Mandrekar, Sumithra J.

7503 Background: Prior to use of novel targeted agents for CLL, debate existed regarding the best chemoimmunotherapy regimen to build upon in patients (pts) with non-del(11q) disease. The role of lenalidomide (L) was also not defined. CALGB 10404 was a randomized phase 2 study addressing these questions. Methods: Pts with untreated CLL requiring therapy were randomized to treatment with fludarabine + rituximab (FR), FR + 6 monthly consolidative treatments of L (5 mg days 1-21/28 x 1 then 10 mg days 1-21/28 x 5) (FR+L), or FR + cyclophosphamide (FCR). Based on pretreatment central interphase cytogenetic screening, pts with del(11q22.3) in at least 20% of cells were excluded from the primary analysis, testing whether 2-year progression-free survival (PFS) rate was improved in non-del(11q) pts within each arm. A target accrual of 103 non-del(11q) pts per arm provided at least 84% power to detect an increase in 2-year PFS rate from 60% to 73%; the critical value was 69% using a single stage design and type I error rate of 4%. Results: A total of 342 non-del(11q) CLL pts were randomized to treatment with FR (n = 123), FR+L (n = 109), or FCR (n = 110). Baseline characteristics were similar across arms. Two-year PFS rates with exact 90% CIs were 64% (57-71%) (FR), 71% (63-78%) (FR+L), and 74% (66-80%) (FCR). Median PFS was significantly shorter with FR compared to FR+L (p = 0.03) and FCR (p < 0.01): 43 (95% CI: 33-50), 66 (95% CI: 45-not reached), and 78 (95% CI: 58-not reached) months respectively. Median overall survival (OS) has not been reached for any arm. OS at 1, 2, and 3 years was similar across arms, although there was a plateau in OS with no events beyond 41 months in the FR+L arm, different from FR/FCR where events continued to occur. The most common adverse events were cytopenias and infections. Conclusions: FR+L and FCR met the protocol defined primary endpoint. FR+L extended PFS relative to FR and a plateau in survival differentiated this arm from the FR/FCR arms. Future studies comparing FR+L to FCR or incorporating L into other novel treatment regimens are justified. Support: U10CA180821, U10CA180882, Celgene. Clinical trial information: NCT00602459.

Background/aims The goal of this article is to illustrate the utility of multi-state models in cancer clinical trials. Our specific aims are to describe multi-state models and how they differ from standard survival methods, to illustrate how multi-state models can facilitate deeper understanding of the treatment effect on multiple paths along the disease process that patients could experience in cancer clinical trials, to explain the differences between multi-state models and time-dependent Cox models, and to briefly describe available software to conduct such analyses. Methods Data from 717 newly diagnosed acute myeloid leukemia patients who enrolled in the CALGB 10603 trial were used as an illustrative example. The current probability-in-state was estimated using the Aalen–Johansen estimator. The restricted mean time in state was calculated as the area under the probability-in-state curves. Cox-type regression was used to evaluate the effect of midostaurin on the various clinical paths. Simulation was conducted using a newly constructed shiny application. All analyses were performed using the R software. Results Multi-state model analyses of CALGB 10603 suggested that the overall improvement in survival with midostaurin seen in the primary analysis possibly resulted from a higher complete remission rate in combination with a lower risk of relapse and of death after complete remission in patients treated with midostaurin. Simulation results, in a three-state illness-death without recovery model, demonstrate that multi-state models and time-dependent Cox models evaluate treatment effects from different frameworks. Conclusion Multi-state models allow detailed evaluation of treatment effects in complex clinical trial settings where patients can experience multiple paths between study enrollment and the final outcome. Multi-state models can be used as a complementary tool to standard survival analyses to provide deeper insights to the effects of treatment in trial settings with complex disease process.

Key PointsBrief lenalidomide consolidation after chemoimmunotherapy is acceptably tolerated and extends PFS and OS in CLL. FCR has superior efficacy compared with FR chemoimmunotherapy for CLL.

SummaryThe landscape of oncology drug development has recently changed with the emergence of molecularly targeted agents and immunotherapies. These new therapeutic agents appear more likely to induce multiple low or moderate grade toxicities rather than dose limiting toxicities. Various model-based dose finding designs and toxicity severity scoring systems have been proposed to account for toxicity grades, but they are difficult to implement because of the use of complicated dose–toxicity models and the requirement to refit the model at each decision of dose escalation and de-escalation. We propose a generalized Bayesian optimal interval design, gBOIN, that accommodates various existing toxicity grade scoring systems under a unified framework. As a model-assisted design, gBOIN derives its optimal decision rule on the basis of the exponential family of distributions but is carried out in a simple way as the algorithm-based design: its decision of dose escalation and de-escalation involves only a simple comparison of the sample mean of the end point with two prespecified dose escalation and de-escalation boundaries. No model fitting is needed. We show that gBOIN has the desirable finite property of coherence and a large sample property of consistency. Numerical studies show that gBOIN yields good performance that is comparable with or superior to that of some existing, more complicated model-based designs. A Web application for implementing gBOIN is freely available from http://www.trialdesign.org.

Background: Phase I trials are designed to determine the safety, tolerability, and recommended phase 2 dose of therapeutic agents for subsequent testing. The dose-finding paradigm has thus traditionally focused on identifying the maximum tolerable dose of an agent or combination therapy under the assumption that there is a non-decreasing relationship between dose–toxicity and dose–efficacy. The dose is typically determined based on the probability of severe toxicity observed during the first treatment cycle. A novel endpoint, the total toxicity profile, was previously developed to account for the multiple toxicity types and grades experienced in the first cycle. More recently, this was extended to a repeated measures design based on the total toxicity profile to account for longitudinal toxicities over multiple treatment cycles in the absence of within-patient correlation. Methods: In this work, we propose to extend the design in the presence of within-patient correlation. Furthermore, we provide a framework to detect a toxicity time trend (toxicity increasing, decreasing, or stable) over multiple treatment cycles. We utilize a linear mixed model in the Bayesian framework, with the addition of Bayesian risk functions for decision-making in dose assignment. Results: The performance of this design was evaluated using simulation studies and real data from a phase I trial. We demonstrated that using available toxicity data from all cycles of treatment improves the accuracy of maximum tolerated dose identification and allows for the detection of a time trend. The performance is consistent regardless of the strength of the within-patient correlation. In addition, the use of a quasi-continuous total toxicity profile score significantly increased the power to detect time trends compared to when binary data only were used. Conclusion: The increased interest in molecularly targeted agents and immunotherapies in oncology necessitates innovative phase I study designs. Our proposed framework provides a tool to tackle some of the challenges presented by these novel agents, specifically through the ability to understand patterns of toxicity over time, which is important in the cases of cumulative or late toxicities.

Abstract The primary objective of phase I oncology trials is to find the MTD. The 3+3 design is easy to implement but performs poorly in finding the MTD. A newer design, such as the modified toxicity probability interval (mTPI) design, provides better accuracy to identify the MTD but tends to overdose patients. We propose the keyboard design, an intuitive Bayesian design that conducts dose escalation and de-escalation based on whether the strongest key, defined as the dosing interval that most likely contains the current dose, is below or above the target dosing interval. The keyboard design can be implemented in a simple way, similar to the traditional 3+3 design, but provides more flexibility for choosing the target toxicity rate and cohort size. Our simulation studies demonstrate that compared with the 3+3 design, the keyboard design has favorable operating characteristics in terms of identifying the MTD. Compared with the mTPI design, the keyboard design is safer, with a substantially lower risk of treating patients at overly toxic doses, and has the better precision to identify the MTD, thereby providing a useful upgrade to the mTPI design. Software freely available at http://www.trialdesign.org facilitates the application of the keyboard design. Clin Cancer Res; 23(15); 3994–4003. ©2017 AACR. See related commentary by Paoletti et al., p. 3977