Evaluation of Candidate Biomarkers of Type 1 Diabetes via the Core for Assay Validation

Recognizing an increasing need for biomarkers that predict clinical outcomes in type 1 diabetes (T1D), JDRF, a major funding organization for T1D research, recently instituted the Core for Assay Validation (CAV) to accelerate the translation of promising assays from discovery to clinical implementation via a process of coordinated evaluation of biomarkers. In this model, the CAV facilitates the validation of candidate assay methods as well as qualification of proposed biomarkers for a specific clinical use in well-characterized patients. We describe here a CAV-driven pilot project aimed at identifying biomarkers that predict the rate of decline in beta cell function after diagnosis. In a formalized pipeline, candidate assays are first assessed for general rationale, technical precision, and biological associations in a cross-sectional cohort. Those with the most favorable characteristics are then applied to placebo arm subjects of T1D intervention trials to assess their predictive correlation with beta cell function. We outline a go/no-go process for advancing candidate assays in a defined qualification pipeline that also allows for the discovery of novel predictive biomarker combinations. This strategy could be a model for other collaborative biomarker development efforts in and beyond T1D

Oral PD-L1 inhibitor GS-4224 selectively engages PD-L1 high cells and elicits pharmacodynamic responses in patients with advanced solid tumors

Background Checkpoint inhibitors targeting the programmed cell death 1 (PD-1)/programmed cell death 1 ligand 1 (PD-L1) pathway are effective therapies in a range of immunogenic cancer types. Blocking this pathway with an oral therapy could benefit patients through greater convenience, particularly in combination regimens, and allow flexible management of immune-mediated toxicities.Methods PD-L1 binding activity was assessed in engineered dimerization and primary cell target occupancy assays. Preclinical antitumor activity was evaluated in ex vivo and in vivo human PD-L1-expressing tumor models. Human safety, tolerability, pharmacokinetics, and biomarker activity were evaluated in an open-label, multicenter, sequential dose-escalation study in patients with advanced solid tumors. Biomarkers evaluated included target occupancy, flow cytometric immunophenotyping, plasma cytokine measurements, and T-cell receptor sequencing.Results GS-4224 binding caused dimerization of PD-L1, blocking its interaction with PD-1 and leading to reversal of T-cell inhibition and increased tumor killing in vitro and in vivo. The potency of GS-4224 was dependent on the density of cell surface PD-L1, with binding being most potent on PD-L1–high cells. In a phase 1 dose-escalation study in patients with advanced solid tumors, treatment was well tolerated at doses of 400–1,500 mg once daily. Administration of GS-4224 was associated with a dose-dependent increase in plasma GS-4224 exposure and reduction in free PD-L1 on peripheral blood T cells, an increase in Ki67 among the PD-1-positive T-cell subsets, and elevated plasma cytokines and chemokines.Conclusions GS-4224 is a novel, orally bioavailable small molecule inhibitor of PD-L1. GS-4224 showed evidence of expected on-target biomarker activity, including engagement of PD-L1 and induction of immune-related pharmacodynamic responses consistent with PD-L1 blockade.Trial registration number NCT04049617

A composite immune signature parallels disease progression across T1D subjects

At diagnosis, most people with type 1 diabetes (T1D) produce measurable levels of endogenous insulin, but the rate at which insulin secretion declines is heterogeneous. To explain this heterogeneity, we sought to identify a composite signature predictive of insulin secretion, using a collaborative assay evaluation and analysis pipeline that incorporated multiple cellular and serum measures reflecting β cell health and immune system activity. The ability to predict decline in insulin secretion would be useful for patient stratification for clinical trial enrollment or therapeutic selection. Analytes from 12 qualified assays were measured in shared samples from subjects newly diagnosed with T1D. We developed a computational tool (DIFAcTO, D ata I ntegration F lexible to Ac count for different T ypes of data and O utcomes) to identify a composite panel associated with decline in insulin secretion over 2 years following diagnosis. DIFAcTO uses multiple filtering steps to reduce data dimensionality, incorporates error estimation techniques including cross-validation and sensitivity analysis, and is flexible to assay type, clinical outcome, and disease setting. Using this novel analytical tool, we identified a panel of immune markers that, in combination, are highly associated with loss of insulin secretion. The methods used here represent a potentially novel process for identifying combined immune signatures that predict outcomes relevant for complex and heterogeneous diseases like T1D. A panel of immune markers that, in combination, are highly associated with loss of insulin secretion in type 1 diabetes was identified using a computational tool