AACR 2022 Poster 3110

Identification of the Phosphorylation Network in PDX and Corresponding Organoid (PDXO) Models Upon Targeted Therapy Treatment Using Deep Phosphoproteomic Analysis

Xiaoxi Xu, Martin Mehnert, Marco Tognetti, Limei Shang, LeileiChen, Jessie Wang, Roland Bruderer, Jakob Vowinckel, Yuehan Feng, Ludovic Bourré, Henry Li

In precision medicine, genomic, transcriptomic, and proteomic data has contributed to the identification of novel driver genes and the molecular-level characterization of cancers. These data have 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 highthroughput-compatible drug screening platforms.

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• How Phosphoproteomic analysis in PDX and PDXO models enables the dissection of signaling networks in a dose-, time-, or pathway-resolved manner
  
  
• How mass spectrometry-based proteomic workflows allowed the quantitative profiling of ~35,000 phospho-sites mapped on >7,800 proteins in these models
  
  
• The predictivity of organoid cultures for serving as a powerful platform to investigate target identification, mechanisms of action, and resistance mechanisms via deep proteomics analysis

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