The study presents a gene expression profile that predicts the clinical outcome of breast cancer. By analyzing the gene expression of 117 young breast cancer patients, researchers identified a gene expression signature that strongly predicts the likelihood of distant metastases in patients with lymph node-negative tumors. This signature includes genes involved in cell cycle, invasion, metastasis, and angiogenesis. Additionally, the study identified a gene expression signature associated with BRCA1 carriers. The gene expression profile outperforms all current clinical parameters in predicting disease outcome and provides a strategy for selecting patients who would benefit from adjuvant therapy. The study used DNA microarray analysis on primary breast tumors of 117 young patients and applied supervised classification to identify the gene expression signature. The researchers selected 98 primary breast cancers and analyzed their gene expression profiles. They identified 5,000 genes that were significantly regulated across the group of samples. Using an unsupervised, hierarchical clustering algorithm, the researchers clustered the 98 tumors based on their similarities measured over these genes. The results showed that the tumors could be divided into two types based on this set of genes. The study also identified a gene expression signature associated with BRCA1 carriers. The researchers found that 16 out of 18 tumors of BRCA1 carriers were found in the bottom branch of the tumor dendrogram, which is consistent with the idea that most BRCA1 mutant tumors are ER negative and have a higher amount of lymphocytic infiltration. The study validated the prognosis classifier using an additional independent set of primary tumors from 19 young, lymph-node-negative breast cancer patients. The classifier showed comparable performance on the validation set and confirmed the predictive power and robustness of prognosis classification using the 70 optimal marker genes. The study also explored the relationship between gene expression and clinical parameters, such as ER status and BRCA1 status. The researchers found that the gene expression profile could be used to decide on adjuvant hormonal therapy and the signature that reveals BRCA1 status may further improve the diagnosis of hereditary breast cancer. Additionally, genes that are overexpressed in tumors with a poor prognosis profile are potential targets for the rational development of new cancer drugs. The study highlights the importance of using gene expression profiles to tailor adjuvant systemic treatment and reduce the cost of breast cancer treatment.The study presents a gene expression profile that predicts the clinical outcome of breast cancer. By analyzing the gene expression of 117 young breast cancer patients, researchers identified a gene expression signature that strongly predicts the likelihood of distant metastases in patients with lymph node-negative tumors. This signature includes genes involved in cell cycle, invasion, metastasis, and angiogenesis. Additionally, the study identified a gene expression signature associated with BRCA1 carriers. The gene expression profile outperforms all current clinical parameters in predicting disease outcome and provides a strategy for selecting patients who would benefit from adjuvant therapy. The study used DNA microarray analysis on primary breast tumors of 117 young patients and applied supervised classification to identify the gene expression signature. The researchers selected 98 primary breast cancers and analyzed their gene expression profiles. They identified 5,000 genes that were significantly regulated across the group of samples. Using an unsupervised, hierarchical clustering algorithm, the researchers clustered the 98 tumors based on their similarities measured over these genes. The results showed that the tumors could be divided into two types based on this set of genes. The study also identified a gene expression signature associated with BRCA1 carriers. The researchers found that 16 out of 18 tumors of BRCA1 carriers were found in the bottom branch of the tumor dendrogram, which is consistent with the idea that most BRCA1 mutant tumors are ER negative and have a higher amount of lymphocytic infiltration. The study validated the prognosis classifier using an additional independent set of primary tumors from 19 young, lymph-node-negative breast cancer patients. The classifier showed comparable performance on the validation set and confirmed the predictive power and robustness of prognosis classification using the 70 optimal marker genes. The study also explored the relationship between gene expression and clinical parameters, such as ER status and BRCA1 status. The researchers found that the gene expression profile could be used to decide on adjuvant hormonal therapy and the signature that reveals BRCA1 status may further improve the diagnosis of hereditary breast cancer. Additionally, genes that are overexpressed in tumors with a poor prognosis profile are potential targets for the rational development of new cancer drugs. The study highlights the importance of using gene expression profiles to tailor adjuvant systemic treatment and reduce the cost of breast cancer treatment.