患者来源胶质母细胞瘤类器官模型的构建及替莫唑胺与洛莫司汀单药和联合用药的敏感性分析

Construction of patient-derived glioblastoma organoid models and sensitivity analysis of temozolomide and lomustine as monotherapy and in combination

摘要

目的 构建胶质母细胞瘤(GBM)患者来源类器官(PDO)模型, 并评估其在替莫唑胺与洛莫司汀单药及联合用药敏感性预测中的应用价值。方法 收集5例经病理确诊、术前未行系统治疗的GBM患者的肿瘤组织, 采用悬浮法构建PDO模型, 并通过HE染色法、免疫荧光(IF)染色法及全外显子组测序(WES)验证其与原发组织的组织学形态及遗传特征一致性, 通过体外3D侵袭实验验证其肿瘤行为学特征。对稳定传代的PDO采用乳酸脱氢酶检测法进行药物敏感性检测, 拟合剂量效应曲线计算IC₅₀; 并通过Chou-Talalay方法计算联合指数(CI)评估药物协同作用。结果 成功构建3例可稳定传代超过3代的GBM PDO模型, 培养成功率为60%, HE和IF结果显示PDO模型与原代肿瘤组织高度一致, WES显示配对PDO保留了原代组织基因组突变异质性的92%以上。基于两种常用化疗药物进行药敏试验, 3例PDO对替莫唑胺的IC₅₀分别为58.52 μmol/L [95% CI (52.17 μmol/L, 65.52 μmol/L)]、2 923 μmol/L [95% CI (1 784 μmol/L, 6 133 μmol/L)] 及187.10 μmol/L [95% CI (159.80 μmol/L, 220.60 μmol/L)]; 对洛莫司汀的IC₅₀分别为12.65 μmol/L [95% CI (10.56 μmol/L, 15.11 μmol/L)]、105.90 μmol/L [95% CI (83.69 μmol/L, 141.30 μmol/L)] 及19.64 μmol/L [95% CI (16.41 μmol/L, 23.57 μmol/L)], 存在显著的个体差异。联合用药实验中, 不同GBM PDO模型在替莫唑胺与洛莫司汀联用后呈现出异质性的剂量效应关系。A141 PDO表现为低剂量拮抗、中等剂量部分协同、高剂量拮抗; A137 PDO与A140 PDO的反应模式更为复杂, 相继经历了低剂量协同、中剂量拮抗后再协同、高剂量再次拮抗的转变。这表明两药联用的协同作用具有严格的中等剂量范围限制。结论 构建的GBM PDO模型成功再现了原代肿瘤组织的形态学、遗传学特征, 保留了肿瘤侵袭的行为学特征, 结合药物敏感性试验能有效模拟患者肿瘤的药物反应异质性, 有望为临床个性化治疗方案的优化调整提供快速、可靠的临床前平台。

Abstract

Objective To establish patient-derived organoid (PDO) models of glioblastoma (GBM) and evaluate their application value in predicting sensitivity to temozolomide and lomustine, both as monotherapies and in combination. Methods Tumor tissues were collected from five patients with pathologically confirmed GBM who had not received any prior systematic treatment. PDO models were constructed using a suspension culture method. The fidelity of the PDOs to the primary tumors was validated by HE staining, immunofluorescence (IF) staining, and whole-exome sequencing (WES) to assess histological morphology and genetic characteristics. Invasive behavioral traits were evaluated using a 3D invasion assay in vitro. Drug sensitivity of stably passaged PDOs was assessed using a lactate dehydrogenase release assay following drug administration. Dose-response curves were fitted to calculate the IC₅₀. Synergistic effects of drug combinations were evaluated by calculating the combination index (CI) using the Chou-Talalay method. Results Three GBM PDO lines capable of stable subculture for over three passages were successfully established, with a culture success rate of 60%. HE and IF staining demonstrated high histological concordance between the PDO models and the primary tumors. WES analysis revealed that the paired PDOs retained up to 92% of the genomic mutation heterogeneity present in the parental tissues. Drug sensitivity testing using two common chemotherapeutic agents showed significant inter-individual variability. The IC₅₀ values of the three PDO lines for temozolomide were 58.52 μmol/L [95% CI (52.17 μmol/L, 65.52 μmol/L)], 2 923 μmol/L [95% CI (1 784 μmol/L, 6 133 μmol/L)], and 187.10 μmol/L [95% CI (159.80 μmol/L, 220.60 μmol/L)], respectively. The IC₅₀ values for lomustine were 12.65 μmol/L [95% CI (10.56 μmol/L, 15.11 μmol/L)], 105.90 μmol/L [95% CI (83.69 μmol/L, 141.30 μmol/L)], and 19.64 μmol/L [95% CI (16.41 μmol/L, 23.57 μmol/L)], respectively. In combination experiments, heterogeneous dose-effect relationships were observed following temozolomide and lomustine treatment across different GBM PDO models. A141 PDO exhibited antagonism at low doses, synergy at some moderate doses, and antagonism at high doses. The response patterns for A137 PDO and A140 PDO were more complex, transitioning through phases of low-dose synergy, moderate-dose antagonism followed by synergy, and reverting to antagonism at high doses. This indicated that the synergistic effect of this drug combination was strictly confined to a specific moderate dose range. Conclusion The GBM PDO models established in this study successfully recapitulate the morphological and genetic features of the primary tumors and retain their invasive behavioral characteristics. When integrated with drug sensitivity testing, these models effectively simulate the heterogeneous drug response profiles of patients' tumors, offering a promising rapid and reliable preclinical platform for guiding and optimizing personalized clinical treatment strategies.

关键词

胶质母细胞瘤 / 类器官 / 患者来源类器官 / 临床前模型 / 药物敏感性 / 替莫唑胺 / 洛莫司汀 / 精准医学

Key words

glioblastoma / organoid / patient-derived organoid / preclinical model / drug sensitivity / temozolomide / lomustine / precision medicine

参考文献

[1] PARK J, KIM D, PARK H C, et al. Tumor microenvironment-preserving gliosarcoma organoids as an in vitro preclinical platform: a comparative analysis with glioblastoma models[J]. J Transl Med, 2025, 23(1): 915. DOI: 10.1186/s12967-025-06952-y.

[2] SCHAFF L R, MELLINGHOFF I K. Glioblastoma and other primary brain malignancies in adults: a review[J]. JAMA, 2023, 329(7): 574. DOI: 10.1001/jama.2023.0023.

[3] 刘妍, 林元强, 李玉宏. 靶向治疗在替莫唑胺耐药胶质瘤治疗中的研究进展[J]. 中国老年学杂志, 2024, 44(24): 6135-6139. DOI: 10.3969/j.issn.1005-9202.2024.24.055.

[4] 刘瑞霞, 张璟, 李晓, 等. 类器官芯片的构建及其在生物医学领域中的应用[J]. 生物化学与生物物理进展, 2026, 53(2): 293-308. DOI: 10.3724/j.pibb.2025.0348.

[5] MOSTEIRO A, DIAO D, BEDIA C, et al. A murine model of glioblastoma initiating cells and human brain organoid xenograft for photodynamic therapy testing[J]. Int J Mol Sci, 2025, 26(18): 8889. DOI: 10.3390/ijms26188889.

[6] 牟健诚, 罗杰, 刘皓天, 等. 精准医疗背景下乳腺癌类器官药物治疗领域的应用与前景[J]. 中国组织工程研究, 2026, 30(19): 5033-5039. DOI: 10.12307/2026.244.

[7] YANG H, CHENG J H, ZHUANG H, et al. Pharmacogenomic profiling of intra-tumor heterogeneity using a large organoid biobank of liver cancer[J]. Cancer Cell, 2024, 42(4): 535-551.e8. DOI: 10.1016/j.ccell.2024.03.004.

[8] RYU S, KIM H S, LEE S, et al. Anti-cancer drug sensitivity testing and preclinical evaluation of the anti-cancer potential of WEE1 inhibitor in triple-negative breast cancer patient-derived organoids and xenograft models[J]. Breast Cancer Res, 2025, 27(1): 113. DOI: 10.1186/s13058-025-02063-0.

[9] ALVES-VALE C, GALVÃO B, SILVESTRE A R, et al. Patient-derived organoids as a model to study tubo-ovarian carcinoma: a pathologist's perspective[J]. J Ovarian Res, 2025, 18(1): 191. DOI: 10.1186/s13048-025-01766-4.

[10] 杨欣怡, 尹毅锐, 黄哲昆, 等. 胃癌类器官在化疗药物敏感性测定中的研究进展[J]. 中国临床医学, 2025, 32(4): 685-691. DOI: 10.12025/j.issn.1008-6358.2025.20240694.

[11] 巩子君, 刘佳颖, 范坤, 等. 肝外胆管癌类器官的建立及影响因素分析[J]. 中国临床医学, 2025, 32(4): 604-609. DOI: 10.12025/j.issn.1008-6358.2025.20250081.

[12] ZHENG C J, WANG P, ZHANG D L, et al. A novel organoid model retaining the glioma microenvironment for personalized drug screening and therapeutic evaluation[J]. Bioact Mater, 2025, 53: 205-217. DOI: 10.1016/j.bioactmat.2025.07.015.

[13] WÖLLNER A, PAUL A, ARQUILLA M, et al. Use of tissue specimens from stereotactic biopsies for patient-derived GBM organoid-based drug testing[J]. Cells, 2025, 14(10): 701. DOI: 10.3390/cells14100701.

[14] 李林波, 边杉. 脑肿瘤类器官的研究进展[J]. 中国细胞生物学学报, 2025, 47(9): 2292-2305. DOI: 10.11844/cjcb.2025.09.0012.

[15] TOMAR M S, KUMAR A, SRIVASTAVA C, et al. Elucidating the mechanisms of Temozolomide resistance in gliomas and the strategies to overcome the resistance[J]. Biochim Biophys Acta Rev Cancer, 2021, 1876(2): 188616. DOI: 10.1016/j.bbcan.2021.188616.

[16] LI H Z, WU Y H, CHEN Y, et al. Overcoming temozolomide resistance in glioma: recent advances and mechanistic insights[J]. Acta Neuropathol Commun, 2025, 13(1): 126. DOI: 10.1186/s40478-025-02046-4.

[17] LIN K T, ZOU C, HUBBARD A, et al. Multiplexed epigenetic memory editing using CRISPRoff sensitizes glioblastoma to chemotherapy[J]. Neuro Oncol, 2025, 27(6): 1443-1457. DOI: 10.1093/neuonc/noaf055.

[18] BELLAMY C, HAJAL C, LIGON K L, et al. Revisiting an old therapy for new, promising combinations: biology and perspectives of lomustine in glioma treatment[J]. Neuro Oncol, 2025, 27(12): 3043-3057. DOI: 10.1093/neuonc/noaf192.

[19] ZHAO R, FENG Q S, XIA Y Y, et al. Iteration of tumor organoids in drug development: simplification and integration[J]. Pharmaceuticals (Basel), 2025, 18(10): 1540. DOI: 10.3390/ph18101540.

[20] CUI G, XUE S, WANG X S, et al. The advancements of organoids push the boundaries of glioblastoma research[J]. Postgrad Med J, 2025, 101(1196): 497-503. DOI: 10.1093/postmj/qgae149.

[21] LEBLANC V G, TRINH D L, ASLANPOUR S, et al. Single-cell landscapes of primary glioblastomas and matched explants and cell lines show variable retention of inter- and intratumor heterogeneity[J]. Cancer Cell, 2022, 40(4): 379-392.e9. DOI: 10.1016/j.ccell.2022.02.016.

[22] LOGUN M, WANG X, SUN Y S, et al. Patient-derived glioblastoma organoids as real-time avatars for assessing responses to clinical CAR-T cell therapy[J]. Cell Stem Cell, 2025, 32(2): 181-190.e4. DOI: 10.1016/j.stem.2024.11.010.

[23] SKARNE N, D'SOUZA R C J, PALETHORPE H M, et al. Personalising glioblastoma medicine: explant organoid applications, challenges and future perspectives[J]. Acta Neuropathol Commun, 2025, 13(1): 6. DOI: 10.1186/s40478-025-01928-x.

[24] BAI L, SU J C. Artificial intelligence virtual organoids (AIVOs)[J]. Bioact Mater, 2026, 59: 45-68. DOI: 10.1016/j.bioactmat.2025.12.030.

[25] MIAO X W, KONG K Y, RONG K W, et al. Construction of biomimetic gradient-structured cartilage organoids and mechanistic study of their application for cartilage rejuvenation[J]. Bioact Mater, 2026, 59: 579-594. DOI: 10.1016/j.bioactmat.2025.12.052.

基金资助

国家自然科学基金青年科学基金(82003220)