Use of a combined gene expression profile in implementing a drug sensitivity predictive model for breast cancer

Xianglan Zhang, In Ho Cha, Ki Yeol Kim

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Purpose Chemotherapy targets all rapidly growing cells, not only cancer cells, and thus is often associated with unpleasant side effects. Therefore, examination of the chemosensitivity based on genotypes is needed in order to reduce the side effects. Materials and Methods Various computational approaches have been proposed for predicting chemosensitivity based on gene expression profiles. A linear regression model can be used to predict the response of cancer cells to chemotherapeutic drugs, based on genomic features of the cells, and appropriate sample size for this method depends on the number of predictors. We used principal component analysis and identified a combined gene expression profile to reduce the number of predictors Results The coefficients of determinanation (R2) of prediction models with combined gene expression and several independent gene expressions were similar. Corresponding F values, which represent model significances were improved by use of a combined gene expression profile, indicating that the use of a combined gene expression profile is helpful in predicting drug sensitivity. Even better, a prediction model can be used even with small samples because of the reduced number of predictors. Conclusion Combined gene expression analysis is expected to contribute to more personalized management of breast cancer cases by enabling more effective targeting of existing therapies. This procedure for identifying a cell-type-specific gene expression profile can be extended to other chemotherapeutic treatments and many other heterogeneous cancer types.

Original languageEnglish
Pages (from-to)116-128
Number of pages13
JournalCancer Research and Treatment
Volume49
Issue number1
DOIs
Publication statusPublished - 2017

Bibliographical note

Funding Information:
This study was supported by a faculty research grant of Yonsei University College of Dentistry for 2015 (6-2015-0003).

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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