Patient-derived in vivo models of human cancer have become a reality, yet their turnaround time is inadequate for clinical applications. Therefore, tailored ex vivo models that faithfully recapitulate in vivo tumour biology are urgently needed. These may especially benefit the management of pancreatic ductal adenocarcinoma (PDAC), where therapy failure has been ascribed to its high cancer stem cell (CSC) content and high density of stromal cells and extracellular matrix (ECM). To date, these features are only partially reproduced ex vivo using organoid and sphere cultures. We have now developed a more comprehensive and highly tuneable ex vivo model of PDAC based on the 3D co-assembly of peptide amphiphiles (PAs) with custom ECM components (PA-ECM). These cultures maintain patient-specific transcriptional profiles and exhibit CSC functionality, including strong in vivo tumourigenicity. User-defined modification of the system enables control over niche-dependent phenotypes such as epithelial-to-mesenchymal transition and matrix deposition. Indeed, proteomic analysis of these cultures reveals improved matrisome recapitulation compared to organoids. Most importantly, patient-specific in vivo drug responses are better reproduced in self-assembled cultures than in other models. These findings support the use of tuneable self-assembling platforms in cancer research and pave the way for future precision medicine approaches.

源自患者的人类癌症体内模型已成为现实，但其周转时间不足以用于临床应用。因此，迫切需要量身定制的体外模型，忠实地概括体内肿瘤生物学。这些可能特别有利于胰腺导管腺癌 (PDAC) 的管理，其中治疗失败归因于其高癌症干细胞 (CSC) 含量和高密度的基质细胞和细胞外基质 (ECM)。迄今为止，这些特征仅在体外使用类器官和球形培养物进行了部分复制。我们现在基于肽两亲物 (PAs) 与定制 ECM 组件 (PA-ECM) 的 3D 共组装开发了一个更全面和高度可调的 PDAC 离体模型。这些培养物保持患者特异性转录谱并表现出 CSC 功能，包括强的体内致瘤性。用户定义的系统修改可以控制依赖生态位的表型，例如上皮-间质转化和基质沉积。事实上，对这些培养物的蛋白质组学分析表明，与类器官相比，基质体的重演得到了改善。最重要的是，患者特异性体内药物反应在自组装培养物中比在其他模型中更好地再现。这些发现支持在癌症研究中使用可调谐自组装平台，并为未来的精准医学方法铺平道路。对这些培养物的蛋白质组学分析表明，与类器官相比，基质体的重演得到了改善。最重要的是，患者特异性体内药物反应在自组装培养物中比在其他模型中更好地再现。这些发现支持在癌症研究中使用可调谐自组装平台，并为未来的精准医学方法铺平道路。对这些培养物的蛋白质组学分析表明，与类器官相比，基质体的重演得到了改善。最重要的是，患者特异性体内药物反应在自组装培养物中比在其他模型中更好地再现。这些发现支持在癌症研究中使用可调谐自组装平台，并为未来的精准医学方法铺平道路。