PO.ET08.01 · 实验与分子治疗
绘制前列腺癌PDX模型对PSMA-617的敏感性图谱以支持联合治疗和耐药研究
Mapping PSMA-617 sensitivity in prostate cancer PDX models to enable combination therapy and resistance studies
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
前列腺癌是男性中第二常被诊断的恶性肿瘤,在全球癌症相关死亡中位居第五。治疗策略包括FDA批准的放射性配体治疗(RLT)[177Lu]Lu-vipivotide tetraxetan([177Lu]Lu-PSMA-617,Pluvicto),适用于在激素治疗和/或化疗进展后的PSMA阳性转移性去势抵抗性前列腺癌(mCRPC)。鉴于[177Lu]Lu-PSMA-617良好的毒性特征,临床研究正在探索将其应用于治疗序列的更早阶段。然而,相当大比例的患者由于内在或获得性耐药机制仅表现出短暂应答或未能应答,这凸显了纳入化疗药物、放射增敏剂、靶向药物和/或免疫治疗的联合方案的必要性。稳健的临床前模型对于评估此类联合策略至关重要,因为它们能够在具有生物学相关性的系统中对治疗疗效进行受控评估。患者来源的异种移植物(PDXs)保留了原始肿瘤的基因组、组织病理学和药理学特征,为临床结局提供可靠的预测能力。因此,经充分验证的PDX模型对于阐明协同相互作用和指导新型联合方法的转化应用至关重要。我们利用了一组基于PSMA表达选择的前列腺癌PDX模型,PSMA表达通过免疫组织化学和AI驱动的图像分析(Visiopharm)进行量化。在治疗前经IHC验证的PSMA表达肿瘤接受约1 mCi(30-37 MBq)的[177Lu]Lu-PSMA-617治疗,并使用SPECT/CT评估肿瘤摄取。纵向监测肿瘤生长,并使用肿瘤生长抑制(TGI)和进展时间(TTP)评估治疗疗效。为模拟获得性耐药,从一个最初敏感的肿瘤(ST1273;XenoSTART)衍生出一个[177Lu]Lu-PSMA-617耐药的PDX(ST1273/RTR;XenoSTART)。该模型可耐受高达30 MBq的[177Lu]Lu-PSMA-617,而PSMA表达和放射性配体摄取在亲本肿瘤和耐药肿瘤之间保持相当。作为概念验证,在ST1273/RTR中评估了以奥拉帕利进行PARP抑制作为放射增敏剂。[177Lu]Lu-PSMA-617单药治疗后29天发生肿瘤复发,而联合治疗将复发延长至49天,显示出增强的抗肿瘤疗效。总的来说,这些经充分表征的前列腺PDX模型——已针对PSMA表达和[177Lu]Lu-PSMA-617应答性进行验证——与全面的临床数据相结合,建立了一个稳健、具有转化相关性的平台,用于对RLT中创新联合策略进行临床前评估。
查看英文原文 English abstract
Prostate cancer represents the second most frequently diagnosed malignancy and ranks fifth in cancer-related mortality worldwide among men. Therapeutic strategies include the FDA-approved radioligand therapy (RLT) [¹⁷⁷Lu]Lu-vipivotide tetraxetan ([¹⁷⁷Lu]Lu-PSMA-617, Pluvicto), indicated for PSMA-positive metastatic castration-resistant prostate cancer (mCRPC) following progression on hormone therapy and/or chemotherapy. Given the favourable toxicity profile of [¹⁷⁷Lu]Lu-PSMA-617, clinical investigations are exploring its application earlier in the therapeutic sequence. Nevertheless, a substantial proportion of patients exhibit only transient responses or fail to respond due to intrinsic or acquired resistance mechanisms, underscoring the need for combination regimens incorporating chemotherapeutics, radiosensitizers, targeted agents, and/or immunotherapies. Robust preclinical models are essential for evaluating such combination strategies, as they enable controlled assessment of treatment efficacy within biologically relevant systems. Patient-derived xenografts (PDXs) retain the genomic, histopathologic, and pharmacologic characteristics of the original tumors, providing reliable predictive power for clinical outcomes. Consequently, well-validated PDX models are critical for elucidating synergistic interactions and guiding translational application of novel combinatorial approaches. We utilized a panel of prostate cancer PDX models selected based on PSMA expression, quantified by immunohistochemistry and AI-driven image analysis (Visiopharm). PSMA-expressing tumors validated by IHC before the treatment were treated with ~1 mCi (30-37 MBq) of [¹⁷⁷Lu]Lu-PSMA-617 and tumor uptake of was evaluated using SPECT/CT. Tumor growth was monitored longitudinally, and therapeutic efficacy was assessed using tumor growth inhibition (TGI) and time to progression (TTP). To model acquired resistance, a [¹⁷⁷Lu]Lu-PSMA-617-resistant PDX (ST1273/RTR; XenoSTART) was derived from an initially sensitive tumor (ST1273; XenoSTART). This model tolerated up to 30 MBq of [¹⁷⁷Lu]Lu-PSMA-617, while PSMA expression and radioligand uptake remained comparable between parental and resistant tumors. As proof-of-concept, PARP inhibition with olaparib was evaluated as a radiosensitizer in ST1273/RTR. Tumor relapse occurred 29 days post-[¹⁷⁷Lu]Lu-PSMA-617 monotherapy, whereas combination treatment extended relapse to 49 days, demonstrating enhanced antitumor efficacy. Collectively, these fully characterized prostate PDX models-validated for PSMA expression and [¹⁷⁷Lu]Lu-PSMA-617 responsiveness-integrated with comprehensive clinical data, establish a robust, translationally relevant platform for preclinical evaluation of innovative combinatorial strategies in RLT.
利益披露 Disclosure
R. Matesanz Sanchez,
Minerva Imaging Employment.
K. Margarete,
Minerva Imaging Employment.
N. Nielsen,
Minerva Imaging Employment.
K. Røpke Jørgensen,
Minerva Imaging Employment.
M. Munk Wessek,
Minerva Imaging Employment.
R. Patricia,
Minerva Imaging Employment.
A. Hessellund Langhave,
Minerva Imaging Employment.
L. Juul Nielsen,
Minerva Imaging Employment.
J. Helle Jane,
Minerva Imaging Employment.
N. Shore,
The START Center for Cancer Research-Carolinas Employment.
S. Gnosa,
Minerva Imaging Employment.
C. Haagen Nielsen,
Minerva Imaging Employment.