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Byline: Dennis F. Moore (1), Thomas D. Brown (2), Michael LeBlanc (2), Steve Dahlberg (2), Thomas P. Miller (3), Suzanne McClure (4), Richard I. Fisher (5) Keywords: menogaril; non-Hodgkin's lymphoma; Southwest Oncology Group Abstract: Purpose: To assess the efficacy and toxicity of menogaril against non-Hodgkin's lymphoma (NHL) in a group of previously treated patients. Patients and methods: Sixty-two eligible patients with a histologic diagnosis of NHL were enrolled, 35 of who had intermediate or high-grade histologies and 27 of who had low-grade lymphomas. Patients with intermediate or high-grade lymphomas had received only 1 prior chemotherapy regimen, while patients with low-grade histologies had received 1 or 2 prior chemotherapy regimens. Menogaril was administered at 160 mg/m.sup.2 intravenously over 1 hour, once every 28 days. Results: Among the 35 patients with intermediate or high-grade lymphomas who were evaluable for response, 6 of 35 patients achieved a partial response (PR) for a response rate of 17% (95% confidence interval: 7%-34%). Median survival in this group of patients was 13 months. For those patients with low-grade lymphoma, 5 of 26 patients achieved a PR for a response rate of 19% (95% confidence interval: 6%-38%). No complete responses were observed in either patient group. The incidence of serious (grade 3 or 4) toxicity for those with intermediate/high-grade and low-grade lymphomas was 43% and 44%, respectively. Most of these toxic effects consisted of reversible myelosuppression. Menogaril was discontinued in 2 patients due to prolonged neutropenia. Cardiotoxicity was observed in 4 patients, requiring discontinuation of the drug in 1 patient. No treatment-related deaths occurred and the overall toxicity was felt to be acceptable. Conclusion: The observed antitumor activity of single agent menogaril against both intermediate/high-grade and low-grade lymphomas was modest. Further exploration of this agent in patients with non-Hodgkin's lymphomas does not seem warranted. Author Affiliation: (1) Wichita CCOP, Wichita, KS, USA (2) NDC Medical Center, Norfolk, VA, USA (3) Arizona Cancer Center, Tucson, AZ, USA (4) University of Texas Medical Branch at Galveston, Galveston, TX, USA (5) Loyola University Strich School of Medicine, Maywood, IL, USA Article History: Registration Date: 03/10/2004

Traditionally, a clinical trial is conducted comparing treatment to standard care for all patients. However, it could be inefficient given patients' heterogeneous responses to treatments, and rapid advances in the molecular understanding of diseases have made biomarker‐based clinical trials increasingly popular. We propose a new targeted clinical trial design, termed as Max‐Impact design, which selects the appropriate subpopulation for a clinical trial and aims to optimize population impact once the trial is completed. The proposed design not only gains insights on the patients who would be included in the trial but also considers the benefit to the excluded patients. We develop novel algorithms to construct enrollment rules for optimizing population impact, which are fairly general and can be applied to various types of outcomes. Simulation studies and a data example from the SWOG Cancer Research Network demonstrate the competitive performance of our proposed method compared to traditional untargeted and targeted designs. [ABSTRACT FROM AUTHOR]

The hazard ratio is widely used to measure or to summarize the magnitude of treatment effects, but it is justifiably difficult to interpret in a meaningful way to patients and perhaps for clinicians as well. In addition, it is most meaningful when the hazard functions are approximately proportional over time. We propose a new measure, termed personalized chance of longer survival. The measure, which quantifies the probability of living longer with one treatment over the another, accounts for individualized characteristics to directly address personalized treatment effects. Hence, the measure is patient focused, which can be used to evaluate subgroups easily. We believe it is intuitive to understand and clinically interpretable in the presence of nonproportionality. Furthermore, because it estimates the probability of living longer by some fixed amount of time, it encodes the probabilistic part of treatment effect estimation. We provide nonparametric estimation and inference procedures that can accommodate censored survival outcomes. We conduct extensive simulation studies, which characterize performance of the proposed method, and data from a large randomized Phase III clinical trial (SWOG S0819) are analyzed using the proposed method. [ABSTRACT FROM AUTHOR]

Summary: Discovering gene–treatment interactions in clinical trials is of rising interest in the era of precision medicine. Non‐parametric statistical learning methods such as trees and random forests are useful tools for building prediction rules. We introduce trees and random forests to the recently proposed case‐only approach for discovering gene–treatment interactions and estimating marker‐specific treatment effects for a dichotomous trial's end points. The motivational example is a case–control genetic association study in the prostate cancer prevention trial, which tested the hypothesis whether finasteride can prevent prostate cancer. We compare this novel approach with the interaction tree method previously proposed. Because of the modelling simplicity—directly targeting at interaction—and the statistical efficiency of the case‐only approach, case‐only trees and random forests yield more accurate prediction of heterogeneous treatment effects and a better measure of variable importance, relative to the interaction tree method which uses data from both cases and controls. Application of the proposed case‐only trees and random forests to the prostate cancer prevention trial study yielded a discovery of genotypes that may influence the prevention effect of finasteride. [ABSTRACT FROM AUTHOR]

Mantle cell lymphoma (MCL) comprises around 6% of all non-Hodgkin's lymphoma (NHL) diagnoses. In younger patients, age less than 60 to 65 years, aggressive induction often followed by consolidation with autologous stem cell transplant has suggested improved outcomes in this population. Less intensive therapies in older patients often followed by maintenance have been studied or are under active investigation. However, despite recent advances, MCL remains incurable, with a median overall survival of around five years. Patients with high-risk disease have particularly poor outcomes. Treatment varies widely across institutions, and to date no randomized trials comparing intensive vs less intensive approaches have been reported. Although recent data have highlighted the heterogeneity of MCL outcomes, patient assessment for treatment selection has largely been driven by patient age with little regard to fitness, disease biology, or disease risk. One critical advance is the finding that minimal residual disease status (MRD) after induction correlates with long-term outcomes. As such, its use as a potential end point could inform clinical trial design. In order to more rapidly improve the outcomes of MCL patients, clinical trials are needed that prospectively stratify patients on the basis of MCL biology and disease risk, incorporate novel agents, and use MRD to guide the need for additional therapy. [ABSTRACT FROM AUTHOR]

Background Clinical trials test the efficacy of a treatment in a select patient population. We examined whether cancer clinical trial patients were similar to nontrial, "real-world" patients with respect to presenting characteristics and survival. Methods We reviewed the SWOG national clinical trials consortium database to identify candidate trials. Demographic factors, stage, and overall survival for patients in the standard arms were compared with nontrial control subjects selected from the Surveillance, Epidemiology, and End Results program. Multivariable survival analyses using Cox regression were conducted. The survival functions from aggregate data across all studies were compared separately by prognosis (≥50% vs <50% average 2-year survival). All statistical tests were two-sided. Results We analyzed 21 SWOG studies (11 good prognosis and 10 poor prognosis) comprising 5190 patients enrolled from 1987 to 2007. Trial patients were younger than nontrial patients (P < .001). In multivariable analysis, trial participation was not associated with improved overall survival for all 11 good-prognosis studies but was associated with better survival for nine of 10 poor-prognosis studies (P < .001). The impact of trial participation on overall survival endured for only 1 year. Conclusions Trial participation was associated with better survival in the first year after diagnosis, likely because of eligibility criteria that excluded higher comorbidity patients from trials. Similar survival patterns between trial and nontrial patients after the first year suggest that trial standard arm outcomes are generalizable over the long term and may improve confidence that trial treatment effects will translate to the real-world setting. Reducing eligibility criteria would improve access to clinical trials. [ABSTRACT FROM AUTHOR]

BACKGROUND: Patients with peripheral T-cell lymphomas (PTCLs) have inferior progression-free survival (PFS) and overall survival (OS) compared with patients who have aggressive B-cell non-Hodgkin lymphoma. Because PTCLs over express multidrug resistance gene 1/P-glycoprotein (MDR-1/P-gp), we devised platinum, etoposide, gemcitabine, and methylprednisolone (PEGS) with agents that are not substrates of the efflux pump. Gemcitabine was included because of its excellent single-agent activity in PTCL. METHODS: Patients who had PTCL with stage II bulky disease, stage III or IV disease with extra-nodal, nodal, and transformed cutaneous presentations were eligible. Patients received intravenous cisplatin 25 mg/m2 on days 1 through 4, etoposide 40 mg/m2 on days 1 through 4, gemcitabine 1000 mg/m2 on day 1, and methylprednisolone 250 mg on days 1 through 4 of a 21-day cycle for 6 cycles. RESULTS: In total, 34 patients were enrolled, 33 were eligible, and 79% were newly diagnosed. Histologic types were PTCL not otherwise specified (n = 15), anaplastic lymphoma kinase (ALK)-negative anaplastic large cell lymphoma (n = 4), angioimmunoblastic T-cell lymphoma (n = 6), or other T-cell non-Hodgkin lymphomas (n = 8). Adverse events included 1 grade 5 infection with grade 3 or 4 neutropenia and 9 grade 4 hematologic toxicities. The overall response rate was 39% (47% in PTCL not otherwise specified, 33% in angioimmunoblastic T-cell lymphoma, 25% in ALK-negative and 38% in other T-cell non-Hodgkin lymphomas). The PFS rate at 2 years was 12% (95% confidence interval, 0.1%-31%), and the median PFS was 7 months. The OS rate at 2 years was 30% (95% confidence interval, 8%-54%), and the median OS was 17 months. Immunohistochemical analysis of P-gp expression revealed strong positivity in a subset of lymphoma cells (n = 6) and tumor endothelium (n = 25). CONCLUSIONS: Overall, PEGS was well tolerated, but OS was not considered promising given the design-specified targets. These results may serve as a benchmark for future comparisons for non-CHOP regimens. Cancer 2013. © 2012 American Cancer Society. [ABSTRACT FROM AUTHOR]

This is the first report of the Southwest oncology group phase II trial of single agent bevacizumab in patients with relapsed, aggressive non-Hodgkin lymphoma (NHL). Fifty-two patients in first or second relapse with diffuse large B-cell or mantle cell lymphoma were enrolled. Patients were treated with bevacizumab at 10 mg/kg every 2 weeks. Therapy was well tolerated with no unexpected toxicities observed. Six-month progression-free survival (PFS) was 16% with a response rate of 2% and median duration of response or stable disease of 5.2 months (range 3.5-72.7). Vascular endothelial growth factor A (VEGF) and VEGF receptor expression was observed in 70% and 65% of specimens, respectively. In an exploratory subgroup analysis, baseline urine VEGF and plasma vascular cell adhesion molecule-1 (VCAM) levels correlated with survival. Prolonged PFS in several patients as well as biomarker studies suggest the VEGF pathway plays an important role in aggressive NHL. Clinical trials combining active chemotherapy regimens with VEGF targeted agents are currently in progress. [ABSTRACT FROM AUTHOR]

Recent results for case–control sampling suggest when the covariate distribution is constrained by gene-environment independence, semiparametric estimation exploiting such independence yields a great deal of efficiency gain. We consider the efficient estimation of the treatment–biomarker interaction in two-phase sampling nested within randomized clinical trials, incorporating the independence between a randomized treatment and the baseline markers. We develop a Newton–Raphson algorithm based on the profile likelihood to compute the semiparametric maximum likelihood estimate (SPMLE). Our algorithm accommodates both continuous phase-one outcomes and continuous phase-two biomarkers. The profile information matrix is computed explicitly via numerical differentiation. In certain situations where computing the SPMLE is slow, we propose a maximum estimated likelihood estimator (MELE), which is also capable of incorporating the covariate independence. This estimated likelihood approach uses a one-step empirical covariate distribution, thus is straightforward to maximize. It offers a closed-form variance estimate with limited increase in variance relative to the fully efficient SPMLE. Our results suggest exploiting the covariate independence in two-phase sampling increases the efficiency substantially, particularly for estimating treatment–biomarker interactions. [ABSTRACT FROM AUTHOR]

We construct interpretable prognostic rules based on a sequence of “box-shaped” regions in the predictor space indexed by the fraction of patients in the prognostic group. In addition, the method can be used as a building block to construct more general prognostic rules based on unions of boxes, or even as a tool to find multiple prognostic groups. Simulations are used to study the properties of the new method and compare it to constructing prognostic groups based on regression trees and linear proportional hazards (PH) models. We consider an example based on data from several completed clinical trials for patients with multiple myeloma. [ABSTRACT FROM AUTHOR]

The article illustrates different techniques on building proportional-hazards models with piecewise constant relative risk functions. It is stated that these techniques can assess a single step-function term or be used as a flexible exploratory survival-data analysis tool. The authors used clinical trial data for myeloma to demonstrate the application of these techniques in the development of prognostic groups.

A regression method is developed for a general class of functionals. A semiparametric linear model is adopted, and the regression parameters are estimated by maximizing a profiled nonparametric or empirical likelihood based on a local estimate of the conditional distribution function. Simulated and real data examples are shown, including an application of quantile regression to censored survival data from a clinical trial for myeloma. [ABSTRACT FROM AUTHOR]

The article discusses randomization in phase II trials in cancer drug development, noting the growing trend in these trials which use progression-free survival (PFS) as the preferred endpoint. It focuses on the review by Sharma and colleagues published in this issue which highlights the value and advantages of this strategy. It notes the significantly improved outcomes of the cited phase II trials of agents with tumor-regressing ability in which improved survival rates have been recorded.

Individualized treatment rules inform tailored treatment decisions based on the patient’s information, where the goal is to optimize clinical benefit for the population. When the clinical outcome of interest is survival time, most of current approaches typically aim to maximize the expected time of survival. We propose a new criterion for constructing Individualized treatment rules that optimize the clinical benefit with survival outcomes, termed as the adjusted probability of a longer survival. This objective captures the likelihood of living longer with being on treatment, compared to the alternative, which provides an alternative and often straightforward interpretation to communicate with clinicians and patients. We view it as an alternative to the survival analysis standard of the hazard ratio and the increasingly used restricted mean survival time. We develop a new method to construct the optimal Individualized treatment rule by maximizing a nonparametric estimator of the adjusted probability of a longer survival for a decision rule. Simulation studies demonstrate the reliability of the proposed method across a range of different scenarios. We further perform data analysis using data collected from a randomized Phase III clinical trial (SWOG S0819). [ABSTRACT FROM AUTHOR]

Baseline circulating markers of bone metabolism are significantly associated with overall survival in men with metastatic hormone-sensitive prostate cancer receiving androgen deprivation therapy. The results of this translational medicine study have critical implications for clinical care and future research. Bone biomarkers are strongly prognostic for overall survival (OS) in men with castration-resistant prostate cancer but not fully established for hormone-sensitive prostate cancer (HSPC). Bone biomarkers in HSPC were prospectively evaluated as part of a phase 3 study of androgen deprivation therapy ± the CYP17 inhibitor orteronel. Patients were randomly divided into training (n = 316) and validation (n = 633) sets. Recursive partitioning and Cox proportional hazard models were employed. Bone resorption (C-telopeptide and pyridinoline) and bone formation markers (C-terminal collagen propeptide and bone alkaline phosphatase) were assessed from patient sera. Of 1279 men, 949 had evaluable baseline bone biomarkers. Optimal cutoffs were identified to define elevated levels of each of the four biomarkers (all p < 0.05) that were associated with worse OS. After adjusting for clinical risk factors in the validation set, elevated bone biomarkers were statistically significantly associated with an increased risk of death (hazard ratios ranging from 1.37 to 1.92). Recursive partitioning algorithms applied to the training set identified three risk groups (low, intermediate, and poor) with differential OS outcomes (median OS: 8.2, 5.1, and 2.1 yr, respectively) based on combinations of bone biomarkers. These results were confirmed in the validation set. In men with HSPC initiating androgen deprivation therapy, bone biomarkers are strongly and independently prognostic for OS. Bone biomarker levels alone or in combination with clinical covariates identify unique subsets of men with differential OS outcomes. These results validate the clinical value of bone biomarker assessment in the HSPC state, extending bone biomarker utility beyond the castration-resistant state. In men with newly diagnosed metastatic prostate cancer, high levels of bone turnover biomarkers are associated with a shorter lifespan. [ABSTRACT FROM AUTHOR]