Analysis of phase II methodologies for single-arm clinical trials with multiple endpoints in rare cancers: An example in Ewing's sarcoma

In search of effective therapies: the current landscape of phase II trials in patients with advanced soft tissue sarcoma

PURPOSE

Soft tissue sarcomas (STS) are diagnosed in 4-6 cases per 100 000 people a year and are associated with an unfavorable prognosis. Around one-third of patients will develop metastatic disease that requires palliative systemic therapy. Current therapeutic options have limited activity, and new treatments are tested, mainly in phase II trials. There is high variability and no standardization of phase II designs. We aimed to analyze the current landscape of phase II studies in STS and evaluate how its statistical design can affect the results.

METHODS

Full-text phase II studies published in STS patients between 2005 and 2020 were identified and analyzed.

RESULTS

We have identified 102 trials, of which 77.4% were single-arm trials, 16.7% were randomized comparative trials (RCT), and 5.9% were randomized noncomparative trials. Including multiple cohorts, 22 randomized and 128 single-arm cohorts were analyzed. Nearly 80% of trials reported full statistical bases of the design. Over 20 different primary endpoints were used, with PFS as the most common in RCT trials (81.8%) and ORR (36.7%) and 3-months progression-free survival (PFS) rate (21.9%) in single-arm trials. Overall, 27.3% of RCT and 37.5% of single-arm trials were positive. Among single-arm trials, studies using 3- or 6-month rates were more often positive than those based on ORR.

CONCLUSIONS

There is high heterogeneity in sarcoma trial designs, mainly in primary-endpoint and hypotheses used for size calculation. There is an unmet need for standardization that will incorporate factors associated with the rarity of the disease, outcomes detected in previous trials and real-life studies, and specific characteristics of new therapeutic agents.

A stochastically curtailed single‐arm phase II trial design for binary outcomes

Phase II clinical trials are a critical aspect of the drug development process. With drug development costs ever increasing, novel designs that can improve the efficiency of phase II trials are extremely valuable.Phase II clinical trials for cancer treatments often measure a binary outcome. The final trial decision is generally to continue or cease development. When this decision is based solely on the result of a hypothesis test, the result may be known with certainty before the planned end of the trial. Unfortunately, there is often no opportunity for early stopping when this occurs.Some existing designs do permit early stopping in this case, accordingly reducing the required sample size and potentially speeding up drug development. However, more improvements can be achieved by stopping early when the final trial decision is very likely, rather than certain, known as stochastic curtailment. While some authors have proposed approaches of this form, these approaches have various limitations.In this work we address these limitations by proposing new design approaches for single-arm phase II binary outcome trials that use stochastic curtailment. We use exact distributions, avoid simulation, consider a wider range of possible designs and permit early stopping for promising treatments. As a result, we are able to obtain trial designs that have considerably reduced sample sizes on average.

Tissue-Engineered Bone Tumor as a Reproducible Human in Vitro Model for Studies of Anticancer Drugs

Studies of anticancer therapies in traditional cell culture models can demonstrate efficacy of direct-acting compounds but lack the 3-dimensional arrangement of the tumor cells and their tissue-specific microenvironments, both of which are important modulators of treatment effects in vivo. Bone cells reside in complex environments that regulate their fate and function. A bioengineered human bone-tumor model has been shown to provide a microphysiological niche for studies of cancer cell behavior. Here, we demonstrate successful transfer between 2 laboratories and utility of this model in efficacy studies using well-established chemotherapeutic agents. The bioengineered human bone-tumor model consisted of Ewing sarcoma (RD-ES) cancer cell aggregates infused into tissue-engineered bone that was grown from human mesenchymal stem cell-derived differentiated into osteoblasts within mineralized bone scaffolds. The tumor model was maintained in culture for over 5 weeks and subjected to clinically relevant doses of linsitinib, doxorubicin, cisplatin, methotrexate, vincristine, dexamethasone, or MAP (methotrexate, doxorubicin, and cisplatin combination). Drug administration cycles were designed to mimic clinical treatment regimens. The bioengineered tumors were evaluated days to weeks after the cessation of treatment to monitor the potential for relapse, using bioengineered bone and ES cell monolayers as controls. Drug binding to the scaffolds and media proteins and gene expression were also evaluated. We show that a bioengineered human bone tumor can be used as a microphysiological model for preclinical studies of anticancer drugs. We found that anticancer efficacy was achieved at concentrations approximating the human Cmax, in contrast to traditional ES cell monolayers. These studies show that the bone-tumor model can be successfully transferred between laboratories and has predictive power in preclinical studies. The effects of drugs on the bone tumors and healthy bone were studied in parallel, in support of the utility of this model for identification of new therapeutic targets.

A modular framework for early-phase seamless oncology trials

BACKGROUND

As our understanding of the etiology and mechanisms of cancer becomes more sophisticated and the number of therapeutic options increases, phase I oncology trials today have multiple primary objectives. Many such designs are now "seamless," meaning that the trial estimates both the maximum tolerated dose and the efficacy at this dose level. Sponsors often proceed with further study only with this additional efficacy evidence. However, with this increasing complexity in trial design, it becomes challenging to articulate fundamental operating characteristics of these trials, such as (1) what is the probability that the design will identify an acceptable, that is., safe and efficacious, dose level? or (2) how many patients will be assigned to an acceptable dose level on average?

METHODS

In this manuscript, we propose a new modular framework for designing and evaluating seamless oncology trials. Each module is comprised of either a dose assignment step or a dose-response evaluation, and multiple such modules can be implemented sequentially. We develop modules from existing phase I/II designs as well as a novel module for evaluating dose-response using a Bayesian isotonic regression scheme.

RESULTS

We also demonstrate a freely available R package called seamlesssim to numerically estimate, by means of simulation, the operating characteristics of these modular trials.

CONCLUSIONS

Together, this design framework and its accompanying simulator allow the clinical trialist to compare multiple different candidate designs, more rigorously assess performance, better justify sample sizes, and ultimately select a higher quality design.