Statistical controversies in clinical research: basket trials, umbrella trials, and other master protocols: a review and examples

Genomics in Clinical trials for Breast Cancer

Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress of genomic technology has transformed our understanding of the genetic landscape of breast cancer, opening new avenues for improved anti-cancer therapeutics. Genomics is critical in developing tailored therapeutics and identifying patients most benefit from these treatments. The next generation of breast cancer clinical trials has incorporated next-generation sequencing technologies into the process, and we have seen benefits. These innovations have led to the approval of better-targeted therapies for patients with breast cancer. Genomics has a role to play in clinical trials, including genomic tests that have been approved, patient selection and prediction of therapeutic response. Multiple clinical trials in breast cancer have been done and are still ongoing, which have applied genomics technology. Precision medicine can be achieved in breast cancer therapy with increased efforts and advanced genomic studies in this domain. Genomics studies assist with patient outcomes improvement and oncology advancement by providing a deeper understanding of the biology behind breast cancer. This article will examine the present state of genomics in breast cancer clinical trials.

Target-Driven Tissue-Agnostic Drug Approvals-A New Path of Drug Development

The regulatory approvals of tumor-agnostic therapies have led to the re-evaluation of the drug development process. The conventional models of drug development are histology-based. On the other hand, the tumor-agnostic drug development of a new drug (or combination) focuses on targeting a common genomic biomarker in multiple cancers, regardless of histology. The basket-like clinical trials with multiple cohorts allow clinicians to evaluate pan-cancer efficacy and toxicity. There are currently eight tumor agnostic approvals granted by the Food and Drug Administration (FDA). This includes two immune checkpoint inhibitors, and five targeted therapy agents. Pembrolizumab is an anti-programmed cell death protein-1 (PD-1) antibody that was the first FDA-approved tumor-agnostic treatment for unresectable or metastatic microsatellite instability-high (MSI-H) or deficient mismatch repair (dMMR) solid tumors in 2017. It was later approved for tumor mutational burden-high (TMB-H) solid tumors, although the TMB cut-off used is still debated. Subsequently, in 2021, another anti-PD-1 antibody, dostarlimab, was also approved for dMMR solid tumors in the refractory setting. Patients with fusion-positive cancers are typically difficult to treat due to their rare prevalence and distribution. Gene rearrangements or fusions are present in a variety of tumors. Neurotrophic tyrosine kinase (NTRK) fusions are present in a range of pediatric and adult solid tumors in varying frequency. Larotrectinib and entrectinib were approved for neurotrophic tyrosine kinase (NTRK) fusion-positive cancers. Similarly, selpercatinib was approved for rearranged during transfection (RET) fusion-positive solid tumors. The FDA approved the first combination therapy of dabrafenib, a B-Raf proto-oncogene serine/threonine kinase (BRAF) inhibitor, plus trametinib, a mitogen-activated protein kinase (MEK) inhibitor for patients 6 months or older with unresectable or metastatic tumors (except colorectal cancer) carrying a BRAFV600E mutation. The most recent FDA tumor-agnostic approval is of fam-trastuzumab deruxtecan-nxki (T-Dxd) for HER2-positive solid tumors. It is important to identify and expeditiously develop drugs that have the potential to provide clinical benefit across tumor types.

Recent Status of Phase I Clinical Trials for Brain Tumors: A Regulatory Science Study of Exploratory Efficacy Endpoints

BACKGROUND

Appropriate exploratory efficacy data from Phase I trials are vital for subsequent phases. Owing to the uniqueness of brain tumors (BTs), use of different strategies to evaluate efficacy is warranted. We studied exploratory efficacy evaluation in Phase I trials involving BTs.

METHODS

Using Clarivate's Cortellis™, 42 Phase I trials of BT interventions conducted from 2020 to 2022 were analyzed for efficacy endpoints, which were set as primary endpoints (PEs) or secondary endpoints (SEs). Additionally, these metrics were compared in two subgroups: trials including only BTs (Group-A) and those including BTs among mixed solid tumors (Group-B).

RESULTS

Selected studies included a median of 1.5 PEs (range, 1-6) and 5 SEs (range, 0-19). Efficacy endpoints were included as PEs and SEs in 2 (5%) and 31 (78%) trials, respectively. Among the latter 31 trials that included 94 efficacy endpoints, 24, 22, 20, 9, and 8 reflected overall response rate (ORR), progression-free survival (PFS), overall survival (OS), duration of response (DOR), and disease control rate (DCR), respectively. ORR for BT was determined using various methods; however, the Response Evaluation Criteria in Solid Tumors (RECIST) was used less frequently in Group-A than in Group-B (p = 0.0039).

CONCLUSIONS

Recent Phase I trials included efficacy endpoints as SEs, with ORR, PFS, or OS included in ~ 50% trials and DOR or DCR in ~ 25%. No established criteria exist for imaging evaluation of BTs. Phase I trials involving mixed solid tumor cohorts revealed challenges in designing methods to assess the exploratory efficacy of BTs.

Biomarker-driven basket trial designs: origins and new methodological developments

Due to increased use of gene sequencing techniques, understanding of cancer on a molecular level has evolved, in terms of both diagnosis and evaluation in response to initial therapies. In parallel, clinical trials meant to evaluate molecularly-driven interventions through assessment of both treatment effects and putative predictive biomarker effects are being employed to advance the goals of precision medicine. Basket trials investigate one or more biomarker-targeted therapies across multiple cancer types in a tumor location agnostic fashion. The review article offers an overview of the traditional forms of such designs, the practical challenges facing each type of design, and then review novel adaptations proposed in the last few years, categorized into Bayesian and Classical Frequentist perspectives. The review article concludes by summarizing potential advantages and limitations of the new trial design solutions.

Advancing randomized controlled trial methodologies: The place of innovative trial design in eating disorders research

Randomized controlled trials can be used to generate evidence on the efficacy and safety of new treatments in eating disorders research. Many of the trials previously conducted in this area have been deemed to be of low quality, in part due to a number of practical constraints. This article provides an overview of established and more innovative clinical trial designs, accompanied by pertinent examples, to highlight how design choices can enhance flexibility and improve efficiency of both resource allocation and participant involvement. Trial designs include individually randomized, cluster randomized, and designs with randomizations at multiple time points and/or addressing several research questions (master protocol studies). Design features include the use of adaptations and considerations for pragmatic or registry-based trials. The appropriate choice of trial design, together with rigorous trial conduct, reporting and analysis, can establish high-quality evidence to advance knowledge in the field. It is anticipated that this article will provide a broad and contemporary introduction to trial designs and will help researchers make informed trial design choices for improved testing of new interventions in eating disorders. PUBLIC SIGNIFICANCE: There is a paucity of high quality randomized controlled trials that have been conducted in eating disorders, highlighting the need to identify where efficiency gains in trial design may be possible to advance the eating disorder research field. We provide an overview of some key trial designs and features which may offer solutions to practical constraints and increase trial efficiency.

Sharing information across patient subgroups to draw conclusions from sparse treatment networks

Network meta-analysis (NMA) usually provides estimates of the relative effects with the highest possible precision. However, sparse networks with few available studies and limited direct evidence can arise, threatening the robustness and reliability of NMA estimates. In these cases, the limited amount of available information can hamper the formal evaluation of the underlying NMA assumptions of transitivity and consistency. In addition, NMA estimates from sparse networks are expected to be imprecise and possibly biased as they rely on large-sample approximations that are invalid in the absence of sufficient data. We propose a Bayesian framework that allows sharing of information between two networks that pertain to different population subgroups. Specifically, we use the results from a subgroup with a lot of direct evidence (a dense network) to construct informative priors for the relative effects in the target subgroup (a sparse network). This is a two-stage approach where at the first stage, we extrapolate the results of the dense network to those expected from the sparse network. This takes place by using a modified hierarchical NMA model where we add a location parameter that shifts the distribution of the relative effects to make them applicable to the target population. At the second stage, these extrapolated results are used as prior information for the sparse network. We illustrate our approach through a motivating example of psychiatric patients. Our approach results in more precise and robust estimates of the relative effects and can adequately inform clinical practice in presence of sparse networks.

Characteristics, Progression, and Output of Randomized Platform Trials: A Systematic Review

Importance

Platform trials have become increasingly common, and evidence is needed to determine how this trial design is actually applied in current research practice.

Objective

To determine the characteristics, progression, and output of randomized platform trials.

Evidence Review

In this systematic review of randomized platform trials, Medline, Embase, Scopus, trial registries, gray literature, and preprint servers were searched, and citation tracking was performed in July 2022. Investigators were contacted in February 2023 to confirm data accuracy and to provide updated information on the status of platform trial arms. Randomized platform trials were eligible if they explicitly planned to add or drop arms. Data were extracted in duplicate from protocols, publications, websites, and registry entries. For each platform trial, design features such as the use of a common control arm, use of nonconcurrent control data, statistical framework, adjustment for multiplicity, and use of additional adaptive design features were collected. Progression and output of each platform trial were determined by the recruitment status of individual arms, the number of arms added or dropped, and the availability of results for each intervention arm.

Findings

The search identified 127 randomized platform trials with a total of 823 arms; most trials were conducted in the field of oncology (57 [44.9%]) and COVID-19 (45 [35.4%]). After a more than twofold increase in the initiation of new platform trials at the beginning of the COVID-19 pandemic, the number of platform trials has since declined. Platform trial features were often not reported (not reported: nonconcurrent control, 61 of 127 [48.0%]; multiplicity adjustment for arms, 98 of 127 [77.2%]; statistical framework, 37 of 127 [29.1%]). Adaptive design features were only used by half the studies (63 of 127 [49.6%]). Results were available for 65.2% of closed arms (230 of 353). Premature closure of platform trial arms due to recruitment problems was infrequent (5 of 353 [1.4%]).

Conclusions and Relevance

This systematic review found that platform trials were initiated most frequently during the COVID-19 pandemic and declined thereafter. The reporting of platform features and the availability of results were insufficient. Premature arm closure for poor recruitment was rare.

New clinical trial design in precision medicine: discovery, development and direction

In the era of precision medicine, it has been increasingly recognized that individuals with a certain disease are complex and different from each other. Due to the underestimation of the significant heterogeneity across participants in traditional "one-size-fits-all" trials, patient-centered trials that could provide optimal therapy customization to individuals with specific biomarkers were developed including the basket, umbrella, and platform trial designs under the master protocol framework. In recent years, the successive FDA approval of indications based on biomarker-guided master protocol designs has demonstrated that these new clinical trials are ushering in tremendous opportunities. Despite the rapid increase in the number of basket, umbrella, and platform trials, the current clinical and research understanding of these new trial designs, as compared with traditional trial designs, remains limited. The majority of the research focuses on methodologies, and there is a lack of in-depth insight concerning the underlying biological logic of these new clinical trial designs. Therefore, we provide this comprehensive review of the discovery and development of basket, umbrella, and platform trials and their underlying logic from the perspective of precision medicine. Meanwhile, we discuss future directions on the potential development of these new clinical design in view of the "Precision Pro", "Dynamic Precision", and "Intelligent Precision". This review would assist trial-related researchers to enhance the innovation and feasibility of clinical trial designs by expounding the underlying logic, which be essential to accelerate the progression of precision medicine.

A generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints

A basket trial simultaneously evaluates a treatment in multiple cancer subtypes, offering an effective way to accelerate drug development in multiple indications. Many basket trials are designed and monitored based on a single efficacy endpoint, primarily the tumor response. For molecular targeted or immunotherapy agents, however, a single efficacy endpoint cannot adequately characterize the treatment effect. It is increasingly important to use more complex endpoints to comprehensively assess the risk-benefit profile of such targeted therapies. We extend the calibrated Bayesian hierarchical modeling approach to monitor phase II basket trials with multiple endpoints. We propose two generalizations, one based on the latent variable approach and the other based on the multinomial-normal hierarchical model, to accommodate different types of endpoints and dependence assumptions regarding information sharing. We introduce shrinkage parameters as functions of statistics measuring homogeneity among subgroups and propose a general calibration approach to determine the functional forms. Theoretical properties of the generalized hierarchical models are investigated. Simulation studies demonstrate that the monitoring procedure based on the generalized approach yields desirable operating characteristics.

A basket trial design based on constrained hierarchical Bayesian model for latent subgroups

It is well known a basket trial consisting of multiple cancer types has the potential of borrowing strength across the baskets defined by the cancer types, leading to an efficient design in terms of sample size and trial duration. The treatment effects in those baskets are often heterogeneous and categorized by the cancer types being sensitive or insensitive to the treatment. Hence, the assumption of exchangeability in many existing basket trials may be violated, and there is a need to design trials without this assumption. In this paper, we simplify the constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) for two classifiers to deal with the potential heterogeneity of treatment effects due to the single classifier of the cancer type. Different baskets are aggregated into subgroups using a latent subgroup modeling approach. The treatment effects are similar and exchangeable to facilitate information borrowing within each latent subgroup. Applying the simplified CHBM-LS approach to the real basket trials where baskets defined by only cancer types shows better performance than other available approaches. Further simulation study also demonstrates this CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios.

Tumor-Specific Activity of Precision Medicines in the NCI-MATCH Trial

PURPOSE

National Cancer Institute Molecular Analysis for Therapy Choice (NCI-MATCH) is a precision medicine basket trial designed to test the effectiveness of treating cancers based on specific genetic changes in patients' tumors, regardless of cancer type. Multiple subprotocols have each tested different targeted therapies matched to specific genetic aberrations. Most subprotocols exhibited low rates of tumor shrinkage as evaluated across all tumor types enrolled. We hypothesized that these results may arise because these precision cancer therapies have tumor type-specific efficacy, as is common among other cancer therapies.

EXPERIMENTAL DESIGN

To test the hypothesis that certain tumor types are more sensitive to specific therapies than other tumor types, we applied permutation testing to tumor volume change and progression-free survival data from 10 published NCI-MATCH subprotocols (together n = 435 patients). FDR was controlled by the Benjamini-Hochberg procedure.

RESULTS

Six of ten subprotocols exhibited statistically significant evidence of tumor-specific drug sensitivity, four of which were previously considered negative based on response rate across all tumors. This signal-finding analysis highlights potential uses of FGFR tyrosine kinase inhibition in urothelial carcinomas with actionable FGFR aberrations and MEK inhibition in lung cancers with BRAF non-V600E mutations. In addition, it identifies low-grade serious ovarian carcinoma with BRAF v600E mutation as especially sensitive to BRAF and MEK co-inhibition (dabrafenib plus trametinib), a treatment that received accelerated FDA approval for advanced solid tumors with BRAF v600E mutation.

CONCLUSIONS

These findings support the value of basket trials because even when precision medicines do not have tumor-agnostic activity, basket trials can identify tumor-specific activity for future study.

Bayesian Methods for Information Borrowing in Basket Trials: An Overview

Basket trials allow simultaneous evaluation of a single therapy across multiple cancer types or subtypes of the same cancer. Since the same treatment is tested across all baskets, it may be desirable to borrow information across them to improve the statistical precision and power in estimating and detecting the treatment effects in different baskets. We review recent developments in Bayesian methods for the design and analysis of basket trials, focusing on the mechanism of information borrowing. We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings.

Adaptive platform trials: the impact of common controls on type one error and power

Platform trials offer a framework to study multiple interventions in one trial with the opportunity of opening and closing arms. The use of common controls can increase efficiency as compared to individual controls. The need for multiplicity adjustment because of common controls is currently a debate among researchers, pharmaceutical companies, and regulators. The impact of common controls on the type one error in a fixed platform trial, i.e. when all treatments start and end recruitment at the same time, has been discussed in the literature before. We complement these findings by investigating the impact of a common control on the type one error and power in a flexible platform trial, i.e. when one arm joins the platform later. We derived the correlation of test statistics to assess the impact of the overlap and compared the results to a trial with individual controls. Furthermore, we evaluate the power, and the impact of multiplicity adjustment on the power in fixed and flexible platform trials. These methodological considerations are complemented by a regulatory guideline review. With multiple arms, the FWER is inflated when no multiplicity adjustment is applied. However, the FWER inflation is smaller with common controls than with individual controls. Even after multiplicity adjustment, a trial with common controls is often beneficial in terms of sample size and power. However, in some cases, the trial with common controls loses the efficiency gain and it might be advisable to run a separate trial rather than joining a platform trial.

Frequentist analysis of basket trials with one-sample Mantel-Haenszel procedures

Recent substantial advances of molecular targeted oncology drug development is requiring new paradigms for early-phase clinical trial methodologies to enable us to evaluate efficacy of several subtypes simultaneously and efficiently. The concept of the basket trial is getting of much attention to realize this requirement borrowing information across subtypes, which are called baskets. Bayesian approach is a natural approach to this end and indeed the majority of the existing proposals relies on it. On the other hand, it required complicated modeling and may not necessarily control the type 1 error probabilities at the nominal level. In this article, we develop a purely frequentist approach for basket trials based on one-sample Mantel-Haenszel procedure relying on a very simple idea for borrowing information under the common treatment effect assumption over baskets. We show that the proposed Mantel-Haenszel estimator for the treatment effect is consistent under two limiting models of the large strata and sparse data limiting models (dually consistent) and propose dually consistent variance estimators. The proposed estimators are interpretable even if the common treatment effect assumptions are violated. Then, we can design basket trials in a confirmatory matter. We also propose an information criterion approach to identify effective subclasses of baskets.

Bayesian sample size determination in basket trials borrowing information between subsets

Basket trials are increasingly used for the simultaneous evaluation of a new treatment in various patient subgroups under one overarching protocol. We propose a Bayesian approach to sample size determination in basket trials that permit borrowing of information between commensurate subsets. Specifically, we consider a randomized basket trial design where patients are randomly assigned to the new treatment or control within each trial subset ("subtrial" for short). Closed-form sample size formulae are derived to ensure that each subtrial has a specified chance of correctly deciding whether the new treatment is superior to or not better than the control by some clinically relevant difference. Given prespecified levels of pairwise (in)commensurability, the subtrial sample sizes are solved simultaneously. The proposed Bayesian approach resembles the frequentist formulation of the problem in yielding comparable sample sizes for circumstances of no borrowing. When borrowing is enabled between commensurate subtrials, a considerably smaller trial sample size is required compared to the widely implemented approach of no borrowing. We illustrate the use of our sample size formulae with two examples based on real basket trials. A comprehensive simulation study further shows that the proposed methodology can maintain the true positive and false positive rates at desired levels.

Improving the efficiency of clinical trials in multiple sclerosis

BACKGROUND

Phase 3 clinical trials for disease-modifying therapies in relapsing-remitting multiple sclerosis (RRMS) have utilized a limited number of conventional designs with a high degree of success. However, these designs limit the types of questions that can be addressed, and the time and cost required. Moreover, trials involving people with progressive multiple sclerosis (MS) have been less successful.

OBJECTIVE

The objective of this paper is to discuss complex innovative trial designs, intermediate and composite outcomes and to improve the efficiency of trial design in MS and broaden questions that can be addressed, particularly as applied to progressive MS.

METHODS

We held an international workshop with experts in clinical trial design.

RESULTS

Recommendations include increasing the use of complex innovative designs, developing biomarkers to enrich progressive MS trial populations, prioritize intermediate outcomes for further development that target therapeutic mechanisms of action other than peripherally mediated inflammation, investigate acceptability to people with MS of data linkage for studying long-term outcomes of clinical trials, use Bayesian designs to potentially reduce sample sizes required for pediatric trials, and provide sustained funding for platform trials and registries that can support pragmatic trials.

CONCLUSION

Novel trial designs and further development of intermediate outcomes may improve clinical trial efficiency in MS and address novel therapeutic questions.

Types and progress of clinical trial design for breast cancer: a narrative review

Background and Objective

In recent years, the field of breast cancer diagnosis and therapy has witnessed rapid technological advances. Concurrently, the emergence of molecular biology and novel detection methodologies has facilitated the transition of breast cancer management into the precision medicine era. The primary objective of this review is to discuss the transformation in the research and development paradigm for breast cancer therapies and strategies.

Methods

We systematically searched PubMed, EMBASE and Cochrane databases for relevant studies published over the past 20 years using keywords including "breast cancer", "clinical trial", "seamless", "master protocol", "umbrella", "basket", "platform", and "precision medicine". Articles were screened for eligibility and key data extracted. The search was limited to English-language publications.

Key Content and Findings

The review identifies three core innovations in breast cancer trial methodology: (I) in terms of research speed, the traditional three-stage drug development models are being substituted by "seamless designs" as exemplified by the immunotherapy combination study NCT0328056. (II) Addressing research breadth, "master protocols" such as basket trials (IMMU-132-01), umbrella trials (FUTURE), and platform trials (I-SPY 2) have been introduced, allowing the simultaneous assessment of multiple treatments or disease subtypes within a singular framework. (III) Pertaining to research precision, newer designs utilize biomarkers such as "enrichment" (seen in EMBRACA and OlympiA trials) and "marker stratification" (as in the SOLAR-1 trial), enabling the identification of appropriate patient subgroups and the provision of tailored therapy strategies, a stark contrast to traditional histopathology-based evaluations.

Conclusions

Clinical trial design in breast cancer research has been revolutionized, moving towards more efficient and targeted strategies. Despite the presence of ethical, logistical, and data complexities, it is anticipated that ongoing technological and regulatory enhancements will pave the way for even more refined research approaches, subsequently influencing future research, clinical practices, and policymaking in breast cancer care.

Unlocking multidimensional cancer therapeutics using geometric data science

Personalised approaches to cancer therapeutics primarily involve identification of patient sub-populations most likely to benefit from targeted drugs. Such a stratification has led to plethora of designs of clinical trials that are often too complex due to the need for incorporating biomarkers and tissue types. Many statistical methods have been developed to address these issues; however, by the time such methodology is available research in cancer has moved on to new challenges and therefore in order to avoid playing catch-up it is necessary to develop new analytic tools alongside. One of the challenges facing cancer therapy is to effectively and appropriately target multiple therapies for sensitive patient population based on a panel of biomarkers across multiple cancer types, and matched future trial designs. We present novel geometric methods (mathematical theory of hypersurfaces) to visualise complex cancer therapeutics data as multidimensional, as well as geometric representation of oncology trial design space in higher dimensions. The hypersurfaces are used to describe master protocols, with application to a specific example of a basket trial design for melanoma, and thus setup a framework for further incorporating multi-omics data as multidimensional therapeutics.

Using dichotomized survival data to construct a prior distribution for a Bayesian seamless Phase II/III clinical trial

Master protocol designs allow for simultaneous comparison of multiple treatments or disease subgroups. Master protocols can also be designed as seamless studies, in which two or more clinical phases are considered within the same trial. They can be divided into two categories: operationally seamless, in which the two phases are separated into two independent studies, and inferentially seamless, in which the interim analysis is considered an adaptation of the study. Bayesian designs are scarcely studied. Our aim is to propose and compare Bayesian operationally seamless Phase II/III designs using a binary endpoint for the first stage and a time-to-event endpoint for the second stage. At the end of Phase II, arm selection is based on posterior (futility) and predictive (selection) probabilities. The results of the first phase are then incorporated into prior distributions of a time-to-event model. Simulation studies showed that Bayesian operationally seamless designs can approach the inferentially seamless counterpart, allowing for an increasing simulated power with respect to the operationally frequentist design.

Tumor-specific activity of precision medicines in the NCI-MATCH trial

Background

NCI-MATCH is a precision medicine basket trial designed to test the effectiveness of treating cancers based on specific genetic changes in patients' tumors, regardless of cancer type. Multiple subprotocols have each tested different targeted therapies matched to specific genetic aberrations. Most subprotocols exhibited low rates of tumor shrinkage as evaluated across all tumor types enrolled. We hypothesized that these results may arise because these precision cancer therapies have tumor type-specific efficacy, as is common among other cancer therapies.

Methods

To test the hypothesis that certain tumor types are more sensitive to specific therapies than other tumor types, we applied permutation testing to tumor volume change and progression-free survival data from ten published NCI-MATCH subprotocols (together n=435 patients). False discovery rate was controlled by the Benjamini-Hochberg procedure.

Results

Six of ten subprotocols exhibited statistically significant evidence of tumor-specific drug sensitivity, four of which were previously considered negative based on response rate across all tumors. This signal-finding analysis highlights potential uses of FGFR tyrosine kinase inhibition in urothelial carcinomas with actionable FGFR aberrations, MEK inhibition in lung cancers with BRAF non-V600E mutations, and MEK inhibition in cholangiocarcinomas with NRAS mutations.

Conclusions

These findings support the value of basket trials because even when precision medicines do not have tumor-agnostic activity, basket trials can identify tumor-specific activity for future study.

Tumor Dynamic Model-Based Decision Support for Phase Ib/II Combination Studies: A Retrospective Assessment Based on Resampling of the Phase III Study IMpower150

PURPOSE

Model-based tumor growth inhibition (TGI) metrics are increasingly incorporated into go/no-go decisions in early clinical studies. To apply this methodology to new investigational combinations requires independent evaluation of TGI metrics in recently completed Phase III trials of effective immunotherapy.

PATIENTS AND METHODS

Data were extracted from IMpower150, a positive, randomized, Phase III study of first-line therapy in 1,202 patients with non-small cell lung cancer. We resampled baseline characteristics and longitudinal sum of longest diameters of tumor lesions of patients from both arms, atezolizumab+ bevacizumab+chemotherapy (ABCP) versus BCP, to mimic Phase Ib/II studies of 15 to 40 patients/arm with 6 to 24 weeks follow-up. TGI metrics were estimated using a bi-exponential TGI model. Effect sizes were calculated as TGI metrics geometric mean ratio (GMR), objective response rate (ORR) difference (d), and progression-free survival (PFS), hazard ratio (HR) between arms. Correct and incorrect go decisions were evaluated as the probability to achieve desired effect sizes in ABCP versus BCP and BCP versus BCP, respectively, across 500 replicated subsamples for each design.

RESULTS

For 40 patients/24 weeks follow-up, correct go decisions based on probability tumor growth rate (KG) GMR <0.90, dORR >0.10, and PFS HR <0.70 were 83%, 69%, and 58% with incorrect go decision rates of 4%, 12%, and 11%, respectively. For other designs, the ranking did not change with TGI metrics consistently overperforming RECIST endpoints. The predicted overall survival (OS) HR was around 0.80 in most of the scenarios investigated.

CONCLUSIONS

Model-based estimate of KG GMR is an exploratory endpoint that informs early clinical decisions for combination studies.

Moving away from one disease at a time: Screening, trial design, and regulatory implications of novel platform technologies

Most rare diseases are caused by single-gene mutations, and as such, lend themselves to a host of new gene-targeted therapies and technologies including antisense oligonucleotides, phosphomorpholinos, small interfering RNAs, and a variety of gene delivery and gene editing systems. Early successes are encouraging, however, given the substantial number of distinct rare diseases, the ability to scale these successes will be unsustainable without new development efficiencies. Herein, we discuss the need for genomic newborn screening to match pace with the growing development of targeted therapeutics and ability to rapidly develop individualized therapies for rare variants. We offer approaches to move beyond conventional "one disease at a time" preclinical and clinical drug development and discuss planned regulatory innovations that are necessary to speed therapy delivery to individuals in need. These proposals leverage the shared properties of platform classes of therapeutics and innovative trial designs including master and platform protocols to better serve patients and accelerate drug development. Ultimately, there are risks to these novel approaches; however, we believe that close partnership and transparency between health authorities, patients, researchers, and drug developers present the path forward to overcome these challenges and deliver on the promise of gene-targeted therapies for rare diseases.

Practical basket design for binary outcomes with control of family-wise error rate

BACKGROUND

A basket trial is a type of clinical trial in which eligibility is based on the presence of specific molecular characteristics across subpopulations with different cancer types. The existing basket designs with Bayesian hierarchical models often improve the efficiency of evaluating therapeutic effects; however, these models calibrate the type I error rate based on the results of simulation studies under various selected scenarios. The theoretical control of family-wise error rate (FWER) is important for decision-making regarding drug approval.

METHODS

In this study, we propose a new Bayesian two-stage design with one interim analysis for controlling FWER at the target level, along with the formulations of type I and II error rates. Since the difficulty lies in the complexity of the theoretical formulation of the type I error rate, we devised the simulation-based method to approximate the type I error rate.

RESULTS

The proposed design enabled adjustment of the cutoff value to control the FWER at the target value in the final analysis. The simulation studies demonstrated that the proposed design can be used to control the well-approximated FWER below the target value even in situations where the number of enrolled patients differed among subpopulations.

CONCLUSIONS

The accrual number of patients is sometimes unable to reach the pre-defined value; therefore, existing basket designs may not ensure defined operating characteristics before beginning the trial. The proposed design that enables adjustment of the cutoff value to control FWER at the target value based on the results in the final analysis would be a better alternative.

A systematic review of basket and umbrella trials in oncology: the importance of tissue of origin and molecular target

INTRODUCTION

We sought to characterise oncology basket and umbrella trials that have been implemented, determine how many have been completed, and calculate the response rate, by tumour type and drug target.

METHODS

We conducted a retrospective, cross-sectional review of PubMed, Embase, and clinicaltrials.gov for all oncology basket and umbrella trials. We included all trials and publications reporting on the results of these trials, and we calculated overall response rates, stratified by tumour type and drug target.

RESULTS

Most basket and umbrella trials are phase II and non-randomised in design. Of the 180 basket trials, 99 (55.0%) had published results and 81 (45.0%) did not. Of the 73 umbrella trials, 28 (38.4%) had published results and 45 (61.6%) did not. The median response rate was 14.0 (IQR: 4.2, 31.2) for basket trials and 17.8 (IQR: 3.8, 40.4) for umbrella trials. These responses varied, depending on tumour type and drug target.

CONCLUSIONS

Understanding what is known about these trials, especially given the limited but heterogenous response reported in these trials, provides context about the strengths and limitations of drugs, especially since several drugs have been approved in recent years for tumour-agnostic indications, based on the results of these types of trials.

An adaptive biomarker basket design in phase II oncology trials

The phase II basket trial in oncology is a novel design that enables the simultaneous assessment of treatment effects of one anti-cancer targeted agent in multiple cancer types. Biomarkers could potentially associate with the clinical outcomes and re-define clinically meaningful treatment effects. It is therefore natural to develop a biomarker-based basket design to allow the prospective enrichment of the trials with the adaptive selection of the biomarker-positive (BM+) subjects who are most sensitive to the experimental treatment. We propose a two-stage phase II adaptive biomarker basket (ABB) design based on a potential predictive biomarker measured on a continuous scale. At Stage 1, the design incorporates a biomarker cutoff estimation procedure via a hierarchical Bayesian model with biomarker as a covariate (HBMbc). At Stage 2, the design enrolls only BM+ subjects, defined as those with the biomarker values exceeding the biomarker cutoff within each cancer type, and subsequently assesses the early efficacy and/or futility stopping through the pre-defined interim analyses. At the end of the trial, the response rate of all BM+ subjects for each cancer type can guide drug development, while the data from all subjects can be used to further model the relationship between the biomarker value and the clinical outcome for potential future research. The extensive simulation studies show that the ABB design could produce a good estimate of the biomarker cutoff to select BM+ subjects with high accuracy and could outperform the existing phase II basket biomarker cutoff design under various scenarios.

Starring Role of Biomarkers and Anticancer Agents as a Major Driver in Precision Medicine of Cancer Therapy

Precision medicine is the most modern contemporary medicine approach today, based on great amount of data on people's health, individual characteristics, and life circumstances, and employs the most effective ways to prevent and cure diseases. Precision medicine in cancer is the most precise and viable treatment for every cancer patient based on the disease's genetic profile. Precision medicine changes the standard one size fits all medication model, which focuses on average responses to care. Consolidating modern methodologies for streamlining and checking anticancer drugs can have long-term effects on understanding the results. Precision medicine can help explicit anticancer treatments using various drugs and even in discovery, thus becoming the paradigm of future cancer medicine. Cancer biomarkers are significant in precision medicine, and findings of different biomarkers make this field more promising and challenging. Naturally, genetic instability and the collection of extra changes in malignant growth cells are ways cancer cells adapt and survive in a hostile environment, for example, one made by these treatment modalities. Precision medicine centers on recognizing the best treatment for individual patients, dependent on their malignant growth and genetic characterization. This new era of genomics progressively referred to as precision medicine, has ignited a new episode in the relationship between genomics and anticancer drug development.

Sample size estimation for cancer randomized trials in the presence of heterogeneous populations

A key issue when designing clinical trials is the estimation of the number of subjects required. Assuming for multicenter trials or biomarker-stratified designs that the effect size between treatment arms is the same among the whole study population might be inappropriate. Limited work is available for properly determining the sample size for such trials. However, we need to account for both, the heterogeneity of the baseline hazards over clusters or strata but also the heterogeneity of the treatment effects, otherwise sample size estimates might be biased. Most existing methods account for either heterogeneous baseline hazards or treatment effects but they dot not allow to simultaneously account for both sources of variations. This article proposes an approach to calculate sample size formula for clustered or stratified survival data relying on frailty models. Both theoretical derivations and simulation results show the proposed approach can guarantee the desired power in worst case scenarios and is often much more efficient than existing approaches. Application to a real clinical trial designs is also illustrated.

Culture media composition influences patient-derived organoid ability to predict therapeutic responses in gastrointestinal cancers

BACKGROUNDA patient-derived organoid (PDO) platform may serve as a promising tool for translational cancer research. In this study, we evaluated PDO's ability to predict clinical response to gastrointestinal (GI) cancers.METHODSWe generated PDOs from primary and metastatic lesions of patients with GI cancers, including pancreatic ductal adenocarcinoma, colorectal adenocarcinoma, and cholangiocarcinoma. We compared PDO response with the observed clinical response for donor patients to the same treatments.RESULTSWe report an approximately 80% concordance rate between PDO and donor tumor response. Importantly, we found a profound influence of culture media on PDO phenotype, where we showed a significant difference in response to standard-of-care chemotherapies, distinct morphologies, and transcriptomes between media within the same PDO cultures.CONCLUSIONWhile we demonstrate a high concordance rate between donor tumor and PDO, these studies also showed the important role of culture media when using PDOs to inform treatment selection and predict response across a spectrum of GI cancers.TRIAL REGISTRATIONNot applicable.FUNDINGThe Joan F. & Richard A. Abdoo Family Fund in Colorectal Cancer Research, GI Cancer program of the Mayo Clinic Cancer Center, Mayo Clinic SPORE in Pancreatic Cancer, Center of Individualized Medicine (Mayo Clinic), Department of Laboratory Medicine and Pathology (Mayo Clinic), Incyte Pharmaceuticals and Mayo Clinic Hepatobiliary SPORE, University of Minnesota-Mayo Clinic Partnership, and the Early Therapeutic program (Department of Oncology, Mayo Clinic).

Platform trials for anaesthesia and perioperative medicine: a narrative review

Large randomised trials provide the most reliable evidence of effectiveness of new treatments in clinical practice. However, the time and resources required to complete such trials can be daunting. An overarching clinical trial platform focused on a single condition or type of surgery, aiming to compare several treatments, with an option to stop any or add in new treatment options, can provide greater efficiency. This has the potential to accelerate knowledge acquisition and identify effective, ineffective, or harmful treatments faster. The master protocol of the platform defines the study population(s) and standardised procedures. Ineffective or harmful treatments can be discarded or study drug dose modified during the life cycle of the trial. Other adaptive elements that can be modified include eligibility criteria, required sample size for any comparison(s), randomisation assignment ratio, and the addition of other promising treatment options. There are excellent opportunities for anaesthetists to establish platform trials in perioperative medicine. Platform trials are highly efficient, with the potential to provide quicker answers to important clinical questions that lead to improved patient care.

Design and analysis of umbrella trials: Where do we stand?

Background

The efficiencies that master protocol designs can bring to modern drug development have seen their increased utilization in oncology. Growing interest has also resulted in their consideration in non-oncology settings. Umbrella trials are one class of master protocol design that evaluates multiple targeted therapies in a single disease setting. Despite the existence of several reviews of master protocols, the statistical considerations of umbrella trials have received more limited attention.

Methods

We conduct a systematic review of the literature on umbrella trials, examining both the statistical methods that are available for their design and analysis, and also their use in practice. We pay particular attention to considerations for umbrella designs applied outside of oncology.

Findings

We identified 38 umbrella trials. To date, most umbrella trials have been conducted in early phase settings (73.7%, 28/38) and in oncology (92.1%, 35/38). The quality of statistical information available about conducted umbrella trials to date is poor; for example, it was impossible to ascertain how sample size was determined in the majority of trials (55.3%, 21/38). The literature on statistical methods for umbrella trials is currently sparse.

Conclusions

Umbrella trials have potentially great utility to expedite drug development, including outside of oncology. However, to enable lessons to be effectively learned from early use of such designs, there is a need for higher-quality reporting of umbrella trials. Furthermore, if the potential of umbrella trials is to be realized, further methodological research is required.

Bayesian and frequentist approaches to sequential monitoring for futility in oncology basket trials: A comparison of Simon's two-stage design and Bayesian predictive probability monitoring with information sharing across baskets

This article discusses and compares statistical designs of basket trial, from both frequentist and Bayesian perspectives. Baskets trials are used in oncology to study interventions that are developed to target a specific feature (often genetic alteration or immune phenotype) that is observed across multiple tissue types and/or tumor histologies. Patient heterogeneity has become pivotal to the development of non-cytotoxic treatment strategies. Treatment targets are often rare and exist among several histologies, making prospective clinical inquiry challenging for individual tumor types. More generally, basket trials are a type of master protocol often used for label expansion. Master protocol is used to refer to designs that accommodates multiple targets, multiple treatments, or both within one overarching protocol. For the purpose of making sequential decisions about treatment futility, Simon's two-stage design is often embedded within master protocols. In basket trials, this frequentist design is often applied to independent evaluations of tumor histologies and/or indications. In the tumor agnostic setting, rarer indications may fail to reach the sample size needed for even the first evaluation for futility. With recent innovations in Bayesian methods, it is possible to evaluate for futility with smaller sample sizes, even for rarer indications. Novel Bayesian methodology for a sequential basket trial design based on predictive probability is introduced. The Bayesian predictive probability designs allow interim analyses with any desired frequency, including continual assessments after each patient observed. The sequential design is compared with and without Bayesian methods for sharing information among a collection of discrete, and potentially non-exchangeable tumor types. Bayesian designs are compared with Simon's two-stage minimax design.

A scoping review on patient heterogeneity in economic evaluations of precision medicine based on basket trials

INTRODUCTION

Considerable challenges in the economic evaluation of precision medicines have been mentioned in previous studies. However, they have not addressed how an economic assessment would be conducted based on basket trials (novel studies for evaluation of precision medicine effects) in which the included populations have specific biomarkers and various cancers. Since basket trial populations have remarkable heterogeneity, this study aims to investigate the concept of heterogeneity and specific method(s) for considering it in economic evaluations through guidelines and studies that could be applicable in economic evaluation based on basket trials.

AREA COVERED

We searched PubMed, Web of Science, Scopus, Google Scholar, and Google to find studies and pharmacoeconomics guidelines. The inclusion criteria included subjects of patient heterogeneity and suggested explicit method(s). Thirty-nine guidelines and 43 studies were included and evaluated. None of these materials mentioned disease types in a target population as a factor causing heterogeneity. Moreover, in economic evaluations, patient heterogeneity has been considered with four general approaches subgroup analysis, individual-based models, sensitivity analysis, and regression models.

EXPERT OPINION

Type of disease is not considered a contributing factor in population heterogeneity, and the probable appropriate method for this issue could be individual-based models.

Risk and benefit for umbrella trials in oncology: a systematic review and meta-analysis

BACKGROUND

Umbrella clinical trials in precision oncology are designed to tailor therapies to the specific genetic changes within a tumor. Little is known about the risk/benefit ratio for umbrella clinical trials. The aim of our systematic review with meta-analysis was to evaluate the efficacy and safety profiles in cancer umbrella trials testing targeted drugs or a combination of targeted therapy with chemotherapy.

METHODS

Our study was prospectively registered in PROSPERO (CRD42020171494). We searched Embase and PubMed for cancer umbrella trials testing targeted agents or a combination of targeted therapies with chemotherapy. We included solid tumor studies published between 1 January 2006 and 7 October 2019. We measured the risk using drug-related grade 3 or higher adverse events (AEs), and the benefit by objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). When possible, data were meta-analyzed.

RESULTS

Of the 6207 records identified, we included 31 sub-trials or arms of nine umbrella trials (N = 1637). The pooled overall ORR was 17.7% (95% confidence interval [CI] 9.5-25.9). The ORR for targeted therapies in the experimental arms was significantly lower than the ORR for a combination of targeted therapy drugs with chemotherapy: 13.3% vs 39.0%; p = 0.005. The median PFS was 2.4 months (95% CI 1.9-2.9), and the median OS was 7.1 months (95% CI 6.1-8.4). The overall drug-related death rate (drug-related grade 5 AEs rate) was 0.8% (95% CI 0.3-1.4), and the average drug-related grade 3/4 AE rate per person was 0.45 (95% CI 0.40-0.50).

CONCLUSIONS

Our findings suggest that, on average, one in five cancer patients in umbrella trials published between 1 January 2006 and 7 October 2019 responded to a given therapy, while one in 125 died due to drug toxicity. Our findings do not support the expectation of increased patient benefit in cancer umbrella trials. Further studies should investigate whether umbrella trial design and the precision oncology approach improve patient outcomes.

Landscape of coronavirus disease 2019 clinical trials: New frontiers and challenges

BACKGROUND/AIM

The number of coronavirus disease 2019 deaths and cases continues to increase globally. Novel therapies are urgently needed to treat patients with coronavirus disease 2019. We sought to provide a critical review of trials designed during the coronavirus disease 2019 pandemic. Our primary goal was to provide a critical review of the landscape of clinical trials designed to address the coronavirus disease 2019 pandemic. Specifically, we were interested in assessing the design of phase II/III and phase III interventional trials.

METHODS

We utilized the ClinicalTrials.gov database to include trials registered between 1 December 2019 and 11 April 2021 to survey the current landscape of clinical trials for coronavirus disease 2019. Variables extracted included: National Clinical Trial number, title, location, sponsor, study type, start date, completion date, gender group, age group, primary outcome, secondary outcome, overall status, and associated references.

RESULTS

About 57% of studies were interventional, 14.5% were phase III trials, and the majority of the therapeutic trials included hospitalized patients. There were 52 primary composite outcomes and 285 unique interventions spanning 10 drug classes. The outcomes, disease severity, and comparators varied substantially across trials, and the trials were often too small to be definitive.

CONCLUSION

These findings are relevant as we strongly advocate for global coordination of efforts through the use of common platforms that enable harmonizing of endpoints, collection of common key variables and clear definition of disease severity to have clinically meaningful results from clinical trials.

Bayesian local exchangeability design for phase II basket trials

We propose an information borrowing strategy for the design and monitoring of phase II basket trials based on the local multisource exchangeability assumption between baskets (disease types). In our proposed local-MEM framework, information borrowing is only allowed to occur locally, that is, among baskets with similar response rate and the amount of information borrowing is determined by the level of similarity in response rate, whereas baskets not considered similar are not allowed to share information. We construct a two-stage design for phase II basket trials using the proposed strategy. The proposed method is compared to competing Bayesian methods and Simon's two-stage design in a variety of simulation scenarios. We demonstrate the proposed method is able to maintain the family-wise type I error rate at a reasonable level and has desirable basket-wise power compared to Simon's two-stage design. In addition, our method is computationally efficient compared to existing Bayesian methods in that the posterior profiles of interest can be derived explicitly without the need for sampling algorithms. R scripts to implement the proposed method are available at https://github.com/yilinyl/Bayesian-localMEM.

Operational complexity versus design efficiency: challenges of implementing a phase IIa multiple parallel cohort targeted treatment platform trial in advanced breast cancer

BACKGROUND

Platform trial designs are used increasingly in cancer clinical research and are considered an efficient model for evaluating multiple compounds within a single disease or disease subtype. However, these trial designs can be challenging to operationalise. The use of platform trials in oncology clinical research has increased considerably in recent years as advances in molecular biology enable molecularly defined stratification of patient populations and targeted therapy evaluation. Whereas multiple separate trials may be deemed infeasible, platform designs allow efficient, parallel evaluation of multiple targeted therapies in relatively small biologically defined patient sub-populations with the promise of increased molecular screening efficiency and reduced time for drug evaluation. Whilst the theoretical efficiencies are widely reported, the operational challenges associated with these designs (complexity, cost, regulatory, resource) are not always well understood. MAIN: In this commentary, we describe our practical experience of the implementation and delivery of the UK plasmaMATCH trial, a platform trial in advanced breast cancer, comprising an integrated screening component and multiple parallel downstream mutation-directed therapeutic cohorts. plasmaMATCH reported its primary results within 3 years of opening to recruitment. We reflect on the operational challenges encountered and share lessons learnt to inform the successful conduct of future trials. Key to the success of the plasmaMATCH trial was well co-ordinated stakeholder engagement by an experienced clinical trials unit with expert methodology and trial management expertise, a federated model of clinical leadership, a well-written protocol integrating screening and treatment components and including justification for the chosen structure and intentions for future adaptions, and an integrated funding model with streamlined contractual arrangements across multiple partners. Findings based on our practical experience include the importance of early engagement with the regulators and consideration of a flexible resource infrastructure to allow adequate resource allocation to support concurrent trial activities as adaptions are implemented in parallel to the continued management of patient safety and data quality of the ongoing trial cohorts.

CONCLUSION

Platform trial designs allow the efficient reporting of multiple treatment cohorts. Operational challenges can be overcome through multidisciplinary engagement, streamlined contracting processes, rationalised protocol and database design and appropriate resourcing.

Characteristics of Early Phase Clinical Trials for Rare Cancers: Insights From Interviews With Stakeholders

Background: Rare cancers occur with an incidence of no more than six cases per 100,000 people according to the definition used by the Surveillance of Rare Cancers in Europe project. For a variety of reasons (low prevalence, cytotoxicity), it is challenging to perform the necessary clinical studies to investigate the safety and efficacy of investigational medicines against such rare malignancies, reformulating even at the earliest stages of the drug development process. This article investigates the differences between phase I rare cancer trials performed in commercial (companies) and non-commercial settings (academic hospitals).

Materials and Methods: The differences were explored through the conduct of semi-structured interviews with three different stakeholder groups: representatives from academia (n = 7), representatives from companies (n = 4) and representatives from patient organizations (n = 4). All the interviews were transcribed verbatim and analyzed in NVivo using the framework method.

Results: According to the interviewees, the academic and commercial stakeholders collaborate in the majority of phase I rare cancer trials. In general, the commercial partner finances the trial, whereas academia is responsible for the execution of the study procedures. The average cost of undertaking these trials is difficult to estimate because it depends on what is specifically requested during the trial. The 3 + 3 study design remains the most widely used design and the use of expansion cohorts is controversial. With regard to the regulatory aspects of phase I rare cancer trials, it was expressed that a good regulatory framework facilitates the conduct of these studies, but that increased regulation and oversight also has drawbacks, e.g., differences in standards between different ethics committees, over interpretation of the rules, insufficient availability of qualified personnel and higher workloads. The patient organization representatives claimed that patients experience no differences in terms of accommodation, compensation and paperwork between the academic and commercial settings or the degree of follow-up. They also believed that the direct input of patients can bring added value to such studies not only with regard to the recruitment process and the feasibility of the study but also the legibility of the informed consent forms.

Conclusion: The growing need for first-in-man trials in rare malignancies needs to be highlighted, as difficult as they are to undertake and to co-develop, not only because rare cancer patients deserve an appropriate treatment, but also because these medicines represent the future of cancer therapy in the precision medicine era. Cooperation of commercial and academic sites are needed. Patient organizations need to be educated to take part in this process.

Adaptive Designs: Benefits and Cautions for Neurosurgery Trials

BACKGROUND

It is well accepted that randomized controlled trials provide the greatest quality of evidence about effectiveness and safety of new interventions. In neurosurgery, randomized controlled trials face challenges, with their use remaining relatively low compared with other clinical areas. Adaptive designs have emerged as a method for improving the efficiency and patient benefit of trials. They allow modifications to the trial design to be made as patient outcome data are collected. The benefit they provide is highly variable, predominantly governed by the time taken to observe the primary endpoint compared with the planned recruitment rate. They also face challenges in design, conduct, and reporting.

METHODS

We provide an overview of the benefits and challenges of adaptive designs, with a focus on neurosurgery applications. To investigate how often an adaptive design may be advantageous in neurosurgery, we extracted data on recruitment rates and endpoint lengths for ongoing neurosurgery trials registered in ClinicalTrials.gov.

RESULTS

We found that a majority of neurosurgery trials had a relatively short endpoint length compared with the planned recruitment period and therefore may benefit from an adaptive trial. However, we did not identify any ongoing ClinicalTrials.gov registered neurosurgery trials that mentioned using an adaptive design.

CONCLUSIONS

Adaptive designs may provide benefits to neurosurgery trials and should be considered for use more widely. Use of some types of adaptive design, such as multiarm multistage, may further increase the number of interventions that can be tested with limited patient and financial resources.

Development of iPSC-based clinical trial selection platform for patients with ultrarare diseases

A "Leap-of-Faith" approach is used to treat patients with previously unknown ultrarare pathogenic mutations, often based on evidence from patients having dissimilar but more prevalent mutations. This uncertainty reflects the need to develop personalized prescreening platforms for these patients to assess drug efficacy before considering clinical trial enrollment. In this study, we report an 18-year-old patient with ultrarare Leigh-like syndrome. This patient had previously participated in two clinical trials with unfavorable responses. We established an induced pluripotent stem cell (iPSC)-based platform for this patient, and assessed the efficacy of a panel of drugs. The iPSC platform validated the safety and efficacy of the screened drugs. The efficacy of three of the screened drugs was also investigated in the patient. After 3 years of treatment, the drugs were effective in shifting the metabolic profile of this patient toward healthy control. Therefore, this personalized iPSC-based platform can act as a prescreening tool to help in decision-making with respect to patient's participation in future clinical trials.

Borrowing information across patient subgroups in clinical trials, with application to a paediatric trial

Background

Clinical trial investigators may need to evaluate treatment effects in a specific subgroup (or subgroups) of participants in addition to reporting results of the entire study population. Such subgroups lack power to detect a treatment effect, but there may be strong justification for borrowing information from a larger patient group within the same trial, while allowing for differences between populations. Our aim was to develop methods for eliciting expert opinions about differences in treatment effect between patient populations, and to incorporate these opinions into a Bayesian analysis.

Methods

We used an interaction parameter to model the relationship between underlying treatment effects in two subgroups. Elicitation was used to obtain clinical opinions on the likely values of the interaction parameter, since this parameter is poorly informed by the data. Feedback was provided to experts to communicate how uncertainty about the interaction parameter corresponds with relative weights allocated to subgroups in the Bayesian analysis. The impact on the planned analysis was then determined.

Results

The methods were applied to an ongoing non-inferiority trial designed to compare antiretroviral therapy regimens in 707 children living with HIV and weighing ≥ 14 kg, with an additional group of 85 younger children weighing < 14 kg in whom the treatment effect will be estimated separately. Expert clinical opinion was elicited and demonstrated that substantial borrowing is supported. Clinical experts chose on average to allocate a relative weight of 78% (reduced from 90% based on sample size) to data from children weighing ≥ 14 kg in a Bayesian analysis of the children weighing < 14 kg. The total effective sample size in the Bayesian analysis was 386 children, providing 84% predictive power to exclude a difference of more than 10% between arms, whereas the 85 younger children weighing < 14 kg provided only 20% power in a standalone frequentist analysis.

Conclusions

Borrowing information from a larger subgroup or subgroups can facilitate estimation of treatment effects in small subgroups within a clinical trial, leading to improved power and precision. Informative prior distributions for interaction parameters are required to inform the degree of borrowing and can be informed by expert opinion. We demonstrated accessible methods for obtaining opinions.

Bayesian basket trial design with false-discovery rate control

BACKGROUND

Recent advances in developing "tumor agnostic" oncology therapies have identified molecular targets that define patient subpopulations in a manner that supersedes conventional criteria for cancer classification. These successes have produced effective targeted therapies that are administered to patients regardless of their tumor histology. Trials have evolved as well with master protocol designs. By blending translational and clinical science, basket trials in particular are well-suited to investigate and develop targeted therapies among multiple cancer histologies. However, basket trials intrinsically involve more complex design decisions, including issues of multiple testing across baskets, and guidance for investigators is needed.

METHODS

The sensitivity of the multisource exchangeability model to prior specification under differing degrees of response heterogeneity is explored through simulation. Then, a multisource exchangeability model design that incorporates control of the false-discovery rate is presented and a simulation study compares the operating characteristics to a design where the family-wise error rate is controlled and to the frequentist approach of treating the baskets as independent. Simulations are based on the original design of a real-world clinical trial, the SUMMIT trial, which investigated Neratinib treatment for a variety of solid tumors. The methods studied here are specific to single-arm phase II trials with binary outcomes.

RESULTS

Values of prior probability of exchangeability in the multisource exchangeability model between 0.1 and 0.3 provide the best trade-offs between gain in precision and bias, especially when per-basket sample size is below 30. Application of these calibration results to a re-analysis of the SUMMIT trial showed that the breast basket exceeded the null response rate with posterior probability of 0.999 while having low posterior probability of exchangeability with all other baskets. Simulations based on the design of the SUMMIT trial revealed that there is meaningful improvement in power even in baskets with small sample size when the false-discovery rate is controlled as opposed to the family-wise error rate. For example, when only the breast basket was active, with a sample size of 25, the power was 0.76 when the false-discovery rate was controlled at 0.05 but only 0.56 when the family-wise error rate was controlled at 0.05, indicating that impractical sample sizes for the phase II setting would be needed to achieve acceptable power while controlling the family-wise error rate in this setting of a trial with 10 baskets.

CONCLUSION

Selection of the prior exchangeability probability based on calibration and incorporation of false-discovery rate control result in multisource exchangeability model designs with high power to detect promising treatments in the context of phase II basket trials.

Modelling approaches for histology-independent cancer drugs to inform NICE appraisals: a systematic review and decision-framework

BACKGROUND

The first histology-independent marketing authorisation in Europe was granted in 2019. This was the first time that a cancer treatment was approved based on a common biomarker rather than the location in the body at which the tumour originated. This research aims to explore the implications for National Institute for Health and Care Excellence appraisals.

METHODS

Targeted reviews were undertaken to determine the type of evidence that is likely to be available at the point of marketing authorisation and the analyses required to support National Institute for Health and Care Excellence appraisals. Several challenges were identified concerning the design and conduct of trials for histology-independent products, the greater levels of heterogeneity within the licensed population and the use of surrogate end points. We identified approaches to address these challenges by reviewing key statistical literature that focuses on the design and analysis of histology-independent trials and by undertaking a systematic review to evaluate the use of response end points as surrogate outcomes for survival end points. We developed a decision framework to help to inform approval and research policies for histology-independent products. The framework explored the uncertainties and risks associated with different approval policies, including the role of further data collection, pricing schemes and stratified decision-making.

RESULTS

We found that the potential for heterogeneity in treatment effects, across tumour types or other characteristics, is likely to be a central issue for National Institute for Health and Care Excellence appraisals. Bayesian hierarchical methods may serve as a useful vehicle to assess the level of heterogeneity across tumours and to estimate the pooled treatment effects for each tumour, which can inform whether or not the assumption of homogeneity is reasonable. Our review suggests that response end points may not be reliable surrogates for survival end points. However, a surrogate-based modelling approach, which captures all relevant uncertainty, may be preferable to the use of immature survival data. Several additional sources of heterogeneity were identified as presenting potential challenges to National Institute for Health and Care Excellence appraisal, including the cost of testing, baseline risk, quality of life and routine management costs. We concluded that a range of alternative approaches will be required to address different sources of heterogeneity to support National Institute for Health and Care Excellence appraisals. An exemplar case study was developed to illustrate the nature of the assessments that may be required.

CONCLUSIONS

Adequately designed and analysed basket studies that assess the homogeneity of outcomes and allow borrowing of information across baskets, where appropriate, are recommended. Where there is evidence of heterogeneity in treatment effects and estimates of cost-effectiveness, consideration should be given to optimised recommendations. Routine presentation of the scale of the consequences of heterogeneity and decision uncertainty may provide an important additional approach to the assessments specified in the current National Institute for Health and Care Excellence methods guide.

FURTHER RESEARCH

Further exploration of Bayesian hierarchical methods could help to inform decision-makers on whether or not there is sufficient evidence of homogeneity to support pooled analyses. Further research is also required to determine the appropriate basis for apportioning genomic testing costs where there are multiple targets and to address the challenges of uncontrolled Phase II studies, including the role and use of surrogate end points.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Evidence Synthesis programme and will be published in full in Health Technology Assessment; Vol. 25, No. 76. See the NIHR Journals Library website for further project information.

Regulatory issues and the potential use of Bayesian approaches for early drug approval systems in Japan

Bayesian methods quantify and interpret the therapeutic effects of investigational drugs based on probability statements of the posterior distribution. However, the basic principle underlying the use of Bayesian methods in registration trials for new drug applications in Japan has not been adequately discussed. Motivated by the two drug approval systems for early approval recently enacted in Japan, we present our perspectives on the application of the Bayesian approach in registration trials in Japan. These are based on discussions among academic, industry, and regulatory experts at invited workshops. Based on the aforementioned early approval systems, we discuss putative common regulatory issues related to the use of the Bayesian approach and introduce instances of clinical trials in which the Bayesian approach is expected to be used. This article provides a well-defined premise for the discussion between industry and regulatory agencies on the use of Bayesian approaches for early drug approval in Japan.

Of some innovations in clinical trial design in hematology and oncology

The design of clinical trials, formalized in the immediate post-war period, has undergone major changes due to therapeutic innovations, particularly the arrival of targeted therapies in onco-hematology. The traditional phase I-II-III regimen is regularly questioned and multiple adaptations are proposed. This article proposes to expose some of these modifications and the issues they lead to.

Master Protocols for Precision Medicine in Oncology: Overcoming Methodology of Randomized Clinical Trials

Randomized clinical trials are considered the milestones of clinical research in oncology, and guided the development and approval of new compounds so far. In the last few years, however, molecular and genomic profiling led to a change of paradigm in therapeutic algorithms of many cancer types, with the spread of different biomarker-driven therapies (or targeted therapies). This scenario of "personalized medicine" revolutionized therapeutic strategies and the methodology of the supporting clinical research. New clinical trial designs are emerging to answer to the unmet clinical needs related to the development of these targeted therapies, overcoming the "classical" structure of randomized studies. Innovative trial designs able to evaluate more than one treatment in the same group of patients or many groups of patients with the same treatment (or both) are emerging as a possible future standard in clinical trial methodology. These are identified as "master protocols", and include umbrella, basket and platform trials. In this review, we described the main characteristics of these new trial designs, focusing on the opportunities and limitations of their use in the era of personalized medicine.

Master protocol trial design for technical feasibility of MR-guided radiotherapy

The master protocol trial design aims to increase efficiency in terms of trial infrastructure and protocol administration which may accelerate development of (technical) innovations in radiation oncology. A master protocol to study feasibility of techniques/software for MR-guided adaptive radiotherapy with the MR-Linac is described and discussed.

Molecular profiling of advanced soft-tissue sarcomas: the MULTISARC randomized trial

BACKGROUND

Soft-tissue sarcomas (STS) represent a heterogeneous group of rare tumors including more than 70 different histological subtypes. High throughput molecular analysis (next generation sequencing exome [NGS]) is a unique opportunity to identify driver mutations that can change the usual one-size-fits-all treatment paradigm to a patient-driven therapeutic strategy. The primary objective of the MULTISARC trial is to assess whether NGS can be conducted for a large proportion of metastatic STS participants within a reasonable time, and, secondarily to determine whether a NGS-guided therapeutic strategy improves participant's outcome.

METHODS

This is a randomized, multicentre, phase II/III trial inspired by the design of umbrella and biomarker-driven trials. The setting plans up to 17 investigational centres across France and the recruitment of 960 participants. Participants aged at least 18 years, with unresectable locally advanced and/or metastatic STS confirmed by the French sarcoma pathological reference network, are randomized according to 1:1 allocation ratio between the experimental arm "NGS" and the standard "No NGS". NGS will be considered feasible if (i) NGS results are available and interpretable, and (ii) a report of exome sequencing including a clinical recommendation from a multidisciplinary tumor board is provided to investigators within 7 weeks from reception of the samples on the biopathological platform. A feasibility rate of more than 70% is expected (null hypothesis: 70% versus alternative hypothesis: 80%). In terms of care, participants randomized in "No NGS" arm and who fail treatment will be able to switch to the NGS arm at the request of the investigator.

DISCUSSION

The MULTISARC trial is a prospective study designed to provide high-level evidence to support the implementation of NGS in routine clinical practice for advanced STS participants, on a large scale.

TRIAL REGISTRATION

clinicaltrial.gov NCT03784014 .