Abstract 5306: Dissecting the formation mechanisms of tertiary lymphoid structures and their impact on prognosis and immunotherapy

Abstract Background: The mortality from gastric cancer is high due to its frequently advanced stage at diagnosis. The standard treatment is mainly surgical, including a comprehensive treatment model of chemotherapy, radiotherapy, targeted therapy and immunotherapy. However, the existing classification of gastric cancer cannot fully guide the precise treatment. TLSs, sometimes also referred to as tertiary lymphoid organs or ectopic lymphoid structures, are organized aggregates of immune cells that arise postnatally in nonlymphoid tissues. The presence of TLSs in tumor environment often indicates a better prognosis. Method: Identifying the expression of TLSs and its maturation status on pathological sections of gastric cancer surgical specimens via HE staining, immunohistochemistry staining and multi-color fluorescence staining. Surgical tumor specimens were collected and analyzed using single-cell RNA sequencing and spatial single-cell transcriptomics sequencing. To identify TLSs artificially by establishing the artificial intelligence machine learning models of TLSs. To explore the mechanism of TLSs formation in the tumor environment in vivo by humanized PDOX mice and in vitro by co-culture systems of gastric organoids and immune cells. Results: HE staining, immunohistochemistry staining and multi-color fluorescence staining of tumor sections identified the expression and mature status of TLSs. Single-cell sequencing and spatial single-cell transcriptomics sequencing found the specific markers to identify the TLSs and analyzed the cell composition and cell communication in the tumor environment. Artificial intelligence machine learning models of TLSs identified the presence of TLSs and its maturation status. Furthermore, in vitro co-culture systems of organoids and immune cells and humanized PDOX mice found that organoids of patients with high TLSs score increased apoptosis of tumor cells while using PD-1 antibody and CTLA-4 antibody. Conclusions: The high expression of mature TLSs indicates a better survival. We establish the artificial intelligence machine learning models of TLSs to identify the presence of TLSs and its mature status. We establish the TLS score to predict the TLS expression and prognosis of patients. Patients with high TLSs score have a better survival and are more sensitive to PD-1 antibody and CTLA-4 antibody. Citation Format: Ben Liu, Wenbo Zhu, Mengyue Qiu, Yanxin Yao, Kexin Chen. Dissecting the formation mechanisms of tertiary lymphoid structures and their impact on prognosis and immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5306.