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Master Protocols in Oncology Trials

A master protocol is defined as a protocol designed with multiple sub-studies, which may have different objectives and involve coordinated efforts to evaluate one or more investigational drugs in one or more disease subtypes within the overall trial structure. Master protocol trials include three trial designs: basket trials, umbrella trials, and platform trials.

FDA guidance released in March 2022 provides recommendations for master protocol trials.

In this blog, we discuss master protocol trial designs, challenges and best practices, and the benefit of these innovative designs in oncology trials.

 

Types of master protocol trials

Basket trials

Basket trials are designed to test a single investigational drug or drug combination in different populations defined by different cancers, disease stages for a specific cancer, histologies, number of prior therapies, genetic or other biomarkers, or demographic characteristics.

 

Umbrella trials

Umbrella trials are designed to evaluate multiple investigational drugs administered as single drugs or as drug combinations in a single disease population.

 

Platform trials

Platform trials are master protocols in which arm(s) can be dropped or added based on knowledge gained from previously evaluated parts of the trial.

 

Figure 1: Basket Trials, Umbrella Trials, and Platform Trials

Image credit: Park, J. J. H., Siden, E., Zoratti, M. J., Dron, L., Harari, O., Singer, J., Lester, R. T., Thorlund, K., & Mills, E. J. (2019). Systematic review of basket trials, umbrella trials, and platform trials: A landscape analysis of master protocols. Trials, 20.

 

Key challenges with master protocol trials

Master protocol trials are inherently complex due to their expansive scope and varied components. Let’s refine these challenges further:

 

Data management and analysis

  • Large amounts of data need efficient integration and processing.
  • Basket trials involve multiple indications and endpoint definitions, and/or response criteria may vary across the indications.
  • Umbrella trials have multiple drugs, leading to complex exposure and safety summaries.
  • Platform trials continuously add new treatment arms, generating a dynamic dataset that requires real-time integration and analysis. This necessitates robust data management systems capable of handling evolving data structures and ensuring consistency across various cohorts.

 

Safety profile considerations

  • Variability in drug effects requires tailored safety monitoring strategies.
  • Adverse events of special interest might need to be defined for each drug separately.

 

Biomarker data complexity

  • Data can be relatively large and complex.
  • Having the data transfer specifications at an early stage is important to ensure that the correct data will be received and in the expected format.
  • Intensive discussion might be needed with biomarker data specialists to define the rules for deriving biomarker/genomic profile of interest.
  • Mapping those data from raw data to SDTM can also be challenging.

 

Statistical Analysis Plan (SAP) and shell development

  • Potential additional complexity for statistical inference (e.g., adaptive features, multiplicity, and Bayesian methods).
  • Require the team to focus on the main objectives of the study, otherwise SAP and shell can become very extensive.
  • The number of tables, figures, and listings can grow significantly, making prioritization essential.
  • Layout complexities arise when need to display numerous columns across multiple cohorts.

 

Operational and reporting challenges

  • Each cohort may follow different timelines, complicating interim and final analyses.
  • Frequent reportings require good planning.
  • CSR(s) strategy (e.g., separate CSR for each cohort versus single CSR) should be defined sufficiently early.

Staying focused on the key study objectives is crucial to prevent data overload and inefficiencies in reporting. Exploratory analyses can be planned in a second step.

 

Comparative Overview: Basket vs. Umbrella vs. Platform Trials

(Click table to enlarge)

 

Final takeaways

Master protocol trials represent a transformative shift in clinical research — enabling the simultaneous evaluation of multiple therapies or disease subtypes under a unified framework. While designs like basket, umbrella, and platform trials offer flexibility and efficiency, they also introduce significant operational, statistical, and data management complexities.

Success is built on early planning, early discussion with safety and biomarker teams, and a focus on core study objectives to ensure meaningful insights and readiness.

Understanding Master Protocol Designs: Platform and Basket Trials

A clinical trial usually seeks to evaluate the effects of a candidate drug in a carefully pre-specified patient population. Every detail of the trial must be outlined in the Clinical Study Protocol (CSP), including the exact inclusion and exclusion criteria for patients, the exact variables to be measured, and the statistical hypotheses to be tested.

Platform trials and basket trials, however, are innovative study designs that allow researchers to explore multiple treatments or target multiple patient populations simultaneously under a single overarching CSP, called a master protocol. Such approaches are elegant in that sponsors may start a new study arm to investigate an additional indication, dose, or inclusion criterion in parallel with the ongoing clinical trial, without needing to write a new CSP for each new study arm (which would also need to be applied for and approved by authorities). On the other hand, the planning and writing of the CSP for platform and basket trials up front requires a lot more effort than that of a traditional study.

Here, I outline benefits and challenges of these groundbreaking methodologies.

Platform trials make it possible to add study arms

Based on a particular disease, a platform trial investigates different treatments, doses, or subgroups of patients, all in different study arms. In particular, it is possible to add study arms that were not predefined in the study protocol: It is possible to start a trial, keep it running over years, and introduce new potential treatments as they appear and after evaluation of the older treatment arms. Platform trials are adaptive, as new parts of the trial may be chosen based on knowledge gained from previously evaluated parts of the trial. Allocation rates between ongoing treatment arms may also be adapted to optimize patient recruitment.

However, platform trials may come with administrative challenges

Platform trials can be notoriously difficult to administer. The CSP (i.e., the master protocol) needs to consider precise instructions for how future decisions will be made regarding the number of interventions active at the same time, the allocation of new patients between interventions and control groups, the frequency of interim evaluations, and the rules for stopping and starting interventions at interim evaluations.

Yet platform trials are helpful in collaborative projects

Despite the administrative challenges, a platform trial may be very beneficial in, for example, collaborative projects between multiple clinics or academic groups worldwide. Multiple groups of researchers may contribute to the larger project, enabling the comparison of different treatment strategies through the streamlined study arms detailed by the master protocol. Research groups may be able to share control groups and quickly adapt to new or evolving therapeutic landscapes. The STAMPEDE prostate cancer study is an example in which 12,000 patients were enrolled between 2005 and 2023.1 Another example is the I-SPY platform trial, in which 28 active interventions against breast cancer have been tested so far since the start of recruitment in 2010.2

 

Basket trials allow for multiple indications

Unlike platform designs, basket designs do not permit adding new treatments during the trial. Instead, while the trial targets a specific therapy, it allows sponsors to test multiple indications. Think of each basket coming with a new set of patients, with their own inclusion and exclusion criteria, to a trial. Each basket will be randomized to its own study arms (usually active and control treatment arms), but the outcome of the study may be a combination of the results from all the different study arms. This way, a proof of concept may be approached early and jointly between, say, different cancer indications that may be candidates for the same drug. The assessment of each indication may be derived given the results of the other indications, for example, using a Bayesian method.

Common criticism of basket trial designs

Basket designs do get criticized for enabling a positive study outcome even in situations where no indication shows sufficient efficacy on its own. This is a justified comment. As you go into a follow-up study to recruit a larger number of patients with a single indication, your amount of evidence from a positive basket trial may be very light for the specific indication. This means the follow-up study has a larger element of gambling than it would have had were the first efficacy study based on that same single indication. We cannot be sure that there really is a treatment effect in one particular indication.

When are basket designs useful?

For the reason mentioned, a basket design makes the most sense when there is clinical reason that all the indications can be improved by the same molecular drug mechanism. Perhaps because the indications were all caused by the same mechanism. In such cases, coherent results in different patient populations do strengthen each other. The BRAF V600 Vemurafenib is an example basket trial in which patients had the same mutation (BRAF V600) but different diagnoses.3 It included 122 patients from 5 indications (NSCLC, Colorectal, Cholangiocarcinoma, ECD or LCH, and Thyroid) plus one “other” basket.

 

Interested in learning more? Download our complimentary ebook, Adaptive Trial Design, which outlines common adaptive trial designs, benefits of adaptive trials, how to optimize your adaptive trial, and a ten-point framework to determine if your trial should be adaptive.

Platform Trials, Master Protocols, and Challenges in Execution

How can we build an efficient statistical protocol for a clinical trial, if we do not know the therapies that will be tested nor the populations on whom the testing will occur? This is the challenge confronting those working to design an ever-increasing number of platform trials. In a recent webinar, Dr. Kyle Wathen, Cytel’s Vice President of Scientific Strategy and Innovation, discussed a method using platform trials to show how to build such trials.

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Myth Busting: Master Protocol Edition

Interest and appetite for master protocols is growing as sponsors consider opportunities in various therapeutic areas beyond oncology. During initial discussions, most quickly recognize the potential operational and inferential benefits of a master protocol; however, as sponsors dive deeper into the details, doubt creeps in and there are a multitude of reasons expressed for not moving forward with a master protocol. Here are some common myths and the facts around them:

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