The chapter "Breast Cancer Metastasis: Markers and Models" by Britta Weigelt, Johannes L. Peterse, and Laura J. van 't Veer discusses the current understanding and challenges in predicting breast cancer metastasis. The authors highlight the limitations of traditional prognostic markers, such as lymph node status and tumor size, in accurately predicting metastasis risk. They emphasize the need for new prognostic markers to better identify patients at high risk of developing metastases and to tailor treatment strategies accordingly. The chapter reviews several recent prognostic markers, including the epidermal growth factor receptor 2 (ERBB2) status, which has been shown to be associated with poor outcome in patients with axillary lymph node metastases. However, the authors note that ERBB2 is not a reliable prognostic marker for patients without lymph node metastases. Other markers, such as the plasminogen activator uPA and its inhibitor PAI1, have been found to be independent prognostic markers of disease-free and overall survival in breast cancer patients. Gene-expression profiling has emerged as a powerful tool for identifying new prognostic markers. The authors describe the development of gene-expression signatures that can predict metastasis risk and patient outcomes. For example, a 70-gene signature identified through gene-expression profiling was found to be the strongest predictor of metastasis-free survival and overall survival in patients with lymph node-negative breast cancer. This signature was validated in independent cohorts and showed significant prognostic value, suggesting that it could be used to tailor therapy for individual patients. The chapter also discusses the integrative model of breast cancer metastasis, which integrates the findings from gene-expression profiling, clinical data, and models of metastasis. This model proposes that primary breast carcinomas with metastatic potential can be distinguished by their gene-expression profiles, and that metastatic-type tumors may harbor seeding subpopulations under the influence of stromal fibroblasts. The authors suggest that this model may explain why some patients develop metastases while others do not, and it highlights the importance of understanding the role of cancer stem cells in metastasis. Finally, the chapter outlines future directions for research, emphasizing the need for new prognostic markers and the development of therapeutic strategies targeting both the cancer cells and the tumor microenvironment.The chapter "Breast Cancer Metastasis: Markers and Models" by Britta Weigelt, Johannes L. Peterse, and Laura J. van 't Veer discusses the current understanding and challenges in predicting breast cancer metastasis. The authors highlight the limitations of traditional prognostic markers, such as lymph node status and tumor size, in accurately predicting metastasis risk. They emphasize the need for new prognostic markers to better identify patients at high risk of developing metastases and to tailor treatment strategies accordingly. The chapter reviews several recent prognostic markers, including the epidermal growth factor receptor 2 (ERBB2) status, which has been shown to be associated with poor outcome in patients with axillary lymph node metastases. However, the authors note that ERBB2 is not a reliable prognostic marker for patients without lymph node metastases. Other markers, such as the plasminogen activator uPA and its inhibitor PAI1, have been found to be independent prognostic markers of disease-free and overall survival in breast cancer patients. Gene-expression profiling has emerged as a powerful tool for identifying new prognostic markers. The authors describe the development of gene-expression signatures that can predict metastasis risk and patient outcomes. For example, a 70-gene signature identified through gene-expression profiling was found to be the strongest predictor of metastasis-free survival and overall survival in patients with lymph node-negative breast cancer. This signature was validated in independent cohorts and showed significant prognostic value, suggesting that it could be used to tailor therapy for individual patients. The chapter also discusses the integrative model of breast cancer metastasis, which integrates the findings from gene-expression profiling, clinical data, and models of metastasis. This model proposes that primary breast carcinomas with metastatic potential can be distinguished by their gene-expression profiles, and that metastatic-type tumors may harbor seeding subpopulations under the influence of stromal fibroblasts. The authors suggest that this model may explain why some patients develop metastases while others do not, and it highlights the importance of understanding the role of cancer stem cells in metastasis. Finally, the chapter outlines future directions for research, emphasizing the need for new prognostic markers and the development of therapeutic strategies targeting both the cancer cells and the tumor microenvironment.