Purpose To investigate the safety and efficacy of durvalumab, a human monoclonal antibody that binds programmed cell death ligand-1 (PD-L1), and the role of PD-L1 expression on clinical response in patients with advanced urothelial bladder cancer (UBC). Methods A phase 1/2 multicenter, open-label study is being conducted in patients with inoperable or metastatic solid tumors. We report here the results from the UBC expansion cohort. Durvalumab (MEDI4736, 10 mg/kg every 2 weeks) was administered intravenously for up to 12 months. The primary end point was safety, and objective response rate (ORR, confirmed) was a key secondary end point. An exploratory analysis of pretreatment tumor biopsies led to defining PD-L1–positive as ≥ 25% of tumor cells or tumor-infiltrating immune cells expressing membrane PD-L1. Results A total of 61 patients (40 PD-L1–positive, 21 PD-L1–negative), 93.4% of whom received one or more prior therapies for advanced disease, were treated (median duration of follow-up, 4.3 months). The most common treatment-related adverse events (AEs) of any grade were fatigue (13.1%), diarrhea (9.8%), and decreased appetite (8.2%). Grade 3 treatment-related AEs occurred in three patients (4.9%); there were no treatment-related grade 4 or 5 AEs. One treatment-related AE (acute kidney injury) resulted in treatment discontinuation. The ORR was 31.0% (95% CI, 17.6 to 47.1) in 42 response-evaluable patients, 46.4% (95% CI, 27.5 to 66.1) in the PD-L1–positive subgroup, and 0% (95% CI, 0.0 to 23.2) in the PD-L1–negative subgroup. Responses are ongoing in 12 of 13 responding patients, with median duration of response not yet reached (range, 4.1+ to 49.3+ weeks). Conclusion Durvalumab demonstrated a manageable safety profile and evidence of meaningful clinical activity in PD-L1–positive patients with UBC, many of whom were heavily pretreated.

clinicaltrials.gov Identifier: NCT01693562.

In the fields of medicine and public health, a common application of areal data models is the study of geographical patterns of disease. When we have several measurements recorded at each spatial location (for example, information on p>/= 2 diseases from the same population groups or regions), we need to consider multivariate areal data models in order to handle the dependence among the multivariate components as well as the spatial dependence between sites. In this article, we propose a flexible new class of generalized multivariate conditionally autoregressive (GMCAR) models for areal data, and show how it enriches the MCAR class. Our approach differs from earlier ones in that it directly specifies the joint distribution for a multivariate Markov random field (MRF) through the specification of simpler conditional and marginal models. This in turn leads to a significant reduction in the computational burden in hierarchical spatial random effect modeling, where posterior summaries are computed using Markov chain Monte Carlo (MCMC). We compare our approach with existing MCAR models in the literature via simulation, using average mean square error (AMSE) and a convenient hierarchical model selection criterion, the deviance information criterion (DIC; Spiegelhalter et al., 2002, Journal of the Royal Statistical Society, Series B64, 583-639). Finally, we offer a real-data application of our proposed GMCAR approach that models lung and esophagus cancer death rates during 1991-1998 in Minnesota counties.

BackgroundA high-quality programmed cell-death ligand 1 (PD-L1) diagnostic assay may help predict which patients are more likely to respond to anti-programmed cell death-1 (PD-1)/PD-L1 antibody-based cancer therapy. Here we describe a PD-L1 immunohistochemical (IHC) staining protocol developed by Ventana Medical Systems Inc. and key analytical parameters of its use in formalin-fixed, paraffin-embedded (FFPE) samples of non-small cell lung cancer (NSCLC) and head and neck squamous cell carcinoma (HNSCC).MethodsAn anti-human PD-L1 rabbit monoclonal antibody (SP263) was optimized for use with the VENTANA OptiView DAB IHC Detection Kit on the automated VENTANA BenchMark ULTRA platform. The VENTANA PD-L1 (SP263) Assay was validated for use with FFPE NSCLC and HNSCC tissue samples in a series of studies addressing sensitivity, specificity, robustness, and precision. Samples from a subset of 181 patients from a Phase 1/2 study of durvalumab (NCT01693562) were analyzed to determine the optimal PD-L1 staining cut-off for enriching the probability of responses to treatment. The scoring algorithm was defined using statistical analysis of clinical response data from this clinical trial and PD-L1 staining parameters in HNSCC and NSCLC tissue. Inter-reader agreement was established by three pathologists who evaluated 81 NSCLC and 100 HNSCC samples across the range of PD-L1 expression levels.ResultsThe VENTANA PD-L1 (SP263) Assay met all pre-defined acceptance criteria. For both cancer types, a cut-off of 25 % of tumor cells with PD-L1 membrane staining of any intensity best discriminated responders from nonresponders. Samples with staining above this value were deemed to have high PD-L1 expression, and those with staining below it were deemed to have low or no PD-L1 expression. Inter-reader agreement on PD-L1 status was 97 and 92 % for NSCLC and HNSCC, respectively.ConclusionsThese results highlight the robustness and reproducibility of the VENTANA PD-L1 (SP263) Assay and support its suitability for use in the evaluation of NSCLC and HNSCC FFPE tumor samples using the devised ≥25 % tumor cell staining cut-off in a clinical setting. The clinical utility of the PD-L1 diagnostic assay as a predictive biomarker will be further validated in ongoing durvalumab studies.Trial registrationClinicalTrials.gov: NCT01693562

The objectives of this analysis were to develop a population pharmacokinetics (PK) model of durvalumab, an anti‐PD‐L1 antibody, and quantify the impact of baseline and time‐varying patient/disease characteristics on PK. Pooled data from two studies (1,409 patients providing 7,407 PK samples) were analyzed with nonlinear mixed effects modeling. Durvalumab PK was best described by a two‐compartment model with both linear and nonlinear clearances. Three candidate models were evaluated: a time‐invariant clearance (CL) model, an empirical time‐varying CL model, and a semimechanistic time‐varying CL model incorporating longitudinal covariates related to disease status (tumor shrinkage and albumin). The data supported a slight decrease in durvalumab clearance with time and suggested that it may be associated with a decrease in nonspecific protein catabolic rate among cancer patients who benefit from therapy. No covariates were clinically relevant, indicating no need for dose adjustment. Simulations indicated similar overall PK exposures following weight‐based and flat‐dosing regimens.