Sargent, Daniel J.

Purpose Prior studies have suggested that patients with stage II/III colon cancer receive similar benefit from intravenous (IV) fluoropyrimidine adjuvant therapy regardless of age. Combination regimens and oral fluorouracil (FU) therapy are now standard. We examined the impact of age on colon cancer recurrence and mortality after adjuvant therapy with these newer options. Patients and Methods We analyzed 11,953 patients age < 70 and 2,575 age ≥ 70 years from seven adjuvant therapy trials comparing IV FU with oral fluoropyrimidines (capecitabine, uracil, or tegafur) or combinations of fluoropyrimidines with oxaliplatin or irinotecan in stage II/III colon cancer. End points were disease-free survival (DFS), overall survival (OS), and time to recurrence (TTR). Results In three studies comparing oxaliplatin-based chemotherapy with IV FU, statistically significant interactions were not observed between treatment arm and age (P interaction = .09 for DFS, .05 for OS, and .36 for TTR), although the stratified point estimates suggested limited benefit from the addition of oxaliplatin in elderly patients (DFS hazard ratio [HR], 0.94; 95% CI, 0.78 to 1.13; OS HR, 1.04; 95% CI, 0.85 to 1.27). No significant interactions by age were detected with oral fluoropyrimidine therapy compared with IV FU; noninferiority was supported in both age populations. Conclusion Patients age ≥ 70 years seemed to experience reduced benefit from adding oxaliplatin to fluoropyrimidines in the adjuvant setting, although statistically, there was not a significant effect modification by age, whereas oral fluoropyrimidines retained their efficacy.

Background Prospective trial design often occurs in the presence of ‘acceptable’ historical control data. Typically, these data are only utilized for treatment comparison in a posteriori retrospective analysis to estimate population-averaged effects in a random-effects meta-analysis. Purpose We propose and investigate an adaptive trial design in the context of an actual randomized controlled colorectal cancer trial. This trial, originally reported by Goldberg et al., succeeded a similar trial reported by Saltz et al., and used a control therapy identical to that tested (and found beneficial) in the Saltz trial. Methods The proposed trial implements an adaptive randomization procedure for allocating patients aimed at balancing total information (concurrent and historical) among the study arms. This is accomplished by assigning more patients to receive the novel therapy in the absence of strong evidence for heterogeneity among the concurrent and historical controls. Allocation probabilities adapt as a function of the effective historical sample size (EHSS), characterizing relative informativeness defined in the context of a piecewise exponential model for evaluating time to disease progression. Commensurate priors are utilized to assess historical and concurrent heterogeneity at interim analyses and to borrow strength from the historical data in the final analysis. The adaptive trial’s frequentist properties are simulated using the actual patient-level historical control data from the Saltz trial and the actual enrollment dates for patients enrolled into the Goldberg trial. Results Assessing concurrent and historical heterogeneity at interim analyses and balancing total information with the adaptive randomization procedure lead to trials that on average assign more new patients to the novel treatment when the historical controls are unbiased or slightly biased compared to the concurrent controls. Large magnitudes of bias lead to approximately equal allocation of patients among the treatment arms. Using the proposed commensurate prior model to borrow strength from the historical data, after balancing total information with the adaptive randomization procedure, provides admissible estimators of the novel treatment effect with desirable bias-variance trade-offs. Limitations Adaptive randomization methods in general are sensitive to population drift and more suitable for trials that initiate with gradual enrollment. Balancing information among study arms in time-to-event analyses is difficult in the presence of informative right-censoring. Conclusions The proposed design could prove important in trials that follow recent evaluations of a control therapy. Efficient use of the historical controls is especially important in contexts where reliance on preexisting information is unavoidable because the control therapy is exceptionally hazardous, expensive, or the disease is rare.

Purpose By using data from North Central Cancer Treatment Group Phase III Trial N0147, a randomized adjuvant trial of patients with stage III colon cancer, we assessed the relationship between smoking and cancer outcomes, disease-free survival (DFS), and time to recurrence (TTR), accounting for heterogeneity by patient and tumor characteristics. Patients and Methods Before random assignment to infusional fluorouracil, leucovorin, and oxaliplatin (FOLFOX) or FOLFOX plus cetuximab, 1,968 participants completed a questionnaire on smoking history and other risk factors. Cox models assessed the association between smoking history and the primary trial outcome of DFS (ie, time to recurrence or death), as well as TTR, adjusting for other clinical and patient factors. The median follow-up was 3.5 years among patients who did not experience events. Results Compared with never-smokers, ever smokers experienced significantly shorter DFS (3-year DFS proportion: 70% v 74%; hazard ratio [HR], 1.21; 95% CI, 1.02 to 1.42). This association persisted after multivariate adjustment (HR, 1.23; 95% CI, 1.02 to 1.49). There was significant interaction in this association by BRAF mutation status (P = .03): smoking was associated with shorter DFS in patients with BRAF wild-type (HR, 1.36; 95% CI, 1.11 to 1.66) but not BRAF mutated (HR, 0.80; 95% CI, 0.50 to 1.29) colon cancer. Smoking was more strongly associated with poorer DFS in those with KRAS mutated versus KRAS wild-type colon cancer (HR, 1.50 [95% CI, 1.12 to 2.00] v HR, 1.09 [95% CI, 0.85 to 1.39]), although interaction by KRAS mutation status was not statistically significant (P = .07). Associations were comparable in analyses of TTR. Conclusion Overall, smoking was significantly associated with shorter DFS and TTR in patients with colon cancer. These adverse relationships were most evident in patients with BRAF wild-type or KRAS mutated colon cancer.

Abstract Background: Colon cancer is categorized by the acquisition of a number of molecular alterations in both oncogenes and in tumor suppressor genes. Although cMYC alterations have been reported in colon cancer cell lines, little other information exists in primary colon tumors. We sought to determine the frequency of cMYC amplification in primary colon cancer and its relationship to other well defined colon tumor alterations. Methods: An initial set of 392 tumors were evaluated. All samples were tested for the BRAF V600E mutation and all were classified as proficient mismatch repair (pMMR) based on both IHC and microsatellite analysis of tumor tissue. A second set of 522 tumors was selected to replicate associations identified in the first set of samples. Similar to the initial cohort, both the BRAF V600E and the mismatch repair status of the tumors were assessed. The second cohort, however, included tumors that were both pMMR and dMMR. In both sets of samples, fluorescent in situ hybridization was used to evaluate cMYC gene amplification. Results: In cohort 1, cMYC amplification was observed in 15 of the 362 (4%) pMMR tumors successfully analyzed (Table 1). cMYC amplification was detected in 10 of 323 (3%) tumors with normal BRAF status vs. 5 of 39 (13%) tumors with altered BRAF status (p=0.015). In the second cohort, cMYC amplification was found in 20 of the 522 (4%) tumors. Only 1 of the 152 (&lt;1%) dMMR tumors had cMYC amplification vs. 19 of the 370 (5%) pMMR tumors (p=0.012). Of the pMMR tumors, cMYC amplification was detected in 13 of 333 (4%) tumors with normal BRAF status vs. 6 of 37 (16%) tumors with altered BRAF status (p=0.007), nearly identical to the first group. Conclusion: cMYC amplification in colon cancer is an infrequent event. However, when present, cMYC amplification occurs almost exclusively in tumors that are pMMR. Additionally, among pMMR tumors, cMYC amplification is associated with the presence of the BRAF V600E mutation. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1151. doi:1538-7445.AM2012-1151

Abstract Background. Autophagy maintains energy metabolism and enables tumor cell survival during cellular stress. Beclin 1 is involved in autophagosome formation during which membrane-bound LC3 binds to p62/sequestosome 1. We reported that ectopic p62 can promote 5-fluorouracil-induced cell death (JBC 286:40002-12, 2011). It is unknown whether these autophagy markers can predict clinical outcome. Materials & Methods. Beclin 1, LC3B, and p62 proteins were analyzed by immunohistochemistry in tissue microarrays containing stage II and III colon carcinomas (N=151) from patients receiving 5-FU or surgery alone. The mean weighted score (intensity x extent) was dichotomized for survival analysis using Cox models. Results. Increased levels of cytoplasmic LC3, Beclin 1, and p62 expression were detected in 21%, 14%, and 68% of cancers, respectively, and were not associated with tumor site, stage or grade. By multivariable analysis, increased LC3 was significantly associated with worse disease-free survival (DFS) (p=0.022), but not overall survival (OS) [p=0.069](Table). Beclin 1 was not prognostic, but increased p62 was significantly associated with improved DFS (p=0.038), but not OS (p= 0.176)[Table]. When restricted to 5-FU-treated patients, increased LC3 was associated with worse DFS [HR= 2.38 (1.19, 4.75, p=0.020), elevated Beclin 1 predicted worse OS [HR=2.83 (1.27, 6.34), p = 0.019], and p62 was not prognostic. Conclusion. LC3 and Beclin 1 overexpression were independently associated with adverse outcome. Increased p62 was associated with better outcome consistent with its ability to promote cell death. These data support strategies to inhibit autophagy for therapeutic advantage. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4112. doi:1538-7445.AM2012-4112

Abstract Purpose: Novel therapies are challenging current drug development standards. Agents with specific biologic targets, unknown dose-efficacy curves, limited toxicity profiles, and in combination therapy require novel statistical designs. We compared the operating characteristics of three model based designs incorporating both toxicity and a measure of efficacy to identify the optimal dose region for combination therapies. Methods: Three designs were considered: continuation ratio model with a trinary outcome, bivariate logistic model treating toxicity and efficacy as independent outcomes, and bivariate probit model allowing for dependent toxicity and efficacy outcomes. All designs utilized a model-based methodology to adaptively determine dose escalation/de-escalation, based on the data observed to date in the trial. The model parameters were estimated using Markov chain Monte Carlo approach in a Bayesian framework. The target threshold for toxicity was set at 33%; the minimum and maximum sample sizes were set at 30 and 45, respectively, for the simulated trials; and the cohort size was 3 with at least nine patients needed to be treated at the proposed combination dose level. The performance of the designs was evaluated in simulation studies under six clinically relevant scenarios. Results: As expected, the ability to correctly identify an optimal dose region was sensitive to the changes in the toxicity and/or efficacy across the dose level combinations. Considering 50% as a benchmark for the recommendation rate for the optimal dose or region (dose levels within 10% of the maximum efficacious dose level), at least one of the three models performed well in recommending the optimal dose region except when the dose-efficacy surface was non-monotone or the dose-toxicity surface was close to the target rate of 33%. All three models worked well in not recommending any dose combination when the starting dose level combination was too toxic. The median sample size across all scenarios ranged from 27–33. Conclusions: Despite favorable operating characteristics, the implementation of these new designs in practice is challenging for a variety of reasons. Adoption of novel designs require a multi-pronged approach that provides a) clinically obvious advantages, b) practical design framework with a transparent decision making process, c) timely access to both toxicity and preliminary efficacy data, and d) portable user-friendly software, or very attentive statisticians. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):C53.

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.