89Zr-immuno-PET using the anti-LAG-3 tracer [89Zr]Zr-BI 754111: demonstrating target specific binding in NSCLC and HNSCC.

PURPOSE: Although lymphocyte activation gene-3 (LAG-3) directed therapies demonstrate promising clinical anti-cancer activity, only a subset of patients seems to benefit and predictive biomarkers are lacking. Here, we explored the potential use of the anti-LAG-3 antibody tracer [89Zr]Zr-BI 754111 as a predictive imaging biomarker and investigated its target specific uptake as well as the correlation of its tumor uptake and the tumor immune infiltration. METHODS: Patients with head and neck (N = 2) or lung cancer (N = 4) were included in an imaging substudy of a phase 1 trial with BI 754091 (anti-PD-1) and BI 754111 (anti-LAG-3). After baseline tumor biopsy and [18F]FDG-PET, patients were given 240 mg of BI 754091, followed 8 days later by administration of [89Zr]Zr-BI 754111 (37 MBq, 4 mg). PET scans were performed 2 h, 96 h, and 144 h post-injection. To investigate target specificity, a second tracer administration was given two weeks later, this time with pre-administration of 40 (N = 3) or 600 mg (N = 3) unlabeled BI 754111, followed by PET scans at 96 h and 144 h post-injection. Tumor immune cell infiltration was assessed by immunohistochemistry and RNA sequencing. RESULTS: Tracer uptake in tumors was clearly visible at the 4-mg mass dose (tumor-to-plasma ratio 1.63 [IQR 0.37-2.89]) and could be saturated by increasing mass doses (44 mg: 0.67 [IQR 0.50-0.85]; 604 mg: 0.56 [IQR 0.42-0.75]), demonstrating target specificity. Tumor uptake correlated to immune cell-derived RNA signatures. CONCLUSIONS: [89Zr]Zr-BI-754111 PET imaging shows favorable technical and biological characteristics for developing a potential predictive imaging biomarker for LAG-3-directed therapies. TRIAL REGISTRATION: ClinicalTrials.gov , NCT03780725. Registered 19 December 2018.

An introduction to causal inference for pharmacometricians.

As formal causal inference begins to play a greater role in disciplines that intersect with pharmacometrics, such as biostatistics, epidemiology, and artificial intelligence/machine learning, pharmacometricians may increasingly benefit from a basic fluency in foundational causal inference concepts. This tutorial seeks to orient pharmacometricians to three such fundamental concepts: potential outcomes, g-formula, and directed acyclic graphs (DAGs).