title: Modeling Expression Plasticity of Genes that Differentiate Drug-sensitive from Drug-resistant Cells to Chemotherapeutic Treatment.
abstract: By measuring gene expression at an unprecedented resolution and throughput, RNA-seq has played a pivotal role in studying biological functions. Its typical application in clinical medicine is to identify the discrepancies of gene expression between two different types of cancer cells, sensitive and resistant to chemotherapeutic treatment, in a hope to predict drug response. Here we modified and used a mechanistic model to identify distinct patterns of gene expression in response of different types of breast cancer cell lines to chemotherapeutic treatment. This model was founded on a mixture likelihood of Poisson-distributed transcript read data, with each mixture component specified by the Skellam function. By estimating and comparing the amount of gene expression in each environment, the model can test how genes alter their expression in response to environment and how different genes interact with each other in the responsive process. Using the modified model, we identified the alternations of gene expression between two cell lines of breast cancer, resistant and sensitive to tamoxifen, which allows us to interpret the expression mechanism of how genes respond to metabolic differences between the two cell types. The model can have a general implication for studying the plastic pattern of gene expression across different environments measured by RNA-seq.
authors.affiliation: Department of Statistics, Pennsylvania State University, State College, PA 16802, USA ; Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA.
authors.forename: Ningtao
authors.initials: N
authors.lastname: Wang
authors.affiliation: Department of Statistics, Pennsylvania State University, State College, PA 16802, USA ; Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA.
authors.forename: Yaqun
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Statistics, Pennsylvania State University, State College, PA 16802, USA ; Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Han
authors.affiliation: Department of Pharmacology, The Pennsylvania State University, Hershey, PA 17033, USA.
authors.forename: Kathryn J
authors.initials: KJ
authors.lastname: Huber
authors.affiliation: Department of Pharmacology, The Pennsylvania State University, Hershey, PA 17033, USA.
authors.forename: Jin-Ming
authors.initials: JM
authors.lastname: Yang
authors.affiliation: Department of Statistics, Pennsylvania State University, State College, PA 16802, USA ; Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA.
authors.forename: Runze
authors.initials: R
authors.lastname: Li
authors.affiliation: Department of Statistics, Pennsylvania State University, State College, PA 16802, USA ; Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA.
authors.forename: Rongling
authors.initials: R
authors.lastname: Wu
keywords: Clustering.; Gene-environment interaction; Phenotypic plasticity; RNA-seq