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Breast cancer subtype specific classifiers of response to neoadjuvant chemotherapy do not outperform classifiers trained on all subtypes.

de Ronde JJ, Bonder MJ, Lips EH, Rodenhuis S, Wessels LF - PLoS ONE (2014)

Bottom Line: We trained predictors of resistance that were specific for a subtype and generic predictors that were not specific for a particular subtype, i.e. trained on all subtypes simultaneously.Through a rigorous double-loop cross-validation we compared the performance of these two types of predictors on the different subtypes on a large set of tumors all profiled on the same expression platform (n = 394).This can be explained by the fact that the generic predictor included HER2 and ER status, features that are predictive over the whole set, but not within this subtype.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands ; Department of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.

ABSTRACT

Introduction: Despite continuous efforts, not a single predictor of breast cancer chemotherapy resistance has made it into the clinic yet. However, it has become clear in recent years that breast cancer is a collection of molecularly distinct diseases. With ever increasing amounts of breast cancer data becoming available, we set out to study if gene expression based predictors of chemotherapy resistance that are specific for breast cancer subtypes can improve upon the performance of generic predictors.

Methods: We trained predictors of resistance that were specific for a subtype and generic predictors that were not specific for a particular subtype, i.e. trained on all subtypes simultaneously. Through a rigorous double-loop cross-validation we compared the performance of these two types of predictors on the different subtypes on a large set of tumors all profiled on the same expression platform (n = 394). We evaluated predictors based on either mRNA gene expression or clinical features.

Results: For HER2+, ER- breast cancer, subtype specific predictor based on clinical features outperformed the generic, non-specific predictor. This can be explained by the fact that the generic predictor included HER2 and ER status, features that are predictive over the whole set, but not within this subtype. In all other scenarios the generic predictors outperformed the subtype specific predictors or showed equal performance.

Conclusions: Since it depends on the specific context which type of predictor - subtype specific or generic- performed better, it is highly recommended to evaluate both specific and generic predictors when attempting to predict treatment response in breast cancer.

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The AUC scores for the best performing predictors on each subtype.AUCs for the (A) HER2 positive subtype; (B) Luminal subtype; (C) Triple negative subtype and (D) HER2 positive and ER negative subtype. The red bars represent the clinical predictors, blue bars the expression based predictors and darker colors represent non-subtype specific predictors. When two boxplots are connected with a u-shaped line, the means of the AUC distributions are significantly different for the experiment represented by the boxplots (two-sided t-test, p<0.05, Bonferroni multiple testing corrected.)
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pone-0088551-g002: The AUC scores for the best performing predictors on each subtype.AUCs for the (A) HER2 positive subtype; (B) Luminal subtype; (C) Triple negative subtype and (D) HER2 positive and ER negative subtype. The red bars represent the clinical predictors, blue bars the expression based predictors and darker colors represent non-subtype specific predictors. When two boxplots are connected with a u-shaped line, the means of the AUC distributions are significantly different for the experiment represented by the boxplots (two-sided t-test, p<0.05, Bonferroni multiple testing corrected.)

Mentions: Figure 2 shows the comparison of the best performing classifiers for the subtype specific and non-subtype specific predictors.


Breast cancer subtype specific classifiers of response to neoadjuvant chemotherapy do not outperform classifiers trained on all subtypes.

de Ronde JJ, Bonder MJ, Lips EH, Rodenhuis S, Wessels LF - PLoS ONE (2014)

The AUC scores for the best performing predictors on each subtype.AUCs for the (A) HER2 positive subtype; (B) Luminal subtype; (C) Triple negative subtype and (D) HER2 positive and ER negative subtype. The red bars represent the clinical predictors, blue bars the expression based predictors and darker colors represent non-subtype specific predictors. When two boxplots are connected with a u-shaped line, the means of the AUC distributions are significantly different for the experiment represented by the boxplots (two-sided t-test, p<0.05, Bonferroni multiple testing corrected.)
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3928239&req=5

pone-0088551-g002: The AUC scores for the best performing predictors on each subtype.AUCs for the (A) HER2 positive subtype; (B) Luminal subtype; (C) Triple negative subtype and (D) HER2 positive and ER negative subtype. The red bars represent the clinical predictors, blue bars the expression based predictors and darker colors represent non-subtype specific predictors. When two boxplots are connected with a u-shaped line, the means of the AUC distributions are significantly different for the experiment represented by the boxplots (two-sided t-test, p<0.05, Bonferroni multiple testing corrected.)
Mentions: Figure 2 shows the comparison of the best performing classifiers for the subtype specific and non-subtype specific predictors.

Bottom Line: We trained predictors of resistance that were specific for a subtype and generic predictors that were not specific for a particular subtype, i.e. trained on all subtypes simultaneously.Through a rigorous double-loop cross-validation we compared the performance of these two types of predictors on the different subtypes on a large set of tumors all profiled on the same expression platform (n = 394).This can be explained by the fact that the generic predictor included HER2 and ER status, features that are predictive over the whole set, but not within this subtype.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands ; Department of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.

ABSTRACT

Introduction: Despite continuous efforts, not a single predictor of breast cancer chemotherapy resistance has made it into the clinic yet. However, it has become clear in recent years that breast cancer is a collection of molecularly distinct diseases. With ever increasing amounts of breast cancer data becoming available, we set out to study if gene expression based predictors of chemotherapy resistance that are specific for breast cancer subtypes can improve upon the performance of generic predictors.

Methods: We trained predictors of resistance that were specific for a subtype and generic predictors that were not specific for a particular subtype, i.e. trained on all subtypes simultaneously. Through a rigorous double-loop cross-validation we compared the performance of these two types of predictors on the different subtypes on a large set of tumors all profiled on the same expression platform (n = 394). We evaluated predictors based on either mRNA gene expression or clinical features.

Results: For HER2+, ER- breast cancer, subtype specific predictor based on clinical features outperformed the generic, non-specific predictor. This can be explained by the fact that the generic predictor included HER2 and ER status, features that are predictive over the whole set, but not within this subtype. In all other scenarios the generic predictors outperformed the subtype specific predictors or showed equal performance.

Conclusions: Since it depends on the specific context which type of predictor - subtype specific or generic- performed better, it is highly recommended to evaluate both specific and generic predictors when attempting to predict treatment response in breast cancer.

Show MeSH
Related in: MedlinePlus