Controversies and Opportunities in the Clinical Daily Use of the 21-Gene Assay for Prognostication and Prediction of Chemotherapy Benefit in HR+/HER2- Early Breast Cancer.
Flavia JacobsMariangela GaudioChiara BenvenutiRita De SanctisArmando SantoroAlberto ZambelliPublished in: Cancers (2022)
Several multigene assays have been developed to help clinicians in defining adjuvant treatment for patients with hormone-receptor-positive (HR+), human epidermal growth factor receptor-2 (HER2)-negative early breast cancer. Despite the 21-gene assay having been available for decades, it has only recently been included in the healthcare systems of several countries. Clinical optimisation of the test remains of critical interest to achieve a greater impact of genomic information in HR+/HER2- early breast cancer. Although current guidelines recommend the use of the 21-gene assay in early breast cancer at intermediate risk of relapse, the implication of the Recurrence Score (RS) in some grey areas still remains uncertain. Our aim is to critically discuss the role of RS in peculiar circumstances. In particular, we focus on the complex integration of genomic data with clinicopathological factors; the potential clinical impact of RS in node-positive premenopausal women and in the neoadjuvant setting; the significance of RS in special histologies and in male patients; and the management and time-optimisation of test ordering. In the absence of robust evidence in these areas, we provide perspectives for improving the use of the 21-gene assay in the decision-making process and guide adjuvant treatment decisions even in challenging cases.
Keyphrases
- early breast cancer
- copy number
- high throughput
- epidermal growth factor receptor
- healthcare
- genome wide
- end stage renal disease
- early stage
- genome wide identification
- endothelial cells
- decision making
- lymph node
- chronic kidney disease
- palliative care
- newly diagnosed
- ejection fraction
- type diabetes
- pregnant women
- gene expression
- metabolic syndrome
- machine learning
- risk assessment
- combination therapy
- rectal cancer
- big data
- prognostic factors
- deep learning
- postmenopausal women
- free survival
- white matter
- single cell