Current issues in dose-finding designs: A response to the US Food and Drug Adminstration's Oncology Center of Excellence Project Optimus.

With the advent of targeted agents and immunological therapies, the medical research community has become increasingly aware that conventional methods for determining the best dose or schedule of a new agent are inadequate. It has been well established that conventional phase I designs cannot reliably identify safe and effective doses. This problem applies, generally, for cytotoxic agents, radiation therapy, targeted agents, and immunotherapies. To address this, the US Food and Drug Administration's Oncology Center of Excellence initiated Project Optimus, with the goal "to reform the dose optimization and dose selection paradigm in oncology drug development." As a response to Project Optimus, the articles in this special issue of Clinical Trials review recent advances in methods for choosing the dose or schedule of a new agent with an overall objective of informing clinical trialists of these innovative designs. This introductory article briefly reviews problems with conventional methods, the regulatory changes that encourage better dose optimization designs, and provides brief summaries of the articles that follow in this special issue.

Safety, efficacy and determinants of response of allogeneic CD19-specific CAR-NK cells in CD19+ B cell tumors: a phase 1/2 trial.

There is a pressing need for allogeneic chimeric antigen receptor (CAR)-immune cell therapies that are safe, effective and affordable. We conducted a phase 1/2 trial of cord blood-derived natural killer (NK) cells expressing anti-CD19 chimeric antigen receptor and interleukin-15 (CAR19/IL-15) in 37 patients with CD19+ B cell malignancies. The primary objectives were safety and efficacy, defined as day 30 overall response (OR). Secondary objectives included day 100 response, progression-free survival, overall survival and CAR19/IL-15 NK cell persistence. No notable toxicities such as cytokine release syndrome, neurotoxicity or graft-versus-host disease were observed. The day 30 and day 100 OR rates were 48.6% for both. The 1-year overall survival and progression-free survival were 68% and 32%, respectively. Patients who achieved OR had higher levels and longer persistence of CAR-NK cells. Receiving CAR-NK cells from a cord blood unit (CBU) with nucleated red blood cells ≤ 8 × 107 and a collection-to-cryopreservation time ≤ 24 h was the most significant predictor for superior outcome. NK cells from these optimal CBUs were highly functional and enriched in effector-related genes. In contrast, NK cells from suboptimal CBUs had upregulation of inflammation, hypoxia and cellular stress programs. Finally, using multiple mouse models, we confirmed the superior antitumor activity of CAR/IL-15 NK cells from optimal CBUs in vivo. These findings uncover new features of CAR-NK cell biology and underscore the importance of donor selection for allogeneic cell therapies. ClinicalTrials.gov identifier: NCT03056339 .

Ex Vivo Expanded Cord Blood Natural Killer Cells Combined with Rituximab and High-Dose Chemotherapy and Autologous Stem Cell Transplantation for B Cell Non-Hodgkin Lymphoma.

Relapse is the major cause of failure of high-dose chemotherapy (HDC) with autologous stem cell transplantation (ASCT) for B cell non-Hodgkin lymphomas (B-NHL). Improvement strategies include use in combination with effective immunotherapies. We hypothesized that the combination of rituximab/HDC/ASCT with expanded cord blood (CB)-derived natural killer (NK) cells is safe and active in B-NHL. Patients with B-NHL age 15 to 70 years and appropriate ASCT candidates were eligible for the study. The CB units were selected without considering HLA match with the recipient. The CB NK cells were expanded from day -19 to day -5. Treatment included rituximab on days -13 and -7, BEAM (carmustine/etoposide/cytarabine/melphalan) on days -13 to -7, lenalidomide on days -7 to -2, CB NK infusion (108/kg) on day -5, and ASCT (day 0). The primary endpoint was 30-day treatment-related mortality (TRM); secondary endpoints included relapse-free survival (RFS), overall survival (OS), and persistence of CB NK cells. We enrolled 20 patients. CB NK cells were expanded a median of 1552-fold with >98% purity and >96% viability. We saw no adverse events attributable to the CB NK cells and 0% 30-day TRM. At median follow-up of 47 months, the RFS and OS rates were 53% and 74%, respectively. CB NK cells were detectable in blood for 2 weeks, independent of HLA-mismatch status. CD16 expression in donor NK cells was correlated favorably with outcome, and homozygosity for the high-affinity CD16 variant (158 V/V) in CB, but not recipient, NK cells was correlated with better outcomes. Our data indicate that the combination of expanded and highly purified CB-derived NK cells with HDC/ASCT for B-NHL is safe. CD16 expression in donor NK cells, particularly if homozygous for the high-affinity CD16 variant, was correlated with better outcomes.

Risk-benefit trade-offs and precision utilities in phase I-II clinical trials.

BACKGROUND: Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life. PURPOSE: While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk-benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups. METHODS AND CONCLUSIONS: Herein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy-toxicity risk-benefit trade-offs. The design chooses each patient's dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma.

Stereotactic body radiotherapy with or without selective dismutase mimetic in pancreatic adenocarcinoma: an adaptive, randomised, double-blind, placebo-controlled, phase 1b/2 trial.

BACKGROUND: Stereotactic body radiotherapy (SBRT) has the potential to ablate localised pancreatic ductal adenocarcinoma. Selective dismutase mimetics sensitise tumours while reducing normal tissue toxicity. This trial was designed to establish the efficacy and toxicity afforded by the selective dismutase mimetic avasopasem manganese when combined with ablative SBRT for localised pancreatic ductal adenocarcinoma. METHODS: In this adaptive, randomised, double-blind, placebo-controlled, phase 1b/2 trial, patients aged 18 years or older with borderline resectable or locally advanced pancreatic cancer who had received at least 3 months of chemotherapy and had an Eastern Cooperative Oncology Group performance status of 0-2 were enrolled at six academic sites in the USA. Eligible patients were randomly assigned (1:1), with block randomisation (block sizes of 6-12) with a maximum of 24 patients per group, to receive daily avasopasem (90 mg) or placebo intravenously directly before (ie, within 180 min) SBRT (50, 55, or 60 Gy in five fractions, adaptively assigned in real time by Bayesian estimates of 90-day safety and efficacy). Patients and physicians were masked to treatment group allocation, but not to SBRT dose. The primary objective was to find the optimal dose of SBRT with avasopasem or placebo as determined by the late onset EffTox method. All analyses were done on an intention-to-treat basis. This study is registered with ClinicalTrials.gov, NCT03340974, and is complete. FINDINGS: Between Jan 25, 2018, and April 29, 2020, 47 patients were screened, of whom 42 were enrolled (median age was 71 years [IQR 63-75], 23 [55%] were male, 19 [45%] were female, 37 [88%] were White, three [7%] were Black, and one [2%] each were unknown or other races) and randomly assigned to avasopasem (n=24) or placebo (n=18); the placebo group was terminated early after failing to meet prespecified efficacy parameters. At data cutoff (June 28, 2021), the avasopasem group satisfied boundaries for both efficacy and toxicity. Late onset EffTox efficacy response was observed in 16 (89%) of 18 patients at 50 Gy and six (100%) of six patients at 55 Gy in the avasopasem group, and was observed in three (50%) of six patients at 50 Gy and nine (75%) of 12 patients at 55 Gy in the placebo group, and the Bayesian model recommended 50 Gy or 55 Gy in five fractions with avasopasem for further study. Serious adverse events of any cause were reported in three (17%) of 18 patients in the placebo group and six (25%) of 24 in the avasopasem group. In the placebo group, grade 3 adverse events within 90 days of SBRT were abdominal pain, acute cholangitis, pyrexia, increased blood lactic acid, and increased lipase (one [6%] each); no grade 4 events occurred. In the avasopasem group, grade 3-4 adverse events within 90 days of SBRT were acute kidney injury, increased blood alkaline phosphatase, haematoma, colitis, gastric obstruction, lung infection, abdominal abscess, post-surgical atrial fibrillation, and pneumonia leading to respiratory failure (one [4%] each).There were no treatment-related deaths but one late death in the avasopasem group due to sepsis in the setting of duodenal obstruction after off-study treatment was reported as potentially related to SBRT. INTERPRETATION: SBRT that uses 50 or 55 Gy in five fractions can be considered for patients with localised pancreatic ductal adenocarcinoma. The addition of avasopasem might further enhance disease outcomes. A larger phase 2 trial (GRECO-2, NCT04698915) is underway to validate these results. FUNDING: Galera Therapeutics.

Interpreting Randomized Controlled Trials.

This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.

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.

A Bayesian feature allocation model for identifying cell subpopulations using CyTOF data.

A Bayesian feature allocation model (FAM) is presented for identifying cell subpopulations based on multiple samples of cell surface or intracellular marker expression level data obtained by cytometry by time of flight (CyTOF). Cell subpopulations are characterized by differences in marker expression patterns, and cells are clustered into subpopulations based on their observed expression levels. A model-based method is used to construct cell clusters within each sample by modeling subpopulations as latent features, using a finite Indian buffet process. Non-ignorable missing data due to technical artifacts in mass cytometry instruments are accounted for by defining a static missingship mechanism. In contrast with conventional cell clustering methods, which cluster observed marker expression levels separately for each sample, the FAM-based method can be applied simultaneously to multiple samples, and also identify important cell subpopulations likely to be otherwise missed. The proposed FAM-based method is applied to jointly analyse three CyTOF datasets to study natural killer (NK) cells. Because the subpopulations identified by the FAM may define novel NK cell subsets, this statistical analysis may provide useful information about the biology of NK cells and their potential role in cancer immunotherapy which may lead, in turn, to development of improved NK cell therapies.

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 Prospective Study of Conventionally Fractionated Dose Constraints for Reirradiation of Primary Brain Tumors in Adults.

PURPOSE: Dose constraints for reirradiation of recurrent primary brain tumors are not well-established. This study was conducted to prospectively evaluate composite dose constraints for conventionally fractionated brain reirradiation. METHODS AND MATERIALS: A single-institution, prospective study of adults with previously irradiated, recurrent brain tumors was performed. For 95% of patients, electronic dosimetry records from the first course of radiation (RT1) were obtained and deformed onto the simulation computed tomography for the second course of radiation (RT2). Conventionally fractionated treatment plans for RT2 were developed that met protocol-assigned dose constraints for RT2 alone and the composite dose of RT1 + RT2. Prospective composite dose constraints were based on histology, interval since RT1, and concurrent bevacizumab. Patients were followed with magnetic resonance imaging including spectroscopy and perfusion studies. Primary endpoint was the rate of symptomatic brain necrosis at 6 months after RT2. RESULTS: Patients were enrolled from March 2017 to May 2018; 20 were evaluable. Eighteen had glioma, 1 had atypical choroid plexus papilloma, and 1 had hemangiopericytoma. Nineteen patients were treated with volumetric modulated arc therapy, and one was treated with protons. Median RT1 dose was 57 Gy (range, 50-60 Gy). Median RT1-RT2 interval was 49 months (range, 9-141 months). Median RT2 dose was 42.4 Gy (range, 36-60 Gy). Median planning target volume was 186 cc (range, 8-468 cc). Nineteen of 20 patients (95%) were free of grade 3+ central nervous system necrosis. One patient had grade 3+ necrosis 2 months after RT2; the patient recovered fully and lived another 18 months until dying of disease progression. Median overall survival from RT2 start for all patients was 13.3 months (95% credible interval, 6.3-20.7); for patients with glioblastoma, 11.5 months (95% credible interval, 6.1-20.1). CONCLUSIONS: Brain reirradiation can be safely performed with conventionally fractionated regimens tailored to previous dose distributions. The prospective composite dose constraints described here are a starting point for future studies of conventionally fractionated reirradiation.

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.

BAYESIAN HIERARCHICAL RANDOM-EFFECTS META-ANALYSIS AND DESIGN OF PHASE I CLINICAL TRIALS.

We propose a curve-free random-effects meta-analysis approach to combining data from multiple phase I clinical trials to identify an optimal dose. Our method accounts for between-study heterogeneity that may stem from different study designs, patient populations, or tumor types. We also develop a meta-analytic-predictive (MAP) method based on a power prior that incorporates data from multiple historical studies into the design and conduct of a new phase I trial. Performances of the proposed methods for data analysis and trial design are evaluated by extensive simulation studies. The proposed random-effects meta-analysis method provides more reliable dose selection than comparators that rely on parametric assumptions. The MAP-based dose-finding designs are generally more efficient than those that do not borrow information, especially when the current and historical studies are similar. The proposed methodologies are illustrated by a meta-analysis of five historical phase I studies of Sorafenib, and design of a new phase I trial.

A Causal Framework for Making Individualized Treatment Decisions in Oncology.

We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT's entry criteria, or a treatment's effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment-covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new patient seen in the clinic, it may be necessary to combine the RCT data with external data from other RCTs, single-arm trials, or preclinical experiments evaluating biological treatment effects. For example, external data may show that treatment effects differ between histological subgroups not recorded in an RCT. A causal diagram may be used to decide whether external observational or experimental data should be obtained and combined with RCT data to compute statistical estimates for making individualized treatment decisions. We use adjuvant treatment of renal cell carcinoma as our motivating example to illustrate how to construct causal diagrams and apply them to guide clinical decisions.

Vorinostat Combined with Busulfan, Fludarabine, and Clofarabine Conditioning Regimen for Allogeneic Hematopoietic Stem Cell Transplantation in Patients with Acute Leukemia: Long-Term Study Outcomes.

Conditioning regimens play a major role in determining disease outcomes following allogeneic hematopoietic stem cell transplantation (allo-HSCT). The use of i.v. busulfan (Bu) as part of conditioning chemotherapy has been shown to be effective in controlling disease relapse; however, disease relapse remains a major cause of death following allo-HSCT. This study was conducted to determine the long-term outcomes of vorinostat with i.v. Bu plus dual nucleoside analogs clofarabine (Clo) and fludarabine (Flu) in the conditioning regimen for patients undergoing allo-HSCT. This was a rapid dose escalation phase I/II study designed to determine whether the addition of vorinostat would improve the efficacy of standard i.v. Bu/Flu/Clo conditioning regimen. This report presents the long-term disease outcomes of this combination in 68 patients with high-risk leukemia, including 31 (46%) with acute lymphoblastic leukemia (ALL) and 37 (54%) with acute myelogenous leukemia (AML) or myelodysplastic syndrome (MDS). Fifty-eight patients (85%) were in morphologic complete remission at time of transplantation, and 38 (56%) received a matched unrelated donor graft. Over the median follow-up of 37.6 months, 29 of the 68 patients died (43%), and the nonrelapse mortality (NRM) rate was 22% (n = 15). The median overall survival and median NRM were not reached. Nineteen patients (28%) experienced disease progression. The median progression-free survival was 36.8 months. Thirty-seven patients (57%) developed grade II-IV acute graft-versus-host disease (GVHD), and 20 patients (31%) developed chronic GVHD. Our results suggest a lack of benefit from adding a short course of vorinostat to i.v. Bu/Flu/Clo conditioning regimens for leukemia patients undergoing allo- HSCT.

A randomized phase III study of pretransplant conditioning for AML/MDS with fludarabine and once daily IV busulfan ± clofarabine in allogeneic stem cell transplantation.

Pretransplant conditioning with Fludarabine (Flu)-Busulfan (Bu) is safe, but clofarabine (Clo) has improved antileukemic activity. Hypothesis: Flu+Clo-Bu (FCB) yields superior progression-free survival (PFS) after allogeneic transplantation. We randomized 250 AML/MDS patients aged 3-70, Karnofsky Score ≥80, with matched donors, to FCB (n = 120) or Flu-Bu (n = 130), stratifying complete remission (CR) vs. No CR, (NCR). HCT-CI scores varied, from 0 to 10. All evaluable patients engrafted. Median follow-up was 66 months (interquartile range: 58-80). Three-year relapse incidence (RI), 25% with FCB, vs. 39% with Flu-Bu (p = 0.018), offset by higher non-relapse mortality, 22.6% (95%CI: 16-30.2%) vs. 12.3% (95%CI: 6.5-19%). Three-year PFS was 52% (95%CI: 44-62%) (FCB), vs. 48% (95%CI: 41-58%) (Flu-Bu). FCB benefited CR patients less, NCR patients age ≤ 60 had 3-year 34% RI (95%CI: 19-49%) (FCB) vs. 56% (95%CI: 38-70%) after Flu-Bu (p = 0.037). NCR patients >60 years had 3-year RI 10.0% (FCB), vs. 56.0%, after Flu-Bu (p = 0.003). Bayesian regression analysis including treatment-covariate interactions showed FCB superiority in NCR patients with low HCT-CI (0-2). Serious adverse event profiles were similar for the regimens. Conditioning with FCB did not improve PFS overall, but improved disease control in NCR patients, mandating confirmatory trials. Remission status and HCT-CI should be considered when using FCB.

A phase 1-2 trial of sitravatinib and nivolumab in clear cell renal cell carcinoma following progression on antiangiogenic therapy.

The accumulation of immune-suppressive myeloid cells is a critical determinant of resistance to anti-programmed death-1 (PD-1) therapy in advanced clear cell renal cell carcinoma (ccRCC). In preclinical models, the tyrosine kinase inhibitor sitravatinib enhanced responses to anti-PD-1 therapy by modulating immune-suppressive myeloid cells. We conducted a phase 1-2 trial to choose an optimal sitravatinib dose combined with a fixed dose of nivolumab in 42 immunotherapy-naïve patients with ccRCC refractory to prior antiangiogenic therapies. The combination demonstrated no unexpected toxicities and achieved an objective response rate of 35.7% and a median progression-free survival of 11.7 months, with 80.1% of patients alive after a median follow-up of 18.7 months. Baseline peripheral blood neutrophil-to-lymphocyte ratio correlated with response to sitravatinib and nivolumab. Patients with liver metastases showed durable responses comparable to patients without liver metastases. In addition, correlative studies demonstrated reduction of immune-suppressive myeloid cells in the periphery and tumor microenvironment following sitravatinib treatment. This study provides a rationally designed combinatorial strategy to improve outcomes of anti-PD-1 therapy in advanced ccRCC.

Phase I Trial of Definitive Concurrent Chemoradiotherapy and Trametinib for KRAS-Mutated Non-Small Cell Lung Cancer.

OBJECTIVE: This phase I trial (NCT01912625) evaluated the safety and pharmacokinetics of definitive concurrent chemoradiotherapy (cCRT) and the radiosensitizer trametinib (MEK1/2 inhibitor) for KRAS-mutated nonmetastatic non-small cell lung cancer (NSCLC). METHODS: Patients received cCRT (carboplatin/paclitaxel and 60 Gy/30 fractions radiotherapy); oral trametinib (7 days/week) commenced on day 1 and completed on the final day of radiotherapy. Dose-finding of trametinib was done using the time-to-event continual reassessment method (TiTE-CRM); dose levels were 0.5mg (level -1), 1mg (initial, level 1), 1.5mg (level 2), and 2mg (level 3). Progression-free (PFS) and overall survival (OS) times were also recorded. RESULTS: Fifteen patients (stage III, variety of KRAS mutations) were treated, with 1/5/4/5 at dose levels -1/1/2/3, respectively. Five patients received dose reductions (n=2, levels 2 and 3; n=1, level 1). Twelve patients completed the full cCRT course. One patient (following 12d trametinib) was taken off protocol for an unrelated/unresolved grade 1 event and later experienced grade 5 sepsis/respiratory failure. There was one grade 4 retinal detachment; grade 3 events included skin rash (n=2) and ventricular dysfunction, pneumonitis, pain, fatigue, and diarrhea (n=1 each). The final dose selected by the TiTE-CRM of trametinib was 1.5 mg. Pharmacokinetic profiles were elucidated and extensively described. At median follow-up of 70 months, median PFS was 11 months and median OS was 38 months. CONCLUSIONS: The MTD for trametinib when combined with cCRT is 1.5 mg, with encouraging preliminary outcomes. This combination merits further study to combine with consolidation durvalumab in non-metastatic KRAS mutant NSCLC.

Utility-based Bayesian personalized treatment selection for advanced breast cancer.

A Bayesian method is proposed for personalized treatment selection in settings where data are available from a randomized clinical trial with two or more outcomes. The motivating application is a randomized trial that compared letrozole plus bevacizimab to letrozole alone as first-line therapy for hormone receptor positive advanced breast cancer. The combination treatment arm had larger median progression-free survival time, but also a higher rate of severe toxicities. This suggests that the risk-benefit trade-off between these two outcomes should play a central role in selecting each patient's treatment, particularly since older patients are less likely to tolerate severe toxicities. To quantify the desirability of each possible outcome combination for an individual patient, we elicited from breast cancer oncologists a utility function that varied with age. The utility was used as an explicit criterion for quantifying risk-benefit trade-offs when making personalized treatment selections. A Bayesian nonparametric multivariate regression model with a dependent Dirichlet process prior was fit to the trial data. Under the fitted model, a new patient's treatment can be selected based on the posterior predictive utility distribution. For the breast cancer trial dataset, the optimal treatment depends on the patient's age, with the combination preferable for patients 70 years or younger and the single agent preferable for patients older than 70.

Lessons Learned From Implementing a Novel Bayesian Adaptive Dose-Finding Design in Advanced Pancreatic Cancer.

PURPOSE: Novel Bayesian adaptive designs provide an effective way to improve clinical trial efficiency. These designs are superior to conventional methods, but implementing them can be challenging. The aim of this article was to describe what we learned while applying a novel Bayesian phase I-II design in a recent trial. METHODS: The primary goal of the trial was to optimize radiation therapy (RT) dose among three levels (low, standard, and high), given either with placebo (P) or an investigational agent (A), for treating locally advanced, radiation-naive pancreatic cancer, deemed appropriate for RT rather than surgery. Up to 48 patients were randomly assigned fairly between RT plus P and RT plus A, with RT dose-finding done within each arm using the late-onset efficacy-toxicity design on the basis of two coprimary end points, tumor response and dose-limiting toxicity, both evaluated at up to 90 days. The random assignment was blinded, but within each arm, unblinded RT doses were chosen adaptively using software developed within the institution. RESULTS: Implementing the design involved double-blind balance-restricted random assignment, real-time assessment of patient outcomes to evaluate the efficacy-toxicity trade-off for each RT dose in each arm to optimize each patient's RT dose adaptively, and transition from a single-center trial to a multicenter trial. We present lessons learned and illustrative documentation. CONCLUSION: Implementing novel Bayesian adaptive trial designs requires close collaborations between physicians, pharmacists, statisticians, data managers, and sponsors. The process is difficult but manageable and essential for efficient trial conduct. Close collaboration during trial conduct is a key component of any trial that includes real-time adaptive decision rules.