Optimizing natural killer cell doses for heterogeneous cancer patients on the basis of multiple event times

Bayesian Safety and Futility Monitoring in Phase II Trials Using One Utility-Based Rule

For phase II clinical trials that determine the acceptability of an experimental treatment based on ordinal toxicity and ordinal response, most monitoring methods require each ordinal outcome to be dichotomized using a selected cut-point. This allows two early stopping rules to be constructed that compare marginal probabilities of toxicity and response to respective upper and lower limits. Important problems with this approach are loss of information due to dichotomization, dependence of treatment acceptability decisions on precisely how each ordinal variable is dichotomized, and ignoring association between the two outcomes. To address these problems, we propose a new Bayesian method, which we call U-Bayes, that exploits elicited numerical utilities of the joint ordinal outcomes to construct one early stopping rule that compares the mean utility to a lower limit. U-Bayes avoids the problems noted above by using the entire joint distribution of the ordinal outcomes, and not dichotomizing the outcomes. A step-by-step algorithm is provided for constructing a U-Bayes rule based on elicited utilities and elicited limits on marginal outcome probabilities. A simulation study shows that U-Bayes greatly improves the probability of determining treatment acceptability compared to conventional designs that use two monitoring rules based on marginal probabilities.

Adaptive Bayesian information borrowing methods for finding and optimizing subgroup-specific doses

In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.

Novel Clinical Trial Designs with Dose Optimization to Improve Long-term Outcomes

Conventional designs for choosing a dose for a new therapy may select doses that are unsafe or ineffective and fail to optimize progression-free survival time, overall survival time, or response/remission duration. We explain and illustrate limitations of conventional dose-finding designs and make four recommendations to address these problems. When feasible, a dose-finding design should account for long-term outcomes, include screening rules that drop unsafe or ineffective doses, enroll an adequate sample size, and randomize patients among doses. As illustrations, we review three designs that include one or more of these features. The first illustration is a trial that randomized patients among two cell therapy doses and standard of care in a setting where it was assumed on biological grounds that dose toxicity and dose-response curves did not necessarily increase with cell dose. The second design generalizes phase I-II by first identifying a set of candidate doses, rather than one dose, randomizing additional patients among the candidates, and selecting an optimal dose to maximize progression-free survival over a longer follow-up period. The third design combines a phase I-II trial and a group sequential randomized phase III trial by using survival time data available after the first stage of phase III to reoptimize the dose selected in phase I-II. By incorporating one or more of the recommended features, these designs improve the likelihood that a selected dose or schedule will be optimal, and thus will benefit future patients and obtain regulatory approval.

Biomarker-based precision dose finding for immunotherapy combined with radiotherapy

Recent success of sequential administration of immunotherapy following radiotherapy (RT), often referred to as immunoRT, has sparked the urgent need for novel clinical trial designs to accommodate the unique features of immunoRT. For this purpose, we propose a Bayesian phase I/II design for immunotherapy administered after standard-dose RT to identify the optimal dose that is personalized for each patient according to his/her measurements of PD-L1 expression at baseline and post-RT. We model the immune response, toxicity, and efficacy as functions of dose and patient's baseline and post-RT PD-L1 expression profile. We quantify the desirability of the dose using a utility function and propose a two-stage dose-finding algorithm to find the personalized optimal dose. Simulation studies show that our proposed design has good operating characteristics, with a high probability of identifying the personalized optimal dose.

Bayesian treatment screening and selection using subgroup-specific utilities of response and toxicity

A Bayesian design is proposed for randomized phase II clinical trials that screen multiple experimental treatments compared to an active control based on ordinal categorical toxicity and response. The underlying model and design account for patient heterogeneity characterized by ordered prognostic subgroups. All decision criteria are subgroup specific, including interim rules for dropping unsafe or ineffective treatments, and criteria for selecting optimal treatments at the end of the trial. The design requires an elicited utility function of the two outcomes that varies with the subgroups. Final treatment selections are based on posterior mean utilities. The methodology is illustrated by a trial of targeted agents for metastatic renal cancer, which motivated the design methodology. In the context of this application, the design is evaluated by computer simulation, including comparison to three designs that conduct separate trials within subgroups, or conduct one trial while ignoring subgroups, or base treatment selection on estimated response rates while ignoring toxicity.

Generalized phase I-II designs to increase long term therapeutic success rate

Designs for early phase dose finding clinical trials typically are either phase I based on toxicity, or phase I-II based on toxicity and efficacy. These designs rely on the implicit assumption that the dose of an experimental agent chosen using these short-term outcomes will maximize the agent's long-term therapeutic success rate. In many clinical settings, this assumption is not true. A dose selected in an early phase oncology trial may give suboptimal progression-free survival or overall survival time, often due to a high rate of relapse following response. To address this problem, a new family of Bayesian generalized phase I-II designs is proposed. First, a conventional phase I-II design based on short-term outcomes is used to identify a set of candidate doses, rather than selecting one dose. Additional patients then are randomized among the candidates, patients are followed for a predefined longer time period, and a final dose is selected to maximize the long-term therapeutic success rate, defined in terms of duration of response. Dose-specific sample sizes in the randomization are determined adaptively to obtain a desired level of selection reliability. The design was motivated by a phase I-II trial to find an optimal dose of natural killer cells as targeted immunotherapy for recurrent or treatment-resistant B-cell hematologic malignancies. A simulation study shows that, under a range of scenarios in the context of this trial, the proposed design has much better performance than two conventional phase I-II designs.

A Bayesian phase I/II design to determine subgroup-specific optimal dose for immunotherapy sequentially combined with radiotherapy

Sequential administration of immunotherapy following radiotherapy (immunoRT) has attracted much attention in cancer research. Due to its unique feature that radiotherapy upregulates the expression of a predictive biomarker for immunotherapy, novel clinical trial designs are needed for immunoRT to identify patient subgroups and the optimal dose for each subgroup. In this article, we propose a Bayesian phase I/II design for immunotherapy administered after standard-dose radiotherapy for this purpose. We construct a latent subgroup membership variable and model it as a function of the baseline and pre-post radiotherapy change in the predictive biomarker measurements. Conditional on the latent subgroup membership of each patient, we jointly model the continuous immune response and the binary efficacy outcome using plateau models, and model toxicity using the equivalent toxicity score approach to account for toxicity grades. During the trial, based on accumulating data, we continuously update model estimates and adaptively randomize patients to admissible doses. Simulation studies and an illustrative trial application show that our design has good operating characteristics in terms of identifying both patient subgroups and the optimal dose for each subgroup.

Leveraging Natural Killer Cell Innate Immunity against Hematologic Malignancies: From Stem Cell Transplant to Adoptive Transfer and Beyond

Numerous recent advancements in T-cell based immunotherapies have revolutionized the treatment of hematologic malignancies. In the race towards the first approved allogeneic cellular therapy product, there is growing interest in utilizing natural killer (NK) cells as a platform for off-the-shelf cellular therapies due to their scalable manufacturing potential, potent anti-tumor efficacy, and superior safety profile. Allogeneic NK cell therapies are now being actively explored in the setting of hematopoietic stem cell transplantation and adoptive transfer. Increasingly sophisticated gene editing techniques have permitted the engineering of chimeric antigen receptors, ectopic cytokine expression, and tumor recognition signals to improve the overall cytotoxicity of NK cell therapies. Furthermore, the enhancement of antibody-dependent cellular cytotoxicity has been achieved through the use of NK cell engagers and combination regimens with monoclonal antibodies that act synergistically with CD16-expressing NK cells. Finally, a greater understanding of NK cell biology and the mechanisms of resistance have allowed the preclinical development of NK checkpoint blockade and methods to modulate the tumor microenvironment, which have been evaluated in early phase trials. This review will discuss the recent clinical advancements in NK cell therapies in hematologic malignancies as well as promising avenues of future research.

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.

BIPSE: A biomarker-based phase I/II design for immunotherapy trials with progression-free survival endpoint

A Bayesian biomarker-based phase I/II design (BIPSE) is presented for immunotherapy trials with a progression-free survival (PFS) endpoint. The objective is to identify the subgroup-specific optimal dose, defined as the dose with the best risk-benefit tradeoff in each biomarker subgroup. We jointly model the immune response, toxicity outcome, and PFS with information borrowing across subgroups. A plateau model is used to describe the marginal distribution of the immune response. Conditional on the immune response, we model toxicity using probit regression and model PFS using the mixture cure rate model. During the trial, based on the accumulating data, we continuously update model estimates and adaptively randomize patients to doses with high desirability within each subgroup. Simulation studies show that the BIPSE design has desirable operating characteristics in selecting the subgroup-specific optimal doses and allocating patients to those optimal doses, and outperforms conventional designs.

Noninvasive Tracking of Natural Killer Cells Using Gold Nanoparticles

Natural killer (NK)-cell-based immunotherapy is emerging as an attractive approach for cancer treatment. However, to facilitate and expedite clinical implementation, important questions must be answered regarding the in vivo functionality and trafficking patterns of the transferred cells. We have recently developed a noninvasive cell-tracking technique, based on gold nanoparticles (GNPs) as cell-labeling and contrast agents for whole-body computed tomography (CT) imaging. Herein, we report the implementation of this technique for longitudinal and quantitative tracking of NK cell kinetics, the migration and biodistribution in tumor-bearing mice. NK cells were successfully labeled with GNPs, without impairing their biological function, as assessed both in vitro, by cytokine release and cytotoxicity assays, and in vivo, using a xenograft model of human tumors. Using CT, we longitudinally tracked the migration of intravenously injected NK cells and observed an accumulation of effector cell clusters at the tumor site, up to 72 h. Fluorescence imaging of the cells over time correlated with ex vivo quantitative analysis of gold content in the tumor, validating the accuracy and reliability of our technique. Our cell-tracking approach thus offers a valuable tool for preclinical studies, as well as for clinical applications, to elucidate the fate of NK cells and promote the implementation of NK-cell-based immunotherapy.

A Phase I-II Basket Trial Design to Optimize Dose-Schedule Regimes Based on Delayed Outcomes

This paper proposes a Bayesian adaptive basket trial design to optimize the dose-schedule regimes of an experimental agent within disease subtypes, called "baskets", for phase I-II clinical trials based on late-onset efficacy and toxicity. To characterize the association among the baskets and regimes, a Bayesian hierarchical model is assumed that includes a heterogeneity parameter, adaptively updated during the trial, that quantifies information shared across baskets. To account for late-onset outcomes when doing sequential decision making, unobserved outcomes are treated as missing values and imputed by exploiting early biomarker and low-grade toxicity information. Elicited joint utilities of efficacy and toxicity are used for decision making. Patients are randomized adaptively to regimes while accounting for baskets, with randomization probabilities proportional to the posterior probability of achieving maximum utility. Simulations are presented to assess the design's robustness and ability to identify optimal dose-schedule regimes within disease subtypes, and to compare it to a simplified design that treats the subtypes independently.

Adaptive Enrichment Designs in Clinical Trials

Adaptive enrichment designs for clinical trials may include rules that use interim data to identify treatment-sensitive patient subgroups, select or compare treatments, or change entry criteria. A common setting is a trial to compare a new biologically targeted agent to standard therapy. An enrichment design's structure depends on its goals, how it accounts for patient heterogeneity and treatment effects, and practical constraints. This article first covers basic concepts, including treatment-biomarker interaction, precision medicine, selection bias, and sequentially adaptive decision making, and briefly describes some different types of enrichment. Numerical illustrations are provided for qualitatively different cases involving treatment-biomarker interactions. Reviews are given of adaptive signature designs; a Bayesian design that uses a random partition to identify treatment-sensitive biomarker subgroups and assign treatments; and designs that enrich superior treatment sample sizes overall or within subgroups, make subgroup-specific decisions, or include outcome-adaptive randomization.

BOIN12: Bayesian Optimal Interval Phase I/II Trial Design for Utility-Based Dose Finding in Immunotherapy and Targeted Therapies

PURPOSE

For immunotherapy, such as checkpoint inhibitors and chimeric antigen receptor T-cell therapy, where the efficacy does not necessarily increase with the dose, the maximum tolerated dose may not be the optimal dose for treating patients. For these novel therapies, the objective of dose-finding trials is to identify the optimal biologic dose (OBD) that optimizes patients' risk-benefit trade-off.

METHODS

We propose a simple and flexible Bayesian optimal interval phase I/II (BOIN12) trial design to find the OBD that optimizes the risk-benefit trade-off. The BOIN12 design makes the decision of dose escalation and de-escalation by simultaneously taking account of efficacy and toxicity and adaptively allocates patients to the dose that optimizes the toxicity-efficacy trade-off. We performed simulation studies to evaluate the performance of the BOIN12 design.

RESULTS

Compared with existing phase I/II dose-finding designs, the BOIN12 design is simpler to implement, has higher accuracy to identify the OBD, and allocates more patients to the OBD. One of the most appealing features of the BOIN12 design is that its adaptation rule can be pretabulated and included in the protocol. During the trial conduct, clinicians can simply look up the decision table to allocate patients to a dose without complicated computation.

CONCLUSION

The BOIN12 design is simple to implement and yields desirable operating characteristics. It overcomes the computational and implementation complexity that plagues existing Bayesian phase I/II dose-finding designs and provides a useful design to optimize the dose of immunotherapy and targeted therapy. User-friendly software is freely available to facilitate the application of the BOIN12 design.

Comparison of Phase I-II designs with parametric or semi-parametric models using two different risk-benefit trade-off criteria

A semi-parametric stochastic ordering model (SPSO) is introduced to characterize functional relationships between dose level and the probabilities of binary Efficacy and Toxicity events. This model is used to implement a Bayesian adaptive phase I-II clinical trial using one of two different optimality criteria, either dose desirability defined as a function of the marginal Efficacy and Toxicity probabilities, or mean utility based on numerical scores of the four possible (Efficacy, Toxicity) events. A simulation study is conducted to compare designs using the SPSO model to the parametric EffTox model described in Thall and Cook, with each (model, optimality criterion) combination. Each of these four designs adaptively assigns patient cohorts to estimated optimal dose levels after restricting assignments to dose levels that are acceptably efficacious and safe. The simulation study shows that different design configurations may have superior performance depending on the assumed true dose-outcome scenario.

A phase I-II design based on periodic and continuous monitoring of disease status and the times to toxicity and death

A Bayesian phase I-II dose-finding design is presented for a clinical trial with four coprimary outcomes that reflect the actual clinical observation process. During a prespecified fixed follow-up period, the times to disease progression, toxicity, and death are monitored continuously, and an ordinal disease status variable, including progressive disease (PD) as one level, is evaluated repeatedly by scheduled imaging. We assume a proportional hazards model with piecewise constant baseline hazard for each continuous variable and a longitudinal multinomial probit model for the ordinal disease status process and include multivariate patient frailties to induce association among the outcomes. A finite partition of the nonfatal outcome combinations during the follow-up period is constructed, and the utility of each set in the partition is elicited. Posterior mean utility is used to optimize each patient's dose, subject to a safety rule excluding doses with an unacceptably high rate of PD, severe toxicity, or death. A simulation study shows that, compared with the proposed design, a simpler design based on commonly used efficacy and toxicity outcomes obtained by combining the four variables described above performs poorly and has substantially smaller probabilities of correctly choosing truly optimal doses and excluding truly unsafe doses.

Bayesian cancer clinical trial designs with subgroup-specific decisions

Two illustrative applications are presented of Bayesian clinical trial designs that make adaptive subgroup-specific decisions based on elicited utilities of patient outcomes to quantify risk-benefit trade-offs. The first design is for a randomized trial to evaluate effects of nutritional prehabilitation on post-operative morbidity in esophageal cancer patients undergoing surgery. The second design is for a dose-finding trial of natural killer cells to treat advanced hematologic malignancies, with five time-to-event outcomes. Each design is based on a Bayesian hierarchical model that borrows strength between subgroups. Computer simulation is used to evaluate each design's properties, including comparison to a simpler design ignoring treatment-subgroup interactions. The simulations show that accounting prospectively for treatment-subgroup interactions yields designs with very desirable properties, is greatly superior to a simplified comparator design that ignores subgroups if treatment-subgroup interactions actually exist, and each design is robust to deviations from the assumed underlying model.

Bayesian Utility-Based Designs for Subgroup-Specific Treatment Comparison and Early-Phase Dose Optimization in Oncology Clinical Trials

PURPOSE

Despite the fact that almost any sample of patients with a particular disease is heterogeneous, most clinical trial designs ignore the possibility that treatment or dose effects may differ between prognostic or biologically defined subgroups. This article reviews two clinical trial designs that make subgroup-specific decisions and compares each to a simpler design that ignores patient heterogeneity. The purpose is to illustrate the benefits of accounting prospectively for treatment-subgroup interactions and how utilities may be used to quantify risk-benefit trade-offs.

METHODS

Two Bayesian clinical trial designs that perform subgroup-specific decision making and inference based on elicited utilities of patient outcomes are reviewed. The first is a randomized comparative trial of nutritional prehabilitation for patients undergoing esophageal resection that has two prognostic subgroups and is based on postoperative morbidity score. The second is a sequentially adaptive trial of natural killer cells for treating hematologic malignancies that is based on five time-to-event outcomes and that performs safety monitoring and optimizes cell dose within six disease subgroups. Computer simulations under a range of different scenarios are presented for each design to establish its operating characteristics and compare it to a more conventional design that ignores patient heterogeneity.

RESULTS

Each design has attractive operating characteristics, is greatly superior to a simplified design that ignores patient subgroups, is robust to deviations from its assumed statistical model, and is feasible to use for conducting trials.

CONCLUSION

Bayesian designs that make subgroup-specific decisions in randomized comparative trials or sequentially adaptive early-phase dose-finding trials are superior to designs that ignore patient heterogeneity. Using elicited utilities of complex patient outcomes to quantify risk-benefit trade-offs provides a practical and ethical basis for decision making and treatment evaluation in clinical trials.

Natural Killer Cells as Allogeneic Effectors in Adoptive Cancer Immunotherapy

Natural killer (NK) cells are attractive within adoptive transfer settings in cancer immunotherapy due to their potential for allogeneic use; their alloreactivity is enhanced under conditions of killer immunoglobulin-like receptor (KIR) mismatch with human leukocyte antigen (HLA) ligands on cancer cells. In addition to this, NK cells are platforms for genetic modification, and proliferate in vivo for a shorter time relative to T cells, limiting off-target activation. Current clinical studies have demonstrated the safety and efficacy of allogeneic NK cell adoptive transfer therapies as a means for treatment of hematologic malignancies and, to a lesser extent, solid tumors. However, challenges associated with sourcing allogeneic NK cells have given rise to controversy over the contribution of NK cells to graft-versus-host disease (GvHD). Specifically, blood-derived NK cell infusions contain contaminating T cells, whose activation with NK-stimulating cytokines has been known to lead to heightened release of proinflammatory cytokines and trigger the onset of GvHD in vivo. NK cells sourced from cell lines and stem cells lack contaminating T cells, but can also lack many phenotypic characteristics of mature NK cells. Here, we discuss the available published evidence for the varying roles of NK cells in GvHD and, more broadly, their use in allogeneic adoptive transfer settings to treat various cancers.