ImmunoPET of Malignant and Normal B Cells with (89)Zr- and (124)I-Labeled Obinutuzumab Antibody Fragments Reveals Differential CD20 Internalization In Vivo

利用(89)Zr和(124)I标记的奥妥珠单抗抗体片段对恶性和正常B细胞进行免疫PET成像,揭示了体内CD20内化的差异

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Abstract

Purpose: The B-cell antigen CD20 provides a target for antibody-based positron emission tomography (immunoPET). We engineered antibody fragments targeting human CD20 and studied their potential as immunoPET tracers in transgenic mice (huCD20TM) and in a murine lymphoma model expressing human CD20.Experimental Design: Anti-CD20 cys-diabody (cDb) and cys-minibody (cMb) based on rituximab and obinutuzumab (GA101) were radioiodinated and used for immunoPET imaging of a murine lymphoma model. Pairwise comparison of obinutuzumab-based antibody fragments labeled with residualizing ((89)Zr) versus non-residualizing ((124)I) radionuclides by region of interest analysis of serial PET images was conducted both in the murine lymphoma model and in huCD20TM to assess antigen modulation in vivoResults:(124)I-GAcDb and (124)I-GAcMb produced high-contrast immunoPET images of B-cell lymphoma and outperformed the respective rituximab-based tracers. ImmunoPET imaging of huCD20TM showed specific uptake in lymphoid tissues. The use of the radiometal (89)Zr as alternative label for GAcDb and GAcMb yielded greater target-specific uptake and retention compared with (124)I-labeled tracers. Pairwise comparison of (89)Zr- and (124)I-labeled GAcDb and GAcMb allowed assessment of in vivo internalization of CD20/antibody complexes and revealed that CD20 internalization differs between malignant and endogenous B cells.Conclusions: These obinutuzumab-based PET tracers have the ability to noninvasively and quantitatively monitor CD20-expression and have revealed insights into CD20 internalization upon antibody binding in vivo Because they are based on a humanized mAb they have the potential for direct clinical translation and could improve patient selection for targeted therapy, dosimetry prior to radioimmunotherapy, and prediction of response to therapy. Clin Cancer Res; 23(23); 7242-52. ©2017 AACR.

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