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.

Healthcare decision-making for tumour-agnostic therapies in Europe: lessons learned.

The tumour-agnostic authorisations of larotrectinib and entrectinib shifted the paradigm for indication setting. European healthcare decision-makers agreed on their therapeutic potential but diverged primarily in identified uncertainties concerning basket trial designs and endpoints, prognostic value of neurotrophic tropomyosin receptor kinase (NTRK) gene fusions, and resistance mechanisms. In addition, assessments of relevant comparators, unmet medical needs (UMNs), and implementation of NTRK-testing strategies diverged. In particular, the tumour-specific reimbursement recommendations and guidelines do not reflect tumour-agnostic thinking. These differences indicate difficulties experienced in these assessments and provide valuable lessons for future disruptive therapies. As we discuss here, early multistakeholder dialogues concerning minimum evidence requirements and involving clinicians are essential.

rBMA: A robust Bayesian Model Averaging Method for phase II basket trials based on informative mixture priors.

Oncology drug research in the last few decades has been driven by the development of targeted agents. In the era of targeted therapies, basket trials are often used to test the antitumor activity of a novel treatment in multiple indications sharing the same genomic alteration. As patient population are further fragmented into biomarker-defined subgroups in basket trials, novel statistical methods are needed to facilitate cross-indication learning to improve the statistical power in basket trial design. Here we propose a robust Bayesian model averaging (rBMA) technique for the design and analysis of phase II basket trials. We consider the posterior distribution of each indication (basket) as the weighted average of three different models which only differ in their priors (enthusiastic, pessimistic and non-informative). The posterior weights of these models are determined based on the effect of the experimental treatment in all the indications tested. In early phase oncology trials, different binary endpoints might be chosen for different indications (objective response, disease control or PFS at landmark times), which makes it even more challenging to borrow information across indications. Compared to previous approaches, the proposed method has the flexibility to support cross-indication learning in the presence of mixed endpoints. We evaluate and compare the performance of the proposed rBMA approach to competing approaches in simulation studies. R scripts to implement the proposed method are available at https://github.com/xwang317/rBMA.

Precision Oncology in Pediatric Cancer Surgery.

Pediatric precision oncology has provided a greater understanding of the wide range of molecular alterations in difficult-to-treat or rare tumors with the aims of increasing survival as well as decreasing toxicity and morbidity from current cytotoxic therapies. In this article, the authors discuss the current state of pediatric precision oncology which has increased access to novel targeted therapies while also providing a framework for clinical implementation in this unique population. The authors evaluate the targetable mutations currently under investigation-with a focus on pediatric solid tumors-and discuss the key surgical implications associated with novel targeted therapies.

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.

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.

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.

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.

Safety and efficacy of trastuzumab biosimilar plus irinotecan or gemcitabine in patients with previously treated HER2 (ERBB2)-positive non-breast/non-gastric solid tumors: a phase II basket trial with circulating tumor DNA analysis.

BACKGROUND: Human epidermal growth factor receptor 2 (HER2) (ERBB2)-directed agents are standard treatments for patients with HER2-positive breast and gastric cancer. Herein, we report the results of an open-label, single-center, phase II basket trial to investigate the efficacy and safety of trastuzumab biosimilar (Samfenet®) plus treatment of physician's choice for patients with previously treated HER2-positive advanced solid tumors, along with biomarker analysis employing circulating tumor DNA (ctDNA) sequencing. METHODS: Patients with HER2-positive unresectable or metastatic non-breast, non-gastric solid tumors who failed at least one prior treatment were included in this study conducted at Asan Medical Center, Seoul, Korea. Patients received trastuzumab combined with irinotecan or gemcitabine at the treating physicians' discretion. The primary endpoint was the objective response rate as per RECIST version 1.1. Plasma samples were collected at baseline and at the time of disease progression for ctDNA analysis. RESULTS: Twenty-three patients were screened from 31 December 2019 to 17 September 2021, and 20 were enrolled in this study. Their median age was 64 years (30-84 years), and 13 patients (65.0%) were male. The most common primary tumor was hepatobiliary cancer (seven patients, 35.0%), followed by colorectal cancer (six patients, 30.0%). Among 18 patients with an available response evaluation, the objective response rate was 11.1% (95% confidence interval 3.1% to 32.8%). ERBB2 amplification was detected from ctDNA analysis of baseline plasma samples in 85% of patients (n = 17), and the ERBB2 copy number from ctDNA analysis showed a significant correlation with the results from tissue sequencing. Among 16 patients with post-progression ctDNA analysis, 7 (43.8%) developed new alterations. None of the patients discontinued the study due to adverse events. CONCLUSIONS: Trastuzumab plus irinotecan or gemcitabine was safe and feasible for patients with previously treated HER2-positive advanced solid tumors with modest efficacy outcomes, and ctDNA analysis was useful for detecting HER2 amplification.

Targeted therapy for intractable cancer on the basis of molecular profiles: An open-label, phase II basket trial (Long March Pathway).

Purpose: We evaluated he effects of molecular guided-targeted therapy for intractable cancer. Also, the epidemiology of druggable gene alterations in Chinese population was investigated. Materials and methods: The Long March Pathway (ClinicalTrials.gov identifier: NCT03239015) is a non-randomized, open-label, phase II trial consisting of several basket studies examining the molecular profiles of intractable cancers in the Chinese population. The trial aimed to 1) evaluate the efficacy of targeted therapy for intractable cancer and 2) identify the molecular epidemiology of the tier II gene alterations among Chinese pan-cancer patients. Results: In the first stage, molecular profiles of 520 intractable pan-cancer patients were identified, and 115 patients were identified to have tier II gene alterations. Then, 27 of these 115 patients received targeted therapy based on molecular profiles. The overall response rate (ORR) was 29.6% (8/27), and the disease control rate (DCR) was 44.4% (12/27). The median duration of response (DOR) was 4.80 months (95% CI, 3.33-27.2), and median progression-free survival (PFS) was 4.67 months (95% CI, 2.33-9.50). In the second stage, molecular epidemiology of 17,841 Chinese pan-cancer patients demonstrated that the frequency of tier II gene alterations across cancer types is 17.7%. Bladder cancer had the most tier-II alterations (26.1%), followed by breast cancer (22.4%), and non-small cell lung cancer (NSCLC; 20.2%). Conclusion: The Long March Pathway trial demonstrated a significant clinical benefit for intractable cancer from molecular-guided targeted therapy in the Chinese population. The frequency of tier II gene alterations across cancer types supports the feasibility of molecular-guided targeted therapy under basket trials.

[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.

Shotgun-2: A Bayesian phase I/II basket trial design to identify indication-specific optimal biological doses.

For novel molecularly targeted agents and immunotherapies, the objective of dose-finding is often to identify the optimal biological dose, rather than the maximum tolerated dose. However, optimal biological doses may not be the same for different indications, challenging the traditional dose-finding framework. Therefore, we proposed a Bayesian phase I/II basket trial design, named "shotgun-2," to identify indication-specific optimal biological doses. A dose-escalation part is conducted in stage I to identify the maximum tolerated dose and admissible dose sets. In stage II, dose optimization is performed incorporating both toxicity and efficacy for each indication. Simulation studies under both fixed and random scenarios show that, compared with the traditional "phase I + cohort expansion" design, the shotgun-2 design is robust and can improve the probability of correctly selecting the optimal biological doses. Furthermore, this study provides a useful tool for identifying indication-specific optimal biological doses and accelerating drug development.

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.

New strategies for the treatment of carcinoma of unknown primary / 中华肿瘤杂志

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.

Challenges, opportunities, and innovative statistical designs for precision oncology trials.

In the era of precision oncology, improved understanding of tumor heterogeneity, particularly at the molecular level, has caused a shift from traditionally histology based cancer drug development to molecularly targeted drug development. The shift to the molecular view of cancer leads to increasingly small cancer populations for clinical trials which may be underpowered using traditional statistical designs. This paradigm shift lead to the recent developments of innovative clinical trial designs to address the challenges from precision oncology clinical trials. Hence, this paper reviewed and described innovative trial designs for precision oncology. Different strategies were discussed to account patient and treatment effect heterogeneity, including precision dose-finding designs that tailor the optimal dose to different patients at different time points, master protocol designs that match patients' molecular alterations with specific targeted agents, and adaptive enrichment designs that dynamically modify eligibility criteria and enroll patients that are most likely to benefit from the novel agents. Despite their superior performance, better understanding of practical barriers is needed to widen their implementation for precision oncology trials. Therefore, this paper also reviewed the practical challenges regarding the implementation of precision oncology clinical trials, along with the strength and weakness of various approaches of precision oncology clinical trial designs.

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.

New Drug Development and Clinical Trial Design by Applying Genomic Information Management.

Depending on the patients' genotype, the same drug may have different efficacies or side effects. With the cost of genomic analysis decreasing and reliability of analysis methods improving, vast amount of genomic information has been made available. Several studies in pharmacology have been based on genomic information to select the optimal drug, determine the dose, predict efficacy, and prevent side effects. This paper reviews the tissue specificity and genomic information of cancer. If the tissue specificity of cancer is low, cancer is induced in various organs based on a single gene mutation. Basket trials can be performed for carcinomas with low tissue specificity, confirming the efficacy of one drug for a single gene mutation in various carcinomas. Conversely, if the tissue specificity of cancer is high, cancer is induced in only one organ based on a single gene mutation. An umbrella trial can be performed for carcinomas with a high tissue specificity. Some drugs are effective for patients with a specific genotype. A companion diagnostic strategy that prescribes a specific drug for patients selected with a specific genotype is also reviewed. Genomic information is used in pharmacometrics to identify the relationship among pharmacokinetics, pharmacodynamics, and biomarkers of disease treatment effects. Utilizing genomic information, sophisticated clinical trials can be designed that will be better suited to the patients of specific genotypes. Genomic information also provides prospects for innovative drug development. Through proper genomic information management, factors relating to drug response and effects can be determined by selecting the appropriate data for analysis and by understanding the structure of the data. Selecting pre-processing and appropriate machine-learning libraries for use as machine-learning input features is also necessary. Professional curation of the output result is also required. Personalized medicine can be realized using a genome-based customized clinical trial design.

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.