PO.ET02.03 · 实验与分子治疗
在评估ADC候选物时,三维肿瘤类器官相较于二维细胞更能预测体内抗癌药理学
3D-tumor organoid, over 2D-cell, is more predictive of in vivo anti-cancer pharmacology in assessing ADC candidates
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
ADC是近年来一种前景广阔的抗癌药物模式,因其通过直接结合肿瘤细胞相关抗原(TAA)实现肿瘤特异性靶向、内化以及载荷释放/细胞毒性诱导,从而拓宽了治疗窗口(TW)。对先导ADC候选物的常规评估通常包括以下步骤:1)基于ELISA的与重组TAA蛋白的结合;2)TAA+的二维细胞培养,用于评估结合、内化和细胞毒性;3)使用TAA+异种移植肿瘤模型进行抗肿瘤药理学评估。这一流程在很大程度上基于二维癌细胞系培养可预测体内肿瘤模型的假设。然而,由于肿瘤(细胞)具有独特的结构,二维检测常常不能反映肿瘤药理学检测结果,导致该流程效率低下。考虑到三维肿瘤类器官比二维细胞培养更能反映肿瘤,我们假设在体内肿瘤药理学之前,增加三维肿瘤类器官培养作为新的评估层级,可以使流程更高效。在本报告中,我们着手检验这一假设,使用了针对PD-L1三个不同表位的三种抗体(Ab1、2和3)及其对应的、具有相同载荷/DAR值的ADC(ADC1、2、3),在若干PD-L1+癌细胞系中检测结合/内化/细胞毒性。尽管对PD-L1重组蛋白或PD-L1+细胞具有相似的高亲和力,但ADC-1在PD-L1+肿瘤细胞系中表现出最强的内化/细胞毒性,而ADC2和3的活性显著较低(差异高达1000倍)。换言之,只有ADC1看起来是有前景的、可进一步开发的ADC候选物。然而,当所有三种ADC使用三维肿瘤类器官进行体外检测时,所有ADC都表现出强效且相当的活性,这与二维培养中所见形成鲜明对比。随后我们在相同的二维细胞来源异种移植肿瘤模型(CDX)中检测这些ADC,其结果与三维肿瘤类器官培养的结果一致,而与二维细胞培养的结果不一致。这些结果证实了三维类器官相比二维细胞具有更好的转化性。总之,这些发现支持一种实用的工作流程,即二维检测作为早期基准,而三维类器官在体内验证之前提供更具预测性的ADC药理学体外读数。我们认为,二维(基准)→三维类器官(转化)→体内(验证)的流程能够更高效地精简临床前ADC筛选。此外,如果二维(单层)培养更能代表正常组织,而三维肿瘤类器官更能代表肿瘤结构这一假设成立,我们可以进一步提出,三维肿瘤与二维细胞培养的差异化检测有助于鉴定更多具有拓宽TW的肿瘤特异性ADC候选物,例如本报告中所述的ADC2和ADC3。
查看英文原文 English abstract
ADC is a promising anti-cancer modality lately for widened therapeutic window (TW) due to tumor-specific targeting by direct binding to tumor cell-associated antigen (TAA), internalization and payload releasing/cytotoxicity induction. The conventional evaluation of lead ADC candidates usually involves the following steps: 1) ELISA-based binding to recombinant TAA protein; 2) TAA + -2D-cell culture for assessing binding, internalization and cytotoxicity; 3) anti-tumor pharmacology using TAA + xenograft tumor models. This process is largely based on the assumption that 2D cancer cell line culture is predictive of in vivo tumor models. However, 2D-assay is frequently not reflective of tumor pharmaology assay from time to time due to distinct architectures of tumor (cell), rendering the process unproductive. Considering 3D tumor organoid being more reflective of tumor than 2D cell culture, we hypothesized that addition of 3D-tumor organoid culture as a new tier of assessment, right before in vivo tumor pharmacology, could make the process more productive. In this report, we set out to test this hypothesis by using three antibodies (Ab1, 2 and 3) against three different epitopes of PD-L1 and their corresponding ADCs (ADC1, 2, 3) with the same payload/DAR-value for binding/ internalizations/cytotoxicity in several PD-L1 + cancer cell lines. Despite similar high-affinity to PD-L1 recombinant protein or PD-L1 + cells, ADC-1 exhibited the strongest internalization/cytotoxicity in PD-L1 + tumor cell lines, whereas ADC2 and 3 showed significantly lower activity (up to 1000x folds in differences). In another word, only ADC1 looks promising as an ADC candidate for further development. However, when all three ADCs were tested in vitro using 3D-tumor organoids, all ADCs demonstrated potent and comparable activities, sharp contrasting to those seen in 2D-cultures. We then tested them in the same 2D-cell-derived xenograft tumor models (CDXs), the results of which are consistent with those from 3D-tumor organoid cultures, but not those from 2D-cell cultures. These confirmed better transnationality of 3D-organoids over 2D-cells. Together, these findings support a practical workflow in which 2D assays serve as early benchmarks, while 3-D organoids provide a more predictive in vitro readout of ADC pharmacology prior to in vivo confirmation. We believe that the process of 2-D (benchmark) → 3-D organoids (translational) → in vivo (validate) can more efficiently streamline preclinical ADC triage. Furthermore, if the hypothesis that 2D (monolayer) culture is more representative of normal tissues, while 3D-tumor organoid is more representative of tumor architecture, is true, we could further suggest that 3D tumor and 2D cell culture differentiation assay could help to identify more tumor-specific ADC candidates with widen TW, e.g. ADC2 and ADC3 described in this report.
利益披露 Disclosure
T. Yang, None.
J. Gao,
CrownBio Employment.
Q. Li,
CrownBio Employment.
J. Wang,
CrownBio Employment.
C. Chen,
Hanx Bio Employment.
H. Li,
Hanx Bio Employment.