Regulatory dendritic cells in the tumor immunoenvironment

The tumor microenvironment is a pivotal factor in tumorigenesis, and especially in progression, as the pathogenesis of cancer critically depends on the complex interactions between various microenvironmental components. A key component of the tumor immunoenvironment is the infiltration of immune cells, which has been proven to play a dual role in tumor growth and progression. This Janus two-faced function of the tumor immunoenvironment is seen in tumor infiltration by T cells, which correlates with improved patient survival, but also with the homing of multiple subsets of immunoregulatory cells that inhibit the antitumor immune response. Regulatory dendritic cells (regDCs) have recently been shown to be induced by tumor-derived factors and represent a new and potentially important player in supporting tumor progression and suppressing the development of antitumor immune responses. Our recent data reveal that different tumor cell lines produce soluble factors that induce polarization of conventional DCs into regDCs, both in vitro and in vivo. These regDCs can suppress the proliferation of pre-activated T cells and are phenotypically and functionally different from their precursors as well as the classical immature conventional DCs. Understanding the biology of regDCs and the mechanisms of their formation in the tumor immunoenvironment will provide a new therapeutic target for re-polarizing protumorigenic immunoregulatory cells into proimmunogenic effector cells able to induce and support effective antitumor immunity.     

Bayesian monitoring of clinical trials with failure-time endpoints

This article presents an aid for monitoring clinical trials with failure-time endpoints based on the Bayesian nonparametric analyses of the data. The posterior distribution is a mixture of Dirichlet processes in the presence of censoring if one assumes a Dirichlet process prior for the survival distribution. Using Gibbs sampling, one can generate random samples from the posterior distribution. With samples from the posterior distributions of treatment-specific survival curves, one can evaluate the current evidence in favor of stopping or continuing the trial based on summary statistics of these survival curves. Because the method is nonparametric, it can easily be used, for example, in situations where hazards cross or are suspected to cross and where relevant clinical decisions might be based on estimating when the integral between the curves might be expected to become positive and in favor of the new but toxic therapy. An example based on an actual trial illustrates the method.     

Joint monitoring and prediction of accrual and event times in clinical trials

In many clinical trials, the primary endpoint is time to an event of interest, for example, time to cardiac attack or tumor progression, and the statistical power of these trials is primarily driven by the number of events observed during the trials. In such trials, the number of events observed is impacted not only by the number of subjects enrolled but also by other factors including the event rate and the follow‐up duration. Consequently, it is important for investigators to be able to monitor and predict accurately patient accrual and event times so as to predict the times of interim and final analyses and enable efficient allocation of research resources, which have long been recognized as important aspects of trial design and conduct. The existing methods for prediction of event times all assume that patient accrual follows a Poisson process with a constant Poisson rate over time; however, it is fairly common in real‐life clinical trials that the Poisson rate changes over time. In this paper, we propose a Bayesian joint modeling approach for monitoring and prediction of accrual and event times in clinical trials. We employ a nonhomogeneous Poisson process to model patient accrual and a parametric or nonparametric model for the event and loss to follow‐up processes. Compared to existing methods, our proposed methods are more flexible and robust in that we model accrual and event/loss‐to‐follow‐up times jointly and allow the underlying accrual rates to change over time. We evaluate the performance of the proposed methods through simulation studies and illustrate the methods using data from a real oncology trial.     

Unique human tumor antigens: immunobiology and use in clinical trials

The individual, unique tumor Ags, which characterize each single tumor, were described 50 years ago in rodents but their molecular characterization was limited to few of them and obtained during the last 20 years. Here we summarize the evidence for the existence and the biological role of such Ags in human tumors, although such evidence was provided only during the last 10 years and by a limited number of studies, a fact leading to a misrepresentation of unique Ags in human tumor immunology. This was also due to the increasing knowledge on the shared, self-human tumor Ags, which have been extensively used as cancer vaccines. In this review, we highlight the biological and clinical importance of unique Ags and suggest how they could be used in clinical studies aimed at assessing their immunogenic and clinical potential both in active and adoptive immunotherapy of human tumors.     

Stopping rules for clinical trials incorporating clinical opinion

A method is described of eliciting a 'range of equivalence', i.e. a range of differences between two treatments over which a group of clinical trial participants would have no clear preference for either treatment. This range of equivalence is then incorporated into a formal stopping rule for the trial using an extension of the group sequential design. Tables for the implementation of the design are presented. The method is discussed in the context of other sequential-trial designs.