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Entscheidungshilfen bei der Therapiewahl für Patientinnen mit Mammakarzinom

Prognose- und Prädiktivfaktoren

Aid in decision making for the choice of therapy for patients with breast cancer

Prognostic and predictive factors

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Der Gynäkologe Aims and scope

Zusammenfassung

Therapieentscheidungen für Patientinnen mit Mammakarzinom basieren v. a. auf der Risikoeinschätzung für Rezidiv oder Tod. Etablierte Prognosefaktoren sind dabei Tumorgröße, Lymhknotenstatus, Steroidhormonrezeptorstatus, Gefäßinvasion, Grading und HER2/neu-Status. Der Östrogenrezeptorstatus ist der stärkste Prädiktivfaktor für das Ansprechen auf Tamoxifentherapie. Der Plasminogenaktivator uPA und sein Inhibitor PAI-1 können das Ansprechen auf eine Chemotherapie in einem gewissen Maße vorhersagen.

Seitdem man mit Genexpressionsanalysen Patientinnen mit guter Prognose von solchen mit schlechter Prognose unterscheiden kann, wird versucht diese Technik zur Prädiktion des Therapieansprechens anzuwenden. Auch mit in Paraffin eingebettetem Gewebe kann anhand der Proteinexpression von Genen eine gute Aussage über die Prognose gemacht werden. Bei der Errechnung des Rückfallrisikos mit Hilfe großer Datensätze wird die Prognose für den jeweiligen Einzelfall bestimmt. Dies ist jedoch noch nicht Bestandteil der Risikoeinschätzung zur Therapieplanung.

Gut evaluierte Prognose- und Prädiktivfaktoren sind unabdingbar für die zukünftige individuelle Therapieplanung für Mammakarzinompatientinnen.

Abstract

Therapeutic decisions for patients with breast cancer are commonly based on the risk estimation of recurrence and death. Several prognostic markers such as tumor size, axillary lymph node status, hormonal receptor status, vessel invasion, grading and HER2/neu-status, which help to predict the response to a specific therapy, are used in planning further treatment. Currently, estrogen receptor status is the strongest predictive factor for a therapy with tamoxifen. For chemotherapy, some predictive factors are also known. The plasminogen activator uPA and his inhibitor PAI-1 can predict the response to chemotherapy to some degree.

Since the publication of large scale gene expression profiles of frozen tumor tissues, patients with a good prognosis can be differentiated from patients with a worse prognosis. Studies evaluating the predictive value of gene expression profiles are ongoing. Other prognostic models, using paraffin embedded tumor material, can also provide valid prognostic information. Furthermore, computer models can calculate the patient’s individual risk of recurrence and death by comparison with large datasets of similar patients. This software is already in use and can help to estimate therapeutic effect.

All of these developments emphasize the increasing importance of prognostic and predictive factors in the management of patients with breast cancer. They will become indispensable for planning an individual therapy for the breast cancer patient.

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  1. Frauenklinik, Universitätsklinikum Erlangen

    P. A. Fasching, M. P. Lux, K. Beckmann, R. Strick &  M. W. Beckmann

  2. Frauenklinik, Universitätsklinikum Erlangen, Universitätsstraße 21–23, 91054, Erlangen

    M. W. Beckmann

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Correspondence to M. W. Beckmann.

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Fasching, P.A., Lux, M.P., Beckmann, K. et al. Entscheidungshilfen bei der Therapiewahl für Patientinnen mit Mammakarzinom. Gynäkologe 38, 388–397 (2005). https://doi.org/10.1007/s00129-005-1680-6

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