van Leeuwen Fijs W B, Buckle Tessa, Kersbergen Ariena, Rottenberg Sven, Gilhuijs Kenneth G A
Department of Radiology and Nuclear Medicine, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands.
Eur J Nucl Med Mol Imaging. 2009 Mar;36(3):406-12. doi: 10.1007/s00259-008-1010-z. Epub 2008 Dec 18.
Using a "spontaneous" mammary mouse tumor model we set out to develop diagnostic approaches for non-invasive P-glycoprotein (P-gp) staging and response prediction.
(99m)Tc-MIBI efflux rates were measured using a gamma camera in three Brca1 (-/-); p53 (-/-) mouse mammary tumors that have different Mdr1a/b expression levels. The efflux rates were quantified in the 10-30-min period after injection. In addition to the P-gp-mediated efflux measurements in untreated tumors, efflux measurements were performed in the presence of the P-gp inhibitor tariquidar. Volumetric doxorubicin response patterns for the different tumors were determined and correlated with the efflux rates.
Combined pre- and post-inhibitor treatment imaging of P-gp-mediated efflux correlated with Mdr1a/b expression: basal (0.0026, p = 0.16), 3-fold Mdr1a/b (0.0074, p = 0.02), and 17-fold Mdr1a and 46-fold Mdr1b (0.012, p = 0.002). Based on the doxorubicin response of these tumors, we generated a computer-aided diagnosis model that predicts the likelihood of drug resistance.
Quantified (99m)Tc-MIBI efflux has potential to: (1) noninvasively assign Mdr1 expression levels, (2) predict the therapeutic impact of a P-gp inhibitor, and (3) noninvasively assess the probability of drug resistance.
利用“自发性”小鼠乳腺肿瘤模型,我们着手开发用于非侵入性P-糖蛋白(P-gp)分期和反应预测的诊断方法。
使用γ相机在三种具有不同Mdr1a/b表达水平的Brca1(-/-);p53(-/-)小鼠乳腺肿瘤中测量(99m)Tc-MIBI流出率。在注射后10 - 30分钟内对流出率进行定量。除了在未治疗肿瘤中进行P-gp介导的流出测量外,还在P-gp抑制剂他林洛尔存在的情况下进行流出测量。确定不同肿瘤的阿霉素体积反应模式,并将其与流出率相关联。
P-gp介导的流出的抑制剂治疗前后联合成像与Mdr1a/b表达相关:基础水平(0.0026,p = 0.16),Mdr1a/b为3倍(0.0074,p = 0.02),Mdr1a为17倍且Mdr1b为46倍(0.012,p = 0.002)。基于这些肿瘤的阿霉素反应,我们生成了一个预测耐药可能性的计算机辅助诊断模型。
定量的(99m)Tc-MIBI流出有潜力:(1)非侵入性地确定Mdr1表达水平,(2)预测P-gp抑制剂的治疗效果,以及(3)非侵入性地评估耐药概率。