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Background/aims This article describes the proposed design of a phase I study evaluating the safety of ceramide nanoliposome and vinblastine among an initial set of 19 possible dose combinations in patients with relapsed/refractory acute myeloid leukemia and patients with untreated acute myeloid leukemia who are not candidates for intensive induction chemotherapy. Methods Extensive collaboration between statisticians and clinical investigators revealed the need to incorporate several adaptive features into the design, including the flexibility of adding or eliminating certain dose combinations based on safety criteria applied to multiple dose pairs. During the design stage, additional dose levels of vinblastine were added, increasing the dimension of the drug combination space and thus the complexity of the problem. Increased complexity made application of existing drug combination dose-finding methods unsuitable in their current form. Results Our solution to these challenges was to adapt a method based on isotonic regression to meet the research objectives of the study. Application of this adapted method is described herein, and a simulation study of the design’s operating characteristics is conducted. Conclusion The aim of this article is to bring to light examples of novel design applications as a means of augmenting the implementation of innovative designs in the future and to demonstrate the flexibility of adaptive designs in satisfying changing design conditions.

Indoleamine 2-3 dioxygenase 1 (IDO1) expression may contribute to immunologic escape by melanoma metastases. However, a recent clinical trial failed to identify any clinical benefits of IDO1 inhibition in patients with unresectable metastatic melanoma, and prior characterizations of IDO1 expression have predominately studied primary lesions and local metastases, generating uncertainty regarding IDO1 expression in distant metastases. We hypothesized that IDO1 expression in such lesions would be low and correlated with decreased overall survival (OS). Metastases from patients (n=96) with stage IIIb to IV melanoma underwent tissue microarray construction and immunohistochemical staining for IDO1. Th1-related gene expression was determined quantitatively. Associations between OS and IDO1 expression were assessed with multivariate models. Of 96 metastatic lesions, 28% were IDOpos, and 85% exhibited IDO1 expression in

Background: /Aims: In early-phase cell therapy trials, each dose level being studied is defined by the number of cells infused into the trial participant. The issue of dose feasibility presents itself when the desired number of cells is not reached in the expansion process. Consequently, dose assignments for some patients may deviate from the planned dose according to the chosen design. Widely used algorithmic designs aren't flexible enough to handle this complication and can lead to the exclusion of safety data from the dose assignment algorithm. This article studies the impact of dose feasibility challenges on the behavior of the 3 + 3 decision rule. Methods: We conducted a simulation study across six dose-feasibility and dose-toxicity scenarios. Trials are simulated using the 3 + 3 algorithm. We present a novel algorithm for random feasibility curve generation. We used this algorithm to conduct a large-scale simulation study across 100 random scenarios. Results: We found that the 3 + 3 has problematic characteristics due to the exclusion of safety data from the algorithm. Ignoring toxicity data can complicate the allocation of subsequent patients in the trial and can bias the final maximum tolerated dose recommendation for the next phase of drug development. Conclusion: Our study demonstrates that excluding safety data from the 3 + 3 algorithm can be detrimental to trial conduct. Furthermore, there are existing methods that are flexible enough to include data that is observed away from the planned dose. We recommend that these methods be used in conducting phase I cell therapy trials.

Background: tumor-infiltrating lymphocytes are prognostic in many human cancers. However, the prognostic value of lymphocytes infiltrating glioblastoma (GBM), and roles in tumor control or progression are unclear. We hypothesized that B and T cell density, and markers of their activity, proliferation, differentiation, or function, would have favorable prognostic significance for patients with GBM. Methods: initial resection specimens from 77 patients with IDH1/2 wild type GBM who received standard-of-care treatment were evaluated with multiplex immunofluorescence histology (mIFH), for the distribution, density, differentiation, and proliferation of T cells and B cells, as well as for the presence of tertiary lymphoid structures (TLS), and IFNγ expression. Immune infiltrates were evaluated for associations with overall survival (OS) by univariate and multivariate Cox proportional hazards modeling. Results: in univariate analyses, improved OS was associated with high densities of proliferating (Ki67+) CD8+ cells (HR 0.36, p = 0.001) and CD20+ cells (HR 0.51, p = 0.008), as well as CD8+Tbet+ cells (HR 0.46, p = 0.004), and RORγt+ cells (HR 0.56, p = 0.04). Conversely, IFNγ intensity was associated with diminished OS (HR 0.59, p = 0.036). In multivariable analyses, adjusting for clinical variables, including age, resection extent, Karnofsky Performance Status (KPS), and MGMT methylation status, improved OS was associated with high densities of proliferating (Ki67+) CD8+ cells (HR 0.15, p < 0.001), and higher ratios of CD8+ cells to CD4+ cells (HR 0.31, p = 0.005). Diminished OS was associated with increases in patient age (HR 1.21, p = 0.005) and higher mean intensities of IFNγ (HR 2.13, p = 0.027). Conclusions: intratumoral densities of proliferating CD8 T cells and higher CD8/CD4 ratios are independent predictors of OS in patients with GBM. Paradoxically, higher mean intensities of IFNγ in the tumors were associated with shorter OS. These findings suggest that survival may be enhanced by increasing proliferation of tumor-reactive CD8+ T cells and that approaches may be needed to promote CD8+ T cell dominance in GBM, and to interfere with the immunoregulatory effects of IFNγ in the tumor microenvironment.

Immunotherapy and chemotherapy combinations have proven to be a safe and efficacious treatment approach in multiple settings. However, it is not clear whether approved doses of chemotherapy developed to achieve a maximum tolerated dose are the ideal dose when combining cytotoxic chemotherapy with immunotherapy to induce immune responses. This trial of a modulated dose chemotherapy and Pembrolizumab, with or without a second immunomodulatory agent, uses a Bayesian design to select the optimal treatment combination by balancing both safety and efficacy of the chemotherapy and immunotherapy agents within each of two cohorts. The simulation study provides evidence that the proposed Bayesian design successfully addresses the primary study aim to identify the optimal dose combination for each of the two independent patient cohorts. This conclusion is supported by the high percentage of simulated trials which select a treatment combination that is both safe and highly efficacious. The proposed trial was funded and was being finalized when the sponsoring company decided not to proceed due to negative findings in another patient population. The proposed trial design will continue to be relevant as multiple chemotherapy and immunotherapy combinations become the standard of care and future research will require evaluating the appropriate doses of various components of multiple drug regimens.

Background/aims: Dose feasibility is a challenge that may arise in the development of adoptive T cell therapies for cancer. In early-phase clinical trials, dose is quantified either by a fixed or per unit body weight number of cells infused. It may not be feasible, however, to administer a patient’s assigned dose due to an insufficient number of cells harvested or functional heterogeneity of the product. The study objective becomes to identify the maximum tolerated dose with high feasibility of being administered. This article describes a new dose-finding method that adaptively accounts for safety and feasibility endpoints in guiding dose allocation. Methods: We propose an adaptive dose-finding method that integrates accumulating feasibility and safety data to select doses for participant cohorts in early-phase trials examining adoptive cell immunotherapy. We sequentially model the probability of dose-limiting toxicity and the probability of feasibility using independent beta-binomial models. The probability model for toxicity borrows information across all dose levels using isotonic regression, allowing participants infused at a lower dose than his or her planned dose to contribute safety data to the dose-finding algorithm. We applied the proposed methodology in a single simulated trial and evaluated its operating characteristics through extensive simulation studies. Results: In simulations conducted for a phase I study of adoptive immunotherapy for newly diagnosed glioblastoma, the proposed method demonstrates the ability to identify accurately the feasible maximum tolerated doses and to treat participants at and around these doses. Over 10 hypothesized scenarios studied, the percentage of correctly selecting the true feasible and maximum tolerated dose ranged from 50% to 90% with sample sizes averaging between 21 and 24 participants. A comparison to the only known existing method accounting for safety and feasibility yields competitive performance. Conclusion: We have developed a new practical adaptive dose-finding method to assess feasibility in early-phase adoptive cell therapy trials. A design that incorporates feasibility, as a function of the quantity and quality of the product manufactured, in addition to safety will have an impact on the recommended phase II doses in studies that evaluate patient outcomes.

This paper studies the notion of coherence in interval-based dose-finding methods. An incoherent decision is either: (i) a recommendation to escalate the dose following an observed dose-limiting toxicity, or (ii) a recommendation to de-escalate the dose following a non-dose-limiting toxicity. In a simulated example, we illustrate that the Bayesian optimal interval method and the Keyboard method are not coherent. We generated dose-limiting toxicity outcomes under an assumed set of true probabilities for a trial of n = 36 patients in cohorts of size 1, and we counted the number of incoherent dosing decisions that were made throughout this simulated trial. Each of the methods studied resulted in 13/36 (36%) incoherent decisions in the simulated trial. Additionally, for two different target dose-limiting toxicity rates, 20% and 30%, and a sample size of n = 30 patients, we randomly generated 100 dose-toxicity curves and tabulated the number of incoherent decisions made by each method in 1000 simulated trials under each curve. For each method studied, the probability of incurring at least one incoherent decision during the conduct of a single trial is greater than 75%. Coherency is an important principle in the conduct of dose-finding trials. Interval-based methods violate this principle for cohorts of size 1 and require additional modifications to overcome this shortcoming. Researchers need take a closer look at the dose assignment behavior of interval-based methods when using them to plan dose-finding studies.

Purpose The phase 1 portion of this multicenter, phase 1/2 study of hypofractionated (HypoFx) prostate bed radiation therapy (RT) as salvage or adjuvant therapy aimed to identify the shortest dose-fractionation schedule with acceptable toxicity. The phase 2 portion aimed to assess the health-related quality of life (QoL) of using this HypoFx regimen. Methods and Materials Eligibility included standard adjuvant or salvage prostate bed RT indications. Patients were assigned to receive 1 of 3 daily RT schedules: 56.6 Gy in 20 Fx, 50.4 Gy in 15 Fx, or 42.6 Gy in 10 Fx. Regional nodal irradiation and androgen deprivation therapy were not allowed. Participants were followed for 2 years after treatment with outcome measures based on prostate-specific antigen levels, toxicity assessments (Common Terminology Criteria for Adverse Events, v4.0), QoL measures (the Expanded Prostate Cancer Index Composite [EPIC] and EuroQol EQ-5D instruments), and out-of-pocket costs. Results There were 32 evaluable participants, and median follow-up was 3.53 years. The shortest dose-fractionation schedule with acceptable toxicity was determined to be 42.6 Gy in 10 Fx, with most patients (23) treated with this schedule. Grade 3 genitourinary (GU) and gastrointestinal (GI) toxicities occurred in 3 patients and 1 patient, respectively. There was 1 grade 4 sepsis event. Higher dose to the hottest 25% of the rectum was associated with increased risk of grade 2+ GI toxicity; no dosimetric factors were found to predict for GU toxicity. There was a significant decrease in the mean bowel, but not bladder, QoL score at 1 year compared with baseline. Prostate-specific antigen failure occurred in 34.3% of participants, using a definition of nadir plus 2 ng/mL. Metastases were more likely to occur in regional lymph nodes (5 of 7) than in bones (2 of 7). The mean out-of-pocket cost for patients during treatment was $223.90. Conclusions We identified 42.6 Gy in 10 fractions as the shortest dose-fractionation schedule with acceptable toxicity in this phase 1/2 study. There was a higher than expected rate of grade 2 to 3 GU and GI toxicity and a decreased EPIC bowel QoL domain with this regimen. Future studies are needed to explore alternative adjuvant/salvage HypoFx RT schedules after radical prostatectomy.

Existing methodology for the design of Phase I-II studies has been intended to search for the optimal regimen, based on a trade-off between toxicity and efficacy, from a set of regimens comprised of doses of a new agent. The underlying assumptions guiding allocation are that the dose-toxicity curve is monotonically increasing, and that the dose-efficacy curve either plateaus or decreases beyond an intermediate dose. This article considers the problem of designing Phase I-II studies that violate these assumptions for both outcomes. The motivating application studies regimens that are not defined by doses of a new agent, but rather a peptide vaccine plus novel adjuvants for the treatment of melanoma. All doses of each adjuvant are fixed, and the regimens vary by the number and selection of adjuvants. This structure produces regimen-toxicity curves that are partially ordered, and regimen-efficacy curves that may deviate from a plateau or unimodal shape. Application of a Bayesian model-based design is described in determining the optimal biologic regimen, based on bivariate binary measures of toxicity and biologic activity. A simulation study of the design's operating characteristics is conducted, and its versatility in handling other Phase I-II problems is discussed.

Purpose Operating characteristics for proposed clinical trial designs provide insight into performance regarding safety and accuracy, allowing the study team and review entities to determine the design's suitability to achieve the study's proposed objectives. Advances in cancer therapeutics have augmented the needs of early phase clinical trial design. Additionally, advances in research on early-phase trial design have led to the availability of a wide range of methods that show vast improvement over outdated approaches. Methods Three trials utilizing variations of the 3 + 3 decision rule are discussed. The protocols lacked detail, including operating characteristics and guidance for decision-making that deviated from the 3 + 3 decision rule and MTD determination. We provide a discussion of the statistical issues associated with each design and operating characteristics for the proposed design compared to alternatives better suited to achieve the aims of each trial. Results Our results illustrate how operating characteristics inform a design's safety and accuracy. Operating characteristics can unmask poor behavior, such as a high percentage of particiapnts exposed to overly toxic doses, a low probability of correctly identifying the MTD, and inappropriate early study termination. Conclusion Selection of early-phase trial design has significant implications on a trial's ability to meet its objectives. Operating characteristics are a necessary component in the design and review of a protocol, determining if the study's objectives can be achieved and documenting the study's scientific validity. Continued use of outdated approaches due to historical acceptance hinders scientific rigor and the effort to move effective agents through the drug development process.

Background/aims: In evaluating the performance of Phase I dose-finding designs, simulation studies are typically conducted to assess how often a method correctly selects the true maximum tolerated dose under a set of assumed dose–toxicity curves. A necessary component of the evaluation process is to have some concept for how well a design can possibly perform. The notion of an upper bound on the accuracy of maximum tolerated dose selection is often omitted from the simulation study, and the aim of this work is to provide researchers with accessible software to quickly evaluate the operating characteristics of Phase I methods using a benchmark. Methods: The non-parametric optimal benchmark is a useful theoretical tool for simulations that can serve as an upper limit for the accuracy of maximum tolerated dose identification based on a binary toxicity endpoint. It offers researchers a sense of the plausibility of a Phase I method’s operating characteristics in simulation. We have developed an R shiny web application for simulating the benchmark. Results: The web application has the ability to quickly provide simulation results for the benchmark and requires no programming knowledge. The application is free to access and use on any device with an Internet browser. Conclusion: The application provides the percentage of correct selection of the maximum tolerated dose and an accuracy index, operating characteristics typically used in evaluating the accuracy of dose-finding designs. We hope this software will facilitate the use of the non-parametric optimal benchmark as an evaluation tool in dose-finding simulation.

Background: This article studies the notion of irrational dose assignment in Phase I clinical trials. This property was recently defined by Zhou and colleagues as a dose assignment that fails to de-escalate the dose when two out of three, three out of six, or four out of six patients have experienced a dose-limiting toxicity event at the current dose level. The authors claimed that a drawback of the well-known continual reassessment method is that it can result in irrational dose assignments. The aim of this article is to examine this definition of irrationality more closely within the conduct of the continual reassessment method. Methods: Over a broad range of assumed dose-limiting toxicity probability scenarios for six study dose levels and a variety of target dose-limiting toxicity rates, we simulated 2000 trials of n = 36 patients. For each scenario, we counted the number of irrational dose assignments that were made by the continual reassessment method, according to the definitions of Zhou and colleagues. For each of the irrational decisions made, we classified the dose assignment as an underdose assignment, a target dose assignment, or an overdose assignment based on the true dose-limiting toxicity probability at that dose. Results: Across eight dose-toxicity scenarios, there were a total of 181,581 dose assignments made in the simulation study. Of these assignments, 8165 (4.5%) decisions were made when two out of three, three out of six, or four out of six patients had experienced a dose-limiting toxicity at the current dose. Of these 8165 decisions, 1505 (18.4%) recommended staying at the current dose level and would therefore be classified as irrational by Zhou and colleagues. Among the irrational decisions, 41.2% were misclassified, meaning they were made either at the true target dose (17.9%) or at a true underdose (23.3%). The remaining 58.8% were made at a true overdose and therefore truly irrational. Overall, irrational dose assignments comprised Conclusion: Zhou and colleagues argue that the behavior of the continual reassessment method is disturbing due to its ability to make irrational dose assignments. These definitions are based on rules that mimic the popular 3 + 3 design, which should not be the benchmark used to construct guidelines for trial conduct of modern Phase I methods. Our study illustrates that these dose assignments occur very seldom in the continual reassessment method and that even when they do occur, they can often be considered sensible when accounting for all accumulated data in the study.

This article considers the problem of designing Phase I-II clinical trials with delayed toxicity and efficacy outcomes. The proposed design is motivated by a Phase I-II study evaluating all-trans retinoic acid (ATRA) in combination with a fixed dose of daratumumab in the treatment of relapsed or refractory multiple myeloma. The primary objective of the study is to identify a dose that maximizes efficacy and has an acceptable level of toxicity. The toxicity endpoint is observed in one cycle of therapy (i.e., 4 weeks) while the efficacy endpoint is assessed after 8 weeks of treatment. The difference in endpoint observation windows causes logistical challenges in conducting the trial, since it is not practical to wait until both outcomes for each patient have been fully observed before sequentially assigning the dose of a newly eligible patient. In order to avoid delays in treatment for newly enrolled patients and to accelerate trial progress, we generalize the time-to-event continual reassessment method (TITE-CRM) to bivariate outcomes. Simulation studies are conducted to evaluate the proposed method, and we found that the proposed design is able to accurately select doses that maximize efficacy and have acceptable toxicity, while using all available information in allocating patients at the time of dose assignment. We compare the proposed methodology to two existing methods in the area.

INTRODUCTION: In contemporary oncology drug development, implementation of novel early-phase designs with the ability to address multiple research objectives are needed to better refine regimens. This paper describes an adaptive design strategy for identifying a range of optimal regimens based on two endpoints within multiple cohorts. The proposed design was developed to address objectives in an early-phase trial of cancer vaccines in combination with agonistic antibodies to CD40 and CD27. MATERIALS AND METHODS: We describe a model-based design strategy that was developed for a trial evaluating the safety and immunogenicity of vaccination with i) peptides plus CD40 antibody and TLR3 ligand, ii) systemic administration of an agonistic CD27 antibody, and c) to assess immune response from i) and ii) compared to optimal controls in participants with stage IIB-IV melanoma. RESULTS AND CONCLUSIONS: The proposed design is a practical adaptive method for use with combined immunotherapy regimens with multiple objectives within multiple cohorts of interest. Further advances in the effectiveness of cancer immunotherapies will require new approaches that include redefining optimal strategies to take multiple regimens forward into later phases, incorporating additional endpoints in the dose selection process and testing drug combination therapies to improve efficacy and reduce toxicity. Our goal is to facilitate acceptance and application of more novel designs in contemporary early development trials.

Optimal approaches to induce T cell infiltration of tumors are not known. Chemokines CXCL9, CXCL10, and CXCL11 support effector T cell recruitment and may be induced by IFN. This study tests the hypothesis that intratumoral administration of IFNγ will induce CXCL9-11 and will induce T cell recruitment and anti-tumor immune signatures in melanoma metastases.Nine eligible patients were immunized with a vaccine comprised of 12 class I MHC-restricted melanoma peptides and received IFNγ intratumorally. Effects on the tumor microenvironment were evaluated in sequential tumor biopsies. Adverse events (AEs) were recorded. T cell responses to vaccination were assessed in PBMC by IFNγ ELISPOT assay. Tumor biopsies were evaluated for immune cell infiltration, chemokine protein expression, and gene expression.Vaccination and intratumoral administration of IFNγ were well tolerated. Circulating T cell responses to vaccine were detected in six of nine patients. IFNγ increased production of chemokines CXCL10, CXCL11, and CCL5 in patient tumors. Neither vaccination alone, nor the addition of IFNγ promoted immune cell infiltration or induced anti-tumor immune gene signatures.The melanoma vaccine induced circulating T cell responses, but it failed to infiltrate metastases, thus highlighting the need for combination strategies to support T cell infiltration. A single intratumoral injection of IFNγ induced T cell-attracting chemokines; however, it also induced secondary immune regulation that may paradoxically limit immune infiltration and effector functions. Alternate dosing strategies or additional combinatorial treatments may be needed to promote trafficking and retention of tumor-reactive T cells in melanoma metastases.

Background Limited options are available for dose-finding clinical trials requiring group-specific dose selection. While conducting parallel trials for groups is an accessible approach to group-specific dose selection, this approach allows for maximum tolerated dose selection that does not align with clinically meaningful group order information. Methods The two-stage continual reassessment method is developed for dose-finding in studies involving three or more groups where group frailty order is known between some but not all groups, creating a partial order. This is an extension of the existing continual reassessment method shift model for two ordered groups. This method allows for dose selection by group, where maximum tolerated dose selection follows the known frailty order among groups. For example, if a group is known to be the most frail, the recommended maximum tolerated dose for this group should not exceed the maximum tolerated dose recommended for any other group. Results With limited alternatives for dose-finding in partially ordered groups, this method is compared to two alternatives: (1) an existing method for dose-finding in partially ordered groups which is less computationally accessible and (2) independent trials for each group using the two-stage continual reassessment method. Simulation studies show that when ignoring information on group frailty, using independent continual reassessment method trials by group, 30% of simulations would result in maximum tolerated dose selection that is out of order between groups. In addition, the two-stage continual reassessment method for partially ordered groups selects the maximum tolerated dose more often and assigns more patients to the maximum tolerated dose compared to using independent continual reassessment method trials within each group. Simulation results for the proposed method and the less computationally accessible approach are similar. Conclusion The proposed continual reassessment method for partially ordered groups ensures appropriate maximum tolerated dose order and improves accuracy of maximum tolerated dose selection, while allowing for trial implementation that is computationally accessible.

The impacts of patient age and gender on immune response (IR) and clinical outcome after cancer vaccines are not known. We hypothesized younger and female patients would have higher IR rates and better survival.Patients with resected stage IIB-IV melanoma in three clinical trials (Mel43, Mel44, Mel48) were vaccinated with 12 melanoma-associated peptides restricted by class I MHC. The cumulative incidence rate of CD8(+) T cell responses (direct interferon-gamma ELIspot assay) by week 7 was compared by age and gender. Overall survival (OS) and disease-free survival (DFS) landmark analyses were compared by Kaplan-Meier estimates and in multivariate analyses.T cell responses were evaluated in 327 patients and detected in 50 % of males and 48 % of females, with no difference in IR by gender or menopausal status. Males had trends toward longer DFS (p = 0.12) and OS (p = 0.09). Cumulative incidence of IR was higher in patients64 years of age versus older patients (p = 0.03). OS and DFS were similar by age group (p0.50). In multivariate modeling, younger age was associated with better IR (OR 0.40, p value 0.003), without an impact of age or gender on clinical outcomes.These data support the hypothesis that older patients are less likely to develop T cell responses to a cancer vaccine. Nonetheless, significant proportions of older patients mount immune responses with comparable survival outcomes. Thus, these data support including older patients in cancer vaccine trials, but suggest value in stratifying patients by age/64 years.

Phase I trials evaluating the safety of multi-drug combinations are becoming more common in oncology. Despite the emergence of novel methodology in the area, it is rare that innovative approaches are used in practice. In this article, we review three methods for Phase I combination studies that are easy to understand and straightforward to implement. We demonstrate the operating characteristics of the designs through illustration in a single trial, as well as through extensive simulation studies, with the aim of increasing the use of novel approaches in phase I combination studies. Design specifications and software capabilities are also discussed.

Dose-finding studies that aim to evaluate the safety of single agents are becoming less common, and advances in clinical research have complicated the paradigm of dose finding in oncology. A class of more complex problems, such as targeted agents, combination therapies and stratification of patients by clinical or genetic characteristics, has created the need to adapt early-phase trial design to the specific type of drug being investigated and the corresponding endpoints. In this article, we describe the implementation of an adaptive design based on a continual reassessment method for heterogeneous groups, modified to coincide with the objectives of a phase I/II trial of stereotactic body radiation therapy (SBRT) in patients with painful osseous metastatic disease. Operating characteristics of the Institutional Review Board (IRB) approved design are demonstrated under various possible true scenarios via simulation studies.

In dose-finding trials of chemotherapeutic agents, the goal of identifying the maximum tolerated dose is usually determined by considering information on toxicity only, with the assumption that the highest safe dose also provides the most promising outlook for efficacy. Trials of molecularly targeted agents challenge accepted dose-finding methods because minimal toxicity may arise over all doses under consideration and higher doses may not result in greater response. In this article, we propose a new early-phase method for trials investigating targeted agents. We provide simulation results illustrating the operating characteristics of our design.

Existing statistical methodology on dose finding for combination chemotherapies has focused on toxicity considerations alone in finding a maximum tolerated dose combination to recommend for further testing of efficacy in a phase II setting. Recently, there has been increasing interest in integrating phase I and phase II trials in order to facilitate drug development. In this article, we propose a new adaptive phase I/II method for dual-agent combinations that takes into account both toxicity and efficacy after each cohort inclusion. The primary objective, both within and at the conclusion of the trial, becomes finding a single dose combination with an acceptable level of toxicity that maximizes efficacious response. We assume that there exist monotone dose–toxicity and dose–efficacy relationships among doses of one agent when the dose of other agent is fixed. We perform extensive simulation studies that demonstrate the operating characteristics of our proposed approach, and we compare simulated results to existing methodology in phase I/II design for combinations of agents.

This paper presents the R package pocrm for implementing and simulating the partial order continual reassessment method (PO-CRM; [1,2]) in Phase I trials of combinations of agents. The aim of this article is to illustrate, through examples of the pocrm package, how the PO-CRM works and how its operating characteristics can inform clinical trial investigators. This should promote the use of the PO-CRM in designing and conducting dose-finding Phase I trials of combinations of agents.

The majority of methods for the design of Phase I trials in oncology are based upon a single course of therapy, yet in actual practice it may be the case that there is more than one treatment schedule for any given dose. Therefore, the probability of observing a dose-limiting toxicity (DLT) may depend upon both the total amount of the dose given, as well as the frequency with which it is administered. The objective of the study then becomes to find an acceptable combination of both dose and schedule. Past literature on designing these trials has entailed the assumption that toxicity increases monotonically with both dose and schedule. In this article, we relax this assumption for schedules and present a dose-schedule finding design that can be generalized to situations in which we know the ordering between all schedules and those in which we do not. We present simulation results that compare our method to other suggested dose-schedule finding methodology.

In studies of combinations of agents in phase I oncology trials, the dose–toxicity relationship may not be monotone for all combinations, in which case the toxicity probabilities follow a partial order. The continual reassessment method for partial orders (PO-CRM) is a design for phase I trials of combinations that leans upon identifying possible complete orders associated with the partial order. This article addresses some practical design considerations not previously undertaken when describing the PO-CRM. We describe an approach in choosing a proper subset of possible orderings, formulated according to the known toxicity relationships within a matrix of combination therapies. Other design issues, such as working model selection and stopping rules, are also discussed. We demonstrate the practical ability of PO-CRM as a phase I design for combinations through its use in a recent trial designed at the University of Virginia Cancer Center. Copyright © 2013 John Wiley & Sons, Ltd.

Infiltration of non-small-cell lung cancer (NSCLC) by CD8Twenty-three primary NSCLC tumors were immunohistochemically stained for CD8 and were assessed using automated software with eight different sampling strategies or the whole tumor. Results of all sampling strategies were compared to the whole tumor counts (paired t tests, Pearson's r). Associations between CD8 densities and overall survival were assessed (log-rank test).Counts from all eight sampling strategies significantly correlated with whole tumor counts (p ≤ 0.001). However, the magnitude of CD8Different tumor sampling strategies may yield discordant TIL density results and different stratification for risk assessment. Small biopsies may be particularly unrepresentative. Random sampling of larger tumor areas is recommended. Enumerating CD8

Infiltration of cancers by T cells is associated with improved patient survival and response to immune therapies; however, optimal approaches to induce T cell infiltration of tumors are not known. This study was designed to assess whether topical treatment of melanoma metastases with the TLR7 agonist imiquimod plus administration of a multipeptide cancer vaccine will improve immune cell infiltration of melanoma metastases.Eligible patients were immunized with a vaccine comprised of 12 melanoma peptides and a tetanus toxoid-derived helper peptide, and imiquimod was applied topically to metastatic tumors daily. Adverse events were recorded, and effects on the tumor microenvironment were evaluated from sequential tumor biopsies. T cell responses were assessed by IFNγ ELIspot assay and T cell tetramer staining. Patient tumors were evaluated for immune cell infiltration, cytokine and chemokine production, and gene expression.Four eligible patients were enrolled, and administration of imiquimod and vaccination were well tolerated. Circulating T cell responses to the vaccine was detected by ex vivo ELIspot assay in 3 of 4 patients. Treatment of metastases with imiquimod induced immune cell infiltration and favorable gene signatures in the patients with circulating T cell responses. This study supports further study of topical imiquimod combined with vaccines or other immune therapies for the treatment of melanoma.

The time-to-event continual reassessment method (TITE-CRM) was proposed to handle the problem of long trial duration in Phase 1 trials as a result of late-onset toxicities. Here, we implement the TITE-CRM in dose–finding trials of combinations of agents. When studying multiple agents, monotonicity of the dose-toxicity curve is not clearly defined. Therefore, the toxicity probabilities follow a partial order, meaning that there are pairs of treatments for which the ordering of the toxicity probabilities is not known at the start of the trial. A CRM design for partially ordered trials (PO-CRM) was recently proposed. Simulation studies show that extending the TITE-CRM to the partial order setting produces results similar to those of the PO-CRM in terms of maximum tolerated dose recommendation yet reduces the duration of the trial.

There has been constant development of novel statistical methods in the design of early-phase clinical trials since the introduction of model-based designs, yet the traditional or modified 3+3 algorithmic design remains the most widely used approach in dose-finding studies. Research has shown the limitations of this traditional design compared with more innovative approaches yet the use of these model-based designs remains infrequent. This can be attributed to several causes including a poor understanding from clinicians and reviewers into how the designs work, and how best to evaluate the appropriateness of a proposed design. These barriers are likely to be enhanced in the coming years as the recent paradigm of drug development involves a shift to more complex dose-finding problems. This article reviews relevant information that should be included in clinical trial protocols to aid in the acceptance and approval of novel methods. We provide practical guidance for implementing these efficient designs with the aim of augmenting a broader transition from algorithmic to adaptive model-guided designs. In addition we highlight issues to consider in the actual implementation of a trial once approval is obtained. Copyright © 2016 John WileySons, Ltd.

Toxicity probability interval designs have received increasing attention as a dose-finding method in recent years. In this study, we compared the two-stage, likelihood-based continual reassessment method (CRM), modified toxicity probability interval (mTPI), and the Bayesian optimal interval design (BOIN) in order to evaluate each method's performance in dose selection for phase I trials. We use several summary measures to compare the performance of these methods, including percentage of correct selection (PCS) of the true maximum tolerable dose (MTD), allocation of patients to doses at and around the true MTD, and an accuracy index. This index is an efficiency measure that describes the entire distribution of MTD selection and patient allocation by taking into account the distance between the true probability of toxicity at each dose level and the target toxicity rate. The simulation study considered a broad range of toxicity curves and various sample sizes. When considering PCS, we found that CRM outperformed the two competing methods in most scenarios, followed by BOIN, then mTPI. We observed a similar trend when considering the accuracy index for dose allocation, where CRM most often outperformed both mTPI and BOIN. These trends were more pronounced with increasing number of dose levels. Copyright © 2016 John WileySons, Ltd.

The majority of phase I methods for multi-agent trials have focused on identifying a single maximum tolerated dose combination (MTDC) among those being investigated. Some published methods in the area have been based on the notion that there is no unique MTDC and that the set of dose combinations with acceptable toxicity forms an equivalence contour in two dimensions. Therefore, it may be of interest to find multiple MTDCs for further testing for efficacy in a phase II setting. In this paper, we present a new dose-finding method that extends the continual reassessment method to account for the location of multiple MTDCs. Operating characteristics are demonstrated through simulation studies and are compared with existing methodology. Some brief discussion of implementation and available software is also provided. Copyright © 2016 John WileySons, Ltd.

Adaptive, model-based, dose-finding methods, such as the continual reassessment method, have been shown to have good operating characteristics. One school of thought argues in favor of the use of parsimonious models, not modeling all aspects of the problem, and using a strict minimum number of parameters. In particular, for the standard situation of a single homogeneous group, it is common to appeal to a one-parameter model. Other authors argue for a more classical approach that models all aspects of the problem. Here, we show that increasing the dimension of the parameter space, in the context of adaptive dose-finding studies, is usually counter productive and, rather than leading to improvements in operating characteristics, the added dimensionality is likely to result in difficulties. Among these are inconsistency of parameter estimates, lack of coherence in escalation or de-escalation, erratic behavior, getting stuck at the wrong level, and, in almost all cases, poorer performance in terms of correct identification of the targeted dose. Our conclusions are based on both theoretical results and simulations. Copyright © 2016 John WileySons, Ltd.

Little is known about the relative performance of competing model-based dose-finding methods for combination phase I trials. In this study, we focused on five model-based dose-finding methods that have been recently developed. We compared the recommendation rates for true maximum-tolerated dose combinations (MTDCs) and over-dose combinations among these methods under 16 scenarios for 3 × 3, 4 × 4, 2 × 4, and 3 × 5 dose combination matrices. We found that performance of the model-based dose-finding methods varied depending on (1) whether the dose combination matrix is square or not; (2) whether the true MTDCs exist within the same group along the diagonals of the dose combination matrix; and (3) the number of true MTDCs. We discuss the details of the operating characteristics and the advantages and disadvantages of the five methods compared.

In oncology, vaccine-based immunotherapy often investigates regimens that demonstrate minimal toxicity overall and higher doses may not correlate with greater immune response. Rather than determining the maximum tolerated dose, the goal of the study becomes locating the optimal biological dose, which is defined as a safe dose demonstrating the greatest immunogenicity, based on some predefined measure of immune response. Incorporation of adjuvants, new or optimized peptide vaccines, and combining vaccines with immune modulators may enhance immune response, with the aim of improving clinical response. Innovative dose escalation strategies are needed to establish the safety and immunogenicity of new immunologic combinations. We describe the implementation of an adaptive design for identifying the optimal treatment strategy in a multi-site, FDA-approved, phase I/II trial of a novel vaccination approach using long-peptides plus TLR agonists for resected stage IIB-IV melanoma. Operating characteristics of the design are demonstrated under various possible true scenarios via simulation studies. Overall performance indicates that the design is a practical Phase I/II adaptive method for use with combined immunotherapy agents. The simulation results demonstrate the method's ability to effectively recommend optimal regimens in a high percentage of trials with manageable sample sizes. The numerical results presented in this work include the type of simulation information that aid review boards in understanding design performance, such as average sample size and frequency of early trial termination, which we hope will augment early-phase trial design in cancer immunotherapy.

Background Most of the current designs used for Phase I dose finding trials in oncology will either involve only a single cytotoxic agent or will impose some implicit ordering among the doses. The goal of the studies is to estimate the maximum tolerated dose (MTD), the highest dose that can be administered with an acceptable level of toxicity. A key working assumption of these methods is the monotonicity of the dose–toxicity curve. Purpose Here we consider situations in which the monotonicity assumption may fail. These studies are becoming increasingly common in practice, most notably, in phase I trials that involve combinations of agents. Our focus is on studies where there exist pairs of treatment combinations for which the ordering of the probabilities of a dose-limiting toxicity cannot be known a priori. Methods We describe a new dose-finding design which can be used for multiple-drug trials and can be applied to this kind of problem. Our methods proceed by laying out all possible orderings of toxicity probabilities that are consistent with the known orderings among treatment combinations and allowing the continual reassessment method (CRM) to provide efficient estimates of the MTD within these orders. The design can be seen to simplify to the CRM when the full ordering is known. Results We study the properties of the design via simulations that provide comparisons to the Bayesian approach to partial orders (POCRM) of Wages, Conaway, and O'Quigley. The POCRM was shown to perform well when compared to other suggested methods for partial orders. Therefore, we comapre our approach to it in order to assess the performance of the new design. Limitations A limitation concerns the number of possible orders. There are dose-finding studies with combinations of agents that can lead to a large number of possible orders. In this case, it may not be feasible to work with all possible orders. Conclusions The proposed design demonstrates the ability to effectively estimate MTD combinations in partially ordered dosefinding studies. Because it relaxes the monotonicity assumption, it can be considered a multivariate generalization of the CRM. Hence, it can serve as a link between single and multiple-agent dosefinding trials.