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

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

De Ronde, J.J.
Bonder, M.J.
Lips, E.H.
Rodenhuis, S.
Wessels, L.F.A.

Electrical Engineering, Mathematics and Computer Science

Computer Science & Engineering

2014-02-18

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+, ER2 breast cancer, subtype specific predictor based on clinical features outperformed the generic, nonspecific 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.

http://resolver.tudelft.nl/uuid:620ebda2-8403-4ffd-b27b-2bbd944a30f3

https://doi.org/10.1371/journal.pone.0088551

Public Library of Science PLOS

1932-6203

Plos One, 9 (2), e88551, 2014

Institutional Repository

journal article

(c) 2014 de Ronde et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.