ESMO E-Learning: Personalised Treatment for Breast Cancer Patients
- Assess the strengths and weaknesses of the main techniques used for the prediction of clinical outcome in breast cancer.
- Evaluate the tests that can predict the efficacy of specific drugs used in the treatment of breast cancer.
- Provide a future direction/strategy for the management of breast cancer patients who are in high risk for disease relapse.
|Title||Duration||Content||CME Points||CME Test|
|Personalised Treatment for Breast Cancer Patients||25 min.||39 slides||1||Take Test|
Predicting the risk for recurrence in breast cancer patients is a critical task in clinics. Although different guidelines have been developed to assist clinicians in selecting patients who should receive adjuvant systemic therapy, it still remains a challenge to distinguish those who would really benefit from it. With the advent of array-based technology and the sequencing of the human genome, new insights into breast cancer biology and prognosis have emerged.
Recent developments have fostered tremendous advances in molecular diagnosis and prognosis of breast cancer. Testing a breast cancer for its genomic signature can help identify which patients will need adjuvant systemic therapy after surgery, and spare its use to those for whom it is not necessary.
This E-Learning module critically evaluates genomic signatures and immunohistochemistry, with the aim to distinguish patients who can be cured without chemotherapy, and those who are not cured by optimal adjuvant therapy. It also tries to summarise the current evidence on the development of tests that predict drug-specific efficacy, as well as the most rational strategy for patients having a high risk for disease relapse.
In the past several years, a number of commercialised multigene prognostic and predictive tests have entered the complex and expanding landscape of breast cancer companion diagnostics. In this module, multigene assays are evaluated as to their scientific validation and current clinical utility. Emphasis is placed on two prognostic gene expression signatures: MammaPrint® and Oncotype DX™. The genomic signatures identify a subset of patients with low risk of metastatic relapse. In addition, the module discusses if there is any prognostic value of Ki67. It also provides the genomic algorithm in order to identify a subset of patients who present with a high risk for relapse despite being optimally treated.
The module further elaborates on tests developed to predict drug-specific sensitivity, especially on TOP2A as an immune response for anthracyclines, and DNA defect for alkylating agents. There are many new promising drugs for breast cancer patients but there are also many targets, and this poses the challenge on the right approach to optimally select the patients. The author discusses the concept of high throughput technologies for molecular screening which will allow one day identifying the target at the sample level.
This E-Learning module is a perfect summary of the current clinical evidence and forward-looking teaching material for further personalisation of breast cancer treatment.
The author has reported no conflicts of interest.