Abstract A073: Digital assessment of tertiary lymphoid structures in cancer using whole slide scans

Abstract Tertiary Lymphoid Structures (TLSs) are organized aggregates of immune cells that form in non-lymphoid tissues under chronic inflammation, including cancer. In most cases, TLS presence in tumors correlates with improved clinical outcomes, underscoring their prognostic and predictive value. However, standardizing TLS assessment remains challenging due to their complex cellular composition, multi-lobular structures, and highly variable appearances within tissue sections. Current TLS assessment methods rely largely on manual identification of these structures, which is inefficient for large datasets and introduces significant variability and potential bias. In this pilot study, we therefore applied a novel digital approach to characterize TLSs in whole-slide images of colorectal cancer tissue sections stained with multiplexed immunofluorescence. This approach enabled comprehensive identification of major lymphocyte subsets within TLSs across entire tissue sections and facilitated assessment of their proliferative activity. Compared to traditional methods that require manual counting and phenotyping of individual TLSs, the whole-slide approach proved far simpler and efficiently distinguished patients with strikingly different levels of immune activity within their TLSs. Notably, we observed strong correlations between the proliferation of B-cells within TLSs and that of T-cell subsets. We also present an automated computational pipeline for TLS identification in whole-slide images, providing a fast, unbiased alternative to manual boundary delineation. Overall, our findings demonstrate that whole-section digital quantification of immune cell activity within TLSs offers substantial advantages over traditional methods. This approach has the potential to accelerate immuno-oncology research by enabling robust correlations between TLS states and clinical outcomes. Ultimately, automated TLS analysis could pave the way for standardized clinical tests, maximizing the value of these structures as potential biomarkers and therapeutic targets. Citation Format: Saem Mul Park, Luis Munoz-Erazo, Shelly Lin, Chun-Jen J Chen, Lisa Y.Y. Zhou, Janet L Rhodes, Taesung Jeon, Sonya Fenton, John L McCall, Roslyn A Kemp, P. Rod Dunbar. Digital assessment of tertiary lymphoid structures in cancer using whole slide scans [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr A073.

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Publication Details

Journal
Cancer Immunology Research
Published
2025-02-23
DOI
https://doi.org/10.1158/2326-6074.io2025-a073
Primary Topic
AI in cancer detection
Type
article
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article

Abstract A073: Digital assessment of tertiary lymphoid structures in cancer using whole slide scans

Janet L Rhodes, P. Rod Dunbar, Saem Mul Park, Taesung Jeon et al.
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Cancer Immunology Research
AI in cancer detection
article

Abstract A073: Digital assessment of tertiary lymphoid structures in cancer using whole slide scans

Janet L Rhodes, P. Rod Dunbar, Saem Mul Park, Taesung Jeon, Sonya Fenton, Roslyn A. Kemp, Luis Munoz‐Erazo, John L McCall, Shelly Lin, Lisa Y.Y. Zhou, J Chen Chun-Jen
article en

Abstract

Abstract Tertiary Lymphoid Structures (TLSs) are organized aggregates of immune cells that form in non-lymphoid tissues under chronic inflammation, including cancer. In most cases, TLS presence in tumors correlates with improved clinical outcomes, underscoring their prognostic and predictive value. However, standardizing TLS assessment remains challenging due to their complex cellular composition, multi-lobular structures, and highly variable appearances within tissue sections. Current TLS assessment methods rely largely on manual identification of these structures, which is inefficient for large datasets and introduces significant variability and potential bias. In this pilot study, we therefore applied a novel digital approach to characterize TLSs in whole-slide images of colorectal cancer tissue sections stained with multiplexed immunofluorescence. This approach enabled comprehensive identification of major lymphocyte subsets within TLSs across entire tissue sections and facilitated assessment of their proliferative activity. Compared to traditional methods that require manual counting and phenotyping of individual TLSs, the whole-slide approach proved far simpler and efficiently distinguished patients with strikingly different levels of immune activity within their TLSs. Notably, we observed strong correlations between the proliferation of B-cells within TLSs and that of T-cell subsets. We also present an automated computational pipeline for TLS identification in whole-slide images, providing a fast, unbiased alternative to manual boundary delineation. Overall, our findings demonstrate that whole-section digital quantification of immune cell activity within TLSs offers substantial advantages over traditional methods. This approach has the potential to accelerate immuno-oncology research by enabling robust correlations between TLS states and clinical outcomes. Ultimately, automated TLS analysis could pave the way for standardized clinical tests, maximizing the value of these structures as potential biomarkers and therapeutic targets. Citation Format: Saem Mul Park, Luis Munoz-Erazo, Shelly Lin, Chun-Jen J Chen, Lisa Y.Y. Zhou, Janet L Rhodes, Taesung Jeon, Sonya Fenton, John L McCall, Roslyn A Kemp, P. Rod Dunbar. Digital assessment of tertiary lymphoid structures in cancer using whole slide scans [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr A073.

Cancer Immunology ResearchVol. 13(2_Supplement)
University of Auckland (NZ), University of Otago (NZ)
Openalex Percentile: Top 99%
AI in cancer detection
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