Toxicity burden score: a novel approach to summarize multiple toxic effects

MC-Keyboard: A Practical Phase I Trial Design for Targeted Therapies and Immunotherapies Integrating Multiple-Grade Toxicities

In targeted therapies and immunotherapies, the occurrence of low-grade (e.g., grade 1-2) toxicities (LGT) is common, while dose-limiting toxicities (DLT) are relatively rare. As a result, conventional phase I trial designs, solely based on DLTs and disregarding milder toxicities, are problematic when evaluating these novel therapies. Methods: To address this issue, we propose a novel phase I design called a multiple-constraint keyboard (MC-Keyboard) that integrates multiple toxicity constraints, accounting for both DLT and LGT, for precise dose escalation and de-escalation, and identification of the maximum tolerated dose (MTD). As a model-assisted design, an important feature of MC-Keyboard is that its dose-escalation or de-escalation rule can be pretabulated and incorporated into the trial protocol before the initiation of the trial, greatly simplifying its implementation. Results: The simulation study showed that the MC-Keyboard had high accuracy in identifying the MTD and is safer than some existing designs. Conclusion: The MC-Keyboard provides a novel, simple, and safe approach to assessing safety and identifying the MTD for targeted therapies and immunotherapies.

Statistical and practical considerations in planning and conduct of dose-optimization trials

The U.S. Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection toward identifying the optimal biological dose that offers the best balance between benefit and risk, rather than the maximum tolerated dose. However, achieving dose optimization is a challenging task that involves a variety of factors and is considerably more complicated than identifying the maximum tolerated dose, both in terms of design and implementation. This article provides a comprehensive review of various design strategies for dose-optimization trials, including phase 1/2 and 2/3 designs, and highlights their respective advantages and disadvantages. In addition, practical considerations for selecting an appropriate design and planning and executing the trial are discussed. The article also presents freely available software tools that can be utilized for designing and implementing dose-optimization trials. The approaches and their implementation are illustrated through real-world examples.

Influence of Gut Microbiota-Mediated Immune Regulation on Response to Chemotherapy

The involvement of the gut microbiota in anti-cancer treatment has gained increasing attention. Alterations to the structure and function of the gut bacteria are important factors in the development of cancer as well as the efficacy of chemotherapy. Recent studies have confirmed that the gut microbiota and related metabolites influence the pharmacological activity of chemotherapeutic agents through interactions with the immune system. This review aims to summarize the current knowledge of how malignant tumor and chemotherapy affect the gut microbiota, how the gut microbiota regulates host immune response, and how interactions between the gut microbiota and host immune response influence the efficacy of chemotherapy. Recent advances in strategies for increasing the efficiency of chemotherapy based on the gut microbiota are also described. Deciphering the complex homeostasis maintained by the gut microbiota and host immunity provides a solid scientific basis for bacterial intervention in chemotherapy.

The Weighted Toxicity Score: Confirmation of a Simple Metric to Communicate Toxicity in Randomized Trials of Systemic Cancer Therapy

INTRODUCTION

FDA's Project Optimus was developed in part to better identify appropriate dose and schedule of cancer therapeutics. The tabular method to summarize patients' maximum toxicity in a clinical trial does not allow for ready comparison to the treatment's benefit. In this manuscript, we apply a simple tool, the weighted toxicity score (WTS), to trials involving lung cancer immunotherapy and chemotherapy, as well as those cited in a recent publication as examples of trials that represent successful reduction of the appropriate dose of anti-cancer agents.

METHODS

PubMed was queried for randomized controlled trials of therapy involving immune checkpoint inhibitors in lung cancer. Trial data from studies highlighting initial success with dose adjustments after FDA approval also were assembled and analyzed according to the WTS procedure described previously, compared to clinical outcomes data.

RESULTS

The WTS provided, with the clinical outcome(s), a data pair that leads to easy interpretation of the expected benefit versus relative toxicity of studies involving immunotherapy or chemoimmunotherapy in lung cancers. The WTS was consistent with the conclusions of the primary studies, helping to quantitate the toxicity difference between treatments in a previously unavailable way.

CONCLUSION

The WTS provides a tool to show the cost in toxicity of therapy in a randomized clinical trial, with applicability to studies involving chemotherapy, immunotherapy, or kinase-directed therapy. Inclusion of a running tally of WTS during conduct of a trial could serve as one means to adjust dosing or to provide feedback during data safety monitoring of a clinical trial.

TITE-gBOIN-ET: Time-to-event generalized Bayesian optimal interval design to accelerate dose-finding accounting for ordinal graded efficacy and toxicity outcomes

One of the primary objectives of an oncology dose-finding trial for novel therapies, such as molecular-targeted agents and immune-oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low or moderate-grade toxicities than dose-limiting toxicities. Besides, for efficacy, evaluating the overall response and long-term stable disease in solid tumors and considering the difference between complete remission and partial remission in lymphoma are preferable. It is also essential to accelerate early-stage trials to shorten the entire period of drug development. However, it is often challenging to make real-time adaptive decisions due to late-onset outcomes, fast accrual rates, and differences in outcome evaluation periods for efficacy and toxicity. To solve the issues, we propose a time-to-event generalized Bayesian optimal interval design to accelerate dose finding, accounting for efficacy and toxicity grades. The new design named "TITE-gBOIN-ET" design is model-assisted and straightforward to implement in actual oncology dose-finding trials. Simulation studies show that the TITE-gBOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment while having comparable or higher performance in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings.

Quasi-partial order continual reassessment method: Applying toxicity scores to cancer dose-finding drug combination trials

The primary endpoint of most dose-finding cancer trials is patient toxicity, and the primary goal is to identify the maximum tolerated dose (MTD), that is, the highest dose that falls below or within a pre-specified toxicity tolerability threshold. Conventionally, dose-finding methods have utilized a binary toxicity endpoint based on whether or not a patient experiences a dose limiting-toxicity (DLT). Improving upon this, in recent years several methods have been developed for modeling toxicity scores, a novel continuous endpoint designed to more precisely estimate patient toxicity burden. Separately, drug-combination trials have become increasingly prevalent, and due to added complexities regarding estimating 'true' dose ordering and potential for more complex patient toxicity profiles, provide an ideal setting which may benefit from the improved precision of toxicity scores. In this paper, we merge two frameworks based on the Continual Reassessment Method (CRM) - the Quasi-CRM and the Partial Order CRM (POCRM) - to propose a novel approach for modeling toxicity scores in a combination-trial setting. We demonstrate that utilizing toxicity scores has the potential to greatly improve correct dose-selection over a variety of trial scenarios. We further present a simple adaptation to the toxicity-score model to control for potential over-dosing issues such that it adheres to the conventional DLT definition and will, at worst, perform equivalently to that of the traditional binary DLT framework. We demonstrate that extending toxicity scores to the combination-trial setting offers potential for improvement over the conventional binary endpoint models.

An overview of the BOIN design and its current extensions for novel early-phase oncology trials

Bayesian Optimal Interval (BOIN) designs are a class of model-assisted dose-finding designs that can be used in oncology trials to determine the maximum tolerated dose (MTD) of a study drug based on safety or the optimal biological dose (OBD) based on safety and efficacy. BOIN designs provide a complete suite for dose finding in early phase trials, as well as a consistent way to explore different scenarios such as toxicity, efficacy, continuous outcomes, delayed toxicity or efficacy and drug combinations in a unified manner with easy access to software to implement most of these designs. Although built upon Bayesian probability models, BOIN designs are operationally simple in general and have good statistical operating characteristics compared to other dose-finding designs. This review paper describes the original BOIN design and its many extensions, their advantages and limitations, the software used to implement them, and the most suitable situation for use of each of these designs. Published examples of the implementation of BOIN designs are provided in the Appendix.

gBOIN-ET: The generalized Bayesian optimal interval design for optimal dose-finding accounting for ordinal graded efficacy and toxicity in early clinical trials

One of the primary objectives of an oncology dose-finding trial for novel therapies, such as molecular targeted agents and immune-oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low- or moderate-grade toxicities than dose-limiting toxicities. Besides, efficacy should be evaluated as an overall response and stable disease in solid tumors and the difference between complete remission and partial remission in lymphoma. This paper proposes the generalized Bayesian optimal interval design for dose-finding accounting for efficacy and toxicity grades. The new design, named "gBOIN-ET" design, is model-assisted, simple, and straightforward to implement in actual oncology dose-finding trials than model-based approaches. These characteristics are quite valuable in practice. A simulation study shows that the gBOIN-ET design has advantages compared with the other model-assisted designs in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings.

Novel longitudinal Multiple Overall Toxicity (MOTox) score to quantify adverse events experienced by patients during chemotherapy treatment: a retrospective analysis of the MRC BO06 trial in osteosarcoma

OBJECTIVES

This study aims at exploring and quantifying multiple types of adverse events (AEs) experienced by patients during cancer treatment. A novel longitudinal score to evaluate the Multiple Overall Toxicity (MOTox) burden is proposed. The MOTox approach investigates the personalised evolution of high overall toxicity (high-MOTox) during the treatment.

DESIGN

Retrospective analysis of the MRC-BO06/EORTC-80931 randomised controlled trial for osteosarcoma.

SETTING

International multicentre population-based study.

PARTICIPANTS

A total of 377 patients with resectable high-grade osteosarcoma, who completed treatment within 180 days after randomisation without abnormal dosages (+25% higher than planned).

INTERVENTIONS

Patients were randomised to six cycles of conventional versus dose-intense regimens of doxorubicin and cisplatin. Non-haematological toxicity data were collected prospectively and graded according to the Common Terminology Criteria for Adverse Events (CTCAE).

MAIN OUTCOME MEASURES

The MOTox score described the overall toxicity burden in terms of multiple toxic AEs, maximum-severity episode and cycle time-dimension. Evolution of high-MOTox was assessed through multivariable models, that investigated the impact of personalised characteristics (eg, achieved chemotherapy dose, previous AEs or biochemical factors) cycle-by-cycle.

RESULTS

A cycle-by-cycle analysis identifies different evolutions of MOTox levels during treatment, detecting differences in patients' health. Mean MOTox values and percentages of patients with high-MOTox decreased cycle-by-cycle from 2.626 to 1.953 and from 57.8% to 36.6%, respectively. High-MOTox conditions during previous cycles were prognostic risk factors for a new occurrence (ORs range from 1.522 to 4.439), showing that patient's history of toxicities played an important role in the evolution of overall toxicity burden during therapy. Conventional regimen may be preferred to dose-intense in terms of AEs at cycles 2-3 (p<0.05).

CONCLUSIONS

The novel longitudinal method developed can be applied to any cancer studies with CTCAE-graded toxicity data. After validation in other studies, the MOTox approach may lead to improvements in healthcare assessment and treatment planning.

TRIAL REGISTRATION NUMBER

ISRCTN86294690; Post-results.

An adaptive gBOIN design with shrinkage boundaries for phase I dose-finding trials

Background

With the emergence of molecularly targeted agents and immunotherapies, the landscape of phase I trials in oncology has been changed. Though these new therapeutic agents are very likely induce multiple low- or moderate-grade toxicities instead of DLT, most of the existing phase I trial designs account for the binary toxicity outcomes. Motivated by a pediatric phase I trial of solid tumor with a continuous outcome, we propose an adaptive generalized Bayesian optimal interval design with shrinkage boundaries, gBOINS, which can account for continuous, toxicity grades endpoints and regard the conventional binary endpoint as a special case.

Result

The proposed gBOINS design enjoys convergence properties, e.g., the induced interval shrinks to the toxicity target and the recommended dose converges to the true maximum tolerated dose with increased sample size.

Conclusion

The proposed gBOINS design is transparent and simple to implement. We show that the gBOINS design has the desirable finite property of coherence and large-sample property of consistency. Numerical studies show that the proposed gBOINS design yields good performance and is comparable with or superior to the competing design.

TITE-gBOIN: Time-to-event Bayesian optimal interval design to accelerate dose-finding accounting for toxicity grades

The new therapeutic agents, such as molecular targeted agents and immuno-oncology therapies, appear more likely to induce multiple toxicities at different grades than dose-limiting toxicities defined in traditional dose-finding trials. In addition, it is often challenging to make adaptive decisions on dose escalation and de-escalation on time because of the fast accrual rate and/or the late-onset toxicity outcomes, causing the potential suspension of the enrollment and the delay of the trials. To address these issues, we propose a time-to-event Bayesian optimal interval design to accelerate the dose-finding process utilizing toxicity grades based on both cumulative and pending toxicity outcomes. The proposed design, named "TITE-gBOIN" design, is a nonparametric and model-assisted design and has the virtues of robustness, simplicity and straightforward to implement in actual oncology dose-finding trials. A simulation study shows that the TITE-gBOIN design has a higher probability of selecting the MTDs correctly and allocating more patients to the MTDs across various realistic settings while reducing the trial duration significantly, therefore can accelerate early-stage dose-finding trials.

Visualizing adverse events in clinical trials using correspondence analysis with R-package visae

Background

Graphical displays and data visualization are essential components of statistical analysis that can lead to improved understanding of clinical trial adverse event (AE) data. Correspondence analysis (CA) has been introduced decades ago as a multivariate technique that can communicate AE contingency tables using two-dimensional plots, while quantifying the loss of information as other dimension reduction techniques such as principal components and factor analysis.

Methods

We propose the application of stacked CA using contribution biplots as a tool to explore differences in AE data among treatments in clinical trials. We defined five levels of refinement for the analysis based on data derived from the Common Terminology Criteria for Adverse Events (CTCAE) grades, domains, terms and their combinations. In addition, we developed a Shiny app built in an R-package, visae, publicly available on Comprehensive R Archive Network (CRAN), to interactively investigate CA configurations based on the contribution to the explained variance and relative frequency of AEs. Data from two randomized controlled trials (RCT) were used to illustrate the proposed methods: NSABP R-04, a neoadjuvant rectal 2 × 2 factorial trial comparing radiation therapy with either capecitabine (Cape) or 5-fluorouracil (5-FU) alone with or without oxaliplatin (Oxa), and NSABP B-35, a double-blind RCT comparing tamoxifen to anastrozole in postmenopausal women with hormone-positive ductal carcinoma in situ.

Results

In the R04 trial (n = 1308), CA biplots displayed the discrepancies between single agent treatments and their combinations with Oxa at all levels of AE classes, such that these discrepancies were responsible for the largest portion of the explained variability among treatments. In addition, an interaction effect when adding Oxa to Cape/5-FU was identified when the distance between Cape+Oxa and 5-FU + Oxa was observed to be larger than the distance between 5-FU and Cape, with Cape+Oxa and 5-FU + Oxa in different quadrants of the CA biplots. In the B35 trial (n = 3009), CA biplots showed different patterns for non-adherent Anastrozole and Tamoxifen compared with their adherent counterparts.

Conclusion

CA with contribution biplot is an effective tool that can be used to summarize AE data in a two-dimensional display while minimizing the loss of information and interpretation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01368-w.

Adverse event burden in older patients with CLL receiving bendamustine plus rituximab or ibrutinib regimens: Alliance A041202

Ibrutinib has superior progression-free survival compared with bendamustine plus rituximab (BR) in older CLL patients, however, differences in treatment duration, six monthly BR cycles versus continuous ibrutinib, complicate adverse event (AE) comparisons. We introduce the AE burden score (AE_sc) to compare AEs, calculated for each patient by summing over products of reporting period length and grade for each all-cause grade 1-4 AE and dividing by the length of time over which AEs are assessed. A total of 176 patients received BR and 361 ibrutinib alone or with six cycles of rituximab. At 38 months median follow-up, 64% remained on ibrutinib. Median AE_sc was higher with BR versus ibrutinib in the first six cycles (7.2 versus 4.9, p < 0.0001). Within ibrutinib arms, median AE_sc decreased significantly to 3.7 after six cycles (p < 0.0001). 10% and 14% of BR and ibrutinib patients discontinued treatment for AEs. In ibrutinib arms, cumulative incidence of grade 3 or higher atrial fibrillation, hypertension, and infection (AEs of clinical interest) at 12 months was 4.5%, 17.5%, and 12.8%, respectively, and increased more slowly thereafter to 7.7%, 25.4%, and 20.5% at 36 months. Analytical tools including the AE_sc and cumulative incidence of AEs can help to better characterize AE burden over time. ClinicalTrials.gov identifier: NCT01886872.

Adaptive Bayesian phase I clinical trial designs for estimating the maximum tolerated doses for two drugs while fully utilizing all toxicity information

The combined treatments with multiple drugs are very common in the contemporary medicine, especially for medical oncology. Therefore, we developed a Bayesian adaptive Phase I clinical trial design entitled escalation with overdoing control using normalized equivalent toxicity score for estimating maximum tolerated dose (MTD) contour of two drug combination (EWOC-NETS-COM) used for oncology trials. The normalized equivalent toxicity score (NETS) as the primary endpoint of clinical trial is assumed to follow quasi-Bernoulli distribution and treated as quasi-continuous random variable in the logistic linear regression model which is used to describe the relationship between the doses of the two agents and the toxicity response. Four parameters in the dose-toxicity model were re-parameterized to parameters with explicit clinical meanings to describe the association between NETS and doses of two agents. Noninformative priors were used and Markov chain Monte Carlo was employed to update the posteriors of the four parameters in dose-toxicity model. Extensive simulations were conducted to evaluate the safety, trial efficiency, and MTD estimation accuracy of EWOC-NETS-COM under different scenarios, using the EWOC as reference. The results demonstrated that EWOC-NETS-COM not only efficiently estimates MTD contour of multiple drugs but also provides better trial efficiency by fully utilizing all toxicity information.

phase1RMD: An R package for repeated measures dose-finding designs with novel toxicity and efficacy endpoints

Traditional dose-finding designs are substantially inefficient for targeted agents and cancer immunotherapies by failing to incorporate efficacy signals, mild and moderate adverse events, and late, cumulative toxicities. However, the lack of user-friendly software is a barrier to the practical use of the novel phase I designs, despite their demonstrated superiority of traditional 3+3 designs. To overcome these barriers, we present an R package, phase1RMD, which provides a comprehensive implementation of novel designs with repeated toxicity measures and early efficacy. A novel phase I repeated measures design that used a continuous toxicity score from multiple treatment cycles was implemented. Furthermore, in studies where preliminary efficacy is evaluated, an adaptive, multi-stage design to identify the most efficacious dose with acceptable toxicity was demonstrated. Functions are provided to recommend the next dose based on the data collected in a phase I trial, as well as to assess trial characteristics given design parameters via simulations. The repeated measure designs accurately estimated both the magnitude and direction of toxicity trends in late treatment cycles, and allocated more patients at therapeutic doses. The R package for implementing these designs is available from the Comprehensive R Archive Network. To our best knowledge, this is the first software that implement novel phase I dose-finding designs that simultaneously accounts for the multiple-grade toxicity events over multiple treatment cycles and a continuous early efficacy outcome. With the software published on CRAN, we will pursue the implementation of these designs in phase I trials in real-life settings.

Activity-Based Costing of Intensity-Modulated Proton versus Photon Therapy for Oropharyngeal Cancer

PURPOSE

In value-based health care delivery, radiation oncologists need to compare empiric costs of care delivery with advanced technologies, such as intensity-modulated proton therapy (IMPT) and intensity-modulated radiation therapy (IMRT). We used time-driven activity-based costing (TDABC) to compare the costs of delivering IMPT and IMRT in a case-matched pilot study of patients with newly diagnosed oropharyngeal (OPC) cancer.

MATERIALS AND METHODS

We used clinicopathologic factors to match 25 patients with OPC who received IMPT in 2011-12 with 25 patients with OPC treated with IMRT in 2000-09. Process maps were created for each multidisciplinary clinical activity (including chemotherapy and ancillary services) from initial consultation through 1 month of follow-up. Resource costs and times were determined for each activity. Each patient-specific activity was linked with a process map and TDABC over the full cycle of care. All calculated costs were normalized to the lowest-cost IMRT patient.

RESULTS

TDABC costs for IMRT were 1.00 to 3.33 times that of the lowest-cost IMRT patient (mean ± SD: 1.65 ± 0.56), while costs for IMPT were 1.88 to 4.32 times that of the lowest-cost IMRT patient (2.58 ± 0.39) (P < .05). Although single-fraction costs were 2.79 times higher for IMPT than for IMRT (owing to higher equipment costs), average full cycle cost of IMPT was 1.53 times higher than IMRT, suggesting that the initial cost increase is partly mitigated by reductions in costs for other, non-RT supportive health care services.

CONCLUSIONS

In this matched sample, although IMPT was on average more costly than IMRT primarily owing to higher equipment costs, a subset of IMRT patients had similar costs to IMPT patients, owing to greater use of supportive care resources. Multidimensional patient outcomes and TDABC provide vital methodology for defining the value of radiation therapy modalities.

On the properties of the toxicity index and its statistical efficiency

Cancer clinical trials typically generate detailed patient toxicity data. The most common measure used to summarize patient toxicity is the maximum grade among all toxicities and it does not fully represent the toxicity burden experienced by patients. In this article, we study the mathematical and statistical properties of the toxicity index (TI), in an effort to address this deficiency. We introduce a total ordering, (T-rank), that allows us to fully rank the patients according to how frequently they exhibit toxicities, and show that TI is the only measure that preserves the T-rank among its competitors. Moreover, we propose a Poisson-Limit model for sparse toxicity data. Under this model, we develop a general two-sample test, which can be applied to any summary measure for detecting differences among two population of toxicity data. We derive the asymptotic power function of this class as well as the asymptotic relative efficiency (ARE) of the members of the class. We evaluate the ARE formula empirically and show that if the data are drawn from a random Poisson-Limit model, the TI is more efficient, with high probability, than the maximum and the average summary measures. Finally, we evaluate our method on clinical trial toxicity data and show that TI has a higher power in detecting the differences in toxicity profile among treatments. The results of this article can be applied beyond toxicity modeling, to any problem where one observes a sparse array of scores on subjects and a ranking based on extreme scores is desirable.

Designing Dose-Finding Phase I Clinical Trials: Top 10 Questions That Should Be Discussed With Your Statistician

In recent years, the landscape in clinical trial development has changed to involve many molecularly targeted agents, immunotherapies, or radiotherapy, as a single agent or in combination. Given their different mechanisms of action and lengths of administration, these agents have different toxicity profiles, which has resulted in numerous challenges when applying traditional designs such as the 3 + 3 design in dose-finding clinical trials. Novel methods have been proposed to address these design challenges such as combinations of therapies or late-onset toxicities. However, their design and implementation require close collaboration between clinicians and statisticians to ensure that the appropriate design is selected to address the aims of the study and that the design assumptions are pertinent to the study drug. The goal of this paper is to provide guidelines for appropriate questions that should be considered early in the design stage to facilitate the interactions between clinical and statistical teams and to improve the design of dose-finding clinical trials for novel anticancer agents.

Bayesian interval-based oncology dose-finding design with repeated quasi-continuous toxicity model

In oncology dose-finding clinical trials, the key to accurately estimating the maximum tolerated dose (MTD) is to use all data efficiently given small sample sizes. Currently, popular designs dichotomize adverse events of various types and grades that occur within the first treatment cycle into binary toxicity outcomes of dose-limiting toxicity (DLT) events. Such compression of toxicity data from multiple treatment cycles causes huge loss of information, often resulting in MTD estimation with large bias and variance. To improve this, a continuous endpoint (the total toxicity profile, TTP) was proposed to incorporate adverse event types and grades. The Bayesian Repeated Measures Design (RMD) was further developed by Yin et al. (2017) to account for the cumulative toxicity information from multiple treatment cycles. However, the existing RMD method selects the dose that minimizes the loss function based on point estimates, which may generate inconsistent results due to small sample sizes in phase I trials. To reduce the variability in dose escalation decision-making, we propose an improved repeated measures design with an interval-based decision rule that selects the dose with the highest posterior probability of falling in a pre-specified target toxicity interval. Through comprehensive simulations, we compared this proposed design with the existing RMD design, along with well-established DLT-based designs such as Continual Reassessment Method (CRM) and Bayesian Logistic Regression Model (BLRM). The results demonstrated that our proposed design outperforms all other designs in terms of accurately identifying the MTD and assigning fewer patients to sub-therapeutic or overly toxic doses.

Adverse event load, onset, and maximum grade: A novel method of reporting adverse events in cancer clinical trials

BACKGROUND

Current adverse event reporting practices do not document longitudinal characteristics of adverse effects, and alternative methods are not easily interpretable and have not been employed by clinical trials. Introducing time parameters in the evaluation of safety that are comprehensive yet easily interpretable could allow for a better understanding of treatment quality. In this study, we developed and applied a novel adverse event reporting method based on longitudinal adverse event changes to aid describing, summarizing, and presenting adverse event profile. We termed it the "Adverse Event Load, Onset, and Maximum Grade" method.

METHODS

We developed two adverse event summary metrics to complement the traditional maximum grade report. Onset time indicates the time period in which the maximum grade for a specific adverse event occurred and was defined as "early" (i.e. maximum grade happened for the first time before 6 weeks) or "late" (i.e. after the 6^th week). Adverse event load indicates the overall severity of a specific adverse event over the entire treatment. Higher adverse event load indicates a worse overall experience. These metrics can be calculated for adverse events with different maximum grades, in treatments with planned changes (e.g. dosage changes), using data sets with different number of adverse event data points between treatments (e.g. treatments with longer cycle lengths may have less adverse event data points) and on data sets with different adverse event data availability (e.g. cycle basis and patient-outcome reports). We tested the utility of this method using individual patient data from two major backbone therapies ("Irinotecan" and "Oxaliplatin") from the N9741 trial available in the Fondation ARCAD database (fondationarcad.org). We investigated profiles of diarrhea, neutropenia/leukopenia, and nausea/vomiting.

RESULTS

Our method provided additional information compared to traditional adverse event reports. For example, for nausea/vomiting, while patients in Irinotecan had a higher risk of experiencing maximum grade 3-4 (15.6% vs 7.6%, respectively; p < 0.001), patients in both groups experienced similar severity over time (adverse effect load = 0.102 and 0.096, respectively; p = 0.26), suggesting that patients in Oxaliplatin experienced a lower-grade but more persistent nausea/vomiting. For neutropenia/leukopenia, more patients in Irinotecan experienced their maximum grade for the first time early in the treatment compared to patients in Oxaliplatin (67.9% vs 41.7%; p < 0.001), regardless of maximum grade. Longitudinal information can help compare treatments or guide clinicians on choosing appropriate interventions for low-grade but persistent adverse event or early adverse event onset.

CONCLUSION

We developed an adverse event reporting method that provides clinically relevant information about treatment toxicity by incorporating two longitudinal adverse event metrics to the traditional maximum grade approach. Future research should establish clinical benchmarks for metrics included in this adverse event reporting method.

Understanding Health-Related Quality of Life in Patients with Mantle Cell Lymphoma

Mantle cell lymphoma (MCL) is a unique lymphoma that is heterogeneous in its clinical course, and lacks consensus treatment approaches. It is often treated with immunochemotherapy at diagnosis and chronic therapies in relapse. Despite significant advances in therapy, MCL remains incurable. Maintaining patients' health-related quality of life (HRQOL) is an important treatment goal. Assessment of HRQOL elucidates the impact of an illness and its treatment on patients' lives. This review highlights the relevance of HRQOL assessment in MCL, evaluates existing evidence, current knowledge gaps and challenges in HRQOL assessment, and defines future directions for improving HRQOL evaluation in MCL patients.

Longitudinal Toxicity over Time (ToxT) analysis to evaluate tolerability: a case study of lenalidomide in the CALGB 50401 (Alliance) trial

Evaluation of tolerability is increasingly relevant for patients with haematological malignancies treated with chronically administered therapies. Adverse events from these agents might affect the ability of patients to tolerate treatment over time. Conventional toxicity tables that include the incidence of high-grade adverse events, defined by the Common Terminology Criteria for Adverse Events, do not provide information on the time profile of these adverse events or reflect the continuous, lower grade symptomatic toxicities that are particularly relevant to treatment tolerability for patients living with indolent disease. Modern approaches to the evaluation and reporting of toxicity that capture the tolerability of treatment to the patient are imperative. In this Viewpoint, we present a focused, pilot, and longitudinal Toxicity over Time analysis of adverse events from lenalidomide and lenalidomide with rituximab in patients with follicular lymphoma treated in the CALGB 50401 (Alliance; NCT00238238) trial to define the trajectory of adverse events and quantify the burden of continuous, low-grade events. Toxicity over Time analyses provided clinically relevant descriptions of neutropenia and fatigue trajectories caused by lenalidomide that were not identified by standard analysis of the maximum grade events defined by the Common Terminology Criteria for Adverse Events. Systematic, rigorous incorporation of patient-reported outcomes in clinical trials will be crucial to our understanding of the tolerability of chronically administered therapies in patients with haematological malignancies.

Dose-finding designs for cumulative toxicities using multiple constraints

This article addresses the concern regarding late-onset dose-limiting toxicities (DLT), moderate toxicities below the threshold of a DLT and cumulative toxicities that may lead to a DLT, which are mostly disregarded or handled in an ad hoc manner when determining the maximum tolerated dose (MTD) in dose-finding cancer clinical trials. An extension of the Time-to-Event Continual Reassessment Method (TITE-CRM) which allows for the specification of toxicity constraints on both DLT and moderate toxicities, and can account for partial information is proposed. The method is illustrated in the context of an Erlotinib dose-finding trial with low DLT rates, but a significant number of moderate toxicities leading to treatment discontinuation in later cycles. Based on simulations, our method performs well at selecting the dose level that satisfies both the DLT and moderate-toxicity constraints. Moreover, it has similar probability of correct selection compared to the TITE-CRM when the true MTD based on DLT only and the true MTD based on grade 2 or higher toxicities alone coincide, but reduces the probability of recommending a dose above the MTD.

An adaptive multi-stage phase I dose-finding design incorporating continuous efficacy and toxicity data from multiple treatment cycles

Phase I designs traditionally use the dose-limiting toxicity (DLT), a binary endpoint from the first treatment cycle, to identify the maximum-tolerated dose (MTD) assuming a monotonically increasing relationship between dose and efficacy. In this article, we establish a general framework for a multi-stage adaptive design where we jointly model a continuous efficacy outcome and continuous/quasi-continuous toxicity endpoints from multiple treatment cycles. The normalized Total Toxicity Profile (nTTP) is used as an illustration for quasi-continuous toxicity endpoints, and we replace DLT with nTTP to take into account multiple grades and types of toxicities. In addition, the proposed design accommodates non-monotone dose-efficacy relationships, and longitudinal toxicity data in effort to capture the adverse events from multiple cycles. Stage 1 of our design uses toxicity data to perform dose-escalation and identify a set of initially allowable (safe) doses; stage 2 of our design incorporates an efficacy outcome to update the set of allowable doses for each new cohort and randomizes the new cohort of patients to the allowable doses with emphasis towards those with higher predicted efficacy. Stage 3 uses all data from all treated patients at the end of the trial to make final recommendations. Simulations showed that the design had a high probability of making the correct dose selection and good overdose control across various dose-efficacy and dose-toxicity scenarios. In addition, the proposed design allows for early termination when all doses are too toxic. To our best knowledge, the proposed dual-endpoint dose-finding design is the first such study to incorporate multiple cycles of toxicities and a continuous efficacy outcome.

National Cancer Institute Breast Cancer Steering Committee Working Group Report on Meaningful and Appropriate End Points for Clinical Trials in Metastatic Breast Cancer

PURPOSE

To provide evidence-based consensus recommendations on choice of end points for clinical trials in metastatic breast cancer, with a focus on biologic subtype and line of therapy.

METHODS

The National Cancer Institute Breast Cancer Steering Committee convened a working group of breast medical oncologists, patient advocates, biostatisticians, and liaisons from the Food and Drug Administration to conduct a detailed curated systematic review of the literature, including original reports, reviews, and meta-analyses, to determine the current landscape of therapeutic options, recent clinical trial data, and natural history of four biologic subtypes of breast cancer. Ongoing clinical trials for metastatic breast cancer in each subtype also were reviewed from ClinicalTrials.gov for planned primary end points. External input was obtained from the pharmaceutic/biotechnology industry, real-world clinical data specialists, experts in quality of life and patient-reported outcomes, and combined metrics for assessing magnitude of clinical benefit.

RESULTS

The literature search yielded 146 publications to inform the recommendations from the working group.

CONCLUSION

Recommendations for appropriate end points for metastatic breast cancer clinical trials focus on biologic subtype and line of therapy and the magnitude of absolute and relative gains that would represent meaningful clinical benefit.

Estimating global treatment toxicity burden from adverse-event data

BACKGROUND

A summary measure that reflects the global toxicity burden of a treatment is essential for comparing therapies. Current toxicity summaries are ad hoc and do not distinguish among the severities and types of toxicities. Here a clinically feasible method for estimating the toxicity burden, based on a prospective evaluation of the toxicity profile of a randomized clinical trial of 746 prostate cancer patients conducted by SWOG, is proposed.

METHODS

For 308 patients who experienced severe toxicities, 2 physicians randomly selected from 14 physicians evaluated each toxicity profile and assigned a visual analogue scale score (0-10) based on their impression of the global burden of toxicities. With mixed-effects models, severity scores and a 10-point toxicity burden score (TBS) were derived from 27 predictors accounting for severe (grade 3) and life-threatening (grade 4) toxicities for each organ class of the Common Terminology Criteria for Adverse Events.

RESULTS

For most organ classes, grade 3 toxicities had a TBS of 4.14 (95% confidence interval [CI], 3.65-4.63), but infections, cardiovascular events, and pulmonary events had a higher TBS with differences of 0.87 (95% CI, 0.53-1.21), 0.88 (95% CI, 0.51-1.25), and 0.73 (95% CI, 0.22-1.24), respectively. Moreover, most grade 4 events had a higher TBS than grade 3 events, except for hemorrhaging, pain, metabolic events, and musculoskeletal events. The intrarater and interrater correlations were 0.91 and 0.59, respectively.

CONCLUSIONS

The burden of toxicity grades differs with toxicity types. A TBS provides a toxicity burden summary that incorporates physicians' perspectives and differentiates between severe and life-threatening toxicities and organ classes. Cancer 2018;124:858-64. © 2017 American Cancer Society.

Early Phase Clinical Trial Designs - State of Play and Adapting for the Future

The process of anti-cancer drug development is complex, with high attrition rates. Factors that may optimise this process include well-constructed and relevant pre-clinical testing and use of biomarkers for patient selection. However, the design of early phase clinical trials will probably play a vital role in both the robust clinical investigation of new targeted therapies and in streamlining drug development. In this overview, we assess current concepts in phase I clinical trials, highlighting issues and opportunities to improve their meaningfulness. The particular challenge of how to design combination trials is addressed, with focus on the potential of new adaptive and model-based designs.

Long-Term Quality of Life of Retroperitoneal Sarcoma Patients Treated with Pre-Operative Radiotherapy and Surgery

Purpose: Retroperitoneal sarcomas (RPS) are connective tissue cancers that are often large and anatomically in close proximity to critical and radiation-sensitive normal structures and organs within the abdomen and pelvis. The management of RPS may include preoperative radiotherapy (RT) and surgery. We aimed to examine how treatment-related toxicities affect patient quality of life (QOL). 

Methods and materials: Within two prospective cohort studies, 48 RPS patients who were treated with preoperative RT from 1998-2012 were recruited and assessed for QOL (EORTC-QLQ-C30) and to determine toxicities potentially related to RT and surgery (graded using CTCAE V.4). Baseline and prospective QOL was available for 11 patients. In the other 37 patients, prospective data were obtained at different time points during their follow-up. Unless stated otherwise, all scores refer to the global QOL subscale.

Results: The patients' median age was 57 (38-82) and RT was administered to a median dose of 45 Gy (41.4-50.4). The median maximum tumor dimension was 16.0 cm (5.7-28) and the majority (35/48) were liposarcomas. The mean pre-RT QOL was 48.5/100. At one month post-RT, the mean QOL improved to 54.2; however, the mean diarrhea symptom scale worsened from baseline (78.3 vs. 18.2, p<0.001). Correspondingly, 54% of patients had gastrointestinal toxicities (92% G1-2 and 8% G3) by the end of RT. At 36 months post-RT, 88% of patients had chronic toxicities (19% G3). RPS patients who survived and are free of recurrence ≥ 36 months had significantly (mean: 75.0; p=0.001) better QOL than at diagnosis. The number of toxicities was significantly (p=0.001) associated with QOL. RT dose, tumor size, patient age, and patient gender were not associated with 36-month QOL. 

Conclusions: Treatment toxicities seem to contribute to QOL recovery during the first 36 months. QOL at 36 months was better than at diagnosis.

Towards using a full spectrum of early clinical trial data: a retrospective analysis to compare potential longitudinal categorical models for molecular targeted therapies in oncology

Following the pattern of phase I clinical trials for cytotoxic drugs, dose-finding clinical trials in oncology of molecularly targeted agents (MTA) aim at determining the maximum tolerated dose (MTD). In classical phase I clinical trials, MTD is generally defined by the number of patients with short-term major treatment toxicities (usually called dose-limiting toxicities, DLT), occurring during the first cycle of study treatment (e.g. within the first 3weeks of treatment). However, S. Postel-Vinay (2011) highlighted that half of grade 3 to 4 toxicities, usually considered as DLT, occur after the first cycle of MTA treatment. In addition, MTAs could induce other moderate (e.g. grade 2) toxicities which could be taken into account depending on their clinical importance, chronic nature and duration. Ignoring these late toxicities may lead to an underestimation of the drug toxicity and to wrong dose recommendations for phase II and III clinical trials. Some methods have been proposed, such as the time-to-event continuous reassessment method (Cheung 2000 and Mauguen 2011), to take into account the late toxicities. We suggest approaches based on longitudinal models (Doussau 2013). We compare several models for longitudinal data, such as transitional or marginal models, to take into account all relevant toxicities occurring during the entire length of the patient treatment (and not just the events within a predefined short-term time-window). These models allow the statistician to benefit from a larger amount of safety data which could potentially improve that accuracy in MTD assessment.

Phase 1 adaptive dose-finding study of neoadjuvant gemcitabine combined with radiation therapy for patients with high-risk extremity and trunk soft tissue sarcoma

BACKGROUND

This study was performed to determine the maximum tolerated dose (MTD) of gemcitabine given concurrently with preoperative, fixed-dose external-beam radiation therapy (EBRT) for patients with resectable, high-risk extremity and trunk soft tissue sarcoma (STS).

METHODS

Gemcitabine was administered on days 1, 8, 22, 29, 43, and 50 with EBRT (50 Gy in 25 fractions over 5 weeks). The gemcitabine MTD was determined with a toxicity severity weight method (TSWM) incorporating 6 toxicity types. The TSWM is a Bayesian procedure that choses each cohort's dose to have a posterior mean total toxicity burden closest to a predetermined clinician-defined target. Clinicopathologic and outcome data were also collected.

RESULTS

Thirty-six patients completed the study. According to the TSWM, the gemcitabine MTD was 700 mg/m(2). At this dose level, 4 patients (24%) experienced grade 4 toxicity; no toxicity-related deaths occurred. All tumors were resected with microscopically negative margins. Pathologic responses of >90% tumor necrosis were achieved in 17 patients (47%); 14 (39%) had complete responses. With a median follow-up of 6.2 years, the 5-year locoregional recurrence-free survival, distant metastasis-free survival, and overall survival rates were 85%, 80%, and 86%, respectively.

CONCLUSIONS

The TSWM combines data from qualitatively different toxicities and can be used to determine the MTD for a drug given as part of a multimodality treatment. Neoadjuvant gemcitabine plus radiation therapy is feasible and safe in patients with high-risk extremity and trunk STS. Major pathologic responses can be achieved, and after complete resection, long-term clinical outcomes are encouraging.

Simple benchmark for complex dose finding studies

While a general goal of early phase clinical studies is to identify an acceptable dose for further investigation, modern dose finding studies and designs are highly specific to individual clinical settings. In addition, as outcome-adaptive dose finding methods often involve complex algorithms, it is crucial to have diagnostic tools to evaluate the plausibility of a method's simulated performance and the adequacy of the algorithm. In this article, we propose a simple technique that provides an upper limit, or a benchmark, of accuracy for dose finding methods for a given design objective. The proposed benchmark is nonparametric optimal in the sense of O'Quigley et al. (2002, Biostatistics 3, 51-56), and is demonstrated by examples to be a practical accuracy upper bound for model-based dose finding methods. We illustrate the implementation of the technique in the context of phase I trials that consider multiple toxicities and phase I/II trials where dosing decisions are based on both toxicity and efficacy, and apply the benchmark to several clinical examples considered in the literature. By comparing the operating characteristics of a dose finding method to that of the benchmark, we can form quick initial assessments of whether the method is adequately calibrated and evaluate its sensitivity to the dose-outcome relationships.

Adaptive designs for dual-agent phase I dose-escalation studies

Anticancer agents used in combination are fundamental to successful cancer treatment, particularly in a curative setting. For dual-agent phase I trials, the goal is to identify drug doses and schedules for further clinical testing. However, current methods for establishing the recommended phase II dose for agents in combination can fail to fully explore drug interactions. With increasing numbers of anticancer drugs requiring testing, new adaptive model-based trial designs that improve on current practice have been proposed, although uptake has been minimal. We describe the methods available and discuss some of the opportunities and challenges faced in dual-agent phase I trials, as well as giving examples of trials in which adaptive designs have been implemented successfully. Improving the design and execution of phase I trials of drug combinations critically relies on collaboration between the statistical and clinical communities to facilitate the implementation of adaptive, model-based designs.