Abstract 1274: Comparative analysis of biological and multi-omics profiling in PDCs and PDOs models

Abstract Patient-derived cells (PDCs), cultured in 2D environment, have limitations in reflecting patient’s characteristics, while patient-derived organoids (PDOs) better capture heterogeneity and complexity of tumor microenvironment. However, PDOs do not always reflect the patient’s condition, and in certain contexts, PDCs may offer distinct advantages. Since most PDCs and PDOs are established from primary tumors, research models from metastatic tumors are necessitated for studying metastatic cancers, like peritoneal metastases. In this study, we aim to compare established PDCs and PDOs models derived from ascites of two advanced gastric cancer patients and to explore the limitations and potential of each model by integrating fundamental characteristics and multi omics analysis. Morphological features in PDCs and PDOs models of each sample were observed using IHC staining and microscopy. Biological features, including proliferation, cell cycle and HER2 expression were evaluated, while multi-omics was conducted to explore comprehensive biological processes and molecular pathways. Drug sensitivity using Paclitaxel, 5-FU, SN-38 and Oxaliplatin was performed to evaluate the drug responsiveness, and underlying mechanisms were explained by multi-omics. In PDCs, both samples (YCC-GO10PDCs and YCC-GO30PDCs) exhibited adherent cell morphology, whereas PDOs showed structural variations, presenting compact (YCC-GO10) and grape-like (YCC-GO30) formations. The HER2 expression in YCC-GO10PDCs was consistent with the patient’s clinical data. At the stabilized stage of PDCs (24h) and PDOs (72h), the cell cycle showed a normal S-phase distribution (YCC-GO10PDCs 26.4%, YCC-GO30PDCs 14.5% and YCC-GO10 21.62%, YCC-GO30 14.87%). In tumor purity of YCC-GO10, PDCs had 99%, while PDOs had 55%. Similarly in tumor mutation burden, as determined by whole genome sequencing, PDCs (16.27) was higher than PDOs (9.93). In proteomics, genes significantly upregulated in PDOs were associated with microenvironment interactions, such as ECM remodeling and stemness-related pathways, while genes upregulated in PDCs were primarily linked to metabolic and proliferative activities, reflecting the biological differences between two models. Drug response of YCC-GO10 showed similar patterns while YCC-GO30 showed higher sensitivities in PDCs than PDOs. Proteomics revealed that COL4A1 gene, associated with oxaliplatin and enhances cancer cell invasiveness and drug resistance, was upregulated in PDOs. In conclusion, established PDCs and PDOs models from patient’s fluid were used for comparative analysis and provided insights into each model’s characteristics. Each research model is expected to serve as representative models reflecting the patient's condition and are anticipated to contribute to the development of personalized treatment. Citation Format: Ye Jin Moon, Woo Sun Kwon, Tae Soo Kim, Harim Koo, Sun Young Rha. Comparative analysis of biological and multi-omics profiling in PDCs and PDOs 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 1274.