Abstract
Abstract PDX models play a crucial role in pre-clinical research for new drugs. In recent years, research based on PDX models has entered the 'Big Data Era.' On the one hand, there are numerous factors influencing drug responses, such as cancer types, genetic backgrounds, clinical treatment histories, and more. Therefore, selecting the appropriate PDX models for drug efficacy testing based on relevant information has become increasingly important. On the other hand, conducting in-depth genomics and transcriptomics analyses on PDX models allows us to gain a deeper understanding of the mechanisms of drug efficacy and drug sensitivity. After years of effort, LIDE have successfully built and maintained over 1800 PDX models from 40 different cancer types. And in this year (2023), we launched LIDE’s PMed-TRIAL PDX database website. As a comprehensive database of PDX models, it includes fundamental PDX clinical information, medication history, growth curves, and standard of care (SOC) data. It also contains whole exome sequencing data and RNA-seq data. Cancer is a complex and heterogeneous disease characterized by tumor heterogeneity, encompassing both inter-tumor diversity (between patients) and intra-tumor variability (within a single patient), thereby complicating drug responsiveness. PDX models preserve heterogeneity of individual patients, and a robust PDX cohort is essential for accurately representing inter-tumor heterogeneity. LIDE’s PDX cohorts effectively capture representative molecular subtypes across various major cancer types, highlighting their potential as a valuable resource for pre-clinical trials. LIDE’s PMed-TRIAL PDX database comes with a concise and efficient web user interface. Users can search and filter models using keywords, such as drug names, gene symbols, and other information. The website can now be accessed via the following URL (https://pmed.lidebiotech.com:8000/). Citation Format: Xinlei Chen, Youbin Guo, Loc Van, Josh Caggiula, Xiaorong Gu, Danyi Wen. PMed-TRIAL PDX database: Integrated multidimensional information to facilitate new drug development [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 3549.
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