A Preclinical Assessment of 89Zr-atezolizumab Identifies a Requirement for Carrier Added Formulations Not Observed with 89Zr-C4.
Anna MorozChia-Yin LeeYung-Hua WangJeffrey C HsiaoNatalia SevillanoCharles TruilletCharles S CraikLawrence FongCheng-I WangKenneth W BaylesPublished in: Bioconjugate chemistry (2018)
The swell of experimental imaging technologies to noninvasively measure immune checkpoint protein expression presents the opportunity for rigorous comparative studies toward identifying a gold standard. 89Zr-atezolizumab is currently in man, and early data show tumor targeting but also abundant uptake in several normal tissues. Therefore, we conducted a reverse translational study both to understand if tumor to normal tissue ratios for 89Zr-atezolizumab could be improved and to make direct comparisons to 89Zr-C4, a radiotracer that we showed can detect a large dynamic range of tumor-associated PD-L1 expression. PET/CT and biodistribution studies in tumor bearing immunocompetent and nu/nu mice revealed that high specific activity 89Zr-atezolizumab (∼2 μCi/μg) binds to PD-L1 on tumors but also results in very high uptake in many normal mouse tissues, as expected. Unexpectedly, 89Zr-atezolizumab uptake was generally higher in normal mouse tissues compared to 89Zr-C4 and lower in H1975, a tumor model with modest PD-L1 expression. Also unexpectedly, reducing the specific activity at least 15-fold suppressed 89Zr-atezo uptake in normal mouse tissues but increased tumor uptake to levels observed with high specific activity 89Zr-C4. In summary, these data reveal that low specific activity 89Zr-atezo may be necessary for accurately measuring PD-L1 in the tumor microenvironment, assuming a threshold can be identified that preferentially suppresses binding in normal tissues without reducing binding to tumors with abundant expression. Alternatively, high specific activity approaches like 89Zr-C4 PET may be simpler to implement clinically to measure the broad dynamic range of PD-L1 expression known to manifest among tumors.
Keyphrases
- pet imaging
- positron emission tomography
- pet ct
- gene expression
- computed tomography
- metabolic syndrome
- stem cells
- type diabetes
- dna methylation
- machine learning
- single cell
- genome wide
- poor prognosis
- signaling pathway
- big data
- drug delivery
- insulin resistance
- mass spectrometry
- transcription factor
- photodynamic therapy
- dna binding