Pathway-structured predictive modeling for multi-level drug response in multiple myeloma

DPADM: a novel algorithm for detecting drug-pathway associations based on high-throughput transcriptional response to compounds

Pathway genes functionally participate in the same biological process. They typically act cooperatively, and none is considered dispensable. The dominant paradigm in drug discovery is the one-to-one strategy, which aims to find the most sensitive drug to act on an individual target. However, many complex diseases, such as cancer, are caused by dysfunction among multiple-gene pathways, not just one. Therefore, identifying pathway genes that are responsive to synthetic compounds in a global physiological environment may be more effective in drug discovery. The high redundancy of crosstalk between biological pathways, though, hints that the covariance matrix, which only connects genes with strong marginal correlations, may miss higher-level interactions, such as group interactions. We herein report the development of DPADM-a Drug-Pathway association Detection Model that infers pathways responsive to specific drugs. This model elucidates higher-level gene-gene interactions by evaluating the conditional dependencies between genes under different drug treatments. The advantage of the proposed method is demonstrated using simulation studies by comparing with another two methods. We applied this model to the Connectivity Map data set (CMap), and demonstrated that DPADM is able to identify many drug-pathway associations, such as mitoxantrone (MTX)- PI3K/AKT association, which targets the topological conditions of DNA transcription. Surprisingly, apart from identifying pathways corresponding to specific drugs, our methodology also revealed new drug-related pathways with functions similarly to those of seed genes.

Bayesian penalized cumulative logit model for high-dimensional data with an ordinal response

Many previous studies have identified associations between gene expression, measured using high-throughput genomic platforms, and quantitative or dichotomous traits. However, we note that health outcome and disease status measurements frequently appear on an ordinal scale, that is, the outcome is categorical but has inherent ordering. Identification of important genes may be useful for developing novel diagnostic and prognostic tools to predict or classify stage of disease. Gene expression data are usually high-dimensional, meaning that the number of genes is much larger than the sample size or number of patients. Herein we describe some existing frequentist methods for modeling an ordinal response in a high-dimensional predictor space. Following Tibshirani (1996), who described the LASSO estimate as the Bayesian posterior mode when the regression coefficients have independent Laplace priors, we propose a new approach for high-dimensional data with an ordinal response that is rooted in the Bayesian paradigm. We show that our proposed Bayesian approach outperforms existing frequentist methods through simulation studies. We then compare the performance of frequentist and Bayesian approaches using a study evaluating progression to hepatocellular carcinoma in hepatitis C infected patients.

BhGLM: Bayesian hierarchical GLMs and survival models, with applications to genomics and epidemiology

SUMMARY

BhGLM is a freely available R package that implements Bayesian hierarchical modeling for high-dimensional clinical and genomic data. It consists of functions for setting up various Bayesian hierarchical models, including generalized linear models (GLMs) and Cox survival models, with four types of prior distributions for coefficients, i.e. double-exponential, Student-t, mixture double-exponential and mixture Student-t. These functions adapt fast and stable algorithms to estimate parameters. BhGLM also provides functions for summarizing results numerically and graphically and for evaluating predictive values. The package is particularly useful for analyzing large-scale molecular data, i.e. detecting disease-associated variables and predicting disease outcomes. We here describe the models, algorithms and associated features implemented in BhGLM.

AVAILABILITY AND IMPLEMENTATION

The package is freely available from the public GitHub repository, https://github.com/nyiuab/BhGLM.