In October 2017, the FDA granted regular approval to axicabtagene ciloleucel, a CD19-directed chimeric antigen receptor (CAR) T-cell therapy, for treatment of adult patients with relapsed or refractory large B-cell lymphoma after two or more lines of systemic therapy. Efficacy was based on complete remission (CR) rate and duration of response (DOR) in 101 adult patients with relapsed or refractory large B-cell lymphoma (median 3 prior systemic regimens) treated on a single-arm trial. Patients received a single infusion of axicabtagene ciloleucel, preceded by lymphodepleting chemotherapy with cyclophosphamide and fludarabine. The objective response rate per independent review committee was 72% [95% confidence interval (CI), 62-81], with a CR rate of 51% (95% CI, 41-62). With a median follow-up of 7.9 months, the median DOR was not reached in patients achieving CR (95% CI, 8.1 months; not estimable, NE), whereas patients with partial remission had an estimated median DOR of 2.1 months (95% CI, 1.3-5.3). Among 108 patients evaluated for safety, serious adverse reactions occurred in 52%. Cytokine release syndrome and neurologic toxicities occurred in 94% and 87% of patients, respectively, leading to implementation of a risk evaluation and mitigation strategy.

Tisagenlecleucel (Kymriah; Novartis Pharmaceuticals) is a CD19-directed genetically modified autologous T-cell immunotherapy. On August 30, 2017, the FDA approved tisagenlecleucel for treatment of patients up to 25 years of age with B-cell precursor acute lymphoblastic leukemia (ALL) that is refractory in second or later relapse. Approval was based on the complete remission (CR) rate, durability of CR, and minimal residual disease (MRD) <0.01% in a cohort of 63 children and young adults with relapsed or refractory ALL treated on a single-arm trial (CCTL019B2202). Treatment consisted of fludarabine and cyclophosphamide followed 2 to 14 days later by a single dose of tisagenlecleucel. The CR rate was 63% (95% confidence interval, 50%-75%), and all CRs had MRD <0.01%. With a median follow-up of 4.8 months, the median duration of response was not reached. Cytokine release syndrome (79%) and neurologic events (65%) were serious toxicities reported in the trial. With implementation of a Risk Evaluation and Mitigation Strategy, the benefit-risk profile was considered acceptable for this patient population with such resistant ALL. A study of safety with 15 years of follow-up is required as a condition of the approval. See related commentary by Geyer, p. 1133 Nonclinical Pharmacology and Toxicology Nonclinical safety studies were conducted with lentivirustransduced T cells prepared from healthy donors and patients in

One of the major problems in the analysis of clinical trials is missing data caused by patients dropping out before study completion. The issue of missing data can result in biased treatment comparisons and can impact the interpretation of study results. Since the missing data mechanism is unknown and unverifiable in most situations, regulatory agencies often request various sensitivity analyses for handling missing data to evaluate the robustness of study results. This article discusses methods used to handle missing data in medical device clinical trials, focusing on tipping-point analysis as a general approach for the assessment of missing data impact. Tipping points are outcomes that result in a change of study conclusion. Such outcomes can be conveyed to clinical reviewers to determine if they are implausibly unfavorable. The analysis aids clinical reviewers in making judgment regarding treatment effect in the study. Three examples with a reasonably representative range of missing data rate are included to illustrate the methods referred.

There is considerable interest among pharmaceutical and other medical product developers in adaptive clinical trials, in which knowledge learned during the course of a trial affects ongoing conduct or analysis of the trial. When the FDA released a draft Guidance document on adaptive design clinical trials in early 2010, expectations were high that it would lead to an increase in regulatory submissions involving adaptive design features, particularly for confirmatory trials. A 6-year (2008-2013) retrospective survey was performed within the Center for Biologics Evaluation and Research (CBER) at the FDA to gather information regarding the submission and evaluation of adaptive design trial proposals. We present an up-to-date summary of adaptive design proposals seen in CBER and provide an overview of our experiences. We share our concerns regarding the statistical issues and operational challenges raised during the review process for adaptive design trials. We also provide general recommendations for developing proposals for such trials. Our motivation in writing this paper was to encourage the best study design proposals to be submitted to CBER. Sometimes these can be adaptive, and sometimes a simpler design is most efficient.

Minimization, a dynamic allocation method, is gaining popularity especially in cancer clinical trials. Aiming to achieve balance on all important prognostic factors simultaneously, this procedure can lead to a substantial reduction in covariate imbalance compared with conventional randomization in small clinical trials. While minimization has generated enthusiasm, some controversy exists over the proper analysis of such a trial. Critics argue that standard testing methods that do not account for the dynamic allocation algorithm can lead to invalid statistical inference. Acknowledging this limitation, the International Conference on Harmonization E9 guideline suggests that 'the complexity of the logistics and potential impact on analyses be carefully evaluated when considering dynamic allocation'. In this article, we investigate the proper analysis approaches to inference in a minimization design for both continuous and time-to-event endpoints and evaluate the validity and power of these approaches under a variety of scenarios both theoretically and empirically. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Immune checkpoint inhibitors (ICIs) such as the anti-PD-1 antibody Nivolumab, achieve remarkable clinical efficacy in patients with late stage cancers. However, only a small subset of patients benefit from this therapy. Numerous clinical trials are underway testing whether combining ICIs with other anticancer therapies can increase this response rate. For example, anti-PD-1/PD-L1 therapy combined with MAP kinase inhibition using BRAF inhibitors (BRAFi) and/or MEK inhibitors (MEKi) are in development for treatment of late stage melanomas. However, the benefits and underlying mechanisms of these combinatorial therapies remain unclear. In the current study, we assess the effects of MAPK inhibition on Nivolumab-induced T cell responses. Using an in vitro mixed lymphocyte reaction assay, we demonstrate that Nivolumab-induced T cell activation is highly heterogeneous. While BRAFi inhibits Nivolumab-induced cytokine production, T cell proliferation, activation markers (CD69, CD25), and Granzyme B in a substantial proportion of donor pairs, a small subset of donor pairs shows an additive effect. MEKi alone significantly inhibits Nivolumab-induced T cell activation; the addition of BRAFi significantly enhances this inhibitory effect. Mechanistically, the effects of BRAFi and/or MEKi on Nivolumab-induced T cell activation may be due to alteration of the activation of the AKT and T cell receptor (TCR) signaling pathways. Our results suggest that MAPK inhibition may not provide a clinical benefit for most melanoma patients being treated with anti-PD-1 therapy.

The absence of reliable, robust, and non-invasive biomarkers for anti- Programmed cell death protein 1 (PD-1) immunotherapy is an urgent unmet medical need for the treatment of cancer patients. No predictive biomarkers have been established based on the direct assessment of T cell functions, the primary mechanism of action of anti-PD-1 therapy. In this study, we established a model system to test T cell functions modulated by Nivolumab using anti-CD3 monoclonal antibody (mAb)-stimulated peripheral blood mononuclear cells (PBMCs), and characterized T cell functions primarily based on the knowledge gained from retrospective observations of patients treated with anti-PD-1 immunotherapy. During a comprehensive cytokine profile assessment to identify potential biomarkers, we found that Nivolumab increases expression of T helper type 1 (Th1) associated cytokines such as interferon-γ (IFN-γ) and interleukin-2 (IL-2) in a subset of donors. Furthermore, Nivolumab increases production of Th2, Th9, and Th17 associated cytokines, as well as many proinflammatory cytokines such as IL-6 in a subset of donors. Conversely, Nivolumab treatment has no impact on T cell proliferation, expression of CD25, CD69, or Granzyme B, and only modestly increases in the expansion of regulatory T cells. Our results suggest that assessment of cytokine production using a simple PBMC-based T cell functional assay could be used as a potential predictive marker for anti-PD-1 immunotherapy.

The approval of tisagenlecleucel and axicabtagene ciloleucel in 2017 marked a milestone in the development of oncology therapies. Since 2017, the breakthrough in treatment or even cure of previously intractable diseases represented by this new class of cancer treatments has continued with subsequent chimeric antigen receptor T (CAR T)-cell approvals. To date, the US Food and Drug Administration has approved five autologous CAR T-cell products for seven indications. A feature of autologous CAR T-cell products that differentiates them from traditional oncology drugs is that they need to be manufactured specifically for each patient. This feature has implications in study design, statistical analyses, and interpretation of study results. In this article, we share our experiences in the statistical review of CAR T-cell products and provide considerations for the design and statistical analyses of CAR T-cell trials. We also describe how the newly adopted estimand framework for clinical trials can help clarify nuanced issues in CAR T-cell trial design.