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Gene expression profiling of breast tumor cell lines to predict for therapeutic response to microtubule-stabilizing
Gais Kadra1, Pascal Finetti, Yves Toiron
1Département de Pharmacologie Moléculaire and U891 INSERM, Centre de Recherche En Cancérologie de Marseille, Institut Paoli-Calmettes, Marseille, France.
Abstract:
Microtubule-targeting agents, including taxanes (Tax) and ixabepilone (Ixa), are important components of modern breast cancer chemotherapy regimens, but no molecular parameter is currently available that can predict for their efficiency. We sought to develop pharmacogenomic predictors of Tax- and Ixa-response from a large panel of human breast tumor cell lines (BTCL), then to evaluate their performance in clinical samples. Thirty-two BTCL, representative of the molecular diversity of breast cancers (BC), were treated in vitro with Tax (paclitaxel (Pac), docetaxel (Doc)), and ixabepilone (Ixa), then classified as drug-sensitive or resistant according to their 50% inhibitory concentrations (IC50s). Baseline gene expression data were obtained using Affymetrix U133 Plus 2.0 human oligonucleotide microarrays. Gene expression set (GES) predictors of response to taxanes were derived, then tested for validation internally and in publicly available gene expression datasets. In vitro IC50s of Pac and Doc were almost identical, whereas some Tax-resistant BTCL retained sensitivity to Ixa. GES predictors for Tax-sensitivity (333 genes) and Ixa-sensitivity (79 genes) were defined. They displayed a limited number of overlapping genes. Both were validated by leave-n-out cross-validation (n = 4; for overall accuracy (OA), P = 0.028 for Tax, and P = 0.0005 for Ixa). The GES predictor of Tax-sensitivity was tested on publicly available external datasets and significantly predicted Pac-sensitivity in 16 BTCL (P = 0.04 for OA), and pathological complete response to Pac-based neoadjuvant chemotherapy in BC patients (P = 0.0045 for OA). Applying Tax and Ixa-GES to a dataset of clinically annotated early BC patients identified subsets of tumors with potentially distinct phenotypes of drug sensitivity: predicted Ixa-sensitive/Tax-resistant BC were significantly (P < 0.05, Fischer's exact test) more frequently ER/PR-positive, Ki67-negative, and luminal subtype than predicted Ixa-resistant/Tax-sensitive BC. Genomic predictors for Tax- and Ixa-sensitivity can be derived from BTCL and may be helpful for better selecting cytotoxic treatment in BC patients.
Insights
Researchers developed gene expression signatures to predict response to taxanes and ixabepilone chemotherapy in breast cancer. These genomic predictors can help personalize treatment selection for breast cancer patients, identifying those likely to benefit from specific agents.
Area of Science:
- Genomics
- Pharmacogenomics
- Oncology
Background:
- Taxanes (Tax) and ixabepilone (Ixa) are key breast cancer chemotherapies, but predicting their effectiveness is challenging.
- No current molecular markers reliably predict patient response to these microtubule-targeting agents.
Purpose of the Study:
- To develop and validate pharmacogenomic predictors for taxane and ixabepilone sensitivity in breast cancer.
- To assess the clinical utility of these predictors in patient samples and identify potential treatment selection strategies.
Main Methods:
- Utilized a panel of 32 breast tumor cell lines (BTCL) with diverse molecular profiles.
- Treated BTCL with paclitaxel, docetaxel, and ixabepilone, classifying them as sensitive or resistant based on IC50 values.
- Obtained gene expression data and derived gene expression set (GES) predictors for taxane and ixabepilone sensitivity.
Main Results:
- Developed distinct GES predictors for taxane-sensitivity (333 genes) and ixabepilone-sensitivity (79 genes) with limited overlap.
- Validated predictors internally and in external datasets, showing significant prediction of paclitaxel sensitivity and pathological complete response in patients.
- Identified distinct tumor phenotypes associated with differential sensitivity to taxanes and ixabepilone in clinical samples.
Conclusions:
- Genomic predictors for taxane and ixabepilone sensitivity can be derived from breast tumor cell lines.
- These predictors show potential for improving the selection of cytotoxic chemotherapy in breast cancer patients.
- Distinct molecular subtypes may exhibit differential responses to taxanes versus ixabepilone, guiding personalized treatment strategies.
