Related Experiment Video
Updated: Sep 4, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Predictive biomarkers for personalized medicine in breast cancer
Sylvie Rodrigues-Ferreira1, Clara Nahmias2
1Gustave Roussy Institute, INSERM U981, Prédicteurs moléculaires et nouvelles cibles en oncologie, Villejuif, France; LabEx LERMIT, Université Paris-Saclay, 92296 Châtenay-Malabry, France; Inovarion, 75005, Paris, France.
Abstract:
Breast cancer is one of the most frequent malignancies among women worldwide. Based on clinical and molecular features of breast tumors, patients are treated with chemotherapy, hormonal therapy and/or radiotherapy and more recently with immunotherapy or targeted therapy. These different therapeutic options have markedly improved patient outcomes. However, further improvement is needed to fight against resistance to treatment. In the rapidly growing area of research for personalized medicine, predictive biomarkers - which predict patient response to therapy - are essential tools to select the patients who are most likely to benefit from the treatment, with the aim to give the right therapy to the right patient and avoid unnecessary overtreatment. The search for predictive biomarkers is an active field of research that includes genomic, proteomic and/or machine learning approaches. In this review, we describe current strategies and innovative tools to identify, evaluate and validate new biomarkers. We also summarize current predictive biomarkers in breast cancer and discuss companion biomarkers of targeted therapy in the context of precision medicine.

