Abstract 3110: Identification of the phosphorylation network in PDX and corresponding organoid (PDXO) models upon targeted therapy treatment using deep phosphoproteomic analysis[MB1

Abstract

Abstract In precision medicine, genomic, transcriptomic, and proteomic data has contributed to the identification of novel driver genes and the molecular-level characterization of cancers. This data has led to a better understanding of drug modulation and resistance mechanisms. Patient-derived tumor models, including patient-derived xenograft (PDX) and organoid counterparts (PDXO), have been increasingly viewed as predictive preclinical cancer models. These models closely recapitulate tumor complexity, enabling the study of tumor identity for personalized medicine. Leveraging our large PDX collection models which are genomically and phenotypically annotated and validated, we have established and characterized a series of PDXO models to be used as scalable and high throughput compatible drug screening platforms. Here we conducted methodical analysis of drug response in paired PDX and PDXO models. In addition, trough deep whole- and phospho-proteomic analysis, we have analyzed and compared the effect of targeted therapies on protein expression and phosphorylation in both PDX and PDXO tumor models. Indeed, one of the most common post-translational modifications that is involved in cell regulation and intracellular signal transduction is reversible protein phosphorylation catalyzed by protein kinases. For this purpose, we have tested a KRAS inhibitor (AMG510), BCR-ABL TKI (Ponatinib) and EGFR TKI (Afatinib) respectively in lung NSCLC model carrying KRAS G12C mutation, colorectal model with RET fusion and Lung NSCLC model with EGFR exon 19 deletion. According to our historical data, a specific relationship between area under the curve value of organoid drug dose response and in vivo tumor growth has been observed, irrespective of the drug treatment. Furthermore, comparison between PDX and PDXO models deploying quantitative proteomic data enabled deep characterization of both global expression and signaling cascades modulated through small molecule inhibitors. Thus, we demonstrate the predictivity of organoid cultures to not only model in vivo drug responses but also to serve as a powerful platform to investigate target identification, mechanism of action and resistance mechanism via deep proteomics analysis. Citation Format: Xiaoxi Xu, Marco Tognetti, Yuehan Feng, Limei Shang, Leilei Chen, Jessie Wang, Roland Bruderer, Ludovic Bourré, Henry Li. Identification of the phosphorylation network in PDX and corresponding organoid (PDXO) models upon targeted therapy treatment using deep phosphoproteomic analysis[MB1] [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3110.