Cancer Res Treat.  2017 Jan;49(1):116-128. 10.4143/crt.2016.085.

Use of a Combined Gene Expression Profile in Implementing a Drug Sensitivity Predictive Model for Breast Cancer

• Zhang X
• Cha IH
• Kim KY

• KMID: 2367509
• DOI: http://doi.org/10.4143/crt.2016.085

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.

Keyword

Gene expression; Drug sensitivity; Combined predictor

MeSH Terms

Breast Neoplasms*
Breast*
Drug Therapy
Gene Expression*
Genotype
Humans
Linear Models
Methods
Principal Component Analysis
Sample Size
Transcriptome*

Figure

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  Fig. 1. The summary of the study plan using two published datasets. G-matrix includes 638 genes, which showed significantly different expression among subtypes of breast cancer cell lines.

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  Fig. 2. Expression patterns of 638 genes in breast cancer cell lines, identified by ANOVA. The vertical and horizontal axes represent gene expressions and breast cancer cell lines, respectively.

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  Fig. 3. Patterns of standardized IC50 scores in 31 breast cancer cell lines. The vertical and horizontal axes represent 98 drugs and 31 breast cancer cell lines, respectively.

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  Fig. 4. Patterns of standardized IC50 of four drugs in 31 breast cancer cell lines. The vertical and horizontal axes represent four drugs and 31 breast cancer cell lines, respectively.

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  Fig. 5. DG-matrix. Correlation pattern of 638 gene expressions and standardized IC50 scores of 98 drugs. The vertical and horizontal axes represent 638 genes and 98 drugs, respectively. Correlation coefficients range from –1 to 1.

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  Fig. 6. The relationship between expressions of doxorubicin-related genes and drug sensitivity of doxorubicin (A) and mitomycin (B). The horizontal and vertical axes represent the standardized IC50 scores and gene expressions, respectively.

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  Fig. 7. Association of gene expressions and chemosensitivity to doxorubicin in breast cancer. Doxorubicin represents IC50 scores against doxorubicin and combined biomarker represents combined gene expression.

Reference

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