Abstract 4909: An RNA-based model for tertiary lymphoid structure (TLS) prediction and classification in pancreatic adenocarcinoma (PDAC)

Abstract Although TLS status possesses prognostic significance in PDAC and can potentially affect chemotherapy outcomes, there is currently a notable lack of RNA sequencing (RNA-seq) models that specialize in TLS identification and classification in PDAC. Here, we developed a model for predicting TLS status (high or low) based on RNA-seq data. Design: Hematoxylin and eosin (H 2; padj 0. 01), was conducted. The LightGBM gradient boosting classifier was then trained on ranked expression data with sequential feature selection to predict TLS-high and TLS-low groups. We trained the model with H 95% CI 0. 04; 1. 48; p0. 05). We present an RNA-based model that stratifies PDAC samples as TLS-high or TLS-low, with predictions that conform to pathological findings. We also found TLS-low samples to associate with worse OS, thus offering an objective means to predict prognoses of PDAC patients based on TLS status. Citation Format: Alexandra Livanova, Andrey Tyshevich, Andrey Kravets, Stanislav Kurpe, Nadezhda Lukashevich, Dmitry Ivchenkov, Daniil Dymov, Anna Belozerova, Kirill Kryukov, Aleksandr Sarachakov, Viktor Svekolkin, Vladimir Kushnarev. An RNA-based model for tertiary lymphoid structure (TLS) prediction and classification in pancreatic adenocarcinoma (PDAC) abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 4909.