130 Chapter 7 INTRODUCTION Treatment with immune checkpoint inhibitors (ICIs), such as anti-programmed death-1 (PD-1), anti-programmed-death ligand-1 (PD-L1), and anti-cytotoxic T-lymphocyte antigen-4 (CTLA-4) monoclonal antibodies, have dramatically changed the treatment landscape for a wide range of malignancies1,2. ICIs aim to restore antitumor immunity by blocking immunosuppressive checkpoints, which are hijacked by cancer cells to avoid destruction by the immune system3. Although ICIs are the breakthrough in cancer therapy of the last decade, it is key to further improve its clinical efficacy. Biomarkers play a central role to better understand the underlying mechanisms of (non)-response and acquired resistance4, and thus tackle the next challenge in immuno-oncology. Biomarkers can be subdivided in blood-based, tissue-based (immunohistochemistry or sequencing), exhaled breath analysis5, and imaging-based biomarkers. As each biomarker has its strengths and limitations, these profiles define their respective roles in the optimization of overall ICI efficacy. For example, if the strategy is to preselect patients who may respond to ICI, this requires a predictive biomarker that is preferably non-invasive, cheap, and standardizable. If the focus is on a deeper understanding of the mechanisms of action of ICIs to base the design of appropriate (combination) therapy on, biomarkers are required that answer questions in a cross-validated method. Here, expenses and the global applicability are less important, but this type of research can accelerate future precision medicine advances, and most importantly, may improve upon current drug development pipelines. One of the challenges we encounter in ICI optimization is to define the optimal assessment of the dynamics of tumor PD-L1 expression and PD-1 expression on immune cell subsets. Our knowledge of the regulatory mechanisms controlling PD-L1 expression, and its interplay with other checkpoint molecules6, is incomplete and complicates the interpretation of static ex vivo assessments7. The assessment of the expression of targeted immune checkpoint molecules on protein level on tumor tissue, such as PD-L1 expression, has become clinical practice even though its predictive value is moderate at best. Methods to classify and quantify tumor PD-L1 expression vary greatly8. Histopathology and immunohistochemistry seemingly fail in providing a complete picture, since not every single metastasis can be biopsied in each patient and there is a reasonable risk of sampling errors and misinterpretation. This is where ICI radiolabeled positron emission tomography (PET) imaging may play an important role. Data on whole-body PD-(L)1 expression obtained from PET radiolabeled antibodies may facilitate a more dynamic assessment of immunooncology treatment. Since several studies have now demonstrated the feasibility of 89Zr-labeled ICI PET imaging in clinical setting9-11, we urge to reflect on the questions that can be addressed by imaging of the PD-1/PD-L1 axis with radiolabeled full antibodies. In other words, should clinical PD-1/PD-L1 imaging be used as a predictive biomarker, for preselection of patients, or might another role better match the biomarker-profile? In this issue of The Journal of Nuclear Medicine, Niemeijer et al. report on the safety and biodistribution of zirconium-89 ([89Zr]Zr)-pembrolizumab, a radiolabeled anti-PD-1 monoclonal antibody9.
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