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Vernalisation requirement is an agriculturally important trait that postpones the development of cold-sensitive floral organs until the spring. The family Rosaceae includes many agriculturally important fruit and berry crops that suffer from crop losses caused by frost injury to overwintering flower buds. Recently, a vernalisation-requiring accession of the Rosaceae model woodland strawberry ( Fragaria vesca) has been identified in northern Norway. Understanding the molecular basis of the vernalisation requirement in this accession would advance the development of strawberry cultivars better adapted to temperate climate., We use gene silencing, gene expression analysis, genetic mapping and population genomics to study the genetic basis of the vernalisation requirement in woodland strawberry., Our results indicate that the woodland strawberry vernalisation requirement is endemic to northern Norwegian population, and mapping data suggest the orthologue of TERMINAL FLOWER1 ( Fv TFL1) as the causal floral repressor. We demonstrate that exceptionally low temperatures are needed to downregulate Fv TFL1 and to make these plants competent to induce flowering at low postvernalisation temperatures in the spring., We show that altered regulation of Fv TFL1 in the northern Norwegian woodland strawberry accession postpones flower induction until the spring, allowing plants to avoid winter injuries of flower buds that commonly occur in temperate regions. [ABSTRACT FROM AUTHOR]

Phase I oncology trials are designed to identify a safe dose with an acceptable toxicity profile. The dose is typically determined based on the probability of severe toxicity observed during the first treatment cycle, although patients continue to receive treatment for multiple cycles. In addition, the toxicity data from multiple types and grades are typically summarized into a single binary outcome of dose-limiting toxicity. A novel endpoint, the total toxicity profile, was previously developed to account for the multiple toxicity types and grades. In this work, we propose to account for longitudinal repeated measures of total toxicity profile over multiple treatment cycles, accounting for cumulative toxicity during dosing-finding. A linear mixed model was utilized in the Bayesian framework, with addition of Bayesian risk functions for decision-making in dose assignment. The performance of this design is evaluated using simulation studies and compared with the previously proposed quasi-likelihood continual reassessment method (QLCRM) design. Twelve clinical scenarios incorporating four different locations of maximum tolerated dose and three different time trends (decreasing, increasing, and no effect) were investigated. The proposed repeated measures design was comparable with the QLCRM when only cycle 1 data were utilized in dose-finding; however, it demonstrated an improvement over the QLCRM when data from multiple cycles were used across all scenarios. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Alcohol consumption is associated with a modest increased risk of colon cancer, but its relationship with colon cancer survival has not been elucidated. Using data from a phase III randomized adjuvant trial, we assessed the association of alcohol consumption with colon cancer outcomes. Patients completed a risk factor questionnaire before randomization to FOLFOX or FOLFOX + cetuximab ( N = 1984). Information was collected on lifestyle factors, including smoking, physical activity and consumption of different types of alcohol. Cox models assessed the association between alcohol consumption and outcomes of disease-free survival (DFS), time-to-recurrence (TTR) and overall survival (OS), adjusting for age, sex, study arm, body mass, smoking, physical activity and performance status. No statistically significant difference in outcomes between ever and never drinkers were noted [hazard ratio (HR)DFS = 0.86, HRTTR = 0.87, HROS = 0.86, p-values = 0.11-0.17]. However, when considering alcohol type, ever consumers of red wine ( n = 628) had significantly better outcomes than never consumers (HRDFS = 0.80, HRTTR = 0.81, HROS = 0.78, p-values = 0.01-0.02). Favorable outcomes were confirmed in patients who consumed 1-30 glasses/month of red wine ( n = 601, HR = 0.80-0.83, p-values = 0.03-0.049); there was a suggestion of more favorable outcomes in patients who consumed >30 glasses/month of red wine ( n = 27, HR = 0.33-0.38, p-values = 0.05-0.06). Beer and liquor consumption were not associated with outcomes. Although alcohol consumption was not associated with colon cancer outcomes overall, mild to moderate red wine consumption was suggestively associated with longer OS, DFS and TTR in stage III colon cancer patients. [ABSTRACT FROM AUTHOR]

The American Joint Committee on Cancer (AJCC) has increasingly recognized the need for more personalized probabilistic predictions than those delivered by ordinal staging systems, particularly through the use of accurate risk models or calculators. However, judging the quality and acceptability of a risk model is complex. The AJCC Precision Medicine Core conducted a 2-day meeting to discuss characteristics necessary for a quality risk model in cancer patients. More specifically, the committee established inclusion and exclusion criteria necessary for a risk model to potentially be endorsed by the AJCC. This committee reviewed and discussed relevant literature before creating a checklist unique to this need of AJCC risk model endorsement. The committee identified 13 inclusion and 3 exclusion criteria for AJCC risk model endorsement in cancer. The emphasis centered on performance metrics, implementation clarity, and clinical relevance. The facilitation of personalized probabilistic predictions for cancer patients holds tremendous promise, and these criteria will hopefully greatly accelerate this process. Moreover, these criteria might be useful for a general audience when trying to judge the potential applicability of a published risk model in any clinical domain. CA Cancer J Clin 2016;66:370-374. © 2016 American Cancer Society. [ABSTRACT FROM AUTHOR]

Large sample sizes are required in randomized clinical trials designed to meet typical one-sided 2.5% α-level and 80% power. This may not be achievable when the disease is rare. We simulated a series of two-arm superiority trials over a 15-year period. The design parameters examined were the α-level and the number of trials conducted over the 15-year period (thus, trial sample size). Different disease severities and accrual rates were considered. The future treatment effect was characterized by its associated hazard rate; different hypotheses of how treatments improve over time were considered. We defined the total survival benefit as the relative difference of the hazard rates at year 15 versus year 0. The optimal design was defined by maximizing the expected total survival benefit, provided that the risk of selecting at year 15 a treatment inferior to the initial control treatment remains below 1%. Compared with two larger trials with typical one-sided 2.5% α-level, performing a series of small trials with relaxed α-levels leads on average to larger survival benefits over a 15-year research horizon, but also to higher risk of selecting a worse treatment at the end of the research period. Under reasonably optimistic assumptions regarding the future treatment effects, optimal designs outperform traditional ones when the disease is severe (baseline median survival ≤ 1 year) and the accrual is ≥100 patients per year, whereas no major improvement is observed in diseases with better prognosis. Trial designs aiming to maximize survival gain over a long research horizon across a series of trials are worth discussing in the context of rare diseases. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

BACKGROUND: Although obesity is an established risk factor for developing colon cancer, its prognostic impact and relation to patient sex in colon cancer survivors remains unclear. METHODS: The authors examined the prognostic and predictive impact of the body mass index (BMI) in patients with stage II and III colon carcinoma (N = 25,291) within the Adjuvant Colon Cancer Endpoints (ACCENT) database. BMI was measured at enrollment in randomized trials of 5-fluorouracil-based adjuvant chemotherapy. Association of BMI with the time to recurrence (TTR), disease-free survival (DFS), and overall survival (OS) were determined using Cox regression models. Statistical tests were 2-sided. RESULTS: During a median follow-up of 7.8 years, obese and underweight patients had significantly poorer survival compared with overweight and normal-weight patients. In a multivariable analysis, the adverse prognostic impact of BMI was observed among men but not among women ( Pinteraction = .0129). Men with class 2 and 3 obesity (BMI ≥35.0 kg/m2) had a statistically significant reduction in DFS (hazard ratio [HR], 1.16; 95% confidence interval [CI], 1.01-1.33; P = .0297) compared with normal-weight patients. Underweight patients had a significantly shorter TTR and reduced DFS (HR, 1.18; 95% CI, 1.09-1.28; P < .0001) that was more significant among men (HR, 1.31; 95% CI, 1.15-1.50; P < .0001) than among women (HR, 1.11; 95% CI, 1.01-1.23; P = .0362; P interaction = .0340). BMI was not predictive of a benefit from adjuvant treatment. CONCLUSIONS: Obesity and underweight status were associated independently with inferior outcomes in patients with colon cancer who received treatment in adjuvant chemotherapy trials. Cancer 2013. © 2013 American Cancer Society. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

A two-stage model for evaluating both trial-level and patient-level surrogacy of correlated time-to-event endpoints has been introduced, using patient-level data when multiple clinical trials are available. However, the associated maximum likelihood approach often suffers from numerical problems when different baseline hazards among trials and imperfect estimation of treatment effects are assumed. To address this issue, we propose performing the second-stage, trial-level evaluation of potential surrogates within a Bayesian framework, where we may naturally borrow information across trials while maintaining these realistic assumptions. Posterior distributions on surrogacy measures of interest may then be used to compare measures or make decisions regarding the candidacy of a specific endpoint. We perform a simulation study to investigate differences in estimation performance between traditional maximum likelihood and new Bayesian representations of common meta-analytic surrogacy measures, while assessing sensitivity to data characteristics such as number of trials, trial size, and amount of censoring. Furthermore, we present both frequentist and Bayesian trial-level surrogacy evaluations of time to recurrence for overall survival in two meta-analyses of adjuvant therapy trials in colon cancer. With these results, we recommend Bayesian evaluation as an attractive and numerically stable alternative in the multitrial assessment of potential surrogate endpoints. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

BACKGROUND: African Americans are more likely to be diagnosed with metastatic colorectal cancer than whites and have shorter survival once they are diagnosed. In this analysis, the authors examined racial differences in clinical outcomes among patients with metastatic colorectal cancer (mCRC) who received bevacizumab. METHODS: The study cohort consisted of 1589 white patients (81.4%) and 227 African American patients (11.6%) with mCRC who received front-line bevacizumab therapy and who were enrolled in a large, predominantly community-based, prospective, observational cohort study. Differences in time-to-event endpoints and response rates were examined by race. Differences in the incidence of baseline and treatment-related toxicities associated with bevacizumab also were examined. Finally, differences in patterns of care by race were explored. RESULTS: The median overall survival was 22.6 months for African Americans and 22.9 months for whites, and the median progression-free survival was 9.5 months for African Americans and 9.8 months for whites. Response rates (complete responses plus partial responses) were 37.5% for African Americans and 46.3% for whites (adjusted odds ratio, 0.67; 95% confidence interval, 0.50-0.90). African Americans had higher rates of baseline diabetes (18.9% vs 11%; P = .002), higher rates of hypertension (52.9% vs 41.4%; P = .001), and worsening hypertension while on therapy (13.7% vs 8.9%; P = .02), but no differences in on-treatment arterial thromboembolic events were observed. CONCLUSIONS: This large observational cohort study of patients with mCRC demonstrated that, when treated in a similar fashion with modern chemotherapy, African Americans and whites had equivalent cancer outcomes. No significant differences in bevacizumab-related toxicity or patterns of care were observed between African Americans and whites. The lower response rate among African Americans deserves further study. Cancer 2012;. © 2011 American Cancer Society. [ABSTRACT FROM AUTHOR]

BACKGROUND: Numeracy and Adjuvant! are 2 web-based calculators that are used widely to estimate the prognosis and potential benefit of adjuvant 5-fluorouracil (5-FU)-based therapy for patients with stage II and III colon cancer. In this study, the authors compared the predicted survival estimates from these models with the actual observed estimates in independent datasets that were derived from a population cohort and from clinical trials. METHODS: The population cohort was derived from the British Columbia Colorectal Cancer Outcomes Unit database, which identified referred patients with stage II and III colon cancer from 1995 to 1996 and from 1999 to 2003. Patients who were enrolled in North Central Cancer Trials Group (NCCTG) trials NCCTG 94651 and NCCTG 914653 were included in the trials dataset. Patient and disease data were used to predict 5-year relapse-free and overall survival using both tools. RESULTS: In the population-based dataset (N = 2033), Adjuvant! offered more reliable predictions of prognosis for patients who underwent surgery alone, but it had reliability similar to that of Numeracy for predicting the prognosis for patients who received adjuvant 5-FU. Both models tended to overestimate survival for patients with stage II disease who received 5-FU. In the trials dataset of patients who underwent and received 5-FU (N = 1729), Numeracy and Adjuvant! demonstrated similar performance and improved correctness. CONCLUSIONS: This independent validation analysis demonstrated that both Numeracy and Adjuvant! had similar predictive performance and acceptable reliability for patients with stage III disease. Survival outcomes of patients with stage II colon cancer who received adjuvant 5-FU were slightly lower than estimated by either model. Cancer 2011;. © 2011 American Cancer Society. [ABSTRACT FROM AUTHOR]

Novel therapies are challenging the standards of drug development. Agents with specific biologic targets, unknown dose-efficacy curves, and limited toxicity mandate novel designs to identify biologically optimal doses. We review two model-based designs that utilize either a proportional odds model or a continuation ratio model to identify an optimal dose of a single or two-agent combination in a Phase I setting utilizing both toxicity and efficacy data. A continual reassessment method with straightforward dose selection criterion using accumulated data from all patients treated until that time point is employed while allowing for separate toxicity and efficacy curves for each drug in a two-drug setting. The simulation studies demonstrate considerable promise, at least theoretically, in the ability of such model-based designs to identify the optimal dose. Despite such favorable operating characteristics, there are several pragmatic challenges that hinder the routine implementation of such model-based designs in practice. We review and offer practical solutions to potentially overcome some of these challenges. The acceptance and integration of these designs in practice may be quicker and easier if they are developed in concert with a clinical paradigm. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article presents a study which aims to develop defective DNA mismatch repair (MMR) prediction model in patients with sporadic colon cancer. Clinical and pathological data from 982 patients with resected tumor, nodes, metastasis (TNM) stage II and III colonic adenocarcinomas were analyzed for microsatellite instability and/or MMR protein expression. Results show that the most important predictors of MMR status were tumor site and histological grade which are helpful in clinical decisions.

Interaction effects have been consistently found important in explaining the variation in outcomes in many scientific research fields. Yet, in practice, variable selection including interactions is complicated due to the limited sample size, conflicting philosophies regarding model interpretability, and accompanying amplified multiple-testing problems. The lack of statistically sound algorithms for automatic variable selection with interactions has discouraged activities in exploring important interaction effects. In this article, we investigated issues of selecting interactions from three aspects: (1) What is the model space to be searched? (2) How is the hypothesis-testing performed? (3) How to address the multiple-testing issue? We propose loss functions and corresponding decision rules that control FDR in a Bayesian context. Properties of the decision rules are discussed and their performance in terms of power and FDR is compared through simulations. Methods are illustrated on data from a colorectal cancer study assessing the chemotherapy treatments and data from a diffuse large-B-cell lymphoma study assessing the prognostic effect of gene expressions. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article provides information on a study that examined the efficacy and toxicity of oxaliplatin combined with irinotecan (IROX) in patients enrolled on the IROX arm of Intergroup Study N9741. It describes methodology used in the study. It concludes that although IROX may be considered in patients intolerant of 5-FU or in patients known to have a dihydropyrimidine dehydrogenase deficiency.

Historically, designs for dose seeking trials using chemotherapeutic drug combinations have been geared towards finding the maximum tolerated dose, with safety as the primary outcome. With target based agents whose dose-efficacy curves are unknown and dose-toxicity relationships may be minimal, alternative designs are needed. In this paper, we propose an extension to an adaptive single agent dose-finding design previously reported. A generalization of the continuation ratio model allowing separate toxicity and efficacy curves for each agent in a dual agent combination, generating a dose success surface for the combination, is proposed. A continual reassessment approach with a straightforward dose selection criterion using the accumulated data from all patients treated to that point is employed. Our simulation studies demonstrate favourable operating characteristics in terms of experimentation and recommendation rates, and the average sample size, under a variety of scenarios. The proposed approach allowing the incorporation of both the toxicity and efficacy of each agent into the identification of an optimal dosing region for a combination is novel and warrants further consideration. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Novel therapies are challenging the standards of drug development. Agents with specific biologic targets and limited toxicity require novel designs to determine doses to be taken forward into larger studies. In this paper, we describe an approach that incorporates both toxicity and efficacy data into the estimation of the biologically optimal dose of an agent in a phase I trial. The approach is based on the flexible continuation-ratio model, and uses straightforward optimal dose selection criteria. Dose selection is based on all patients treated up until that time point, using a continual reassessment method approach. Dose-outcome curves considered include monotonically increasing, monotonically decreasing, and unimodal curves. Our simulation studies demonstrate that the proposed design, which we call TriCRM, has favourable operating characteristics. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]