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Summary The randomized phase III ELOQUENT-2 study (NCT01239797) evaluated the efficacy and safety of elotuzumab + lenalidomide/dexamethasone (ELd) versus lenalidomide/dexamethasone (Ld) in relapsed/refractory multiple myeloma. ELd reduced the risk of disease progression/death by 30% versus Ld (hazard ratio [HR] 0·70). Median time from diagnosis was 3·5 years. We present extended 3-year follow-up data. Endpoints included progression-free survival (PFS), overall response rate (ORR) and interim overall survival (OS). Exploratory post-hoc analyses included impact of time from diagnosis and prior lines of therapy on PFS, and serum M-protein dynamic modelling. ORR was 79% (ELd) and 66% (Ld) (P = 0·0002). ELd reduced the risk of disease progression/death by 27% versus Ld (HR 0·73; P = 0·0014). Interim OS demonstrated a trend in favour of ELd (P = 0·0257); 1-, 2- and 3-year rates with ELd versus Ld were: 91% versus 83%, 73% versus 69% and 60% versus 53%. In patients with ≥ median time from diagnosis and one prior therapy, ELd resulted in a 53% reduction in the risk of progression/death versus Ld (HR 0·47). Serum M-protein dynamic modelling showed slower tumour regrowth with ELd. Adverse events were comparable between arms. ELd provided a durable and clinically relevant improvement in efficacy, with minimal incremental toxicity.

Purpose A Topoisomerase 1 (Top1) poison is frequently included in the treatment regimens for metastatic colorectal cancer (mCRC). However, no predictive biomarkers for Top1 poisons are available. We here report a study on the TOP1 gene copy number in CRC patients and its association with patient prognosis and tumor cell proliferation. Experimental design The study included TOP1 and CEN-20 fluorescence in situ hybridization ( FISH) analyses on formalin fixed paraffin embedded (FFPE) tissue sections from 154 stage III CRC chemonaive patients. The frequencies of aberration in the TOP1 gene copy number, the CEN-20 copy number and the TOP1 /CEN-20 ratio were analyzed and associated with overall survival (OS), time to recurrence (TTR) and in a subgroup analysis of rectal cancer patients only with time to local recurrence (LR in RC). Moreover, the TOP1 and CEN-20 copy numbers were correlated with the tumor Ki67 proliferation index. Results 35.7% of the tumors had an increased TOP1 copy number above 4 n gene copies per cell and 28.6% and 9.7% had a TOP1 /CEN-20 ratio ≥1.5 or ≥2.0, respectively. The TOP1 copy number and the TOP1 /CEN-20 ratios were separately added into multivariate analyses as continuous variables, in which also age, gender, primary tumor location and Ki67 status were added as covariates. In contrast to the TOP1 /CEN-20 ratio, the TOP1 copy number was significantly associated with OS (HR: 0.62; 95% CI: 0.42–0.90; p = 0.01). Neither the TOP1 copy number nor the ratio was significantly associated with TTR and only the TOP1 /CEN-20 ratio was significantly associated with LR in RC (HR: 0.25; 95% CI: 0.08–0.83; p = 0.02). No significant correlation was found between the TOP1 copy number and proliferation, while a weak and inverse correlation between the CEN-20 copy number and proliferation was observed. Conclusions This study showed that increased TOP1 gene copy numbers are frequent findings in cancer cells in stage III CRC tumors but unrelated to the proliferative status of the tumors. The association with prognosis is important to consider when planning and analyzing future studies investigating TOP1 as a potential predictive biomarker for Top1 poisons.

Evaluation (or assessment)-time bias can arise in oncology trials that study progression-free survival (PFS) when randomized groups have different patterns of timing of assessments. Modelling or computer simulation is sometimes used to explore the extent of such bias; valid results require building such simulations under realistic assumptions concerning the timing of assessments. This paper considers a trial that used a logrank test where computer simulations were based on unrealistic assumptions that severely overestimated the extent of potential bias. The paper shows that seemingly small differences in assumptions can lead to dramatic differences in the apparent operating characteristics of logrank tests.

Indoleamine 2,3‐dioxygenase 1 (IDO1) is a key enzyme associated with immunomodulation through its regulation of the tryptophan‐kynurenine (Kyn) pathway in advanced cancers, including metastatic renal cell carcinoma (mRCC). However, the failure of IDO1 inhibitors when used in combination with immune checkpoint inhibitors (ICIs), as observed in clinical trials, raises a number of questions. This study aimed to investigate the association of tryptophan 2,3‐dioxygenase (TDO) and IDO1 with cancer development and resistance to immunotherapy in patients with RCC. In our analysis of RCC tissue samples, tissue Kyn levels were elevated in advanced‐stage RCC and correlated well with TDO expression levels in RCC tumor cells. In patients with mRCC, TDO rather than IDO1 was expressed in RCC tumor cells, showing a strong association with Kyn expression. Furthermore, immunohistochemical staining of TDO was strongly associated with the staining intensity of forkhead box P3, as well as ICI therapy response and survival in patients with mRCC. Our study is the first to show that TDO expression in tumor tissues is associated with progression and survival, confirming its potential as a predictive biomarker of primary resistance to immunotherapy in patients with mRCC. Our findings suggest that strategies aimed at inhibiting TDO, rather than IDO1, in combination with ICI therapy may aid in the control of mRCC progression., This study aimed to investigate the association of tryptophan 2,3‐dioxygenase (TDO) and indoleamine 2,3‐dioxygenase 1 (IDO1) with cancer development and resistance to immunotherapy in renal cell carcinoma (RCC) patients. Our results suggest that TDO expression in tumor cells is associated with kynurenine accumulation, progression, and survival, confirming its potential as a predictive biomarker of primary resistance to immunotherapy in RCC patients. We believe that our study makes a significant contribution to the literature because, to the best of our knowledge, it is the first to show that the accumulation of kynurenine induced by TDO in tumor tissues is associated with progression and survival. Further, we believe that this paper will be of interest to the readership of your journal because our results strongly recommend the testing of specific TDO inhibitors or dual inhibitors of IDO1 and TDO with ICI treatment in future preclinical and clinical trials focusing on mRCC.

To the Editor: In the Letter of Response from Watkins and Rukazenkov (J Cell Mol Med 2010), the paragraph starting with ‘It is important to note…’ summarizes potential sources of error in our pooled analysis. We were aware of the points correctly raised by Watkins and Rukazenkov when conducting our analysis and we agree that it is important to see the limitations of the performed study; these have been carefully detailed in the discussion part of the paper. The pooled analysis strived for a concise summary of the published data and we acknowledge that simplifying assumptions like the exponential distribution were indispensible to accomplish this. We would like to point out that the simplifications were applied to all treatments in an identical way and that a weighted pooled analysis was performed to adjust for different sample sizes. That this process would favour only one treatment and/or be detrimental to another treatment can therefore be ruled out. In many places within the paper we point to the need to exercise caution when interpreting the presented data, for example, it is repeatedly noted that the conclusions from the pooled analysis only apply to the considered patient pool and may not be readily extrapolated to an unseen patient population. Similarly, the reported P values gained from permutation runs are statistically valid, but apply as any conditional test to the considered patient pool only. Watkins and Rukazenkov refer correctly to randomized clinical trials (RCTs) as the gold standard to evaluate treatments; however, the pooled analysis of published studies provides a relevant view of the accumulated treatment experience in its own right. We consider this summary as a worthwhile contribution to the scientific/medical discussion in the area. Finally, we note that new RCT data have recently become available. The OPTIMAL study, a randomized phase III trial conducted only in patients with epidermal growth factor receptor (EGFR) activating mutation-positive non small cell lung cancer (NSCLC), evaluated first-line platinum doublet chemotherapy versus erlotinib. This study (n = 165) was reported at the ESMO congress in October 2010 and showed a hazard ratio for progression-free survival of 0.16 in favour of the erlotinib-treated patients. The median progression free survival (PFS) of 13.1 months in the erlotinib-treated group and 4.6 in the chemotherapy-treated group further support the results of our pooled analyses.

Recently molecularly targeted agents and immunotherapy have been advanced for the treatment of relapse or refractory cancer patients, where disease progression-free survival or event-free survival is often a primary endpoint for the trial design. However, methods to evaluate two-stage single-arm phase II trials with a time-to-event endpoint are currently processed under an exponential distribution, which limits application of real trial designs. In this paper, we developed an optimal two-stage design, which is applied to the four commonly used parametric survival distributions and a non-parametric logspline distribution. The proposed method has advantages compared to existing methods in that the choice of underlying survival model is more flexible and the power of the study is more adequately addressed. Therefore, the proposed optimal two-stage design can be routinely used for single-arm phase II trial designs with a time-to-event endpoint as a complement to the commonly used Simon’s two-stage design for the binary outcome.

A two-stage enrichment design is a type of adaptive design, which extends a stratified design with a futility analysis on the marker negative cohort at the first stage, and the second stage can be either a targeted design with only the marker positive stratum, or still the stratified design with both marker strata, depending on the result of the interim futility analysis. In this paper we consider the situation where the marker assay and the classification rule are possibly subject to error. We derive the sequential tests for the global hypothesis as well as the component tests for the overall cohort and the marker-positive cohort. We discuss the power analysis with the control of the type-I error rate and show the adverse impact of the misclassification on the powers. We also show the enhanced power of the two-stage enrichment over the one-stage design, and illustrate with examples of the recent successful development of immunotherapy in non-small-cell lung cancer.

In this paper, we derive the joint distribution of progression-free and overall survival as a function of transition probabilities in a multistate model. No assumptions on copulae or latent event times are needed and the model is allowed to be non-Markov. From the joint distribution, statistics of interest can then be computed. As an example, we provide closed formulas and statistical inference for Pearson's correlation coefficient between progression-free and overall survival in a parametric framework. The example is inspired by recent approaches to quantify the dependence between progression-free survival, a common primary outcome in Phase 3 trials in oncology and overall survival. We complement these approaches by providing methods of statistical inference while at the same time working within a much more parsimonious modeling framework. Our approach is completely general and can be applied to other measures of dependence. We also discuss extensions to nonparametric inference. Our analytical results are illustrated using a large randomized clinical trial in breast cancer.

Conventionally, evaluation of a new drug, A, is done in three phases. Phase I is based on toxicity to determine a "maximum tolerable dose" (MTD) of A, phase II is conducted to decide whether A at the MTD is promising in terms of response probability, and if so a large randomized phase III trial is conducted to compare A to a control treatment, C , usually based on survival time or progression free survival time. It is widely recognized that this paradigm has many flaws. A recent approach combines the first two phases by conducting a phase I-II trial, which chooses an optimal dose based on both efficacy and toxicity, and evaluation of A at the selected optimal phase I-II dose then is done in a phase III trial. This paper proposes a new design paradigm, motivated by the possibility that the optimal phase I-II dose may not maximize mean survival time with A. We propose a hybridized design, which we call phase I-II/III, that combines phase I-II and phase III by allowing the chosen optimal phase I-II dose of A to be re-optimized based on survival time data from phase I-II patients and the first portion of phase III. The phase I-II/III design uses adaptive randomization in phase I-II, and relies on a mixture model for the survival time distribution as a function of efficacy, toxicity, and dose. A simulation study is presented to evaluate the phase I-II/III design and compare it to the usual approach that does not re-optimize the dose of A in phase III.

This paper considers methods for estimating the association between progression‐free and overall survival in oncology trials. Copula‐based, nonparametric, and illness‐death model–based methods are reviewed. In addition, the approach based on an underlying illness‐death model is generalized to allow general parametric models. The performance of these methods, in terms of bias and efficiency, is investigated through simulation and also illustrated using data from a clinical trial of treatments for colon cancer. The simulations suggest that the illness‐death model–based method provides good estimates of Kendall's τ across several scenarios. In some situations, copula‐based methods perform well but their performance is sensitive to the choice of copula. The Clayton copula is most appropriate in scenarios, which might realistically reflect an oncology trial, but the use of copula models in practice is questionable.

Progression-free survival is an increasingly popular end point in oncology clinical trials. A complete blinded independent central review (BICR) is often required by regulators in an attempt to reduce the bias in progression-free survival (PFS) assessment. In this paper, we propose a new methodology that uses a sample-based BICR as an audit tool to decide whether a complete BICR is needed. More specifically, we propose a new index, the differential risk, to measure the reading discordance pattern, and develop a corresponding hypothesis testing procedure to decide whether the bias in local evaluation is acceptable. Simulation results demonstrate that our new index is sensitive to the change of discordance pattern; type I error is well controlled in the hypothesis testing procedure, and the calculated sample size provides the desired power. Copyright © 2016 John Wiley & Sons, Ltd.

In oncology clinical trials, progression-free survival (PFS), generally defined as the time from randomization until disease progression or death, has been a key endpoint to support licensing approval. In the U.S. Food and Drug Administration guidance for industry, May 2007, concerning the PFS as the primary or co-primary clinical trial endpoint, it is recommended to have tumor assessments verified by an independent review committee blinded to study treatments, especially in open-label studies. It is considered reassuring about the lack of reader-evaluation bias if treatment effect estimates from the investigators' and independent review committees' evaluations agree. The agreement between these evaluations may vary for subjects with short or long PFS, while there exist no such statistical quantities that can completely account for this temporal pattern of agreements. Therefore, in this paper, we propose a new method to assess temporal agreement between two time-to-event endpoints, while the two event times are assumed to have a positive probability of being identical. This method measures agreement in terms of the two event times being identical at a given time or both being greater than a given time. Overall scores of agreement over a period of time are also proposed. We propose a maximum likelihood estimation to infer the proposed agreement measures using empirical data, accounting for different censoring mechanisms, including reader's censoring (event from one reader dependently censored by event from the other reader). The proposed method is demonstrated to perform well in small samples via extensive simulation studies and is illustrated through a head and neck cancer trial.

Background Epidermal growth factor-tyrosine kinase inhibitors (EGFR-TKIs) are one of the effective medicines in advanced lung adenocarcinoma leptomeningeal metastasis (LM) treatment in recent years. Methods This paper reports two cases of advanced lung adenocarcinoma with exon-EGFR19 mutation. LM occurred during gefitinib treatment with an extracranial condition under control. Later, erlotinib was adopted with a recurrent response. Results The progression free survival of both patients was over six months and their LM was under control for six and a half and nine months, respectively. LM may be sensitive on different levels to different EGFR-TKIs. Conclusion Erlotinib can be used to replace gefitinib if intracranial tumor progress occurs during gefitinib treatment. However, our conclusion still needs to be proven by further clinical research.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Serum concentrations of rituximab influence its clinical efficacy in follicular lymphoma (FL), but its concentration–effect relationship has not been described by pharmacokinetic–pharmacodynamic (PK–PD) modelling. • The genetic polymorphism of FCGR3A influences rituximab efficacy and its in vitro concentration–effect relationship. • Increasing rituximab dose and/or number of infusions may lead to a better clinical response in FL. WHAT THIS PAPER ADDS • This study is the first to describe the concentration–effect relationship of rituximab in populations of FL patients. • This PK–PD model relates progression-free survival with rituximab concentrations and takes into account the influence of FCGR3A polymorphism. • Clinical trials testing new dosing regimens of rituximab can be designed using this PK–PD model. AIM Rituximab has dramatically improved the survival of patients with non-Hodgkin's lymphomas (NHL), but the dosing regimen currently used should be optimized. However, the concentration–effect relationship of rituximab has never been described by pharmacokinetic–pharmacodynamic (PK–PD) modelling, precluding the simulation of new dosing regimens. The aim of this study was to develop a PK–PD model of rituximab in relapsed/resistant follicular NHL (FL). METHODS A model describing the relationship between rituximab concentrations and progression-free survival (PFS) was developed using data extracted from the pivotal study, which evaluated 151 relapsed/resistant FL patients. The influence of FCGR3A genetic polymorphism on the efficacy of rituximab was quantified using data from 87 relapsed/resistant FL patients. The predictive performance of the model was analysed using two independent datasets: a study that evaluated rituximab combined with chemotherapy [rituximab, cyclophosphamide, vincristine, adriamycin and prednisone (R-CHOP)] in 334 relapsed/resistant FL patients and a study that evaluated rituximab monotherapy in 47 asymptomatic FL patients with known FCGR3A genotype. RESULTS For R-CHOP, observed and model-predicted PFS (90% confidence interval) at 24 months were 0.50 and 0.48 (0.40, 0.56), respectively, for the observation arm, and 0.62 and 0.59 (0.50, 0.65), respectively, for the rituximab maintenance arm. For rituximab monotherapy, observed and predicted PFS at 24 months were 0.67 and 0.63, respectively, for FCGR3A-V/V patients, and 0.41 and 0.36 (0.25, 0.49), respectively, for FCGR3A-F carriers. CONCLUSIONS Our model provides a satisfactory prediction of PFS at 24 months. It can be used to simulate new dosing regimens of rituximab in populations of FL patients and should improve the design of future clinical trials.

Among the surrogate endpoints for overall survival (OS) in oncology trials, progression-free survival (PFS) is more and more taking the leading role. Although there have been some empirical investigations on the dependence structure between OS and PFS (in particular between the median OS and the median PFS), statistical models are almost non-existing. This paper aims at filling this gap by introducing an easy-to-handle model based on exponential time-to-event distributions that describe the dependence structure between OS and PFS. Based on this model, explicit formulae for individual correlations are derived together with a lower bound for the correlation of OS and PFS, which is given by the fraction of the two medians for OS and PFS. Two methods on how to estimate the parameter of the model from real data are discussed. One method is based on a maximum-likelihood estimator whereas the other method uses a plug-in approach. Three examples from non-small cell lung cancer are considered. In the first example, the parameters of the model are determined and the estimated survival curce is compared with the observed one. The second example explains how to obtain sample size estimates for OS based on assumptions on median PFS and OS. Finally, the third example provides a way of modelling and quantifying confounding effects that might explain a levelling of differences in OS although a difference in PFS is observed.