Citations

Providing personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract Biomarkers are critical to targeted therapies, as they may identify patients more likely to benefit from a treatment. Several prospective designs for biomarker-directed therapy have been previously proposed, differing primarily in the study population, randomization scheme, or both. Recognizing the need for randomization, yet acknowledging the possibility of promising but inconclusive results after a stage I cohort of randomized patients, we propose a 2-stage phase II design on marker-positive patients that allows for direct assignment in a stage II cohort. In stage I, marker-positive patients are equally randomized to receive experimental treatment or control. Stage II has the option to adopt “direct assignment” whereby all patients receive experimental treatment. Through simulation, we studied the power and type I error rate of our design compared with a balanced randomized two-stage design, and conducted sensitivity analyses to study the effect of timing of stage I analysis, population shift effects, and unbalanced randomization. Our proposed design has minimal loss in power (<1.8%) and increased type I error rate (<2.1%) compared with a balanced randomized design. The maximum increase in type I error rate in the presence of a population shift was between 3.1% and 5%, and the loss in power across possible timings of stage I analysis was less than 1.2%. Our proposed design has desirable statistical properties with potential appeal in practice. The direct assignment option, if adopted, provides for an “extended confirmation phase” as an alternative to stopping the trial early for evidence of efficacy in stage I. Clin Cancer Res; 18(16); 4225–33. ©2012 AACR.

Purpose Limited data exist regarding the outcomes of adjuvant therapy in younger patients with stage II and III colon cancer. We examined disease-free survival (DFS), overall survival (OS), recurrence-free interval (RFI), and grade 3+ adverse events (AEs) in younger patients in the 33,574 patient Adjuvant Colon Cancer Endpoints Group data set. Patients and Methods Individual patient data from 24 randomized phase III clinical trials were obtained for survival outcomes, which included 10 clinical trials for AE outcomes. Two age-based cutoff points were used to define younger patients: age younger than 40 years and younger than 50 years. Adjuvant therapy benefit analyses were limited to the nine clinical trials in which the investigational chemotherapeutic arm demonstrated benefit. Results One thousand seven hundred fifty-eight patients (5.2%) were younger than 40 years, 5,817 patients (17.3%) were younger than 50 years, and only 299 patients (0.9%) were younger than 30 years. No meaningful differences in sex or stage were noted in younger versus older patients. Younger and older patients did not differ in RFI (age, < 40 years: hazard ratio [HR], 1.0; P = .62 and age < 50 years: HR, 1.02; P = .35). Younger patients (both cutoff points), had longer OS and DFS than older patients. In trials demonstrating adjuvant therapy benefit, similar DFS benefit was observed by age. Younger patients experienced less leukopenia and stomatitis, but more frequent nausea/vomiting. Conclusion Among patients on clinical trials, younger and older patients with stage II and III colon cancer had similar RFI and adjuvant therapy benefit. Younger patients have longer OS and DFS, which is likely primarily because of fewer competing causes of death. Adjuvant therapy is beneficial for colon cancer in patients younger than 50 years who meet typical clinical trial eligibility criteria.

Background Traditional clinical trial designs strive to definitively establish the superiority of an experimental treatment, which results in risk-adverse criteria and large sample sizes. Increasingly, common cancers are recognized as consisting of small subsets with specific aberrations for targeted therapy, making large trials infeasible. Purpose To compare the performance of different trial design strategies over a long-term research horizon. Methods We simulated a series of two-treatment superiority trials over 15 years using different design parameters. Trial parameters examined included the number of positive trials to establish superiority (one-trial vs. two-trial rule), α level (2.5%–50%), and the number of trials in the 15-year period, K (thus, trial sample size). The design parameters were evaluated for different disease scenarios, accrual rates, and distributions of treatment effect. Metrics used included the overall survival gain at a 15-year horizon measured by the hazard ratio (HR), year 15 versus year 0. We also computed the expected total survival benefit and the risk of selecting as new standard of care at year 15 a treatment inferior to the initial control treatment, P(detrimental effect). Results Expected survival benefits over the 15-year horizon were maximized when more (smaller) trials were conducted than recommended under traditional criteria, using the criterion of one positive trial (vs. two), and relaxing the α value from 2.5% to 20%. Reducing the sample size and relaxing the α value also increased the likelihood of selecting an inferior treatment at the end. The impact of α and K on the survival benefit depended on the specific disease scenario and accrual rate: greater gains for relaxing α in diseases with good outcome and/or low accrual rates and greater gains for increasing K for diseases with poor outcomes. Trials with smaller sample size did not perform well when using stringent (standard) level of evidence. For each disease scenario and accrual rate studied, the optimal design, defined as the design that the maximized expected total survival benefit while constraining P(detrimental effect) < 2.5%, specified α = 20% or 10%, and a sample size considerably smaller than that recommended by the traditional designs. The results were consistent under different assumed distributions for treatment effect. Limitations The simulations assumed no toxicity issues and did not consider interim analyses. Conclusions It is worthwhile to consider a design paradigm that seeks to maximize the expected survival benefit across a series of trials, over a longer research horizon. In today’s environment of constrained, biomarker-selected populations, our results indicate that smaller sample sizes and larger α values lead to greater long-term survival gains compared to traditional large trials designed to meet stringent criteria with a low efficacy bar.

Abstract Purpose: Obesity is associated with an increased risk of colon cancer. However, the influence of body mass index (BMI) upon the outcome of colon cancer survivors and its relationship to gender remains unknown. Experimental Design: BMI (kg/m2) was calculated and categorized in patients with TNM stage II and III colon carcinomas (n= 4,381) enrolled in 7 randomized trials of 5-fluorouracil-based adjuvant chemotherapy. Cox proportional hazards modeling was used to determine the association of BMI with disease-free and overall survival. Results: Among colon cancer patients, 868 (20%) were obese (BMI ≥30 kg/m2) of which 606 were class 1 (BMI 30–34 kg/m2) and 262 were class 2,3 (BMI ≥35 kg/m2). Obese versus normal-weight patients were more likely to be younger, have distal tumors, show intact DNA mismatch repair (MMR), and have increased lymph node metastases (p &lt;0.017). The association of BMI and MMR status was dependent upon patient age (p= 0.031). In a multivariate analysis, BMI was significantly associated with both disease-free (p=0.030) and overall survival (p=0.0017). Men with class 2,3 obesity showed reduced overall survival compared to normal weight men [HR 1.35 (1.02, 1.79); p=0.039]. Women with class I obesity had reduced overall survival [HR 1.24 (1.01, 1.53), p=0.045] compared to normal weight women. BMI was not predictive of therapeutic benefit. Conclusions: Obesity is associated with intact DNA mismatch repair status and was an adverse, independent prognostic variable in colon cancer survivors. Interventions to reduce obesity have the potential to improve patient outcomes in colon cancer survivors. Citation Information: Cancer Prev Res 2010;3(1 Suppl):B111.

It is widely accepted that traditional models of clinical investigation are becoming unsustainable in oncology and that trials must become more efficient in matching effective treatments to the patients most likely to benefit. In 2008, gastrointestinal oncologists from many countries began a collaboration to improve the design and conduct of clinical trials in their field, through the auspices of a French/U.S. charitable foundation, ARCAD. Whether this model of academic collaboration will be judged a success will depend on the quality of its scientific output during the next few years and whether this output, alongside that of other scientists, groups, and institutions, ultimately leads to more efficient trials and improved treatment options for patients.

PurposeAlthough the importance of obesity in colon cancer risk and outcome is recognized, the association of body mass index (BMI) with DNA mismatch repair (MMR) status is unknown.Patients and MethodsBMI (kg/m2) was determined in patients with TNM stage II or III colon carcinomas (n = 2,693) who participated in randomized trials of adjuvant chemotherapy. The association of BMI with MMR status and survival was analyzed by logistic regression and Cox models, respectively.ResultsOverall, 427 (16%) tumors showed deficient MMR (dMMR), and 630 patients (23%) were obese (BMI ≥ 30 kg/m2). Obesity was significantly associated with younger age (P = .021), distal tumor site (P = .012), and a lower rate of dMMR tumors (10% v 17%; P < .001) compared with normal weight. Obesity remained associated with lower rates of dMMR (odds ratio, 0.57; 95% CI, 0.41 to 0.79; P < .001) after adjusting for tumor site, stage, sex, and age. Among obese patients, rates of dMMR were lower in men compared with women (8% v 13%; P = .041). Obesity was associated with higher recurrence rates (P = .0034) and independently predicted worse disease-free survival (DFS; hazard ratio [HR], 1.37; 95% CI, 1.14 to 1.64; P = .0010) and overall survival (OS), whereas dMMR predicted better DFS (HR, 0.59; 95% CI, 0.47 to 0.74; P < .001) and OS. The favorable prognosis of dMMR was maintained in obese patients.ConclusionColon cancers from obese patients are less likely to show dMMR, suggesting obesity-related differences in the pathogenesis of colon cancer. Although obesity was independently associated with adverse outcome, the favorable prognostic impact of dMMR was maintained among obese patients.