Biomarker-Stratified Phase III Clinical Trials: Enhancement with a Subgroup-Focused Sequential Design

Latest Developments in "Adaptive Enrichment" Clinical Trial Designs in Oncology

As cancer has become better understood on the molecular level with the evolution of gene sequencing techniques, considerations for individualized therapy using predictive biomarkers (those associated with a treatment's effect) have shifted to a new level. In the last decade or so, randomized "adaptive enrichment" clinical trials have become increasingly utilized to strike a balance between enrolling all patients with a given tumor type, versus enrolling only a subpopulation whose tumors are defined by a potential predictive biomarker related to the mechanism of action of the experimental therapy. In this review article, we review recent innovative design extensions and adaptations to adaptive enrichment designs proposed during the last few years in the clinical trial methodology literature, both from Bayesian and frequentist perspectives.

Design and reporting of phase III oncology trials with prospective biomarker validation

BACKGROUND

Phase III trials with prospective biomarker validation are essential to drug development in the era of personalized oncology. However, concerns have emerged regarding the design and reporting of phase III trials with prospective biomarker validation.

METHODS

We searched MEDLINE for phase III oncology trials with prospective biomarker validation published in high-impact medical journals from 2011 to 2020. Information regarding trial design and reporting were extracted. Descriptive methods were used to summarize the results.

RESULTS

We identified 45 phase III trials with prospective biomarker validation. There was a trend for increasing use of biomarker validation phase III trials (from 1 trial in 2011 to 12 trials in 2020). For 39 (86.7%) trials, results in biomarker-negative population were either listed as an exploratory subgroup analysis (62.2%) or not mentioned in the methods (24.4%). Twenty-one (46.7%) trials were originally designed without biomarker validation but were then apparently modified to incorporate prospective biomarker validation after trial commencement, albeit only 15 (33.3%) trials reported this change. Treatment effect and primary outcome values in biomarker-negative patients were not reported in 24.4% and 40.0% trials, respectively. For 18 trials with statistically significant results in the overall population, only 7 trials reported a hazard ratio less than 0.8 in the biomarker-negative population.

CONCLUSIONS

Although biomarker validation in phase III trials have been increasingly used in the past decade, issues regarding changes in trial design after commencement without disclosure, underreporting of results in biomarker-negative groups, and recommending treatment in biomarker negative groups despite modest effects require substantial improvement.

Bayesian two-stage sequential enrichment design for biomarker-guided phase II trials for anticancer therapies

Biomarker-guided phase II trials have become increasingly important for personalized cancer treatment. In this paper, we propose a Bayesian two-stage sequential enrichment design for such biomarker-guided trials. We assumed that all patients were dichotomized as marker positive or marker negative based on their biomarker status; the positive patients were considered more likely to respond to the targeted drug. Early stopping rules and adaptive randomization methods were embedded in the design to control the number of patients receiving inferior treatment. At the same time, a Bayesian hierarchical model was used to borrow information between the positive and negative control arms to improve efficiency. Simulation results showed that the proposed design achieved higher empirical power while controlling the type I error and assigned more patients to the superior treatment arms. The operating characteristics suggested that the design has good performance and may be useful for biomarker-guided phase II trials for evaluating anticancer therapies.

Cardiac-sparing radiotherapy for locally advanced non-small cell lung cancer

BACKGROUND

We have carried out a study to determine the scope for reducing heart doses in photon beam radiotherapy of locally advanced non-small cell lung cancer (LA-NSCLC).

MATERIALS AND METHODS

Baseline VMAT plans were created for 20 LA-NSCLC patients following the IDEAL-CRT isotoxic protocol, and were re-optimized after adding an objective limiting heart mean dose (MDHeart). Reductions in MDHeart achievable without breaching limits on target coverage or normal tissue irradiation were determined. The process was repeated for objectives limiting the heart volume receiving ≥ 50 Gy (VHeart-50-Gy) and left atrial wall volume receiving ≥ 63 Gy (VLAwall-63-Gy).

RESULTS

Following re-optimization, mean MDHeart, VHeart-50-Gy and VLAwall-63-Gy values fell by 4.8 Gy and 2.2% and 2.4% absolute respectively. On the basis of associations observed between survival and cardiac irradiation in an independent dataset, the purposefully-achieved reduction in MDHeart is expected to lead to the largest improvement in overall survival. It also led to useful knock-on reductions in many measures of cardiac irradiation including VHeart-50-Gy and VLAwall-63-Gy, providing some insurance against survival being more strongly related to these measures than to MDHeart. The predicted hazard ratio (HR) for death corresponding to the purposefully-achieved mean reduction in MDHeart was 0.806, according to which a randomized trial would require 1140 patients to test improved survival with 0.05 significance and 80% power. In patients whose baseline MDHeart values exceeded the median value in a published series, the average MDHeart reduction was particularly large, 8.8 Gy. The corresponding predicted HR is potentially testable in trials recruiting 359 patients enriched for greater MDHeart values.

CONCLUSIONS

Cardiac irradiation in RT of LA-NSCLC can be reduced substantially. Of the measures studied, reduction of MDHeart led to the greatest predicted increase in survival, and to useful knock-on reductions in other cardiac irradiation measures reported to be associated with survival. Potential improvements in survival can be trialled more efficiently in a population enriched for patients with greater baseline MDHeart levels, for whom larger reductions in heart doses can be achieved.

Frequentist rules for regulatory approval of subgroups in phase III trials: A fresh look at an old problem

Background

The number of Phase III trials that include a biomarker in design and analysis has increased due to interest in personalised medicine. For genetic mutations and other predictive biomarkers, the trial sample comprises two subgroups, one of which, say B+ is known or suspected to achieve a larger treatment effect than the other B−. Despite treatment effect heterogeneity, trials often draw patients from both subgroups, since the lower responding B− subgroup may also gain benefit from the intervention. In this case, regulators/commissioners must decide what constitutes sufficient evidence to approve the drug in the B− population.

Methods and Results

Assuming trial analysis can be completed using generalised linear models, we define and evaluate three frequentist decision rules for approval. For rule one, the significance of the average treatment effect in B− should exceed a pre-defined minimum value, say ZB−>L. For rule two, the data from the low-responding group B− should increase statistical significance. For rule three, the subgroup-treatment interaction should be non-significant, using type I error chosen to ensure that estimated difference between the two subgroup effects is acceptable. Rules are evaluated based on conditional power, given that there is an overall significant treatment effect. We show how different rules perform according to the distribution of patients across the two subgroups and when analyses include additional (stratification) covariates in the analysis, thereby conferring correlation between subgroup effects.

Conclusions

When additional conditions are required for approval of a new treatment in a lower response subgroup, easily applied rules based on minimum effect sizes and relaxed interaction tests are available. Choice of rule is influenced by the proportion of patients sampled from the two subgroups but less so by the correlation between subgroup effects.

Sample size re-estimation in adaptive enrichment design

Clinical trial participants are often heterogeneous, which is a fundamental problem in the rapidly developing field of precision medicine. Participants heterogeneity causes considerable difficulty in the current phase III trial designs. Adaptive enrichment designs provide a flexible and intuitive solution. At the interim analysis, we enrich the subgroup of trial participants who have a higher likelihood to benefit from the new treatment. However, it is critical to control the level of the test size and maintain adequate power after enrichment of certain subgroup of participants. We develop two adaptive enrichment strategies with sample size re-estimation and verify their feasibility and practicability through extensive simulations and sensitivity analyses. The simulation studies show that the proposed methods can control the overall type I error rate and exhibit competitive improvement in terms of statistical power and expected sample size. The proposed designs are exemplified with a real trial application.