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Prediction and prognosis: impact of gene expression profiling in personalized treatment of breast cancer patients.

Mallmann MR, Staratschek-Jox A, Rudlowski C, Braun M, Gaarz A, Wolfgarten M, Kuhn W, Schultze JL - EPMA J (2010)

Bottom Line: Consequently, new gene expression based prognostic and predictive tests are emerging that promise an improvement in predicting survival and therapy response.Moreover, pattern-based approaches have also been developed to predict response to endocrine therapy or particular chemotherapy regimens.Irrespective of current pitfalls such as lack of validation and standardization, these pattern-based biomarkers will prove useful for clinical decision making in the near future, especially if more patients get access to this form of personalized medicine.

View Article: PubMed Central - PubMed

Affiliation: Department of Obstetrics & Gynecology, Center for Integrated Oncology, University Hospital of Bonn, Sigmund-Freud-Strasse 25, 53105 Bonn, Germany ; LIMES (Life and Medical Sciences Bonn) Institute, Genomics and Immunoregulation, University Bonn, Carl-Troll-Strasse 31, 53115 Bonn, Germany.

ABSTRACT
Breast cancer is a complex disease, whose heterogeneity is increasingly recognized. Despite considerable improvement in breast cancer treatment and survival, a significant proportion of patients seems to be over- or undertreated. To date, single clinicopathological parameters show limited success in predicting the likelihood of survival or response to endocrine therapy and chemotherapy. Consequently, new gene expression based prognostic and predictive tests are emerging that promise an improvement in predicting survival and therapy response. Initial evidence has emerged that this leads to allocation of fewer patients into high-risk groups allowing a reduction of chemotherapy treatment. Moreover, pattern-based approaches have also been developed to predict response to endocrine therapy or particular chemotherapy regimens. Irrespective of current pitfalls such as lack of validation and standardization, these pattern-based biomarkers will prove useful for clinical decision making in the near future, especially if more patients get access to this form of personalized medicine.

No MeSH data available.


Related in: MedlinePlus

Current clinocopathologic decision making. Patients are currently allocated into clinical risk groups by several mechanisms. Clinical parameters such as tumor size, lymph-node status and age as well as pathologic parameters such as histologic grading, hormone receptor status and HER2-status are main factors for risk assignment in breast cancer therapy. This risk assignment results in allocation into a low risk group that may be properly treated with hormonal therapy only or other treatments and a high risk group mainly treated with chemotherapy if no patient specific contradictions apply (e.g. waiving of anthracycline-based chemotherapy in patients with existing heart failure). The intermediate risk group due to uncertain outcome is mainly treated with chemotherapy the best choice of therapy currently under intense clinical studies
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Fig1: Current clinocopathologic decision making. Patients are currently allocated into clinical risk groups by several mechanisms. Clinical parameters such as tumor size, lymph-node status and age as well as pathologic parameters such as histologic grading, hormone receptor status and HER2-status are main factors for risk assignment in breast cancer therapy. This risk assignment results in allocation into a low risk group that may be properly treated with hormonal therapy only or other treatments and a high risk group mainly treated with chemotherapy if no patient specific contradictions apply (e.g. waiving of anthracycline-based chemotherapy in patients with existing heart failure). The intermediate risk group due to uncertain outcome is mainly treated with chemotherapy the best choice of therapy currently under intense clinical studies

Mentions: Breast cancer is the most common cancer of women in the western world. The overall death rate has been significantly reduced in the last decades, but depending on subtype and stage, still a significant portion of patients will suffer from relapse or even die of the disease [1, 2]. While up to 70% of patients with breast cancer can be cured nowadays, a significant proportion of these patients is overtreated. It remains a challenge to identify those patients who will indeed profit from current treatment strategies and also to develop innovative concepts for patients currently at high-risk for relapse after treatment. For this reason, the identification of reliable prognostic biomarkers together with the development of clinically efficient therapies is urgently needed [3]. Today, the prognostic clustering of breast cancer in daily routine relies on the determination of a limited set of molecular markers (e.g. estrogen receptor (ER), progesterone receptor (PR) and epidermal-growth-factor receptor 2 (HER2, also referred to as Her2/neu, ErbB-2)) mostly by semi-quantitative assays e.g. by immunohistochemistry (Fig. 1). Clearly, some of these markers are first examples of personalized medicine and targeted treatment since for instance only the determination of ER-expression by immunohistochemistry allows for a directed anti-hormonal therapy with receptor blockade or inhibition, or both. [4]. Moreover HER2-overexpression has paved the way for anti-HER2 treatment with the humanized monoclonal antibody trastuzumab [5–7] or the small-molecule inhibitor of the tyrosine kinase domains of HER1 and HER2, lapatinib [8–10]. The best HER2-targeted treatment option together with chemotherapy in patients with metastasized but operable breast cancer is currently assessed in clinical trials [11].Fig. 1


Prediction and prognosis: impact of gene expression profiling in personalized treatment of breast cancer patients.

Mallmann MR, Staratschek-Jox A, Rudlowski C, Braun M, Gaarz A, Wolfgarten M, Kuhn W, Schultze JL - EPMA J (2010)

Current clinocopathologic decision making. Patients are currently allocated into clinical risk groups by several mechanisms. Clinical parameters such as tumor size, lymph-node status and age as well as pathologic parameters such as histologic grading, hormone receptor status and HER2-status are main factors for risk assignment in breast cancer therapy. This risk assignment results in allocation into a low risk group that may be properly treated with hormonal therapy only or other treatments and a high risk group mainly treated with chemotherapy if no patient specific contradictions apply (e.g. waiving of anthracycline-based chemotherapy in patients with existing heart failure). The intermediate risk group due to uncertain outcome is mainly treated with chemotherapy the best choice of therapy currently under intense clinical studies
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3405335&req=5

Fig1: Current clinocopathologic decision making. Patients are currently allocated into clinical risk groups by several mechanisms. Clinical parameters such as tumor size, lymph-node status and age as well as pathologic parameters such as histologic grading, hormone receptor status and HER2-status are main factors for risk assignment in breast cancer therapy. This risk assignment results in allocation into a low risk group that may be properly treated with hormonal therapy only or other treatments and a high risk group mainly treated with chemotherapy if no patient specific contradictions apply (e.g. waiving of anthracycline-based chemotherapy in patients with existing heart failure). The intermediate risk group due to uncertain outcome is mainly treated with chemotherapy the best choice of therapy currently under intense clinical studies
Mentions: Breast cancer is the most common cancer of women in the western world. The overall death rate has been significantly reduced in the last decades, but depending on subtype and stage, still a significant portion of patients will suffer from relapse or even die of the disease [1, 2]. While up to 70% of patients with breast cancer can be cured nowadays, a significant proportion of these patients is overtreated. It remains a challenge to identify those patients who will indeed profit from current treatment strategies and also to develop innovative concepts for patients currently at high-risk for relapse after treatment. For this reason, the identification of reliable prognostic biomarkers together with the development of clinically efficient therapies is urgently needed [3]. Today, the prognostic clustering of breast cancer in daily routine relies on the determination of a limited set of molecular markers (e.g. estrogen receptor (ER), progesterone receptor (PR) and epidermal-growth-factor receptor 2 (HER2, also referred to as Her2/neu, ErbB-2)) mostly by semi-quantitative assays e.g. by immunohistochemistry (Fig. 1). Clearly, some of these markers are first examples of personalized medicine and targeted treatment since for instance only the determination of ER-expression by immunohistochemistry allows for a directed anti-hormonal therapy with receptor blockade or inhibition, or both. [4]. Moreover HER2-overexpression has paved the way for anti-HER2 treatment with the humanized monoclonal antibody trastuzumab [5–7] or the small-molecule inhibitor of the tyrosine kinase domains of HER1 and HER2, lapatinib [8–10]. The best HER2-targeted treatment option together with chemotherapy in patients with metastasized but operable breast cancer is currently assessed in clinical trials [11].Fig. 1

Bottom Line: Consequently, new gene expression based prognostic and predictive tests are emerging that promise an improvement in predicting survival and therapy response.Moreover, pattern-based approaches have also been developed to predict response to endocrine therapy or particular chemotherapy regimens.Irrespective of current pitfalls such as lack of validation and standardization, these pattern-based biomarkers will prove useful for clinical decision making in the near future, especially if more patients get access to this form of personalized medicine.

View Article: PubMed Central - PubMed

Affiliation: Department of Obstetrics & Gynecology, Center for Integrated Oncology, University Hospital of Bonn, Sigmund-Freud-Strasse 25, 53105 Bonn, Germany ; LIMES (Life and Medical Sciences Bonn) Institute, Genomics and Immunoregulation, University Bonn, Carl-Troll-Strasse 31, 53115 Bonn, Germany.

ABSTRACT
Breast cancer is a complex disease, whose heterogeneity is increasingly recognized. Despite considerable improvement in breast cancer treatment and survival, a significant proportion of patients seems to be over- or undertreated. To date, single clinicopathological parameters show limited success in predicting the likelihood of survival or response to endocrine therapy and chemotherapy. Consequently, new gene expression based prognostic and predictive tests are emerging that promise an improvement in predicting survival and therapy response. Initial evidence has emerged that this leads to allocation of fewer patients into high-risk groups allowing a reduction of chemotherapy treatment. Moreover, pattern-based approaches have also been developed to predict response to endocrine therapy or particular chemotherapy regimens. Irrespective of current pitfalls such as lack of validation and standardization, these pattern-based biomarkers will prove useful for clinical decision making in the near future, especially if more patients get access to this form of personalized medicine.

No MeSH data available.


Related in: MedlinePlus