89Zr-atezolizumab imaging as a non-invasive approach to assess clinical response to PD-L1 blockade in cancer

89Zr-atezolizumab 成像作为一种非侵入性方法评估癌症对 PD-L1 阻断的临床反应

阅读:5
作者:Frederike Bensch, Elly L van der Veen, Marjolijn N Lub-de Hooge, Annelies Jorritsma-Smit, Ronald Boellaard, Iris C Kok, Sjoukje F Oosting, Carolina P Schröder, T Jeroen N Hiltermann, Anthonie J van der Wekken, Harry J M Groen, Thomas C Kwee, Sjoerd G Elias, Jourik A Gietema, Sandra Sanabria Bohorque

Abstract

Programmed cell death protein-1/ligand-1 (PD-1/PD-L1) blockade is effective in a subset of patients with several tumor types, but predicting patient benefit using approved diagnostics is inexact, as some patients with PD-L1-negative tumors also show clinical benefit1,2. Moreover, all biopsy-based tests are subject to the errors and limitations of invasive tissue collection3-11. Preclinical studies of positron-emission tomography (PET) imaging with antibodies to PD-L1 suggested that this imaging method might be an approach to selecting patients12,13. Such a technique, however, requires substantial clinical development and validation. Here we present the initial results from a first-in-human study to assess the feasibility of imaging with zirconium-89-labeled atezolizumab (anti-PD-L1), including biodistribution, and secondly test its potential to predict response to PD-L1 blockade (ClinicalTrials.gov identifiers NCT02453984 and NCT02478099). We imaged 22 patients across three tumor types before the start of atezolizumab therapy. The PET signal, a function of tracer exposure and target expression, was high in lymphoid tissues and at sites of inflammation. In tumors, uptake was generally high but heterogeneous, varying within and among lesions, patients, and tumor types. Intriguingly, clinical responses in our patients were better correlated with pretreatment PET signal than with immunohistochemistry- or RNA-sequencing-based predictive biomarkers, encouraging further development of molecular PET imaging for assessment of PD-L1 status and clinical response prediction.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。