In Vitro and In Vivo Drug-Response Profiling Using Patient-Derived High-Grade Glioma.
In Vitro and In Vivo Drug-Response Profiling Using Patient-Derived High-Grade Glioma.
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使用患者衍生的高级神经胶质瘤进行体外和体内药物反应分析。
DOI：
10.3390/cancers15133289
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发表时间：
2023-06-22
期刊：
影响因子：
5.2
通讯作者：
Spicer, Timothy P.
中科院分区：
文献类型：
--
作者：
Rajan, Robin G.;Fernandez-Vega, Virneliz;Sperry, Jantzen;Nakashima, Jonathan;Do, Long H.;Andrews, Warren;Boca, Simina;Islam, Rezwanul;Chowdhary, Sajeel A.;Seldin, Jan;Souza, Glauco R.;Scampavia, Louis;Hanafy, Khalid A.;Vrionis, Frank D.;Spicer, Timothy P.
迄今为止，由于 GBM 肿瘤的高度异质性、抵抗性和侵袭性表型，对抗治疗耐药和失败的个性化和综合方法受到限制。我们提出了 GBM 的综合基因组、体外和体内功能治疗范例。我们的研究利用患者来源的 3D 类器官，因为它们在体外测定和体内 PDX 小鼠模型中与亲代肿瘤具有更好的一致性。有效药物的 3D 类器官的体外 HTS 与 RNAseq 分析相结合，可识别差异富集的基因组通路和基因靶标，从而能够快速生成临床相关信息。当使用体内 PDX 小鼠肿瘤生长模型进行验证时，这将创建一个强大的精准医学范例。快速实施的个体化药物反应预测模型为医生提供了可操作的信息，以对抗 GBM 的复发或治疗耐药。此外，它是一个可扩展的工作流程，其中包括尚未批准用于 GBM 但在临床试验中显示出前景的化合物。背景：基因组分析不能单独预测肿瘤细胞在体内微环境中行为的复杂性及其对治疗的敏感性。该研究的目的是利用患者来源的 GBM 肿瘤样本建立功能性药物预测模型，进行药物疗效的体外测试，然后进行体内验证，以克服严格的药物基因组学方法的缺点。方法：对培养为 3D 类器官的患者来源的 GBM 肿瘤进行高通量体外药理学测试，提供了一种具有成本效益的、临床和表型相关的模型，包括肿瘤可塑性和基质。 RNAseq 分析补充了这 128 种化合物的筛选，以预测更有效和针对患者的药物组合，并使用流式细胞术评估额外的肿瘤干性。体内 PDX 小鼠模型快速验证（50 天）并确定突变影响以及药物疗效。我们提出了一个具有代表性的 GBM 病例，其中三个肿瘤在初次就诊时被切除，第一次复发时未经任何治疗，以及在放疗和化疗后第二次复发，全部来自同一患者。结果：分子和体外筛选有助于确定针对该患者的多种途径的有效药物靶点以及 cobimetinib 和 vemurafenib 的协同药物组合，这在一定程度上得到了体内肿瘤生长评估的支持。每次肿瘤迭代都显示出显着不同的干性和耐药性。结论：我们利用分子、体外和体内方法的综合模型提供了患者的肿瘤反应随治疗和时间而漂移的直接证据，正如肿瘤概况的动态变化所证明的那样，这可能会影响人们如何从药理学上解决这种漂移。
To date, personalized and comprehensive approaches to combat treatment resistances and failures are limited due to the highly heterogeneous, resistive, and invasive phenotype of GBM tumors. We present an integrative genomic, in vitro, and in vivo functional treatment paradigm for GBM. Our study utilizes patient-derived 3D organoids, as they have better concordance with the parent tumor for the in vitro assays and in vivo PDX mouse model. In vitro HTS of the 3D organoids for effective drugs combined with RNAseq analysis to identify differentially enriched genomic pathways and gene targets has enabled rapid generation of clinically relevant information. When supplemented with validation using in vivo PDX mouse models of tumor growth, this creates a robust precision medicine paradigm. Rapidly implemented individualized drug response prediction models thus provide actionable information for the physician to combat recurrences or treatment resistances in GBM. Moreover, it is a scalable workflow, which includes compounds not yet approved for GBM but showing promise in clinical trials. Background: Genomic profiling cannot solely predict the complexity of how tumor cells behave in their in vivo microenvironment and their susceptibility to therapies. The aim of the study was to establish a functional drug prediction model utilizing patient-derived GBM tumor samples for in vitro testing of drug efficacy followed by in vivo validation to overcome the disadvantages of a strict pharmacogenomics approach. Methods: High-throughput in vitro pharmacologic testing of patient-derived GBM tumors cultured as 3D organoids offered a cost-effective, clinically and phenotypically relevant model, inclusive of tumor plasticity and stroma. RNAseq analysis supplemented this 128-compound screening to predict more efficacious and patient-specific drug combinations with additional tumor stemness evaluated using flow cytometry. In vivo PDX mouse models rapidly validated (50 days) and determined mutational influence alongside of drug efficacy. We present a representative GBM case of three tumors resected at initial presentation, at first recurrence without any treatment, and at a second recurrence following radiation and chemotherapy, all from the same patient. Results: Molecular and in vitro screening helped identify effective drug targets against several pathways as well as synergistic drug combinations of cobimetinib and vemurafenib for this patient, supported in part by in vivo tumor growth assessment. Each tumor iteration showed significantly varying stemness and drug resistance. Conclusions: Our integrative model utilizing molecular, in vitro, and in vivo approaches provides direct evidence of a patient’s tumor response drifting with treatment and time, as demonstrated by dynamic changes in their tumor profile, which may affect how one would address that drift pharmacologically.
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影响因子：
7.4
作者：
Liu L;Yu L;Li Z;Li W;Huang W
通讯作者：
Huang W
影响因子：
11.2
作者：
Galli, R;Binda, E;Vescovi, A
通讯作者：
Vescovi, A
源自患者的异种移植的前景：小鼠和人类的最佳计划。
DOI：
10.1158/1078-0432.ccr-12-2408
发表时间：
2012-10-01
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Kopetz S;Lemos R;Powis G
通讯作者：
Powis G
影响因子：
11.2
作者：
Hubert CG;Rivera M;Spangler LC;Wu Q;Mack SC;Prager BC;Couce M;McLendon RE;Sloan AE;Rich JN
通讯作者：
Rich JN
影响因子：
50.3
作者：
Letai A;Bhola P;Welm AL
通讯作者：
Welm AL