Predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarrays

BACKGROUND: Genetic markers hold great promise for refining our ability to establish precise prognostic prediction for diseases. The development of comprehensive gene expression microarray technology has allowed the selection of relevant marker genes from a large pool of candidate genes in early-phased, developmental prognostic marker studies. The primary analytical task in such studies is to select a small fraction of relevant genes, typically from a list of significant genes, for further investigation in subsequent studies. RESULTS: We develop a methodology for predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarrays. Key components in this methodology include building prediction models, assessing predictive performance of prediction models, and assessing significance of prediction results. As particular specifications, we assume Cox proportional hazard models with a compound covariate. For assessing predictive accuracy, we propose to use the cross-validated log partial likelihood. To assess significance of prediction results, we apply permutation procedures in cross-validated prediction. As an additional key component peculiar to prognostic prediction, we also consider incorporation of standard prognostic factors. The methodology is evaluated using both simulated and real data. CONCLUSION: The developed methodology for prognostic prediction using a subset of significant genes can provide new insights based on predictive capability, possibly incorporating standard prognostic factors, in selecting a fraction of relevant genes for subsequent studies

分子診断法・治療法の開発のための臨床研究の計画と解析

Open House, ISM in Tachikawa, 2011.7.14統計数理研究所オープンハウス（立川）、H23.7.14ポスター発

Bayesian ranking and selection methods using hierarchical mixture models in microarray studies.

The main purpose of microarray studies is screening to identify differentially expressed genes as candidates for further investigation. Because of limited resources in this stage, prioritizing or ranking genes is a relevant statistical task in microarray studies. In this article, we develop 3 empirical Bayes methods for gene ranking on the basis of differential expression, using hierarchical mixture models. These methods are based on (i) minimizing mean squared errors of estimation for parameters, (ii) minimizing mean squared errors of estimation for ranks of parameters, and (iii) maximizing sensitivity in selecting prespecified numbers of differential genes, with the largest effect. Our methods incorporate the mixture structures of differential and nondifferential components in empirical Bayes models to allow information borrowing across differential genes, with separation from nuisance, nondifferential genes. The accuracy of our ranking methods is compared with that of conventional methods through simulation studies. An application to a clinical study for breast cancer is provided

個別化医療の開発と検証のための統計的方法論の研究

ISM Online Open House, 2020.10.27統計数理研究所オープンハウス（オンライン開催）、R2.10.27ポスター発

個別化医療の開発と検証のための統計的方法論の研究

ISM Online Open House, 2021.6.18統計数理研究所オープンハウス（オンライン開催）、R3.6.18ポスター発

予測医療に向けた臨床試験デザインと解析に関する研究

Open House, ISM in Tachikawa, 2012.6.15統計数理研究所オープンハウス（立川）、H24.6.15ポスター発

More Powerful Selective Kernel Tests for Feature Selection

Refining one's hypotheses in the light of data is a common scientific practice; however, the dependency on the data introduces selection bias and can lead to specious statistical analysis. An approach for addressing this is via conditioning on the selection procedure to account for how we have used the data to generate our hypotheses, and prevent information to be used again after selection. Many selective inference (a.k.a. post-selection inference) algorithms typically take this approach but will "over-condition" for sake of tractability. While this practice yields well calibrated statistic tests with controlled false positive rates (FPR), it can incur a major loss in power. In our work, we extend two recent proposals for selecting features using the Maximum Mean Discrepancy and Hilbert Schmidt Independence Criterion to condition on the minimal conditioning event. We show how recent advances in multiscale bootstrap makes conditioning on the minimal selection event possible and demonstrate our proposal over a range of synthetic and real world experiments. Our results show that our proposed test is indeed more powerful in most scenarios.Comment: Accepted to AISTATS 202

Root canal treatment with high-frequency waves in rats

The purpose of this study was to develop a high-frequency wave therapy model in rats and to investigate the influence of high-frequency waves on root canal treatment, which may provide a novel strategy for treating apical periodontitis. Root canal treatments with and without high-frequency wave irradiation were performed on the mandibular first molars of 10-week-old male Wistar rats. The mesial roots were evaluated radiologically, bacteriologically, and immunohistochemically. At 3 weeks after root canal treatment, lesion volume had decreased significantly more in the irradiated group than in the non-irradiated group, indicating successful development of the high-frequency therapy model. The use of high-frequency waves provided no additional bactericidal effect after root canal treatment. However, high-frequency wave irradiation was found to promote healing of periapical lesions on the host side through increased expression of fibroblast growth factor 2 and transforming growth factor-β1 and could therefore be useful as an adjuvant nonsurgical treatment for apical periodontitis

Assessment of the functional efficacy of root canal treatment with high-frequency waves in rats

The purpose of this study was to develop a high-frequency wave therapy model in rats and to investigate the influence of high-frequency waves on root canal treatment, which may provide a novel strategy for treating apical periodontitis. Root canal treatments with and without high-frequency wave irradiation were performed on the mandibular first molars of 10-week-old male Wistar rats. The mesial roots were evaluated radiologically, bacteriologically, and immunohistochemically. At 3 weeks after root canal treatment, lesion volume had decreased significantly more in the irradiated group than in the non-irradiated group, indicating successful development of the high-frequency therapy model. The use of high-frequency waves provided no additional bactericidal effect after root canal treatment. However, high-frequency wave irradiation was found to promote healing of periapical lesions on the host side through increased expression of fibroblast growth factor 2 and transforming growth factor-β1 and could therefore be useful as an adjuvant nonsurgical treatment for apical periodontitis.Assessment of the functional efficacy of root canal treatment with high-frequency waves in rats. Saori Matsui, et al. PLOS ONE. 2020.9(29) doi.org/10.1371/journal.pone.023966

Study protocol of the SACURA trial: a randomized phase III trial of efficacy and safety of UFT as adjuvant chemotherapy for stage II colon cancer

BACKGROUND: Adjuvant chemotherapy for stage III colon cancer is internationally accepted as standard treatment with established efficacy, but the usefulness of adjuvant chemotherapy for stage II colon cancer remains controversial. The major Western guidelines recommend adjuvant chemotherapy for “high-risk stage II” cancer, but this is not clearly defined and the efficacy has not been confirmed. METHODS/DESIGN: SACURA trial is a multicenter randomized phase III study which aims to evaluate the superiority of 1-year adjuvant treatment with UFT to observation without any adjuvant treatment after surgery for stage II colon cancer in a large population, and to identify “high-risk factors of recurrence/death” in stage II colon cancer and predictors of efficacy and adverse events of the chemotherapy. Patients aged between 20 and 80 years with curatively resected stage II colon cancer are randomly assigned to a observation group or UFT adjuvant therapy group (UFT at 500–600 mg/day as tegafur in 2 divided doses after meals for 5 days, followed by 2-day rest. This 1-week treatment cycle is repeated for 1 year). The patients are followed up for 5 years until recurrence or death. Treatment delivery and adverse events are entered into a web-based case report form system every 3 months. The target sample size is 2,000 patients. The primary endpoint is disease-free survival, and the secondary endpoints are overall survival, recurrence-free survival, and incidence and severity of adverse events. In an additional translational study, the mRNA expression of 5-FU-related enzymes, microsatellite instability and chromosomal instability, and histopathological factors including tumor budding are assessed to evaluate correlation with recurrences, survivals and adverse events. DISCUSSION: A total of 2,024 patients were enrolled from October 2006 to July 2010. The results of this study will provide important information that help to improve the therapeutic strategy for stage II colon cancer. TRIAL REGISTRATION: ClinicalTrials.gov NCT00392899