Despite many years of translational research in breast cancer, very few new biomarkers have been implemented for clinical use beyond estrogen receptor, progesterone receptor, and HER2. The main reason is that many promising biomarkers are clinically validated but lack analytical and clinical utility. One explanation is that proper validation of the predictive ability of the biomarker in independent datasets, and with a pre-planned statistical analysis, is not always performed. Thus, there is a need to identify new biomarkers or new ways to subclassify breast cancer patients that are reproducible and easy to implement in the clinical setting but, more importantly, that improve patient's outcomes.

The goal of this Research Topic is to present the latest advances in this field. We welcome original research articles, reviews, and methods focusing on:

- novel approaches for breast cancer subclassification, in particular using machine learning algorithms and advanced genomic platforms
- identification and/or validation of new biomarkers based on molecular data alone, or in combination with clinical-pathological or imaging data (or any new data type)
- application of new biomarkers from an analytical point-of-view


Topic Editor Aleix Prat is on the Scientific Advisory Board of Oncolytics and has participated in advisory boards from following companies: Roche, Pfizer, Novartis, Lilly, Nanostring Technologies and Daiichi. Topic Editor Mothaffar Rimawi is a consultant for following companies: Genentech, Novartis, Daiichi, and Macrogenics.

Keywords: Genomics, DNA, RNA, biomarker, subtypes, validation

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.