Several patient-derived tumor models emerged recently as robust preclinical drug-testing platforms. However, their potential to guide clinical therapy remained unclear. Here, we report a model called patient-derived tumor-like cell clusters (PTCs). PTCs result from the self-assembly and proliferation of primary epithelial, fibroblast, and immune cells, which structurally and functionally recapitulate original tumors. PTCs enabled us to accomplish personalized drug testing within 2 weeks after obtaining the tumor samples. The defined culture conditions and drug concentrations in the PTC model facilitate its clinical application in precision oncology. PTC tests of 59 patients with gastric, colorectal, or breast cancers revealed an overall accuracy of 93% in predicting their clinical outcomes. We implemented PTC to guide chemotherapy selection for a patient with mucinous rectal adenocarcinoma who experienced recurrence with metastases after conventional therapy. After three cycles of a nonconventional therapy identified by the PTC, the patient showed a positive response. These findings need to be validated in larger clinical trials, but they suggest that the PTC model could be prospectively implemented in clinical decision-making for therapy selection.

最近出现了几种源自患者的肿瘤模型，作为强大的临床前药物测试平台。但是，它们指导临床治疗的潜力仍不清楚。在这里，我们报告一个称为患者源性肿瘤样细胞簇（PTC）的模型。PTC是由原代上皮细胞，成纤维细胞和免疫细胞的自组装和增殖产生的，它们在结构和功能上概括了原始肿瘤。PTC使我们能够在获得肿瘤样本后的2周内完成个性化药物测试。PTC模型中定义的培养条件和药物浓度有助于其在精密肿瘤学中的临床应用。对59例胃癌，结肠直肠癌或乳腺癌患者的PTC测试显示，在预测其临床结局方面，总体准确性为93％。我们实施PTC来指导黏液性直肠腺癌患者的常规化疗后化疗复发的化疗选择。在PTC确定的三个非常规治疗周期后，患者显示出阳性反应。这些发现需要在更大的临床试验中得到验证，但是它们表明PTC模型可以在治疗选择的临床决策中前瞻性地实施。