Modeling expression plasticity of genes that differentiate drug-sensitive from drug-resistant cells to chemotherapeutic treatment

Ningtao Wang, Yaqun Wang, Hao Han, Kathryn J. Huber, Jin Ming Yang, Runze Li, Rongling Wu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)349-356
Number of pages8
JournalCurrent Genomics
Volume15
Issue number5
DOIs
StatePublished - Jan 1 2014

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

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