Clinical trials design and treatment tailoring: General principles applied to breast cancer research

Biomarker-Guided Non-Adaptive Trial Designs in Phase II and Phase III: A Methodological Review

Biomarker-guided treatment is a rapidly developing area of medicine, where treatment choice is personalised according to one or more of an individual’s biomarker measurements. A number of biomarker-guided trial designs have been proposed in the past decade, including both adaptive and non-adaptive trial designs which test the effectiveness of a biomarker-guided approach to treatment with the aim of improving patient health. A better understanding of them is needed as challenges occur both in terms of trial design and analysis. We have undertaken a comprehensive literature review based on an in-depth search strategy with a view to providing the research community with clarity in definition, methodology and terminology of the various biomarker-guided trial designs (both adaptive and non-adaptive designs) from a total of 211 included papers. In the present paper, we focus on non-adaptive biomarker-guided trial designs for which we have identified five distinct main types mentioned in 100 papers. We have graphically displayed each non-adaptive trial design and provided an in-depth overview of their key characteristics. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. Our comprehensive review provides guidance for those designing biomarker-guided trials.

Time-dependent endpoints as predictors of overall survival in multiple myeloma

Background

Supporting health care sector decisions using time-dependent endpoints (TDEs) such as time to progression (TTP), progression-free survival (PFS), and event-free survival (EFS) remains controversial. This study estimated the quantitative relationship between median TDE and median overall survival (OS) in multiple myeloma (MM) patients.

Methods

Studies (excluding allogeneic transplantation) published from 1970 to 2011 were systematically searched (PubMed). The nonparametric Spearman’s rank correlation coefficient measured the association between median TDE and OS. The quantitative relationship between TDEs and OS was estimated with a two-step approach to a simultaneous Tobit model.

Results

We identified 153 studies: 230 treatment arms, 22,696 patients and mean study duration of 3.8 years. Mean of median TDEs was 22.5 months and median OS was 39.1 months. Correlation coefficients of median TTP, PFS, and EFS with median OS were 0.51 (P = 0.003), 0.75 (P < 0.0001), and 0.84 (P < 0.0001), respectively. We estimate a 2.5 month (95% confidence interval, 1.7–3.2) increase in median OS for each additional month reported for median TDEs. There was no evidence that this relationship differed by type of surrogate.

Conclusion

TDEs predict OS in MM patients; this relationship may be valuable in clinical trial design, drug comparisons, and economic evaluation.

Clinical trial design in the age of molecular profiling

The accelerating science of molecular profiling has necessitated a rapid evolution in clinical trial design. Traditional clinical research begins with Phase I studies to characterize dose-limiting toxicities and defines maximally tolerated doses of drugs in small numbers of patients. Traditional Phase II studies test these drugs at the doses discovered during Phase I drug development in small numbers of patients evaluating efficacy and safety. Phase III studies test new therapies to demonstrate improved activity or improved tolerability compared with a standard of care regimen or a placebo. The rapid advances in the understanding of signal transduction, and the identification of new potential diagnostic and therapeutic targets, now require the design and implementation of molecular clinical trials that are very different than traditional Phase I, II, or III trials. The main differentiating factor is the use of a molecular end point to stratify a subset of patients to receive a specific treatment regimen. This chapter focuses on the issues surrounding (a) the definition of clinical end points and the assessment of tumor response; (b) clinical trial design models to define the targeted pathway; and (c) the need for appropriate biomarkers to monitor the response.

Integrating biomarkers in clinical trials

Biomarkers have a growing role in clinical trials. With the advent of the targeted therapy era, molecular biomarkers in particular are becoming increasingly important within both clinical research and clinical practice. This article focuses on biomarkers that anticipate the prognosis of individual patients ('prognostic' biomarkers) and on biomarkers that predict how individual patients will respond to specific treatments ('predictive' biomarkers, also called 'effect modifiers'). Specific Phase II and III clinical trial designs are discussed in detail for their ability to validate the biomarker and/or to establish the effect of an experimental therapy in patient populations defined by the presence or absence of the biomarker. Contemporary biomarker-based clinical trials in oncology are used as examples.

Role of biomarkers to predict outcomes and response to therapy

Molecular markers are not established in management of bladder cancer. There is, however, a limit to the ability of clinical and pathological parameters to predict patients at high risk for urothelial carcinoma of the bladder (UCB) recurrence or mortality. The assessment of molecular biomarkers in surgical UCB specimens offers additional information on the biology of the disease, and might improve the prediction of oncologic end points. There is also a potential for molecular biomarkers to predict the response to adjuvant or neoadjuvant therapies. Furthermore, markers may guide targeted therapy. Prospective trials are needed to validate the clinical benefit of assessing expression patterns of molecular biomarkers.

Randomized clinical trials with biomarkers: design issues

Clinical biomarker tests that aid in making treatment decisions will play an important role in achieving personalized medicine for cancer patients. Definitive evaluation of the clinical utility of these biomarkers requires conducting large randomized clinical trials (RCTs). Efficient RCT design is therefore crucial for timely introduction of these medical advances into clinical practice, and a variety of designs have been proposed for this purpose. To guide design and interpretation of RCTs evaluating biomarkers, we present an in-depth comparison of advantages and disadvantages of the commonly used designs. Key aspects of the discussion include efficiency comparisons and special interim monitoring issues that arise because of the complexity of these RCTs. Important ongoing and completed trials are used as examples. We conclude that, in most settings, randomized biomarker-stratified designs (ie, designs that use the biomarker to guide analysis but not treatment assignment) should be used to obtain a rigorous assessment of biomarker clinical utility.

Molecular biomarkers for urothelial carcinoma of the bladder: challenges in clinical use

Conventional clinical and pathological parameters are limited in their capacity to detect patients with urothelial carcinoma of the bladder (UCB) who are at high risk for recurrence or mortality. The assessment of molecular biomarkers in surgical UCB specimens offers additional information on the biology of the disease, and might improve the prediction of oncologic end points. A wide range of candidate biomarkers, including key cell-cycle regulators, apoptotic markers and specific growth factors, have been reported to be of prognostic value. To date, however, no molecular biomarker for UCB has been introduced into clinical practice, mainly owing to insufficient validation and the absence of prospective studies. Knowledge about the value of molecular biomarkers in predicting the response to adjuvant or neoadjuvant therapies is also lacking. Prospective trials need to be initiated in high-risk patients selected on the basis of the expression patterns of molecular biomarkers that have already passed the initial steps towards clinical utility.

Bayesian adaptive design for targeted therapy development in lung cancer--a step toward personalized medicine

BACKGROUND

With the advancement in biomedicine, many biologically targeted therapies have been developed. These targeted agents, however, may not work for everyone. Biomarker profiles can be used to identify effective targeted therapies. Our goals are to characterize the molecular signature of individual tumors, offer the best-fit targeted therapies to patients in a study, and identify promising agents for future development.

METHODS

We propose an outcome-based adaptive randomization trial design for patients with advanced stage non-small cell lung cancer. All patients have baseline biopsy samples taken for biomarker assessment prior to randomization to treatments. The primary endpoint of this study is the disease control rate at 8 weeks after randomization. The Bayesian probit model is used to characterize the disease control rate. Patients are adaptively randomized to one of four treatments with the randomization rate based on the updated disease control rate from the accumulated data in the trial. For each biomarker profile, high-performing treatments have higher randomization rates, and vice versa. An early stopping rule is implemented to suspend low-performing treatments from randomization.

RESULTS

Based on extensive simulation studies, with a total of 200 evaluable patients, our trial has desirable operating characteristics to: (1) identify effective agents with a high probability; (2) suspend ineffective agents; and (3) treat more patients with effective agents that correspond to their biomarker profiles. Our trial design continues to update and refine the estimates as the trial progresses.

LIMITATIONS

This biomarker-based trial requires biopsible tumors and a two-week turn around time for biomarker profiling before randomization. Additionally, in order to learn from the interim data and adjust the randomization rate accordingly, the outcome-based adaptive randomization design is applicable only for trials when the endpoint can be assessed in a relative short period of time.

CONCLUSION

Bayesian adaptive randomization trial design is a smart, novel, and ethical design. In conjunction with an early stopping rule, it can be used to efficiently identify effective agents, eliminate ineffective ones, and match effective treatments with patients' biomarker profiles. The proposed design is suitable for the development of targeted therapies and provides a rational design for personalized medicine.

A perspective on risks that impede development of drugs to modify the course of Alzheimer's disease: can they be reduced?

Discovery, development, and testing of new drugs with novel mechanisms and new indications are extremely risky. The number of new drugs introduced into clinical practice with new mechanisms and novel indications has not increased during recent years in spite of advances in biologic science. The sources of uncertainty leading to failure in drug development are discussed in three categories: biologic or target risk, clinical development uncertainty, and market uncertainty. Efforts to develop drugs that modify the course of Alzheimer's disease and/or delay the initial clinical manifestations of disease are subject to all three types of risk. Opportunities for government, academic researchers, advocacy groups, and the pharmaceutical industry to mitigate some of these risks and possibly speed the introduction of new therapies are mentioned.