Dose Finding with Escalation with Overdose Control (EWOC) in Cancer Clinical Trials

Traditionally, the major objective in phase I trials is to identify a working-dose for subsequent studies, whereas the major endpoint in phase II and III trials is treatment efficacy. The dose sought is typically referred to as the maximum tolerated dose (MTD). Several statistical methodologies have been proposed to select the MTD in cancer phase I trials. In this manuscript, we focus on a Bayesian adaptive design, known as escalation with overdose control (EWOC). Several aspects of this design are discussed, including large sample properties of the sequence of doses selected in the trial, choice of prior distributions, and use of covariates. The methodology is exemplified with real-life examples of cancer phase I trials. In particular, we show in the recently completed ABR-217620 (naptumomab estafenatox) trial that omitting an important predictor of toxicity when dose assignments to cancer patients are determined results in a high percent of patients experiencing severe side effects and a significant proportion treated at sub-optimal doses.Comment: Published in at http://dx.doi.org/10.1214/10-STS333 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Modeling Correlated Time-Varying Covariate Effects In A Cox-Type Regression Model

In this paper, I extend the proposed model by McKeague and Tighiouart (2000) to handle time-varying correlated covariate effects for the analysis of survival data. I use the conditional predictive ordinates (CPO’s) for model comparison and the methodology is illustrated by an application to nasopharynx cancer survival data. A reversible jump MCMC sampler to estimate the CPO’s will be presented

A Bayesian seamless phase I-II trial design with two stages for cancer clinical trials with drug combinations

The use of drug combinations in clinical trials is increasingly common during the last years since a more favorable therapeutic response may be obtained by combining drugs. In phase I clinical trials, most of the existing methodology recommends a one unique dose combination as "optimal", which may result in a subsequent failed phase II clinical trial since other dose combinations may present higher treatment efficacy for the same level of toxicity. We are particularly interested in the setting where it is necessary to wait a few cycles of therapy to observe an efficacy outcome and the phase I and II population of patients are different with respect to treatment efficacy. Under these circumstances, it is common practice to implement two-stage designs where a set of maximum tolerated dose combinations is selected in a first stage, and then studied in a second stage for treatment efficacy. In this article we present a new two-stage design for early phase clinical trials with drug combinations. In the first stage, binary toxicity data is used to guide the dose escalation and set the maximum tolerated dose combinations. In the second stage, we take the set of maximum tolerated dose combinations recommended from the first stage, which remains fixed along the entire second stage, and through adaptive randomization, we allocate subsequent cohorts of patients in dose combinations that are likely to have high posterior median time to progression. The methodology is assessed with extensive simulations and exemplified with a real trial

Adaptive and Sequential Methods for Clinical Trials

This special issue describes state-of-the-art statistical research in adaptive and sequential methods and the application of such methods in clinical trials. It provides 1 review article and 5 research articles contributed by some of the leading experts in this field. The review article gives a comprehensive overview of the outstanding methodology in the current literature that is related to adaptive and sequential clinical trials, while each of the 5 research articles addresses specific critical issues in contemporary clinical trials, as summarized below

Value Of A Boehmian At A Point And At Infinity

We define the notion of a value of a Boehmian at a point and study its properties. We prove that a Boehmian which has a value at a point is a Borel measure in a neighborhood of that point. We also define the notion of a value of a Boehmian at infinity

Designs of Early Phase Cancer Trials with Drug Combinations

We discuss several innovative phase I and phase I--II designs for early phase cancer clinical trial with drug combinations focusing on continuous dose levels of both agents. For phase I trials with drug combinations, the main objective is to estimate the maximum tolerated dose (MTD) curve in the two-dimensional Cartesian plane. A parametric model is used to describe the relationship between the doses of the two agents and the probability of dose-limiting toxicity (DLT). Trial design proceeds using cohorts of two patients receiving doses according to univariate escalation with overdose control (EWOC) or continual reassessment method (CRM). At the end of the trial, the MTD is estimated as a function of Bayes estimates of the model parameters. Furthermore, we present the model where a fraction of DLTs can be attributed to one or both agents, and show how the parametric designs can be adapted to account for an unknown fraction of attributable DLTs. We also consider the inclusion of a binary baseline covariate to describe sub-groups with different frailty levels. In phase I--II trials, it may not be possible to evaluate efficacy in a short window of time. In this case, two-stage designs are frequently employed. First, a set of maximum tolerated dose combinations is selected. Next, the selected set is then tested for efficacy, sometimes in a different patient population than that used in the first stage. We discuss binary and time-to-event endpoints to identify dose combinations along the MTD curve with maximum probability of efficacy in the second stage

Incorporating a Patient Dichotomous Characteristic in Cancer Phase I Clinical Trials Using Escalation with Overdose Control

We describe a design for cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility. The goal is to estimate the maximum tolerated dose (MTD) given patient’s specific dichotomous covariate value. The design is Bayesian adaptive and is an extension of escalation with overdose control (EWOC). We will assess the performance of this method by comparing the following designs via extensive simulations: (1) design using a covariate; patients are accrued to the trial sequentially and the dose given to a patient depends on his/her baseline covariate value, (2) design ignoring the covariate; patients are accrued to the trial sequentially and the dose given to a patient does not depend on his/her baseline covariate value, and (3) design using separate trials; in each group, patients are accrued to the trial sequentially and EWOC is implemented in each group. These designs are compared with respect to safety of the trial and efficiency of the estimates of the MTDs via extensive simulations. We found that ignoring a significant baseline binary covariate in the model results in a substantial number of patients being overdosed. On the other hand, accounting for a nonsignificant covariate in the model has practically no effect on the safety of the trial and efficiency of the estimates of the MTDs

Cancer epithelia-derived mitochondrial DNA is a targetable initiator of a paracrine signaling loop that confers taxane resistance.

Stromal-epithelial interactions dictate cancer progression and therapeutic response. Prostate cancer (PCa) cells were identified to secrete greater concentration of mitochondrial DNA (mtDNA) compared to noncancer epithelia. Based on the recognized coevolution of cancer-associated fibroblasts (CAF) with tumor progression, we tested the role of cancer-derived mtDNA in a mechanism of paracrine signaling. We found that prostatic CAF expressed DEC205, which was not expressed by normal tissue-associated fibroblasts. DEC205 is a transmembrane protein that bound mtDNA and contributed to pattern recognition by Toll-like receptor 9 (TLR9). Complement C3 was the dominant gene targeted by TLR9-induced NF-κB signaling in CAF. The subsequent maturation complement C3 maturation to anaphylatoxin C3a was dependent on PCa epithelial inhibition of catalase in CAF. In a syngeneic tissue recombination model of PCa and associated fibroblast, the antagonism of the C3a receptor and the fibroblastic knockout of TLR9 similarly resulted in immune suppression with a significant reduction in tumor progression, compared to saline-treated tumors associated with wild-type prostatic fibroblasts. Interestingly, docetaxel, a common therapy for advanced PCa, further promoted mtDNA secretion in cultured epithelia, mice, and PCa patients. The antiapoptotic signaling downstream of anaphylatoxin C3a signaling in tumor cells contributed to docetaxel resistance. The inhibition of C3a receptor sensitized PCa epithelia to docetaxel in a synergistic manner. Tumor models of human PCa epithelia with CAF expanded similarly in mice in the presence or absence of docetaxel. The combination therapy of docetaxel and C3 receptor antagonist disrupted the mtDNA/C3a paracrine loop and restored docetaxel sensitivity