was designed to identify lymphoid structures such as lymphoid aggregates and TLS by analyzing the X and Y coordinates of immune cells. For this study, CD20 and CD8 markers were used to define B cells and T cells, respectively. The cell size was standardized to 10 microns, and a query radius of 160 microns was selected to account for possible empty spaces between cells. A lymphoid aggregate was defined as an area containing 50 or more B and/or T cells within a 160-micron radius of a central B or T cell. Cells already counted in one LA were excluded from subsequent LA assessments. <h3>Results</h3> The algorithm successfully identified immune cells and determined their spatial distributions. In a test dataset, the algorithm counted 107,847 CD68+ cells, 39,746 CD8+ cells, and 29,775 CD20+ cells. When the search radius was adjusted to 160 microns, 84 aggregate centers were found, with the coordinates of each center provided. These centers matched the findings from the pathologist annotations. These findings demonstrate the algorithm’s capability to detect and locate LS within the tumor microenvironment. <h3>Conclusions</h3> The algorithm developed and tested in this study provides an effective tool for identifying lymphoid structures based on the spatial distribution of CD8+ and/or CD20+ immune cells. It is designed flexible to incorporate B cell, T cell and dendritic cells markers. This method enhances our understanding of the immune landscape in tumors and could be instrumental in studying the role of TLS in cancer progression and treatment response.

Regular and Young Investigator Award Abstracts
The University of Texas MD Anderson Cancer Center (US)
Openalex Percentile: Top 75%
Immunotherapy and Immune Responses
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