Point/counterpoint: randomized versus single-arm phase II clinical trials for patients with newly diagnosed glioblastoma

Two-stage response adaptive randomization designs for multi-arm trials with binary outcome

In recent years, adaptive randomization methods have gained significant popularity in clinical research and trial design due to their ability to provide both efficiency and flexibility in adjusting the statistical procedures of ongoing clinical trials. For a study to compare multiple treatments, a multi-arm two-stage design could be utilized to select the best treatment from the first stage and further compare that treatment with control in the second stage. The traditional design used equal randomization in both stages. To better utilize the interim results from the first stage, we propose to develop response adaptive randomization two-stage designs for a multi-arm clinical trial with binary outcome. Two allocation methods are considered: (1) an optimal allocation based on a sequential design; (2) the play-the-winner rule. Optimal multi-arm two-stage designs are obtained under three criteria: minimizing the expected number of failures, minimizing the average expected sample size, and minimizing the expected sample size under the null hypothesis. Simulation studies show that the proposed adaptive design based on the play-the-winner rule has good performance. A phase II trial for patients with pancreas adenocarcinoma and a germline BRCA/ PALB2 mutation was used to illustrate the application of the proposed response adaptive randomization designs.

Leveraging external control data in the design and analysis of neuro-oncology trials: Pearls and perils

BACKGROUND

Randomized controlled trials have been the gold standard for evaluating medical treatments for many decades but they are often criticized for requiring large sample sizes. Given the urgent need for better therapies for glioblastoma, it has been argued that data collected from patients treated with the standard regimen can provide high-quality external control data to supplement or replace concurrent control arm in future glioblastoma trials.

METHODS

In this article, we provide an in-depth appraisal of the use of external control data in the context of neuro-oncology trials. We describe several clinical trial designs with particular attention to how external information is utilized and address common fallacies that may lead to inappropriate adoptions of external control data.

RESULTS

Using 2 completed glioblastoma trials, we illustrate the use of an assessment tool that lays out a blueprint for assembling a high-quality external control data set. Using statistical simulations, we draw caution from scenarios where these approaches can fall short on controlling the type I error rate.

CONCLUSIONS

While this approach may hold promise in generating informative data in certain settings, this sense of optimism should be tampered with a healthy dose of skepticism due to a myriad of design and analysis challenges articulated in this review. Importantly, careful planning is key to its successful implementation.

Tacrolimus After rATG and Infliximab Induction Immunosuppression-RIMINI Trial

BACKGROUND

Infliximab selectively targets recently activated effector cells and, as an induction agent, might enable the safe elimination of mycophenolate from maintenance immunosuppression in kidney transplantation.

METHODS

This is a phase II international multicenter open-label single-arm confidence interval (CI)-based clinical trial of the BIO-DrIM EU consortium aimed at assessing the efficacy and safety of rabbit antithymocyte globulin and infliximab induction in kidney transplantation. Sixty-seven primary kidney transplant recipients at low risk (panel-reactive antibodies <20%, no donor-specific antibodies [DSA]) received rabbit antithymocyte globulin (2 × 1.5 mg/kg, postoperative days 0 and 1) and infliximab (5 mg/kg, postoperative day 2), followed by mycophenolate-free tacrolimus-based immunosuppression for 12 mo. The primary endpoint was efficacy failure, defined as a composite of acute rejection, graft loss, or poor graft function (estimated glomerular filtration rate <40 mL/min) at 12 mo and was based on the endpoint of the comparator study. Additionally, a historical propensity-matched control cohort was established.

RESULTS

Primary endpoint occurred in 22 of 67 patients (32.84%), with upper bound of an exact 1-sided 95% CI of 43.47%, which met the predefined criteria (efficacy failure of <40% and upper-bound 95% CI of <50%) and was similar in the historical matched cohort. By 12 mo, 79.1% of patients remained on the study protocol. Lower rates of BK replication (6% versus 22.4%; P  = 0.013) but higher rates of de novo DSAs (11.9% versus 1.5%; P  = 0.039) were observed in the study cohort.

CONCLUSIONS

A similar efficacy of the study immunosuppression regimen to the comparator study and the historical matched cohort was found. However, a higher de novo DSA emergence points to an increased risk of antibody-mediated rejection (NCT04114188).

Inaugural Results of the Individualized Screening Trial of Innovative Glioblastoma Therapy: A Phase II Platform Trial for Newly Diagnosed Glioblastoma Using Bayesian Adaptive Randomization

PURPOSE

The Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) is a phase II platform trial that uses response adaptive randomization and genomic profiling to efficiently identify novel therapies for phase III testing. Three initial experimental arms (abemaciclib [a cyclin-dependent kinase [CDK]4/6 inhibitor], neratinib [an epidermal growth factor receptor [EGFR]/human epidermal growth factor receptor 2 inhibitor], and CC-115 [a deoxyribonucleic acid-dependent protein kinase/mammalian target of rapamycin inhibitor]) were simultaneously evaluated against a common control arm. We report the results for each arm and examine the feasibility and conduct of the adaptive platform design.

PATIENTS AND METHODS

Patients with newly diagnosed O6-methylguanine-DNA methyltransferase-unmethylated glioblastoma were eligible if they had tumor genotyping to identify prespecified biomarker subpopulations of dominant glioblastoma signaling pathways (EGFR, phosphatidylinositol 3-kinase, and CDK). Initial random assignment was 1:1:1:1 between control (radiation therapy and temozolomide) and the experimental arms. Subsequent Bayesian adaptive randomization was incorporated on the basis of biomarker-specific progression-free survival (PFS) data. The primary end point was overall survival (OS), and one-sided P values are reported. The trial is registered with ClinicalTrials.gov (identifier: NCT02977780).

RESULTS

Two hundred thirty-seven patients were treated (71 control; 73 abemaciclib; 81 neratinib; 12 CC-115) in years 2017-2021. Abemaciclib and neratinib were well tolerated, but CC-115 was associated with ≥ grade 3 treatment-related toxicity in 58% of patients. PFS was significantly longer with abemaciclib (hazard ratio [HR], 0.72; 95% CI, 0.49 to 1.06; one-sided P = .046) and neratinib (HR, 0.72; 95% CI, 0.50 to 1.02; one-sided P = .033) relative to the control arm but there was no PFS benefit with CC-115 (one-sided P = .523). None of the experimental therapies demonstrated a significant OS benefit (P > .05).

CONCLUSION

The INSIGhT design enabled efficient simultaneous testing of three experimental agents using a shared control arm and adaptive randomization. Two investigational arms had superior PFS compared with the control arm, but none demonstrated an OS benefit. The INSIGhT design may promote improved and more efficient therapeutic discovery in glioblastoma. New arms have been added to the trial.

A critical analysis of neuro-oncology clinical trials

BACKGROUND

Limitations in trial design, accrual, and data reporting impact efficient and reliable drug evaluation in cancer clinical trials. These concerns have been recognized in neuro-oncology but have not been comprehensively evaluated. We conducted a semi-automated survey of adult interventional neuro-oncology trials, examining design, interventions, outcomes, and data availability trends.

METHODS

Trials were selected programmatically from ClinicalTrials.gov using primary malignant central nervous system tumor classification terms. Regression analyses assessed design and accrual trends; effect size analysis utilized survival rates among trials investigating survival.

RESULTS

Of 3038 reviewed trials, most trials reporting relevant information were nonblinded (92%), single group (65%), nonrandomized (51%), and studied glioblastomas (47%) or other gliomas. Basic design elements were reported by most trials, with reporting increasing over time (OR = 1.24, P < .00001). Trials assessing survival outcomes were estimated to assume large effect sizes of interventions when powering their designs. Forty-two percent of trials were completed; of these, 38% failed to meet their enrollment target, with worse accrual over time (R = -0.94, P < .00001) and for US versus non-US based trials (OR = 0.5, P < .00001). Twenty-eight percent of completed trials reported partial results, with greater reporting for US (34.6%) versus non-US based trials (9.3%, P < .00001). Efficacy signals were detected by 15%-23% of completed trials reporting survival outcomes.

CONCLUSION

Low randomization rates, underutilization of controls, and overestimation of effect size, particularly pronounced in early-phase trials, impede generalizability of results. Suboptimal designs may be driven by accrual challenges, underscoring the need for cooperative efforts and novel designs. The limited results reporting highlights the need to incentivize data reporting and harmonization.

Bayesian approaches to include real-world data in clinical studies

Randomized clinical trials have been the mainstay of clinical research, but are prohibitively expensive and subject to increasingly difficult patient recruitment. Recently, there is a movement to use real-world data (RWD) from electronic health records, patient registries, claims data and other sources in lieu of or supplementing controlled clinical trials. This process of combining information from diverse sources calls for inference under a Bayesian paradigm. We review some of the currently used methods and a novel non-parametric Bayesian (BNP) method. Carrying out the desired adjustment for differences in patient populations is naturally done with BNP priors that facilitate understanding of and adjustment for population heterogeneities across different data sources. We discuss the particular problem of using RWD to create a synthetic control arm to supplement single-arm treatment only studies. At the core of the proposed approach is the model-based adjustment to achieve equivalent patient populations in the current study and the (adjusted) RWD. This is implemented using common atoms mixture models. The structure of such models greatly simplifies inference. The adjustment for differences in the populations can be reduced to ratios of weights in such mixtures. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Current drug development and trial designs in neuro-oncology: report from the first American Society of Clinical Oncology and Society for Neuro-Oncology Clinical Trials Conference

Successful drug development for people with cancers of the CNS has been challenging. There are multiple barriers to successful drug development including biological factors, rarity of the disease, and ineffective use of clinical trials. Based upon a series of presentations at the First Central Nervous System Clinical Trials Conference hosted by the American Society of Clinical Oncology and the Society for Neuro-Oncology, we provide an overview on drug development and novel trial designs in neuro-oncology. This Review discusses the challenges of therapeutic development in neuro-oncology and proposes strategies to improve the drug discovery process by enriching the pipeline of promising therapies, optimising trial design, incorporating biomarkers, using external data, and maximising efficacy and reproducibility of clinical trials.

Integrating non-concurrent controls in the analyses of late-entry experimental arms in multi-arm trials with a shared control group in the presence of parameter drift

BACKGROUND

Under a master protocol, open platform trials allow new experimental treatments to enter an existing clinical trial. Whether late-entry experimental treatments should be compared to all available or concurrently randomized controls is not well established. Using all available data can increase power and precision; however, drift in population parameters can yield biased estimates and impact type I error rate.

METHODS

We explored the application of methods developed to incorporate historical controls in two-arm trials to the analysis of a late-entry arm in a simulated open platform trial under varying scenarios of parameter drift. Methods explored include test-then-pool, fixed power prior, dynamic power prior, and multi-source exchangeability model approaches.

RESULTS/CONCLUSIONS

Simulated trial results confirm that in the presence of no drift, naively pooling all controls increases power and produces more precise, unbiased estimates when compared to using concurrent controls only. However, under drift, pooling can result in type I error rate inflation or deflation and biased estimates. In the presence of parameter drift, methods that partially borrow non-concurrent data, either through a static weighting mechanism or through methods that allow the heterogeneity between non-concurrent and concurrent data to determine the degree of borrowing, are superior to naively pooling the data. However, compared to using concurrent controls only, these approaches cannot guarantee type I error control or unbiased estimates. Thus, concurrent controls should be used as comparators in confirmatory studies.

Novel Clinical Trial Designs in Neuro-Oncology

Scientific and technologic advances have led to a boon of candidate therapeutics for patients with malignancies of the central nervous system. The path from drug development to clinical use has generally followed a regimented order of sequential clinical trial phases. The recent increase in novel therapies, however, has strained the regulatory process and unearthed limitations of the current system, including significant cost, prolonged development time, and difficulties in testing therapies for rarer tumors. Novel clinical trial designs have emerged to increase efficiencies in clinical trial conduct to better evaluate and bring impactful drugs to patients in a timely manner. In order to better capture meaningful benefits for brain tumor patients, new endpoints to complement or replace traditional endpoints are also an increasingly important consideration. This review will explore the current challenges in the current clinical trial landscape and discuss novel clinical trial concepts, including consideration of limitations and risks of novel trial designs, within the context of neuro-oncology.

Next Steps for Immunotherapy in Glioblastoma

Outcomes for glioblastoma (GBM) patients undergoing standard of care treatment remain poor. Here we discuss the portfolio of previously investigated immunotherapies for glioblastoma, including vaccine therapy and checkpoint inhibitors, as well as novel emerging therapeutic approaches. In addition, we explore the factors that potentially influence response to immunotherapy, which should be considered in future research aimed at improving immunotherapy efficacy.

Glioblastoma Clinical Trials: Current Landscape and Opportunities for Improvement

Therapeutic advances for glioblastoma have been minimal over the past 2 decades. In light of the multitude of recent phase III trials that have failed to meet their primary endpoints following promising preclinical and early-phase programs, a Society for Neuro-Oncology Think Tank was held in November 2020 to prioritize areas for improvement in the conduct of glioblastoma clinical trials. Here, we review the literature, identify challenges related to clinical trial eligibility criteria and trial design in glioblastoma, and provide recommendations from the Think Tank. In addition, we provide a data-driven context with which to frame this discussion by analyzing key study design features of adult glioblastoma clinical trials listed on ClinicalTrials.gov as "recruiting" or "not yet recruiting" as of February 2021.

High-grade glioma therapy: adding flexibility in trial design to improve patient outcomes

INTRODUCTION

Outcomes for patients with high grade gliomas have changed little over the past thirty years. This realization prompted renewed efforts to increase flexibility in the design and conduct of clinical brain tumor trials.

AREAS COVERED

This manuscript reviews the development of clinical trial methods, challenges and considerations of flexible clinical trial designs, approaches to improve identification and testing of active agents for high grade gliomas, and evaluation of their delivery to the central nervous system.

EXPERT OPINION

Flexibility can be introduced in clinical trials in several ways. Flexible designs tout smaller sample sizes, adaptive modifications, fewer control arms, and inclusion of multiple arms in one study. Unfortunately, modifications in study designs cannot address two challenges that are largely responsible for the lack of progress in treating high grade gliomas: 1) the identification of active pharmaceutical agents and 2) the delivery of these agents to brain tumor tissue in therapeutic concentrations. To improve the outcomes of patients with high grade gliomas efforts must be focused on the pre-clinical screening of drugs for activity, the ability of these agents to achieve therapeutic concentrations in non-enhancing tumors, and a willingness to introduce novel compounds in minimally pre-treated patient populations.

An Overview of Phase 2 Clinical Trial Designs

Clinical trials are studies to test new treatments in humans. Typically, these treatments are evaluated over several phases to assess their safety and efficacy. Phase 1 trials are designed to evaluate the safety and tolerability of a new treatment, typically with a small number of patients (eg, 20-80), generally spread across several dose levels. Phase 2 trials are designed to determine whether the new treatment has sufficiently promising efficacy to warrant further investigation in a large-scale randomized phase 3 trial, as well as to further assess safety. These studies usually involve a few hundred patients. This article provides an overview of some of the most commonly used phase 2 designs for clinical trials and emphasizes their critical elements and considerations. Key references to some of the most commonly used phase 2 designs are given to allow the reader to explore in more detail the critical aspects when planning a phase 2 trial. A comparison of 3 potential designs in the context of the NRG-HN002 trial is presented to complement the discussion about phase 2 trials.

Leveraging external data in the design and analysis of clinical trials in neuro-oncology

Integration of external control data, with patient-level information, in clinical trials has the potential to accelerate the development of new treatments in neuro-oncology by contextualising single-arm studies and improving decision making (eg, early stopping decisions). Based on a series of presentations at the 2020 Clinical Trials Think Tank hosted by the Society of Neuro-Oncology, we provide an overview on the use of external control data representative of the standard of care in the design and analysis of clinical trials. High-quality patient-level records, rigorous methods, and validation analyses are necessary to effectively leverage external data. We review study designs, statistical methods, risks, and potential distortions in using external data from completed trials and real-world data, as well as data sources, data sharing models, ongoing work, and applications in glioblastoma.

Toward the Next Generation of High-Grade Glioma Clinical Trials in the Era of Precision Medicine

ABSTRACT

In the era of precision medicine, there is a desire to harness our improved understanding of genomic and molecular underpinnings of gliomas to develop therapies that can be tailored to individual patients and tumors. With the rapid development of novel therapies, there has been a growing need to develop smart clinical trials that are designed to efficiently test promising agents, identify therapies likely to benefit patients, and discard ineffective therapies. We review clinical trial design in gliomas and developments designed to address the unique challenges of precision medicine. To provide an overview of this topic, we examine considerations for endpoints and response assessment, biomarkers, and novel clinical trial designs such as adaptive platform trials in the testing of new therapies for glioma patients.

Imposing Phase II and Phase III Clinical Trials of Targeted Drugs for Glioblastoma: Current Status and Progress

The most common primary intracranial tumor is glioma, among which glioblastoma (GBM) has the worst prognosis. Because of the high degree of malignancy of GBM and frequent recurrence after surgery, postoperative therapy, including chemotherapy, radiotherapy, targeted therapy, and immunotherapy, is particularly important. A wide variety of targeted drugs have undergone phase III clinical trials for patients with GBM, but these drugs do not work for all patients, and few patients in these trials have prolonged overall survival. In this review, some imposing phase III clinical trials of targeted drugs for glioma are introduced, and some prospective phase II clinical trials that have been completed or are in progress are summarized. In addition, the mechanisms of these drugs are briefly introduced, and deficiencies of these clinical trials are analyzed. This review aims to provide a comprehensive overview of current research on targeted drugs for glioma to clarify future research directions.

Inefficiencies in phase II to phase III transition impeding successful drug development in glioblastoma

Background

Improving outcomes of patients with glioblastoma (GBM) represents a significant challenge in neuro-oncology. We undertook a systematic review of key parameters of phase II and III trials in GBM to identify and quantify the impact of trial design on this phenomenon.

Methods

Studies between 2005 and 2019 inclusive were identified though MEDLINE search and manual bibliography searches. Phase II studies (P2T) were restricted to those referenced by the corresponding phase III trials (P3T). Clinical and statistical characteristics were extracted. For each P3T, corresponding P2T data was “optimally matched,” where same drug was used in similar schedule and similar population; “suboptimally matched” if dis-similar schedule and/or treatment setting; or “lacking.” Phase II/III transition data were compared by Pearson Correlation, Fisher’s exact or chi-square testing.

Results

Of 20 P3Ts identified, 6 (30%) lacked phase II data. Of the remaining 14 P3T, 9 had 1 prior P2T, 4 had 2 P2T, and 1 had 3 P2T, for a total of 20 P3T-P2T pairs (called dyads). The 13 “optimally matched” dyads showed strong concordance for mPFS (r² = 0.95, P < .01) and mOS (r² = 0.84, P < .01), while 7 “suboptimally matched” dyads did not (P > .05). Overall, 7 P3Ts underwent an ideal transition from P2T to P3T. “Newly diagnosed” P2Ts with mPFS < 14 months and/or mOS< 22 months had subsequent negative P3Ts. “Recurrent” P2Ts with mPFS < 6 months and mOS< 12 months also had negative P3Ts.

Conclusion

Our findings highlight the critical role of optimally designed phase II trials in informing drug development for GBM.

Reduced-Dose Radiation Therapy for HPV-Associated Oropharyngeal Carcinoma (NRG Oncology HN002)

Reducing radiation treatment dose could improve the quality of life (QOL) of patients with good-risk human papillomavirus–associated oropharyngeal squamous cell carcinoma (OPSCC). Whether reduced-dose radiation produces disease control and QOL equivalent to standard chemoradiation is not proven.

A Review of Perspectives on the Use of Randomization in Phase II Oncology Trials

Historically, phase II oncology trials assessed a treatment's efficacy by examining its tumor response rate in a single-arm trial. Then, approximately 25 years ago, certain statistical and pharmacological considerations ignited a debate around whether randomized designs should be used instead. Here, based on an extensive literature review, we review the arguments on either side of this debate. In particular, we describe the numerous factors that relate to the reliance of single-arm trials on historical control data and detail the trial scenarios in which there was general agreement on preferential utilization of single-arm or randomized design frameworks, such as the use of single-arm designs when investigating treatments for rare cancers. We then summarize the latest figures on phase II oncology trial design, contrasting current design choices against historical recommendations on best practice. Ultimately, we find several ways in which the design of recently completed phase II trials does not appear to align with said recommendations. For example, despite advice to the contrary, only 66.2% of the assessed trials that employed progression-free survival as a primary or coprimary outcome used a randomized comparative design. In addition, we identify that just 28.2% of the considered randomized comparative trials came to a positive conclusion as opposed to 72.7% of the single-arm trials. We conclude by describing a selection of important issues influencing contemporary design, framing this discourse in light of current trends in phase II, such as the increased use of biomarkers and recent interest in novel adaptive designs.

To randomize, or not to randomize, that is the question: using data from prior clinical trials to guide future designs

BACKGROUND

Understanding the value of randomization is critical in designing clinical trials. Here, we introduce a simple and interpretable quantitative method to compare randomized designs versus single-arm designs using indication-specific parameters derived from the literature. We demonstrate the approach through application to phase II trials in newly diagnosed glioblastoma (ndGBM).

METHODS

We abstracted data from prior ndGBM trials and derived relevant parameters to compare phase II randomized controlled trials (RCTs) and single-arm designs within a quantitative framework. Parameters included in our model were (i) the variability of the primary endpoint distributions across studies, (ii) potential for incorrectly specifying the single-arm trial's benchmark, and (iii) the hypothesized effect size. Strengths and weaknesses of RCT and single-arm designs were quantified by various metrics, including power and false positive error rates.

RESULTS

We applied our method to show that RCTs should be preferred to single-arm trials for evaluating overall survival in ndGBM patients based on parameters estimated from prior trials. More generally, for a given effect size, the utility of randomization compared with single-arm designs is highly dependent on (i) interstudy variability of the outcome distributions and (ii) potential errors in selecting standard of care efficacy estimates for single-arm studies.

CONCLUSIONS

A quantitative framework using historical data is useful in understanding the utility of randomization in designing prospective trials. For typical phase II ndGBM trials using overall survival as the primary endpoint, randomization should be preferred over single-arm designs.

Long-term trends in glioblastoma survival: implications for historical control groups in clinical trials

Background

Historical controls continue to be used in early-phase brain tumor trials. We aim to show that historical changes in survival trends for glioblastoma (GBM) call into question the use of noncontemporary controls.

Methods

We analyzed data from 46 106 primary GBM cases from the SEER database (1998-2016). We performed trend analysis on survival outcomes (2-year survival probability, median survival, and hazard ratios) and patient characteristics (age, sex, resection extent, and treatment type).

Results

In 2005-2016 (ie, the post-Stupp protocol era), fitting a parameter independently to each year, there was a demonstrable increase in median survival (R² = 0.81, P < .001) and 2-year survival probability (R² = 0.55, P = .006) for GBM. Trend analysis of the hazard ratio showed a significant time-dependent downward trend (R² = 0.62, P = .002). When controlling, via multivariable Cox regression modeling, for age, sex, resection extent, and treatment type, there was a persistent downward trend in hazard ratios with increases in calendar time, especially in the most recent data.

Conclusion

Contemporary GBM patients face a different overall hazard profile from their historical counterparts, which is evident in changes in measures of patient survival and parametric hazard modeling. Though there was a plateau in these measures before 2005 (pre-Stupp protocol), there is no evidence of a new plateau in recent years even when controlling for known prognostic factors (age, sex, resection extent, and treatment type), suggesting that it may be insufficient to match contemporary patients and noncontemporary controls on the basis of these factors.

The clinical trials landscape for glioblastoma: is it adequate to develop new treatments?

Background

There have been few treatment advances for patients with glioblastoma (GBM) despite increasing scientific understanding of the disease. While factors such as intrinsic tumor biology and drug delivery are challenges to developing efficacious therapies, it is unclear whether the current clinical trial landscape is optimally evaluating new therapies and biomarkers.

Methods

We queried ClinicalTrials.gov for interventional clinical trials for patients with GBM initiated between January 2005 and December 2016 and abstracted data regarding phase, status, start and end dates, testing locations, endpoints, experimental interventions, sample size, clinical presentation/indication, and design to better understand the clinical trials landscape.

Results

Only approximately 8%-11% of patients with newly diagnosed GBM enroll on clinical trials with a similar estimate for all patients with GBM. Trial duration was similar across phases with median time to completion between 3 and 4 years. While 93% of clinical trials were in phases I-II, 26% of the overall clinical trial patient population was enrolled on phase III studies. Of the 8 completed phase III trials, only 1 reported positive results. Although 58% of the phase III trials were supported by phase II data with a similar endpoint, only 25% of these phase II trials were randomized.

Conclusions

The clinical trials landscape for GBM is characterized by long development times, inadequate dissemination of information, suboptimal go/no-go decision making, and low patient participation.

Design and Evaluation of an External Control Arm Using Prior Clinical Trials and Real-World Data

PURPOSE

We discuss designs and interpretable metrics of bias and statistical efficiency of "externally controlled" trials (ECT) and compare ECT performance to randomized and single-arm designs.

EXPERIMENTAL DESIGN

We specify an ECT design that leverages information from real-world data (RWD) and prior clinical trials to reduce bias associated with interstudy variations of the enrolled populations. We then used a collection of clinical studies in glioblastoma (GBM) and RWD from patients treated with the current standard of care to evaluate ECTs. Validation is based on a "leave one out" scheme, with iterative selection of a single-arm from one of the studies, for which we estimate treatment effects using the remaining studies as external control. This produces interpretable and robust estimates on ECT bias and type I errors.

RESULTS

We developed a model-free approach to evaluate ECTs based on collections of clinical trials and RWD. For GBM, we verified that inflated false positive error rates of standard single-arm trials can be considerably reduced (up to 30%) by using external control data.

CONCLUSIONS

The use of ECT designs in GBM, with adjustments for the clinical profiles of the enrolled patients, should be preferred to single-arm studies with fixed efficacy thresholds extracted from published results on the current standard of care.

Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT): A Bayesian Adaptive Platform Trial to Develop Precision Medicines for Patients With Glioblastoma

PURPOSE

Adequately prioritizing the numerous therapies and biomarkers available in late-stage testing for patients with glioblastoma (GBM) requires an efficient clinical testing platform. We developed and implemented INSIGhT (Individualized Screening Trial of Innovative Glioblastoma Therapy) as a novel adaptive platform trial (APT) to develop precision medicine approaches in GBM.

METHODS

INSIGhT compares experimental arms with a common control of standard concurrent temozolomide and radiation therapy followed by adjuvant temozolomide. The primary end point is overall survival. Patients with newly diagnosed unmethylated GBM who are IDH R132H mutation negative and with genomic data available for biomarker grouping are eligible. At the initiation of INSIGhT, three experimental arms (neratinib, abemaciclib, and CC-115), each with a proposed genomic biomarker, are tested simultaneously. Initial randomization is equal across arms. As the trial progresses, randomization probabilities adapt on the basis of accumulating results using Bayesian estimation of the biomarker-specific probability of treatment impact on progression-free survival. Treatment arms may drop because of low probability of treatment impact on overall survival, and new arms may be added. Detailed information on the statistical model and randomization algorithm is provided to stimulate discussion on trial design choices more generally and provide an example for other investigators developing APTs.

CONCLUSION

INSIGhT (NCT02977780) is an ongoing novel biomarker-based, Bayesian APT for patients with newly diagnosed unmethylated GBM. Our goal is to dramatically shorten trial execution timelines while increasing scientific power of results and biomarker discovery using adaptive randomization. We anticipate that trial execution efficiency will also be improved by using the APT format, which allows for the collaborative addition of new experimental arms while retaining the overall trial structure.

Current state of clinical trials in breast cancer brain metastases

Background

Breast cancer brain metastases (BCBM) are the final frontier in neuro-oncology for which more efficacious therapies are required. In this work, we explore clinical trials in BCBM, and determine the shortcomings in the development of new BCBM therapies to shed light on potential areas for enhancement.

Methods

On July 9, 2018, we searched ClinicalTrials.gov for all interventional and therapeutic clinical trials involving BCBM, without limiting for date or location. Information on trial characteristics, including phase, status, start and end dates, study design, primary endpoints, selection criteria, sample size, experimental interventions, results, and publications were collected and analyzed.

Results

Fifty-three trials fulfilled the selection criteria. Median trial duration across phases ranged between 3 and 6 years. More than half of the trials were conducted in the United States. Although 94% of the trials were in early phases (I-II), 20% of patients were in phase III trials. Two phase III trials were anteceded by phase II trials that were non-randomized; one reported positive results. Approximately one-third of the trials were completed, whereas 23% of trials were terminated early; mostly due to inadequate enrollment. Only 13% of all trials and 22% of completed trials had published results directly linked to their primary outcomes.

Conclusions

The low number of trials and accrual numbers, the lack of diversity, and the scarcity of published results represent the main troubles in clinical BCBM research. Optimization of BCBM trials is necessary to achieve effective therapies.

Highlighting the need for reliable clinical trials in glioblastoma

INTRODUCTION

Several recent phase III studies have attempted to improve the dismal survival seen in glioblastoma patients, with disappointing results despite prior promising phase II data. Areas covered: A literature review of prior phase II and phase III studied in glioblastoma was performed to help identify possible areas of concern. Numerous issues in previous phase II trials for glioblastoma were found that may have contributed to these discouraging outcomes and discordant results. Expert commentary: These concerns include the improper selection of therapeutics warranting investigation in a phase III trial, suboptimal design of phase II studies (often lacking a control arm), absence of molecular data, the use of imaging criteria as a surrogate endpoint, and a lack of pharmacodynamic testing. Hopefully, by recognizing prior phase II trial limitations that contributed to failed phase III trials, we can adapt quickly to improve our ability to accurately discover survival-prolonging treatments for glioblastoma patients.