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胃癌高通量化疗药物筛选系统(Cure-GA)。

High-Throughput Chemotherapeutic Drug Screening System for Gastric Cancer (Cure-GA).

作者信息

Lee Jieun, Kim In Hee, Seol Donghyeok, Lee Sangjun, Yoo Mira, Lee Tae-Kyeong, Yoon So Hee, Lee Eunju, Hwang Duyeong, Kang So Hyun, Park Young Suk, Ku Bosung, Jeon Sang Youl, Choi Yongmun, Jung Keehoon, Kim Ji-Won, Kim Jin Won, Ahn Sang-Hoon, Lee Keun-Wook, Kim Hyung-Ho, Oh Hyeon Jeong, Lee Dong Woo, Suh Yun-Suhk

机构信息

Department of Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.

Department of Precision Medicine, Medical and Bio Decision (MBD Co., Ltd), Suwon, Republic of Korea.

出版信息

Ann Surg Oncol. 2025 May;32(5):3781-3795. doi: 10.1245/s10434-024-16850-0. Epub 2025 Jan 23.

Abstract

BACKGROUND

Three dimensional (3D) cell cultures can be effectively used for drug discovery and development but there are still challenges in their general application to high-throughput screening. In this study, we developed a novel high-throughput chemotherapeutic 3D drug screening system for gastric cancer, named 'Cure-GA', to discover clinically applicable anticancer drugs and predict therapeutic responses.

METHODS

Primary cancer cells were isolated from 143 fresh surgical specimens by enzymatic treatment. Cell-Matrigel mixtures were automatically printed onto the micropillar surface then stabilized in an optimal culture medium for 3 days to form tumoroids. These tumoroids were exposed in the drug-containing media for 7 days. Cell viability was measured by fluorescence imaging and adenosine triphosphate assays. On average, 0.31 ± 0.23 g of fresh tumor tissue yielded 4.05×10 ± 4.38×10 viable cells per sample.

RESULTS

Drug response results were successfully acquired from 103 gastric cancer tissues (success rate = 72%) within 13 ± 2 days, averaging 6.4 ± 2.7 results per sample. Pearson correlation analysis showed viable cell numbers significantly impacted drug data acquisition (p < 0.00001). Tumoroids retained immunohistochemical characteristics, mutation signatures, and gene expression consistent with primary tumors. Drug reactivity data enabled prediction of synergistic drug correlations. Additionally, a multiparameter index-based prognosis model for patients undergoing gastrectomy followed by adjuvant XELOX was developed, showing significant differences in 1-year recurrence-free survival rates between drug responders and non-responders (p < 0.0001).

CONCLUSIONS

The Cure-GA platform enables rapid evaluation of chemotherapeutic responses using patient-derived tumoroids, providing clinicians with crucial insights for personalized treatment strategies and improving therapeutic outcomes.

摘要

背景

三维(3D)细胞培养可有效用于药物发现与开发,但在其广泛应用于高通量筛选方面仍存在挑战。在本研究中,我们开发了一种用于胃癌的新型高通量化疗3D药物筛选系统,名为“Cure - GA”,以发现临床适用的抗癌药物并预测治疗反应。

方法

通过酶处理从143份新鲜手术标本中分离出原发性癌细胞。将细胞 - 基质胶混合物自动打印到微柱表面,然后在最佳培养基中稳定3天以形成类肿瘤。将这些类肿瘤暴露于含药培养基中7天。通过荧光成像和三磷酸腺苷测定法测量细胞活力。平均而言,每份样本0.31±0.23克新鲜肿瘤组织可产生4.05×10±4.38×10个活细胞。

结果

在13±2天内成功从103个胃癌组织中获得药物反应结果(成功率 = 72%),每个样本平均有6.4±2.7个结果。Pearson相关分析表明活细胞数量对药物数据获取有显著影响(p < 0.00001)。类肿瘤保留了与原发性肿瘤一致的免疫组化特征、突变特征和基因表达。药物反应性数据能够预测协同药物相关性。此外,还开发了一种基于多参数指标的胃切除术后辅助XELOX患者预后模型,显示药物反应者和无反应者之间1年无复发生存率存在显著差异(p < 0.0001)。

结论

Cure - GA平台能够使用患者来源的类肿瘤快速评估化疗反应,为临床医生提供制定个性化治疗策略的关键见解并改善治疗效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2412/11976768/84b83e0711b1/10434_2024_16850_Fig1_HTML.jpg

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