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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

Predictive gene expression based assignment to chemotherapy. Patients which harbor a high-risk gene expression profile according to Fig. 3 and which can undergo chemotherapy according to clinical risk management (e.g. age, comorbidities, ECOG status) might be further stratified. By extraction of gene expression profilers predictive for specific chemotherapies, patients are allocated to the treatment they profit most of
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Fig4: Predictive gene expression based assignment to chemotherapy. Patients which harbor a high-risk gene expression profile according to Fig. 3 and which can undergo chemotherapy according to clinical risk management (e.g. age, comorbidities, ECOG status) might be further stratified. By extraction of gene expression profilers predictive for specific chemotherapies, patients are allocated to the treatment they profit most of

Mentions: Taken together, the new set of gene expression based profilers to predict prognosis show an exciting high prognostic power. Although they show different power among each other to predict prognosis of untreated patients, they are of equal value or even outcompete traditional decision criteria like Adjuvant! Online, the St. Gallen consensus or the NIH consensus criteria. With these new prognostic markers, in the near future stratification of patients into risk groups according to the gene expression profile of their tumors might be feasible (Fig. 3). The former high number of patients with intermediate-risk tumors might be allocated to either the gene expression profile high-risk group or the gene expression profile low-risk group. Patients in the gene expression profile high-risk group might undergo chemotherapy and hormonal therapy if ER-receptor positive. Patients in the gene expression profile low-risk group might be objected to hormonal therapy, minimizing unnecessary chemotherapy. Prognosis using pattern-based biomarkers might therefore spare many patients with a low risk of recurrence chemotherapy that they otherwise would have assigned to chemotherapy (Fig. 4).Fig. 3


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)

Predictive gene expression based assignment to chemotherapy. Patients which harbor a high-risk gene expression profile according to Fig. 3 and which can undergo chemotherapy according to clinical risk management (e.g. age, comorbidities, ECOG status) might be further stratified. By extraction of gene expression profilers predictive for specific chemotherapies, patients are allocated to the treatment they profit most of
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Related In: Results  -  Collection

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

Fig4: Predictive gene expression based assignment to chemotherapy. Patients which harbor a high-risk gene expression profile according to Fig. 3 and which can undergo chemotherapy according to clinical risk management (e.g. age, comorbidities, ECOG status) might be further stratified. By extraction of gene expression profilers predictive for specific chemotherapies, patients are allocated to the treatment they profit most of
Mentions: Taken together, the new set of gene expression based profilers to predict prognosis show an exciting high prognostic power. Although they show different power among each other to predict prognosis of untreated patients, they are of equal value or even outcompete traditional decision criteria like Adjuvant! Online, the St. Gallen consensus or the NIH consensus criteria. With these new prognostic markers, in the near future stratification of patients into risk groups according to the gene expression profile of their tumors might be feasible (Fig. 3). The former high number of patients with intermediate-risk tumors might be allocated to either the gene expression profile high-risk group or the gene expression profile low-risk group. Patients in the gene expression profile high-risk group might undergo chemotherapy and hormonal therapy if ER-receptor positive. Patients in the gene expression profile low-risk group might be objected to hormonal therapy, minimizing unnecessary chemotherapy. Prognosis using pattern-based biomarkers might therefore spare many patients with a low risk of recurrence chemotherapy that they otherwise would have assigned to chemotherapy (Fig. 4).Fig. 3

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