一种将多组学与患者特异性3D细胞模型相整合的工作流程，用于在临床相关的时间范围内探究精准医学策略

The complexity and heterogeneity of cancers within and between patients has led to the precision medicine paradigm of cancer therapy where treatment should be personalized to individual patients to achieve maximal outcomes. Large scale endeavours such as the NCI-MATCH study [1] have investigated the benefits of tumor DNA sequencing, in order to best match the patient to a targeted therapy for their cancer. These studies demonstrate improved outcomes with matching approaches, but raise questions about genetic influences on drug resistance, suggesting sequencing alone may not be sufficient to guide molecular targeted therapies. New Approach Methodologies (NAMs) such as tumoroids are patient-derived cancer cells that grow as 3D self-organized, multicellular structures that maintain key characteristics of the patient tumor of origin including genotype and transcriptomic profiles, as well as biological behaviors. These NAMs serve as valuable tools for studying tumor biology and patient-specific responses to various anti-cancer therapies. Here, we utilize the RASTRUM TM Allegro platform from Inventia Life Science and Gibco TM OncoPro TM colorectal cancer (CRC) Tumoroid Cell Lines (ThermoFisher Scientific [2]) to easily create plug and play patient-derived 3D cell models within synthetic PEG-based hydrogel matrices, which were tuned to mimic the stiffness and ECM composition of CRC tumors. Coupling these biologically-relevant tumoroid models with molecular insights from comprehensive proteomics and transcriptomics characterization, we demonstrate the utility of this workflow for throughput drug screening and determining personalized therapy responses in clinically-relevant timeframes. Our approach provides a scalable framework for evaluating precision medicine approaches using biologically-relevant patient-derived tumoroids. Future work will focus on the development of processes to support direct dissociation and generation of patient-derived models from specimens from a variety of cancer types. Utilization of the RASTRUM platform in these precision medicine workflows to generate real-time drug sensitivity data, could be leveraged to inform clinical treatment decision making. References 1. O'Dwyer P.J. et al. The NCI-MATCH trial: Lessons for precision oncology. Nature Medicine 2024, 29(6):1349-1357. 2. Paul, C.D. et al. Long-term maintenance of patient-specific characteristics in tumoroids from six cancer indications. Scientific Reports 2025, 15(1):3933.