PO.TB04.08 · 肿瘤生物学

整合多组学分析揭示PDX与类器官模型中保守的肿瘤相关抗原(TAAs)谱,助力推进ADC开发

Integrated multi-omics analysis reveals conserved tumor-associated antigens (TAAs) profiles in PDX and organoid models for advancing ADC development

海报缩略图:整合多组学分析揭示PDX与类器官模型中保守的肿瘤相关抗原(TAAs)谱,助力推进ADC开发
编号 7540 展板 21 时间 4/22 09:00–12:00 区域 Section 32 主讲 Xiaolong Tu, PhD
分会场 Tumor Models and Assays: In Vitro, In Vivo
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作者与单位 Authors & Affiliations

Xiaolong Tu1, Likun Zhang1, Jie Lin1, Hengyuan Liu1, Jun Zhou1, Marrit Putker2, Ludovic Bourre2, Julie Myer2

1Crown Bioscience, Taicang, China,2Crown Bioscience, Inc., San Diego, CA

摘要 Abstract

中文摘要
引言 抗体药物偶联物(ADCs)的开发要求肿瘤相关抗原(TAAs)表达稳定可靠以确保疗效,因此需要具有预测价值的临床前模型。患者来源异种移植(PDXs)保留了患者肿瘤的特征,是转化肿瘤学研究的中流砥柱,而患者来源类器官(PDOs)和PDX来源类器官(PDXOs)近年来作为强大的体外3D系统崭露头角,它们在保留原始组织关键生物学特征的同时提供了更强的可扩展性。然而,它们在维持TAA谱方面的保真度需要多组学验证。本研究评估了跨平台以及PDXs与配对类器官之间TAA的一致性。 方法 我们使用IHC在涵盖18种癌症类型的约1000个PDX模型上分析了18种临床相关的TAAs。IHC定量采用HALO AI平台进行分析以生成H-Score,该数据与Crown Bioscience数据库中的RNA-seq和MS质谱蛋白质组学数据整合,以确定相关系数。为评估模型的可转化性,选取了包含10种关键TAAs的聚焦panel,在约400个已表征PDXs的子集与其配对PDOs/PDXOs之间进行IHC评估,从而实现跨模型比较。 结果 我们在这一大型PDXs队列中进行的整合多组学分析表明,就所研究的18种TAAs而言,蛋白表达(H-Score)与转录组学和蛋白质组学数据之间具有高度一致性,包括HER2(R RNAseq=0.871,R Proteomics=0.765)、TROP2(R RNAseq=0.852,R Proteomics=0.775)、Nectin-4(R RNAseq=0.679,R Proteomics=0.861)、DLL3(R RNAseq=0.75,R Proteomics=0.698)、CEACAM5(R RNAseq=0.799,R Proteomics=0.743)。至关重要的是,在PDXs与其配对PDOs/PDXOs模型之间的TAAs蛋白表达模式中观察到极高的一致性,包括TROP2(R=0.946,P<0.0001)、Nectin-4(R=0.772,P<0.0001)、DLL3(R=0.819,P<0.0001)、HER3(R=0.659,P<0.0001)、Claudin 18.2(R=0.775,P<0.0001)。类器官与其体内对应物之间IHC强度和异质性特征的一致性进一步支持了这种分子保真度。 结论 本研究在大型PDX队列中验证了TAAs表达在多组学平台间的一致性。更重要的是,我们提供了有力证据,表明PDOs/PDXOs模型在维持其相应PDX肿瘤的TAA表达图谱方面表现出卓越的保真度,证明PDOs/PDXOs是从最初的靶点验证和先导抗体表征到临床试验中生物标志物驱动的患者选择策略制定过程中高度可靠且极具价值的工具。
查看英文原文 English abstract
Introduction The development of antibody-drug conjugates (ADCs) requires reliable tumor-associated antigens (TAAs) expression for efficacy, necessitating predictive preclinical models. Patient-derived xenografts (PDXs) preserve patient tumor characteristics, which serve as a mainstay in translational oncology research, while patient-derived organoids (PDOs) and PDX-derived organoids (PDXOs) have recently emerged as powerful in vitro 3D systems that offer enhanced scalability while retaining key biological features of the original tissue. However, their fidelity in maintaining TAA profiles requires multi-omics validation. This study evaluates TAA consistency across platforms and between PDXs and matched organoids. Methods We analyzed 18 clinically relevant TAAs using IHC on ~1000 PDX models across 18 cancer types. IHC quantification was analyzed using HALO AI platform to generate H-Score, this data was integrated with RNA-seq and MS-mass spectra-proteomics from Crown Bioscience's database to determine the correlation coefficients. To assess model translatability, a focused panel of 10 key TAAs was selected for IHC assessment between a subset of ~400 characterized PDXs and their paired PDOs/PDXOs, enabling a cross-model comparison. Results Our integrated multi-omics analysis within the extensive PDXs cohort demonstrated a high degree of concordance between protein expression (H-Score) and both transcriptomics and proteomics data for the 18 investigated TAAs, including HER2(R RNAseq =0.871,R Proteomics =0.765), TROP2(R RNAseq =0.852, R Proteomics =0.775), Nectin-4(R RNAseq =0.679, R Proteomics =0.861), DLL3(R RNAseq =0.75, R Proteomics =0.698), CEACAM5(R RNAseq =0.799, R Proteomics =0.743). Critically, a remarkably high degree of concordance was observed in TAAs protein expression patterns between PDXs and their paired PDOs/PDXOs models, including TROP2(R=0.946, P<0.0001), Nectin-4(R=0.772, P<0.0001), DLL3(R=0.819, P<0.0001), HER3(R=0.659, P<0.0001), Claudin 18.2(R=0.775, P<0.0001). The consistency of IHC intensity and heterogeneity characteristics between organoids and their in vivo counterparts further supports this molecular fidelity. Conclusion This study validated TAAs expression concordance across multi-omics platforms in a large PDX cohort. More significantly, we deliver compelling evidence that PDOs/PDXOs models exhibit exceptional fidelity in maintaining the TAA expression landscape of their corresponding PDX tumors, demonstrating PDOs/PDXOs as highly reliable and invaluable tools from initial target validation and lead antibody characterization to the formulation of biomarker-driven patient selection strategies in clinical trials.
利益披露 Disclosure
X. Tu, None.. L. Zhang, None.. J. Lin, None.. H. Liu, None.. J. Zhou, None.. M. Putker, None.. L. Bourre, None.. J. Myer, None.

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