开发源自患者的胰腺癌三维生物打印模型。

Hang Sun, Yan Wang, Minghao Sun, Xindi Ke, Changcan Li, Bao Jin, Mingchang Pang, Yanan Wang, Shangze Jiang, Liwei Du, Shunda Du, Shouxian Zhong, Haitao Zhao, Yuan Pang, Yongliang Sun, Zhiying Yang, Huayu Yang, Yilei Mao
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引用次数: 0

摘要

简介:胰腺癌(PC)仍然是一种具有挑战性的恶性肿瘤,辅助化疗对于提高患者术后生存率至关重要。然而,由于胰腺癌的内在异质性,必须采取个性化的治疗策略,这就凸显了对可靠临床前模型的需求:本研究旨在利用三维生物打印(3DP)技术开发新型患者来源临床前 PC 模型:方法:利用 3DP 技术建立患者来源的 PC 模型。方法:利用 3DP 技术建立了患者来源的 PC 模型,并进行了基因组和组织学分析,以确定这些模型的特征,并将其与相应的患者组织进行比较。对 PC 3DP 模型进行化疗药物敏感性测试,并分析其与临床结果的相关性:研究成功建立了 PC 3DP 模型,建模成功率为 86.96%。这些模型保留了与患者组织一致的基因组和组织学特征。药物敏感性测试显示,PC 3DP 模型之间存在明显的异质性,反映了临床变异性以及与临床结果的潜在相关性:PC 3DP 模型展示了其作为可靠临床前工具的实用性,保留了关键的基因组和组织学特征。重要的是,这些模型中的药物敏感性特征显示出与临床结果的潜在相关性,表明它们在定制治疗策略和预测患者预后方面大有可为。为了证实其潜在的临床实用性,有必要在更大的患者群体中进行进一步验证。
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Developing Patient-Derived 3D-Bioprinting models of pancreatic cancer.

Introduction: Pancreatic cancer (PC) remains a challenging malignancy, and adjuvant chemotherapy is critical in improving patient survival post-surgery. However, the intrinsic heterogeneity of PC necessitates personalized treatment strategies, highlighting the need for reliable preclinical models.

Objectives: This study aimed to develop novel patient-derived preclinical PC models using three-dimensional bioprinting (3DP) technology.

Methods: Patient-derived PC models were established using 3DP technology. Genomic and histological analyses were performed to characterize these models and compare them with corresponding patient tissues. Chemotherapeutic drug sensitivity tests were conducted on the PC 3DP models, and correlations with clinical outcomes were analyzed.

Results: The study successfully established PC 3DP models with a modeling success rate of 86.96%. These models preserved genomic and histological features consistent with patient tissues. Drug sensitivity testing revealed significant heterogeneity among PC 3DP models, mirroring clinical variability, and potential correlations with clinical outcomes.

Conclusion: The PC 3DP models demonstrated their utility as reliable preclinical tools, retaining key genomic and histological characteristics. Importantly, drug sensitivity profiles in these models showed potential correlations with clinical outcomes, indicating their promise in customizing treatment strategies and predicting patient prognoses. Further validation with larger patient cohorts is warranted to confirm their potential clinical utility.

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