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A Journal on Obstetrics and Gynecology

Indexed/Abstracted in: EMBASE, PubMed/MEDLINE, Scopus, Emerging Sources Citation Index

Frequency: Bi-Monthly

ISSN 0026-4784

Online ISSN 1827-1650


Minerva Ginecologica 2010 December;62(6):599-611



New prognostic and predictive factors in breast cancer

Schmidt M. 1, Gehrmann M. 2,3, Hengstler J. G. 4, Koelbl H. 1

1 Department of Obstetrics and Gynecology, Johannes Gutenberg-University, Mainz, Germany;
2 Siemens Healthcare Diagnostic Products, Cologne, Germany;
3 Bayer Technology Services, Leverkusen, Germany;
4 IfADo- Leibniz Research Centre for Working Environment and Human Factors (IfADo), TU Dortmund University, Dortmund, Germany

There are two major questions regarding systemic therapy of breast cancer: Firstly, which patients should be treated, and secondly, how should these patients be treated? Prognostic factors aim to foresee the outcome of patients irrespective of treatment while predictive factors intend to assess the outcome of patients receiving a certain systemic therapy and thus are intimately associated with sensitivity or resistance to therapy. Ideally, a predictive factor is also a therapeutic target as it is the case with estrogen receptor (ER) or HER-2. In order to avoid over- as well as under-treatment, it is advisable to select the appropriate treatment strategy on the basis of a careful risk assessment for each individual patient. Additionally to time-honoured clinicopathological factors additional prognostic factors like urokinase-type plasminogen activator (uPA)/plasminogen activator inhibitor 1 (PAI-1) or multiparameter gene-expression analyses have shown promising results especially in node-negative breast cancer. These multigene profiles offer new insights in breast cancer biology, like the important role of the tumor-associated immune system. ER, HER-2 and potentially newer prognostic factors like epithelial cell adhesion molecule (Ep-CAM) bridge the gap from prognosis to prediction and serve as therapeutic targets. This should allow us to quantify the risk of progression in each individual patient and tailor treatment accordingly, leading to a more personalized treatment recommendation.

language: English


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