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.

Assessing the hierarchical beta-binomial model as a basic information sharing tool in basket trials.

The majority of statistical methods to share information in basket trials are based on a Bayesian hierarchical model with a common normal distribution for the logit-transformed response rates. The methods are of varying complexity, yet they all use this basic model. Generally, complexity is an obstacle for the application in clinical trials and that includes the use of the logit-transformation. The transformation complicates the model and impedes a direct interpretation of the hyperparameters. On the other hand, there exist basket trial designs which directly work on the probability scale of the response rate which facilitates the understanding of the model for many stakeholders. In order to reduce unnecessary complexity, we considered using a hierarchical beta-binomial model instead of the transformed models. This article investigates whether this approach is a practicable alternative to the commonly applied sharing tools based on a logit-transformation of the response rates. For this purpose, we performed a systematic comparison of the two models, starting with the distributional assumptions for the response rates, continuing with the Bayesian behavior together with binomial data in an independent setting and ended with a simulation study for the hierarchical model under various data and prior scenarios. All Bayesian comparisons require equal starting points, wherefore we propose a calibration procedure to choose similar priors for the models. The evaluation of the sharing property additionally required an evaluation measure for simulation results, which we derived in this work. The conclusion of the comparison is that the hierarchical beta-binomial model is a feasible alternative basic model to share information in basket trials.

A win ratio-based framework to combine multiple clinical endpoints in exploratory basket trials.

In contemporary exploratory phase of oncology drug development, there has been an increasing interest in evaluating investigational drug or drug combination in multiple tumor indications in a single basket trial to expedite drug development. There has been extensive research on more efficiently borrowing information across tumor indications in early phase drug development including Bayesian hierarchical modeling and the pruning-and-pooling methods. Despite the fact that the Go/No-Go decision for subsequent Phase 2 or Phase 3 trial initiation is almost always a multi-facet consideration, the statistical literature of basket trial design and analysis has largely been limited to a single binary endpoint. In this paper we explore the application of considering clinical priorities of multiple endpoints based on matched win ratio to the basket trial design and analysis. The control arm data will be simulated for each tumor indication based on the corresponding null assumptions that could be heterogeneous across tumor indications. The matched win ratio matching on the tumor indication can be performed for individual tumor indication, pooled data, or the pooled data after pruning depending on whether an individual evaluation or a simple pooling or a pruning-and-pooling method is used. We conduct the simulation studies to evaluate the performance of proposed win ratio-based framework and the results suggest the proposed framework could provide desirable operating characteristics.

Generalized triple outcome decision-making in basket trials.

Making the go/no-go decision is critical in Phase II (or Ib) clinical trials. The conventional decision-making framework based on a binary hypothesis testing has been gradually replaced by the TODeM (Triple Outcome Decision-Making) which has three zones of outcomes: go, no-go, and consider. The TODeM provides more flexibility in decision-making with considering both of statistical significance and clinical relevance. However, Bayesian methods (e.g. EXNEX, MUCE, etc.) for the information borrowing are still based on the binary decision-making framework. We propose a new decision-making process G-TODeM (Generalized Triple Outcome Decision-Making) to apply those Bayesian methods with information borrowing across different cohorts to the TODeM framework. Essentially, the information borrowed from other cohorts can shrink the consider zone of the inference cohort.

Innovative trial design in precision oncology.

Genomic profiling technologies have enabled the development of targeted therapies designed to target specific biomarkers and molecular pathways involved in the pathophysiology of tumor initiation, metastasis, and drug resistance. In recent years, clinical trials with innovative design focus on the development of novel agents based on specific patient molecular alterations or other tumor characteristics and include patients with heterogenous tumor types. Precision oncology studies with innovative design associated with novel dose-finding approaches and data analysis focusing on subgroups of patients are characteristic of master protocols. Real-world data, patient-reported outcomes, and N-of-1 trials enhance the knowledge base of evidence to deliver personalized treatment to patients. Master protocols accelerate drug development by enabling simultaneous multiple sub-studies that match the patient's tumor molecular profile with experimental treatment arms. However, the increased flexibility of precision oncology trials is often associated with small subpopulations of patients, which may be underpowered to draw statistically robust conclusions. Despite their limitations, innovative clinical trials continue to rapidly translate the emerging discoveries of novel drugs into unprecedented clinical outcomes in patients with cancer and to accelerate the implementation of precision oncology.

A comparison of Bayesian information borrowing methods in basket trials and a novel proposal of modified exchangeability-nonexchangeability method.

Recent innovation in trial design to improve study efficiency has led to the development of basket trials in which a single therapeutic treatment is tested on several patient populations, each of which forms a basket. In a common setting, patients across all baskets share a genetic marker and as such, an assumption can be made that all patients may have a homogeneous response to treatments. Bayesian information borrowing procedures utilize this assumption to draw on information regarding the response in one basket when estimating the response rate in others. This can improve power and precision of estimates particularly in the presence of small sample sizes, however, can come at a cost of biased estimates and an inflation of error rates, bringing into question validity of trial conclusions. We review and compare the performance of several Bayesian borrowing methods, namely: the Bayesian hierarchical model (BHM), calibrated Bayesian hierarchical model (CBHM), exchangeability-nonexchangeability (EXNEX) model and a Bayesian model averaging procedure. A generalization of the CBHM is made to account for unequal sample sizes across baskets. We also propose a modification of the EXNEX model that allows for better control of a type I error. The proposed method uses a data-driven approach to account for the homogeneity of the response data, measured through Hellinger distances. Through an extensive simulation study motivated by a real basket trial, for both equal and unequal sample sizes across baskets, we show that in the presence of a basket with a heterogeneous response, unlike the other methods discussed, this model can control type I error rates to a nominal level whilst yielding improved power.

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.

[New strategies for the treatment of carcinoma of unknown primary].

Carcinoma of unknown primary (CUP) is a kind of metastatic tumor whose primary origin cannot be identified after adequate examination and evaluation. The main treatment modality of CUP is empiric chemotherapy, and the median overall survival time is less than 1 year. Compared with immunohistochemistry, novel method based on gene expression profiling have improved the sensitivity and specificity of CUP detection, but its guiding value for treatment is still controversial. The approval of immune checkpoint inhibitors and pan-cancer antitumor agents has improved the prognosis of patients with CUP, and targeted therapy and immunotherapy based on specific molecular characteristics are the main directions of future research. Given the high heterogeneity and unique clinicopathological characteristics of CUP, "basket trial" is more suitable for clinical trial design in CUP.

Monotonicity conditions for avoiding counterintuitive decisions in basket trials.

In a basket trial, a new treatment is tested in different subgroups, called the baskets. In oncology, the baskets usually comprise patients with different primary tumor sites but a common biomarker. Most basket trials are uncontrolled phase II trials and investigate a binary endpoint such as tumor response. To combine the data of baskets that show a similar response to the treatment, many basket trial designs use Bayesian borrowing methods. This increases the power compared to a basketwise analysis. However, it can lead to posterior probabilities that are not monotonically increasing in the number of responses. We show that, as a consequence, two types of counterintuitive decisions can arise-one that occurs within a single trial and one that occurs when the results are compared between different trials. We propose two monotonicity conditions for the inference in basket trials. Using a design recently proposed by Fujikawa and colleagues, we investigate the case of a single-stage basket trial with equal sample sizes in all baskets and show that, as the number of baskets increases, these conditions are violated for a wide range of different borrowing strengths. We show that in the investigated scenarios pruning baskets can help to ensure that the monotonicity conditions hold and investigate how this affects type I error rate and power.

Phase 2 study of TAS-117, an allosteric akt inhibitor in advanced solid tumors harboring phosphatidylinositol 3-kinase/v-akt murine thymoma viral oncogene homolog gene mutations.

TAS-117 is a potent and selective allosteric pan-v-akt murine thymoma viral oncogene homolog (Akt) inhibitor. We conducted a single-arm single-center phase 2 study of TAS-117 in heavily treated patients with tumors refractory to systemic chemotherapy and harboring phosphatidylinositol 3-kinase (PI3K)/Akt mutations. Patients with gastrointestinal (GI) cancers were orally administered 16 mg TAS-117 daily, and those with non-GI tumors were administered 24 mg on a 4 days on/3 days off schedule. The primary endpoint was overall response rate (ORR). Secondary endpoints included disease control rate (DCR), progression-free survival (PFS), overall survival (OS), PFS ratio, safety, and tolerability. Thirteen patients were enrolled: eight with non-GI (breast, ovarian, endometrial, and non-small cell lung) and five with GI (colon, rectal, gastric, and gallbladder) cancers. Ten patients were treated with TAS-117 after ≥ 4 lines of therapy. Twelve patients showed PIK3 catalytic subunit alpha (PIK3CA) mutations; one harbored an Akt1E17K mutation. The median treatment duration was 1.4 months; the median number of treatment cycles was 2. The ORR was 8 %, and DCR was 23 %. The median PFS and OS were 1.4 and 4.8 months, respectively. Grade 3-4 treatment-related adverse events were anorexia (grade 3, 8 %) and hyperglycemia (grade 3, 8 %; grade 4, 8 %).Grade 3-4 treatment-related adverse events occurred in 27 % of grade 3 anorexia (9 %) and hyperglycemia (grade 3, 8 %; grade 4, 9\%). TAS-117 showed limited antitumor activity and manageable toxicity. Clinical efficacy was observed in patients with ovarian cancer harboring PIK3CA E545K mutations and in patients with breast cancer harboring PIK3CA H1047R and Akt1E17K mutations.Trial registration: This study was retrospectively registered with ClinicalTrial.gov (NCT03017521 on January 11, 2017).

ODYSSEY clinical trial design: a randomised global study to evaluate the efficacy and safety of dolutegravir-based antiretroviral therapy in HIV-positive children, with nested pharmacokinetic sub-studies to evaluate pragmatic WHO-weight-band based dolutegravir dosing.

BACKGROUND: Dolutegravir (DTG)-based antiretroviral therapy (ART) is highly effective and well-tolerated in adults and is rapidly being adopted globally. We describe the design of the ODYSSEY trial which evaluates the efficacy and safety of DTG-based ART compared with standard-of-care in children and adolescents. The ODYSSEY trial includes nested pharmacokinetic (PK) sub-studies which evaluated pragmatic World Health Organization (WHO) weight-band-based DTG dosing and opened recruitment to children < 14 kg while dosing was in development. METHODS: ODYSSEY (Once-daily DTG based ART in Young people vS. Standard thErapY) is an open-label, randomised, non-inferiority, basket trial comparing the efficacy and safety of DTG + 2 nucleos(t) ides (NRTIs) versus standard-of-care (SOC) in HIV-infected children < 18 years starting first-line ART (ODYSSEY A) or switching to second-line ART (ODYSSEY B). The primary endpoint is clinical or virological failure by 96 weeks. RESULTS: Between September 2016 and June 2018, 707 children weighing ≥14 kg were enrolled; including 311 ART-naïve children and 396 children starting second-line. 47% of children were enrolled in Uganda, 21% Zimbabwe, 20% South Africa, 9% Thailand, 4% Europe. 362 (51%) participants were male; median age [range] at enrolment was 12.2 years [2.9-18.0]. 82 (12%) children weighed 14 to < 20 kg, 135 (19%) 20 to < 25 kg, 206 (29%) 25 to < 35 kg, 284 (40%) ≥35 kg. 128 (18%) had WHO stage 3 and 60 (8%) WHO stage 4 disease. Challenges encountered include: (i) running the trial across high- to low-income countries with differing frequencies of standard-of-care viral load monitoring; (ii) evaluating pragmatic DTG dosing in PK sub-studies alongside FDA- and EMA-approved dosing and subsequently transitioning participants to new recommended doses; (iii) delays in dosing information for children weighing 3 to < 14 kg and rapid recruitment of ART-naïve older/heavier children, which led to capping recruitment of participants weighing ≥35 kg in ODYSSEY A and extending recruitment (above 700) to allow for ≥60 additional children weighing between 3 to < 14 kg with associated PK; (iv) a safety alert associated with DTG use during pregnancy, which required a review of the safety plan for adolescent girls. CONCLUSIONS: By employing a basket design, to include ART-naïve and -experienced children, and nested PK sub-studies, the ODYSSEY trial efficiently evaluates multiple scientific questions regarding dosing and effectiveness of DTG-based ART in children. TRIAL REGISTRATION: NCT, NCT02259127 , registered 7th October 2014; EUDRACT, 2014-002632-14, registered 18th June 2014 ( https://www.clinicaltrialsregister.eu/ctr-search/trial/2014-002632-14/ES ); ISRCTN, ISRCTN91737921 , registered 4th October 2014.

Optimal one-stage design and analysis for efficacy expansion in Phase I oncology trials.

BACKGROUND: Contemporary Phase I oncology trials often include efficacy expansion in various tumor indications post dose finding. Preliminary anti-tumor activity from efficacy expansion can aid Go/No-Go decision for Phase 2 or Phase 3 initiation. Tumor cohorts in efficacy expansion are commonly analyzed independently in practice, which are often underpowered due to small sample size. Pooled analysis is also sometimes conducted, but it ignores the heterogeneity of the anti-tumor activity across cohorts. METHODS: We propose an optimal one-stage design and analysis strategy for the efficacy expansion to assess whether the treatment is effective. Allowing heterogeneous anti-tumor effects across tumor cohorts, inactive cohorts are pruned, and the potentially active cohorts are pooled together to gain study power. For a prospective design with a target power, the total sample size across all cohorts is minimized; or for an ad hoc analysis with pre-specified sample size for each cohort, the pruning criteria are optimized to achieve maximum power. The global type I error is controlled after proper multiplicity adjustment, and a penalty adjusted significance level is used for the pooled test. RESULTS: Simulation studies show that the proposed optimal design has desirable operating characteristics in increasing the overall power and detecting more true positive tumor cohorts. CONCLUSION: The proposed optimal design and analysis strategy provides a practical approach to design and analyze heterogeneous efficacy expansion cohorts in a basket setting with global type I and type II error being controlled.

A new basket trial design based on clustering of homogeneous subpopulations.

As new findings in oncology suggest a focus on individualized and targeted therapies, the demand for adequate clinical trial designs rises, whereby the focus is mainly on early development phases (phase I and II). Phase II oncology trials are often planned and analysed by Simon two-stage design, which corresponds to a one-armed trial design with the option to stop early for futility. Whereas a classical phase II study focuses on one tumour type and location, the relatively new basket trial design allows testing the efficacy of a single drug simultaneously in a number of patient subsets, which correspond to different tumour types. Such trials can be analysed in various ways, including separate analyses of all baskets or by pooling across all baskets. The work presented here tries to find an adequate compromise between these two extremes by implying rules for clustering some baskets, which are reasonably homogeneous. By means of Monte-Carlo simulations, we compare the efficiency of our proposed cluster-based basket trial design with a standard approach proposed recently which only allows for complete pooling or separate analyses. The results suggest that our new design offers a considerable advantage in power, sensitivity and specificity as well as in average sample size compared to the standard approach. The proposed clustering design is an attractive option to conduct basket trials in oncology with higher efficiency and better performance.

Categories, components, and techniques in a modular construction of basket trials for application and further research.

Basket trials have become a virulent topic in medical and statistical research during the last decade. The core idea of them is to treat patients, who express the same genetic predisposition-either personally or their disease-with the same treatment irrespective of the location of the disease. The location of the disease defines each basket and the pathway of the treatment uses the common genetic predisposition among the baskets. This opens the opportunity to share information among baskets, which can consequently increase the information of the basket-wise response with respect to the investigated treatment. This further allows dynamic decisions regarding futility and efficacy of individual baskets during the ongoing trial. Several statistical designs have been proposed on how a basket trial can be conducted and this has left an unclear situation with many options. The different designs propose different mathematical and statistical techniques, different decision rules, and also different trial purposes. This paper presents a broad overview of existing designs, categorizes them, and elaborates their similarities and differences. A uniform and consistent notation facilitates the first contact, introduction, and understanding of the statistical methodologies and techniques used in basket trials. Finally, this paper presents a modular approach for the construction of basket trials in applied medical science and forms a base for further research of basket trial designs and their techniques.

Basket Studies: Redefining Clinical Trials in the Era of Genome-Driven Oncology.

Understanding a tumor's detailed molecular profile has become increasingly necessary to deliver the standard of care for patients with advanced cancer. Innovations in both tumor genomic sequencing technology and the development of drugs that target molecular alterations have fueled recent gains in genome-driven oncology care. "Basket studies," or histology-agnostic clinical trials in genomically selected patients, represent one important research tool to continue making progress in this field. We review key aspects of genome-driven oncology care, including the purpose and utility of basket studies, biostatistical considerations in trial design, genomic knowledgebase development, and patient matching and enrollment models, which are critical for translating our genomic knowledge into clinically meaningful outcomes.

Vemurafenib in non-small-cell lung cancer patients with BRAFV600 and BRAFnonV600 mutations.

BACKGROUND: BRAF mutations occurring in 1%-5% of patients with non-small-cell lung cancer (NSCLC) are therapeutic targets for these cancers but the impact of the exact mutation on clinical activity is unclear. The French National Cancer Institute (INCA) launched the AcSé vemurafenib trial to assess the efficacy and safety of vemurafenib in cancers with various BRAF mutations. We herein report the results of the NSCLC cohort. PATIENTS AND METHODS: Tumour samples were screened for BRAF mutations in INCA-certified molecular genetic centres. Patients with BRAF-mutated tumours progressing after ≥1 line of treatment were proposed vemurafenib 960 mg twice daily. Between October 2014 and July 2018, 118 patients were enrolled in the NSCLC cohort. The primary outcome was the objective response rate (ORR) assessed every 8 weeks (RECIST v1.1). A sequential Bayesian approach was planned with an inefficacy bound of 10% for ORR. If no early stopping occurred, the treatment was of interest if the estimated ORR was ≥30% with a 90% probability. Secondary outcomes were tolerance, response duration, progression-free survival (PFS), and overall survival (OS). RESULTS: Of the 118 patients enrolled, 101 presented with a BRAFV600 mutation and 17 with BRAFnonV600 mutations; the median follow-up was 23.9 months. In the BRAFnonV600 cohort, no objective response was observed and this cohort was stopped. In the BRAFV600 cohort, 43/96 patients had objective responses. The mean Bayesian estimated success rate was 44.9% [95% confidence intervals (CI) 35.2%-54.8%]. The ORR had a 99.9% probability of being ≥30%. Median response duration was 6.4 months, median PFS was 5.2 months (95% CI 3.8-6.8), and OS was 10 months (95% CI 6.8-15.7). The vemurafenib safety profile was consistent with previous publications. CONCLUSION: Routine biomarker screening of NSCLC should include BRAFV600 mutations. Vemurafenib monotherapy is effective for treating patients with BRAFV600-mutated NSCLC but not those with BRAFnonV600 mutations. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02304809.

A Bayesian basket trial design that borrows information across strata based on the similarity between the posterior distributions of the response probability.

Basket trials simultaneously evaluate the effect of one or more drugs on a defined biomarker, genetic alteration, or molecular target in a variety of disease subtypes, often called strata. A conventional approach for analyzing such trials is an independent analysis of each of the strata. This analysis is inefficient as it lacks the power to detect the effect of drugs in each stratum. To address these issues, various designs for basket trials have been proposed, centering on designs using Bayesian hierarchical models. In this article, we propose a novel Bayesian basket trial design that incorporates predictive sample size determination, early termination for inefficacy and efficacy, and the borrowing of information across strata. The borrowing of information is based on the similarity between the posterior distributions of the response probability. In general, Bayesian hierarchical models have many distributional assumptions along with multiple parameters. By contrast, our method has prior distributions for response probability and two parameters for similarity of distributions. The proposed design is easier to implement and less computationally demanding than other Bayesian basket designs. Through a simulation with various scenarios, our proposed design is compared with other designs including one that does not borrow information and one that uses a Bayesian hierarchical model.

Variance prior specification for a basket trial design using Bayesian hierarchical modeling.

BACKGROUND: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared across baskets. METHODS: A common approach used to capture the correlated binary endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a non-randomized basket trial and investigate three popular prior specifications: an inverse-gamma prior on the basket-level variance, a uniform prior and half-t prior on the basket-level standard deviation. RESULTS: From our simulation study, we can see that the inverse-gamma prior is highly sensitive to the input hyperparameters. When the prior mean value of the variance parameter is set to be near zero (≤0.5) , this can lead to unacceptably high false-positive rates (≥40%) in some scenarios. Thus, use of this prior requires a fully comprehensive sensitivity analysis before implementation. Alternatively, we see that a prior that places sufficient mass in the tail, such as the uniform or half-t prior, displays desirable and robust operating characteristics over a wide range of prior specifications, with the caveat that the upper bound of the uniform prior and the scale parameter of the half-t prior must be larger than 1. CONCLUSION: Based on the simulation results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a majority of the density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior or half-t prior on the standard deviation.

Umbrella and basket trials in oncology: ethical challenges.

BACKGROUND: Novel precision oncology trial designs, such as basket and umbrella trials, are designed to test new anticancer agents in more effective and affordable ways. However, they present some ethical concerns referred to scientific validity, risk-benefit balance and informed consent. Our aim is to discuss these issues in basket and umbrella trials, giving examples of two ongoing cancer trials: NCI-MATCH (National Cancer Institute - Molecular Analysis for Therapy Choice) and Lung-MAP (Lung Cancer Master Protocol) study. MAIN BODY: We discuss three ethical requirements for clinical trials which may be challenged in basket and umbrella trial designs. Firstly, we consider scientific validity. Thanks to the new trial designs, patients with rare malignancies have the opportunity to be enrolled and benefit from the trial, but due to insufficient accrual, the trial may generate clinically insignificant findings. Inadequate sample size in study arms and the use of surrogate endpoints may result in a drug approval without confirmed efficacy. Moreover, complexity, limited quality and availability of tumor samples may not only introduce bias and result in unreliable and unrepresentative findings, but also can potentially harm patients and assign them to an inappropriate therapy arm. Secondly, we refer to benefits and risks. Novel clinical trials can gain important knowledge on the variety of tumors, which can be used in future trials to develop effective therapies. However, they offer limited direct benefits to patients. All potential participants must wait about 2 weeks for the results of the genetic screening, which may be stressful and produce anxiety. The enrollment of patients whose tumors harbor multiple mutations in treatments matching a single mutation may be controversial. As to informed consent - the third requirement we discuss, the excessive use of phrases like "personalized medicine", "tailored therapy" or "precision oncology" might be misleading and cause personal convictions that the study protocol is designed to fulfill the individual health-related needs of participants. CONCLUSIONS: We suggest that further approaches should be implemented to enhance scientific validity, reduce misunderstandings and risks, thus maximizing the benefits to society and to trial participants.

Bayesian basket trial design with exchangeability monitoring.

Precision medicine endeavors to conform therapeutic interventions to the individuals being treated. Implicit to the concept of precision medicine is heterogeneity of treatment benefit among patients and patient subpopulations. Thus, precision medicine challenges conventional paradigms of clinical translational which have relied on estimates of population-averaged effects to guide clinical practice. Basket trials comprise a class of experimental designs used to study solid malignancies that are devised to evaluate the effectiveness of a therapeutic strategy among patients defined by the presence of a particular drug target (often a genetic mutation) rather than a particular tumor histology. Acknowledging the potential for differential effectiveness on the basis of traditional criteria for cancer subtyping, evaluations of treatment effectiveness are conducted with respect to the "baskets" which collectively represent a partition of the targeted patient population consisting of discrete subtypes. Yet, designs of early basket trials have been criticized for their reliance on basketwise analysis strategies that suffered from limited power in the presence of imbalanced enrollment as well as failed to convey to the clinical community evidentiary measures for consistent effectiveness among the studied clinical subtypes. This article presents novel methodology for sequential basket trial design formulated with Bayesian monitoring rules. Interim analyses are based a novel hierarchical modeling strategy for sharing information among a collection of discrete potentially nonexchangeable subtypes. The methodology is demonstrated by analysis as well as permutation and simulation studies based on a recent basket trial designed to estimate the effectiveness of vemurafenib in BRAFV600 mutant non-melanoma among six primary disease sites and histologies.