PO.ET05.03 · 实验与分子治疗
靶向CAFs与肿瘤细胞的空间模式以实现精准的基质靶向放射性核素治疗
Targeting spatial patterns of CAFs and tumor cells for precision stroma-targeted radionuclide therapy
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
基质靶向成像,尤其是成纤维细胞激活蛋白抑制剂(FAPI)PET-CT,已在多种癌症类型中展现出显著的诊断成功。相比之下,使用¹⁷⁷Lu标记化合物的基质靶向放射性核素治疗尽管在肿瘤病灶内实现了足够的宏观蓄积,但临床疗效有限(缓解率:10%-20%)。因此我们假设,癌相关成纤维细胞(CAFs)与肿瘤细胞之间的微观空间组织在微观尺度上关键性地影响¹⁷⁷Lu的瘤内分布和辐射剂量沉积。本研究的主要目的是探究空间模式如何影响¹⁷⁷Lu的分布和剂量沉积,并通过实验验证不同空间模式下的差异性治疗结局。以甲状腺髓样癌(MTC)作为代表性模型,我们通过配准、自动化图像分割和空间分布分析,对来自15例患者的279张组织切片进行了定量组织病理学分析。这一分析识别出两种反复出现的空间模式:一种是"包绕"型,其中CAFs在肿瘤细胞簇周围形成连续的环状结构;另一种是"浸润"型,其特征为网状CAF网络中散布着分散的肿瘤巢。这些模式在原发肿瘤、淋巴结转移灶和浸润灶中均一致地被观察到。蒙特卡洛模拟揭示了不同空间模式间¹⁷⁷Lu剂量分布高度异质。浸润型病灶显示出有效的剂量递送,而包绕型病灶的剂量沉积主要局限于富含CAF的外周区域。我们使用6% GelMA 30水凝胶建立了模拟这两种模式的生物学相关3D生物打印模型,该水凝胶与人甲状腺的力学性质(10-20 kPa)相匹配。免疫组化和免疫荧光分析证实了其生物学保真度。使用[¹⁷⁷Lu]Lu-FS-86的放射自显影显示出不同的摄取模式——在CAF环中浓聚(包绕型)对比均质分布(浸润型)——验证了其功能相关性。
这些模型证实,浸润模式允许¹⁷⁷Lu剂量的均质分布并导致显著的细胞死亡,而包绕型则表现出有限的穿透和有限的应答。重要的是,我们在包括肺癌、肝癌、乳腺癌和甲状腺髓样癌在内的多种癌症类型中观察到一致的治疗结局,表明基质靶向放射性核素的疗效依赖于空间模式而非肿瘤类型。这一空间分类系统的建立为基于不同空间模式识别最佳基质靶向放射性核素提供了框架,展现出显著的临床转化潜力。
查看英文原文 English abstract
Stroma-targeted imaging, particularly fibroblast activation protein inhibitor (FAPI) PET-CT, has demonstrated remarkable diagnostic success across multiple cancer types. In contrast, stroma-targeted radionuclide therapy using ¹⁷⁷Lu-labeled compounds showed limited clinical efficacy (response rate: 10%-20%), despite achieving sufficient macroscopic accumulation within tumor lesions. We therefore hypothesize that the microscopic spatial organization between cancer-associated fibroblasts (CAFs) and tumor cells critically influences intratumoral distribution and radiation dose deposition of ¹⁷⁷Lu at the microscale. The primary objective of this study is to investigate how spatial pattern affects ¹⁷⁷Lu distribution and dose deposition, and to experimentally validate the differential therapeutic outcomes across different spatial patterns.Using medullary thyroid carcinoma (MTC) as a representative model, we performed quantitative histopathological analysis of 279 tissue sections from 15 patients through co-registration, automated image segmentation, and spatial distribution profiling. This identified two recurrent spatial patterns: a "surrounding" type, where CAFs form a continuous ring around tumor clusters, and an "infiltrative" type characterized by a reticular CAF network with dispersed tumor nests. These patterns were consistently observed across primary tumors, lymph node metastases, and invasive foci.Monte Carlo simulations revealed highly heterogeneous ¹⁷⁷Lu dose distributions across spatial patterns. While infiltrative lesions showed effective dose delivery, surrounding lesions exhibited dose deposition primarily confined to CAF-rich peripheries. We established biologically relevant 3D bioprinted models mimicking both patterns using 6% GelMA 30 hydrogel, which matches human thyroid mechanical properties (10-20 kPa). Immunohistochemical and immunofluorescence analyses confirmed biological fidelity. Autoradiography with [¹⁷⁷Lu]Lu-FS-86 demonstrated distinct uptake patterns - concentrated in CAF rings (surrounding) versus homogeneous distribution (infiltrative) - validating functional relevance.
These models confirmed that the infiltrative pattern allowed homogeneous dose distribution of 177 Lu and significant cell death, whereas the surrounding type exhibited limited penetration and limited response. Importantly, we observed consistent therapeutic outcomes across multiple cancer types including lung, liver, breast, and medullary thyroid carcinomas, indicating that stroma-targeted radionuclide efficacy is spatial pattern-dependent rather than tumor type-dependent. The establishment of this spatial classification system provides a framework for identifying optimal stroma-targeting radionuclides based on distinct spatial patterns, demonstrating significant potential for clinical translation.
利益披露 Disclosure
Y. Sun, None..
Y. Yang, None..
G. Zhu, None..
J. Zhang, None..
X. Fan, None..
J. Wang, None..
Y. Liu, None..
S. Liu, None..
Y. Lin, None..
X. Cui, None..
Z. Liu, None..
Z. Kong, None.