Practical use of meta-analyses in predicting disease risk, outcome, and therapy response in breast cancer

Kahán Zsuzsanna (1), Tari Gergely (2), Enyedi Márton (3, 4), Haracska Lajos (3, 4)
(1) Szegedi Tudományegyetem, Általános Orvostudományi Kar, Onkoterápiás Klinika, Szeged
(2) Szegedi Tudományegyetem, Általános Orvostudományi Kar, Magatartástudományi Intézet, Szeged
(3) Delta Bio 2000 Kft., Szeged
(4) Magyar Tudományos Akadémia, Szegedi Biológiai Kutatóközpont, Genetikai Intézet, Szeged

Germinal BRCA status influences patient care both in early and advanced/metastatic breast cancer. Ideally, the patient should make the decision on the type of surgery or the avoidance of radiotherapy being aware of the BRCA status; based on the most recent clinical studies, this knowledge may infl uence the type of chemotherapy in the neoadjuvant, adjuvant, or metastatic setting or may raise the use of emerging targeted therapies. DNA-targeting cytostatic agents, mostly platinum agents and PARP inhibitors that act by inducing synthetic lethality, provide specific therapies in BRCA-mutant cases. The optimum place and sequence of these specifi c agents in treatment, however, are not known yet. International guidelines promote BRCA testing for the specification of treatment strategy in all HER2-negative advanced/metastatic breast cancer cases (NCCN) or at least in all cases when, based on certain predictors, the presence of mutations is likely (ESMO). Recently, the methods employed for BRCA testing have improved immensely and are widely available through the services of various providers. For the identification of the mutation, sequencing of the whole genes is needed, which can be achieved faster and more cost-effi ciently using next-generation sequencing (NGS) platforms compared to previous methods. It is the responsibility of the physician to consider the possibility of BRCA mutations and to raise the issue of BRCA testing to the patient if the family history, the age, previous malignant disease(s) of the patient, or the cancer features are suggestive of genetic risk.

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