Shuichi Kawano

Identification of Genes Upregulated in ALK-Positive and EGFR/KRAS/ALK-Negative Lung Adenocarcinomas

Activation of the EGFR, KRAS, and ALK oncogenes defines 3 different pathways of molecular pathogenesis in lung adenocarcinoma. However, many tumors lack activation of any pathway (triple-negative lung adenocarcinomas) posing a challenge for prognosis and treatment. Here, we report an extensive genome-wide expression profiling of 226 primary human stage I-II lung adenocarcinomas that elucidates molecular characteristics of tumors that harbor ALK mutations or that lack EGFR, KRAS, and ALK mutations, that is, triple-negative adenocarcinomas. One hundred and seventy-four genes were selected as being upregulated specifically in 79 lung adenocarcinomas without EGFR and KRAS mutations. Unsupervised clustering using a 174-gene signature, including ALK itself, classified these 2 groups of tumors into ALK-positive cases and 2 distinct groups of triple-negative cases (groups A and B). Notably, group A triple-negative cases had a worse prognosis for relapse and death, compared with cases with EGFR, KRAS, or ALK mutations or group B triple-negative cases. In ALK-positive tumors, 30 genes, including ALK and GRIN2A, were commonly overexpressed, whereas in group A triple-negative cases, 9 genes were commonly overexpressed, including a candidate diagnostic/therapeutic target DEPDC1, that were determined to be critical for predicting a worse prognosis. Our findings are important because they provide a molecular basis of ALK-positive lung adenocarcinomas and triple-negative lung adenocarcinomas and further stratify more or less aggressive subgroups of triple-negative lung ADC, possibly helping identify patients who may gain the most benefit from adjuvant chemotherapy after surgical resection.

Bayesian Information Criterion and Selection of the Number of Factors in Factor Analysis Models

In maximum likelihood exploratory factor analysis, the estimates of unique variances can often turn out to be zero or negative, which makes no sense from a statistical point of view. In order to overcome this diculty, we employ a Bayesian approach by specifying a prior distribution for the variances of unique factors. The factor analysis model is estimated by EM algorithm, for which we provide the expectation and maximization steps within a general framework of EM algorithms. Crucial issues in Bayesian factor analysis model are the choice of adjusted parameters including the number of factors and also the hyper-parameters for the prior distribution. The choice of these parameters can be viewed as a model selection and evaluation problem. We derive a model selection criterion for evaluating a Bayesian factor analysis model. Monte Carlo simulations are conducted to investigate the eectiveness of the proposed procedure. A real data example is also given to illustrate our procedure. We observe that our modeling procedure prevents the occurrence of improper solutions and also chooses the appropriate number of factors objectively.

Semi-Supervised Logistic Discrimination via Regularized Gaussian Basis Expansions

The problem of constructing classification methods based on both labeled and unlabeled data sets is considered for analyzing data with complex structures. We introduce a semi-supervised logistic discriminant model with Gaussian basis expansions. Unknown parameters included in the logistic model are estimated by regularization method along with the technique of EM algorithm. For selection of adjusted parameters, we derive a model selection criterion from Bayesian viewpoints. Numerical studies are conducted to investigate the effectiveness of our proposed modeling procedures.

Selection of tuning parameters in bridge regression models via Bayesian information criterion

We consider bridge regression models, which can produce a sparse or non-sparse model by controlling a tuning parameter in the penalty term. A crucial part of a model building strategy is the selection of the values for adjusted parameters, such as regularization and tuning parameters. Indeed, this can be viewed as a problem in selecting and evaluating the model. We propose a Bayesian selection criterion for evaluating bridge regression models. This criterion enables us to objectively select the values of the adjusted parameters. We investigate the effectiveness of our proposed modeling strategy with some numerical examples.

Identifying Gene Pathways Associated with Cancer Characteristics via Sparse Statistical Methods

We propose a statistical method for uncovering gene pathways that characterize cancer heterogeneity. To incorporate knowledge of the pathways into the model, we define a set of activities of pathways from microarray gene expression data based on the Sparse Probabilistic Principal Component Analysis (SPPCA). A pathway activity logistic regression model is then formulated for cancer phenotype. To select pathway activities related to binary cancer phenotypes, we use the elastic net for the parameter estimation and derive a model selection criterion for selecting tuning parameters included in the model estimation. Our proposed method can also reverse-engineer gene networks based on the identified multiple pathways that enables us to discover novel gene-gene associations relating with the cancer phenotypes. We illustrate the whole process of the proposed method through the analysis of breast cancer gene expression data.

Sparse principal component regression with adaptive loading

Principal component regression (PCR) is a two-stage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variables. Note that the principal components are obtained from only explanatory variables and not considered with the response variable. To address this problem, we propose the sparse principal component regression (SPCR) that is a one-stage procedure for PCR. SPCR enables us to adaptively obtain sparse principal component loadings that are related to the response variable and select the number of principal components simultaneously. SPCR can be obtained by the convex optimization problem for each parameter with the coordinate descent algorithm. Monte Carlo simulations and real data analyses are performed to illustrate the effectiveness of SPCR.

Semi-supervised logistic discrimination via graph-based regularization

We address the problem of constructing a nonlinear discriminant procedure based on both labeled and unlabeled data sets. A semi-supervised logistic model with Gaussian basis functions is presented along with the technique of graph-based regularization. A crucial issue in modeling process is the choice of tuning parameters included in the nonlinear semi-supervised logistic models. In order to select these adjusted parameters, we derive model selection criteria from the viewpoints of information theory and also the Bayesian approach. Some numerical examples are given to investigate the effectiveness of our proposed semi-supervised modeling strategies.

Model selection criteria for the varying-coefficient modelling via regularized basis expansions

We address the problem of constructing varying-coefficient models based on basis expansions along with the technique of regularization. A crucial point in our modeling procedure is the selection of smoothing parameters in the regularization method. In order to choose the parameters objectively, we derive model selection criteria from the viewpoints of information-theoretic and Bayesian approach. We demonstrate the effectiveness of proposed modeling strategy through Monte Carlo simulations and analyzing a real data set.Varying-coefficient models (VCMs) are useful tools for analysing longitudinal data. They can effectively describe the relationship between predictors and responses repeatedly measured. VCMs estimated by regularization methods are strongly affected by values of regularization parameters, and therefore selecting these values is a crucial issue. In order to choose these parameters objectively, we derive model selection criteria for evaluating VCMs from the viewpoints of information-theoretic and Bayesian approach. Models are estimated by the method of regularization with basis expansions, and then they are evaluated by model selection criteria. We demonstrate the effectiveness of the proposed criteria through Monte Carlo simulations and real data analysis.

Sparse principal component regression for generalized linear models

Abstract Principal component regression (PCR) is a widely used two-stage procedure: principal component analysis (PCA), followed by regression in which the selected principal components are regarded as new explanatory variables in the model. Note that PCA is based only on the explanatory variables, so the principal components are not selected using the information on the response variable. We propose a one-stage procedure for PCR in the framework of generalized linear models. The basic loss function is based on a combination of the regression loss and PCA loss. An estimate of the regression parameter is obtained as the minimizer of the basic loss function with a sparse penalty. We call the proposed method sparse principal component regression for generalized linear models (SPCR-glm). Taking the two loss function into consideration simultaneously, SPCR-glm enables us to obtain sparse principal component loadings that are related to a response variable. However, a combination of loss functions may cause a parameter identification problem, but this potential problem is avoided by virtue of the sparse penalty. Thus, the sparse penalty plays two roles in this method. We apply SPCR-glm to two real datasets, doctor visits data and mouse consomic strain data.

Semi-supervised logistic discrimination via labeled data and unlabeled data from different sampling distributions

This article addresses the problem of classification method based on both labeled and unlabeled data, where we assume that a density function for labeled data is different from that for unlabeled data. We propose a semi-supervised logistic regression model for classification problem along with the technique of covariate shift adaptation. Unknown parameters involved in proposed models are estimated by regularization with expectation and maximization (EM) algorithm. A crucial issue in the modeling process is the choices of adjusted parameters in our semi-supervised logistic models. In order to select the parameters, a model selection criterion is derived from an information-theoretic approach. Some numerical studies show that our modeling procedure performs well in various cases.