Abstract 5005: Integrative multi-omics characterization of syngeneic tumor models

Abstract Background: Mouse syngeneic models are widely used to study tumor-immune interactions and evaluate the efficacy of immunotherapies. Proteins serve as a link between the genetic code and phenotype, and can better reflect the dynamic state of a cell. However, the large-scale profiling of proteomics and phosphoproteomics data using syngeneic models has been limited. This study aims to reveal molecular mechanisms underlying tumor development and therapeutic response using multi-omics data generated from syngeneic models. Methods: Untreated fresh tumor tissues (around 500 mm3) from 12 mouse syngeneic models (H22, EMT6, Hepa16, CT26, MC38, Pan02, A20, Renca, RM1, LL2, B16F10 and B16BL6) were collected. Tissues were analyzed using RNA-seq, proteomics and phosphoproteomics. Efficacies of immune checkpoint inhibitors (ICIs) including anti-PD-1 (RMP1-14), anti-PD-L1 (10F.9G2) and anti-CTLA4 (9D9 and 9H10) were tested in each model over a period of 2-3 weeks. Tumor growth inhibition (TGI) values were calculated as: TGI = 1 - ΔT(t)/ΔC(t), where ΔT(t) and ΔC(t) represent average changes in tumor volume from the start of experiment to time t in the treatment and vehicle group, respectively. Models with TGI>=0.45 for at least two ICIs were defined as “responder”, while models with TGI<=0.40 for all ICIs were defined as “non-responder”. Tumor purity scores were inferred with the ESTIMATE software. Differential Expressed Genes (DEGs), Differentially Expressed Proteins (DEPs) and Differentially Phosphorylated Sites (DPSs) were calculated with the limma package. Gene Set Enrichment Analysis (GSEA) was performed using the clusterProfiler package. Kinase Substrate Enrichment Analysis (KSEA) was finished using the KSEAapp package. Network analysis was performed with the STRINGdb package. Results: Estimation of stromal and immune cells using specific genes with transcriptomics and proteomics data revealed that B16BL6 and B16F10 models had the highest tumor purity and low percentage of immune and stromal cells, while A20 model had more immune cell infiltration, and other solid tumor models had relatively higher stromal cell percentage. Next, at RNA and protein levels, genes positively correlated with the drug efficacy were enriched in interferon related pathways and immune response pathways for all ICIs. Furthermore, Dnmt3a and Igfr2 were identified as potential resistant biomarkers for immunotherapy efficacy. Finally, a user-friendly website was designed for both direct acquisition of results generated in this study and deep exploration by simply clicking to choose combinations of parameters and generate new results. Conclusion: This study presents a comprehensive integrated multi-omics analysis using transcriptomics, proteomics and phosphoproteomics data collected from 12 widely used mouse syngeneic models, which provides insights into molecular mechanisms and pathways involved in tumor development and therapeutic response. Citation Format: Zihan Xu, Binchen Mao, Wubin Qian, Xiaobo Chen, Sheng Guo. Integrative multi-omics characterization of syngeneic tumor models [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 5005.