Abstract 4909: An RNA-based model for tertiary lymphoid structure (TLS) prediction and classification in pancreatic adenocarcinoma (PDAC)
Authors: Alexandra Livanova, Andrey Tyshevich, Andrey Kravets, Stanislav Kurpe, Nadezhda Lukashevich, Dmitry Ivchenkov, Daniil Dymov, Anna Belozerova, Kirill Kryukov, Aleksandr Sarachakov, Viktor Svekolkin, Vladimir Kushnarev
Journal: Cancer Research (2024)

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&amp;E) whole slide images of PDAC samples from The Cancer Genome Atlas (TCGA, n = 118) and Clinical Proteomic Tumor Analysis Consortium (CPTAC, n = 129) were used to detect intratumoral and borderline TLSs followed by TLS density measurements (units/mm2) by an experienced pathologist. The samples were then stratified into TLS-high and TLS-low groups based on median density values. Next, we used deconvolution by Kassandra algorithm to identify cell subtypes enriched in each TLS group based on gene expression (RNA-seq) data. Calculation of ssGSEA scores for gene signatures corresponding to cell subtypes and TLS structures was performed, along with survival analysis. Differential expression analysis between TLS-high and TLS-low samples, followed by functional enrichment (|logFC| &amp;gt; 2; padj &amp;lt; 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&amp;E staining annotations and ranked RNA expression data from TCGA or CPTAC samples (total n = 167). The remaining 80 were designated as hold-out samples. The weighted F1 score was computed as a performance metric. Findings: Median density of detected TLSs in the TCGA and CPTAC samples was 0.012 units/mm2. Kassandra deconvolution revealed B-cell enrichment, but fibroblast and M2 macrophage depletion in the TLS-high group. Our calculated ssGSEA scores of previously described TLS gene signatures, along with those of different B-cell subtypes and follicular dendritic cells, showed significant association with the TLS-high group. Genes associated with B-cell proliferation, differentiation, and signaling (CD19, CD22, CD79A, CD79B, and CR2) were also upregulated in this group. Comparing the performance of our RNA-based model on the validation dataset with manual TLS classification by a pathologist, we obtained an F1 weighted score of 0.72 and ROC-AUC score of 0.77. Thus, the TLS predictions by our model concurred with the TLS classification based on H&amp;E annotations and pathological evaluation. Moreover, patients in the predicted TLS-low group had worse overall survival (OS) compared to the TLS-high group (Log(HR) = 0.76; 95% CI [0.04; 1.48]; p&amp;lt;0.05). 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(6_Suppl):Abstract nr 4909.