Genomic Biomarker for Predicting Response in Early Breast Cancer Patients

Human solid tumors, like breast cancers, represent a heterogeneous group of diseases that is best demonstrated through many differences in clinical outcomes within homogenously treated patient sets. It is my belief that through a better understanding of the genomics and biology of a given cancer type, we can make important advancements in predicting patient response to therapy, which will ultimately lead to improvements in patient outcomes. Advances in genomic technologies, and drug developments, have already led to improved outcomes for women with breast cancer; nevertheless, there is still a pressing need for additional therapeutic options guided by predictive biomarkers for aggressive tumors; there is also the question of whether patients with good prognoses need aggressive treatments. Resistance to endocrine therapies has been mainly studied in HR+/HER2- BC and includes down-regulation of oestrogen receptor (ER) expression, altered expression of ER co-regulators, presence of ER mutations, ligand-independent activation of ER and co-activators by growth factor receptor kinases. However, those mechanisms might differ between HER2+ and HER2- tumours, in part due to the differential distribution of intrinsic subtypes within each BC subgroup. My research goal is to develop predictive signatures of aromatase inhibitor response and address the clinical challenge of identifying patients who are likely to benefit from each of the specific therapies. In this webinar, I will discuss the current and the potential future clinical implications of molecular subtyping and immune features on HR+ breast cancer tumors to predict treatment response. 

Learning Objectives:

1. Recall how machine learning can play a role in molecular research.
2. Discuss how subtyping can help determine association with clinical outcome.
3. Visualize how multi-omics/spatial genomics heterogeneity can offer new insights into treatment response.