Asanao Shimokawa

A combination of TERT promoter mutation and MGMT methylation status predicts clinically relevant subgroups of newly diagnosed glioblastomas

The prognostic impact of TERT mutations has been controversial in IDH-wild tumors, particularly in glioblastomas (GBM). The controversy may be attributable to presence of potential confounding factors such as MGMT methylation status or patients’ treatment. This study aimed to evaluate the impact of TERT status on patient outcome in association with various factors in a large series of adult diffuse gliomas. We analyzed a total of 951 adult diffuse gliomas from two cohorts (Cohort 1, n = 758; Cohort 2, n = 193) for IDH1/2, 1p/19q, and TERT promoter status. The combined IDH/TERT classification divided Cohort 1 into four molecular groups with distinct outcomes. The overall survival (OS) was the shortest in IDH wild-type/TERT mutated groups, which mostly consisted of GBMs (P < 0.0001). To investigate the association between TERT mutations and MGMT methylation on survival of patients with GBM, samples from a combined cohort of 453 IDH-wild-type GBM cases treated with radiation and temozolomide were analyzed. A multivariate Cox regression model revealed that the interaction between TERT and MGMT was significant for OS (P = 0.0064). Compared with TERT mutant-MGMT unmethylated GBMs, the hazard ratio (HR) for OS incorporating the interaction was the lowest in the TERT mutant-MGMT methylated GBM (HR, 0.266), followed by the TERT wild-type-MGMT methylated (HR, 0.317) and the TERT wild-type-MGMT unmethylated GBMs (HR, 0.542). Thus, patients with TERT mutant-MGMT unmethylated GBM have the poorest prognosis. Our findings suggest that a combination of IDH, TERT, and MGMT refines the classification of grade II-IV diffuse gliomas.

Comparison of splitting methods on survival tree.

Abstract We compare splitting methods for constructing survival trees that are used as a model of survival time based on covariates. A number of splitting criteria on the classification and regression tree (CART) have been proposed by various authors, and we compare nine criteria through simulations. Comparative studies have been restricted to criteria that suppose the survival model for each terminal node in the final tree as a non-parametric model. As the main results, the criteria using the exponential log-likelihood loss, log-rank test statistics, the deviance residual under the proportional hazard model, or square error of martingale residual are recommended when it appears that the data have constant hazard with the passage of time. On the other hand, when the data are thought to have decreasing hazard with passage of time, the criterion using the two-sample test statistic, or square error of deviance residual would be optimal. Moreover, when the data are thought to have increasing hazard with the passage of time, the criterion using the exponential log-likelihood loss, or impurity that combines observed times and the proportion of censored observations would be the best. We also present the results of an actual medical research to show the utility of survival trees.

A comparative study on splitting criteria of a survival tree based on the Cox proportional model.

ABSTRACT We treat the situations that the effect of covariates on hazard is differed in subgroups of patients. To handle this situation, we can consider the hybrid model of the Cox model and tree-structured model. Through simulation studies, we compared several splitting criteria for constructing this hybrid model. As a result, the criterion using the degree of the improvement in the negative maximum partial log-likelihood obtained by splitting showed a good performance for many situations. We also present the results obtained by applying this tree model in an actual medical research study to show its utility.

Mixed Hidden Markov Models for Clinical Research with Discrete Repeated Measurements

A hidden Markov model (HMM) is a method for analyzing a sequence of transitions for a set of data by considering the outcomes Y to be output from latent state X, which has the Markov property. The HMM has been widely applied, with applications that include speech recognition, genomic analysis, and finance forecasting. The HMM was originally a method for dealing with single-process data. Thus, it is a natural extension to apply it to data with a repeated measure structure by incorporating random effects in it. This is called the mixed hidden Markov model (MHMM). With this extension, the MHMM was recently applied to clinical research data with repeated measurements, e.g. multiple sclerosis, alcohol consumption, and primary biliary cirrhosis. In relation to parameter inference, because regular HMM methods can be used in an MHMM framework, some legacy knowledge is applicable. The likelihood can be obtained by simply adding a random effect parameter to a single process HMM, and the conventional maximum-likelihood method can be used for parameter estimation. On the other hand, much work must still be performed. For instance, the mathematical property of the maximum likelihood estimator has not yet been thoroughly examined. In this study, the asymptotic normality and consistency of the maximum likelihood estimator of the MHMM concerned with time points are examined via simulation, and found that these properties were almost fine. These methods are applied to actual study data, and future perspectives are provided in the conclusion.

A Bayesian equivalency test for two independent binomial proportions

ABSTRACT In clinical trials, it is often necessary to perform an equivalence study. The equivalence study requires actively denoting equivalence between two different drugs or treatments. Since it is not possible to assert equivalence that is not rejected by a superiority test, statistical methods known as equivalency tests have been suggested. These methods for equivalency tests are based on the frequency framework; however, there are few such methods in the Bayesian framework. Hence, this article proposes a new index that suggests the equivalency of binomial proportions, which is constructed based on the Bayesian framework. In this study, we provide two methods for calculating the index and compare the probabilities that have been calculated by these two calculation methods. Moreover, we apply this index to the results of actual clinical trials to demonstrate the utility of the index.

Application of Survival Tree Based on Texture Features Obtained through MRI of Patients with Brain Metastases from Breast Cancer

The information obtained by magnetic resonance imaging (MRI) is considered to possess great potential for providing the prognosis of cancer patients, although not been established. The goal of this study was to evaluate the covariates of the texture patterns obtained from MRI scans of patients with breast cancer brain metastases, which influence the survival time prognosis. The data of forty patients were analyzed using 29 covariates. Twenty-six covariates, which are focused on the texture patterns, were calculated from the gray-level co-occurrence matrix and wavelet coefficients obtained by transform of preoperative T1-weighted MRI scans. The remaining three covariates were age, Karnofsky Performance Scale, and the indicator of whether solitary or multiple metastases were present. These covariates are commonly used as the prognostic factors in medical research. The tree structure prognosis models were constructed by applying the survival tree method to these covariates. The obtained survival trees separated the patients into two or three groups between which there was a statistically significant distance. For the purpose of comparison, Cox regression analyses were performed to determine which covariates showed significant predictive values. All the covariates selected in the Cox analysis and survival tree method were texture features only. In particular, the energy of the gray-level co-occurrence matrix and wavelet coefficients showed a high performance in tree structure analysis. From these results, we conclude that the features obtained from simple medical images can be used to estimate the prognosis of brain metastases patients.