PD-L1 Positron Emission Tomography Imaging in Patients With Non-Small Cell Lung Cancer: Preliminary Results of the ImmunoPET Phase 0 Study

Navigating the landscape of PD-1/PD-L1 imaging tracers: from challenges to opportunities

Immunotherapy targeted to immune checkpoint inhibitors, such as the program cell death receptor (PD-1) and its ligand (PD-L1), has revolutionized cancer treatment. However, it is now well-known that PD-1/PD-L1 immunotherapy response is inconsistent among patients. The current challenge is to customize treatment regimens per patient, which could be possible if the PD-1/PD-L1 expression and dynamic landscape are known. With positron emission tomography (PET) imaging, it is possible to image these immune targets non-invasively and system-wide during therapy. A successful PET imaging tracer should meet specific criteria concerning target affinity, specificity, clearance rate and target-specific uptake, to name a few. The structural profile of such a tracer will define its properties and can be used to optimize tracers in development and design new ones. Currently, a range of PD-1/PD-L1-targeting PET tracers are available from different molecular categories that have shown impressive preclinical and clinical results, each with its own advantages and disadvantages. This review will provide an overview of current PET tracers targeting the PD-1/PD-L1 axis. Antibody, peptide, and antibody fragment tracers will be discussed with respect to their molecular characteristics and binding properties and ways to optimize them.

Diagnostic Positron Emission Tomography Imaging with Zirconium-89 Desferrioxamine B Squaramide: From Bench to Bedside

Molecular imaging with antibodies radiolabeled with positron-emitting radionuclides combines the affinity and selectivity of antibodies with the sensitivity of Positron Emission Tomography (PET). PET imaging allows the visualization and quantification of the biodistribution of the injected radiolabeled antibody, which can be used to characterize specific biological interactions in individual patients. This characterization can provide information about the engagement of the antibody with a molecular target such as receptors present in elevated levels in tumors as well as providing insight into the distribution and clearance of the antibody. Potential applications of clinical PET with radiolabeled antibodies include identifying patients for targeted therapies, characterization of heterogeneous disease, and monitoring treatment response.Antibodies often take several days to clear from the blood pool and localize in tumors, so PET imaging with radiolabeled antibodies requires the use of a radionuclide with a similar radioactive half-life. Zirconium-89 is a positron-emitting radionuclide that has a radioactive half-life of 78 h and relatively low positron emission energy that is well suited to radiolabeling antibodies. It is essential that the zirconium-89 radionuclide be attached to the antibody through chemistry that provides an agent that is stable in vivo with respect to the dissociation of the radionuclide without compromising the biological activity of the antibody.This Account focuses on our research using a simple derivative of the bacterial siderophore desferrioxamine (DFO) with a squaramide ester functional group, DFO-squaramide (DFOSq), to link the chelator to antibodies. In our work, we produce conjugates with an average ∼4 chelators per antibody, and this does not compromise the binding of the antibody to the target. The resulting antibody conjugates of DFOSq are stable and can be easily radiolabeled with zirconium-89 in high radiochemical yields and purity. Automated methods for the radiolabeling of DFOSq-antibody conjugates have been developed to support multicenter clinical trials. Evaluation of several DFOSq conjugates with antibodies and low molecular weight targeting agents in tumor mouse models gave PET images with high tumor uptake and low background. The promising preclinical results supported the translation of this chemistry to human clinical trials using two different radiolabeled antibodies. The potential clinical impact of these ongoing clinical trials is discussed.The use of DFOSq to radiolabel relatively low molecular weight targeting molecules, peptides, and peptide mimetics is also presented. Low molecular weight molecules typically clear the blood pool and accumulate in target tissue more rapidly than antibodies, so they are usually radiolabeled with positron-emitting radionuclides with shorter radioactive half-lives such as fluorine-18 (t1/2 ∼ 110 min) or gallium-68 (t1/2 ∼ 68 min). Radiolabeling peptides and peptide mimetics with zirconium-89, with its longer radioactive half-life (t1/2 = 78 h), could facilitate the centralized manufacture and distribution of radiolabeled tracers. In addition, the ability to image patients at later time points with zirconium-89 based agents (e.g. 4-24 h after injection) may also allow the delineation of small or low-uptake disease sites as the delayed imaging results in increased clearance of the tracer from nontarget tissue and lower background signal.

Bifunctional octadentate pseudopeptides as Zirconium-89 chelators for immuno-PET applications

BACKGROUND

Positron emission tomography (PET) is a highly sensitive method that provides fine resolution images, useful in the field of clinical diagnostics. In this context, Zirconium-89 (89Zr)-based imaging agents have represented a great challenge in molecular imaging with immuno-PET, which employs antibodies (mAbs) as biological vectors. Indeed, immuno-PET requires radionuclides that can be attached to the mAb to provide stable in vivo conjugates, and for this purpose, the radioactive element should have a decay half-life compatible with the time needed for the biodistribution of the immunoglobulin. In this regard, 89Zr is an ideal radioisotope for immuno-PET because its half-life perfectly matches the in vivo pharmacokinetics of mAbs.

RESULTS

The main objective of this work was the design and synthesis of a series of bifunctional octadentate pseudopeptides able to generate stable 89Zr complexes. To achieve this, here we investigated hydroxamate, N-methylhydroxamate and catecholate chelating moieties in complexing radioactive zirconium. N-methylhydroxamate proved to be the most effective 89Zr-chelating group. Furthermore, the increased flexibility and hydrophilicity obtained by using polyoxyethylene groups spacing the hydroxamate units led to chelators capable of rapidly forming (15 min) stable and water-soluble complexes with 89Zr under mild reaction conditions (aqueous environment, room temperature, and physiological pH) that are mandatory for complexation reactions involving biomolecules. Additionally, we report challenge experiments with the competitor ligand EDTA and metal ions such as Fe3+, Zn2+ and Cu2+. In all examined conditions, the chelators demonstrated stability against transmetallation. Finally, a maleimide moiety was introduced to apply one of the most promising ligands in bioconjugation reactions through Thiol-Michael chemistry.

CONCLUSION

Combining solid phase and solution synthesis techniques, we identified novel 89Zr-chelating molecules with a peptide scaffold. The adopted chemical design allowed modulation of molecular flexibility, hydrophilicity, as well as the decoration with different zirconium chelating groups. Best results in terms of 89Zr-chelating properties were achieved with the N-methyl hydroxamate moiety. The Zirconium complexes obtained with the most effective compounds were water-soluble, stable to transmetallation, and resistant to peptidases for at least 6 days. Further studies are needed to assess the potential of this novel class of molecules as Zirconium-chelating agents for in vivo applications.

Enhancing anti-CD274 (PD-L1) targeting through combinatorial immunotherapy with bispecific antibodies and fusion proteins: from preclinical to phase II clinical trials

INTRODUCTION

Immune checkpoint inhibitors have achieved great success in the treatment of many different types of cancer. Programmed cell death protein ligand 1 (PD-L1, CD274) is a major immunosuppressive immune checkpoint and a target for several already approved monoclonal antibodies. Despite this, novel strategies are under development, as the overall response remains low.

AREAS COVERED

In this review, an overview of the current biomarkers for response to PD-L1 inhibitor treatment is given, followed by a discussion of potential novel biomarkers, including tumor mutational burden and circulating tumor DNA. Combinatorial immunotherapy is a potential novel strategy to increase the response to PD-L1 inhibitor treatment and currently, several interesting bispecific antibodies as well as bispecific fusion proteins are undergoing early clinical investigation. We focus on substances targeting PD-L1 and a secondary target, and a secondary immunomodulatory target like CTLA-4, TIGIT, or CD47.

EXPERT OPINION

Overall, the presented studies show anti-tumor activity of these combinatorial immunotherapeutic approaches. However, still relatively low response rates suggest a need for better biomarkers.

Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics

BACKGROUND

Immune checkpoint inhibitors (ICIs) have emerged as one of the most promising first-line therapeutics in the management of non-small cell lung cancer (NSCLC). However, only a subset of these patients responds to ICIs, highlighting the clinical need to develop better predictive and prognostic biomarkers. This study will leverage pre-treatment imaging profiles to develop survival risk models for NSCLC patients treated with first-line immunotherapy.

METHODS

Advanced NSCLC patients (n = 149) were retrospectively identified from two institutions who were treated with first-line ICIs. Radiomics features extracted from pretreatment imaging scans were used to build the predictive models for progression-free survival (PFS) and overall survival (OS). A compendium of five feature selection methods and seven machine learning approaches were utilized to build the survival risk models. The concordance index (C-index) was used to evaluate model performance.

RESULTS

From our results, we found several combinations of machine learning algorithms and feature selection methods to achieve similar performance. K-nearest neighbourhood (KNN) with ReliefF (RL) feature selection was the best-performing model to predict PFS (C-index = 0.61 and 0.604 in discovery and validation cohorts), while XGBoost with Mutual Information (MI) feature selection was the best-performing model for OS (C-index = 0.7 and 0.655 in discovery and validation cohorts).

CONCLUSION

The results of this study highlight the importance of implementing an appropriate feature selection method coupled with a machine learning strategy to develop robust survival models. With further validation of these models on external cohorts when available, this can have the potential to improve clinical decisions by systematically analyzing routine medical images.