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Clin Adv Hematol Oncol. 2015 Jun;13(6 Suppl 6):14-24.

A Comparison of Breast Cancer Multianalyte Assays With Algorithmic Analyses (MAAA) for Their Net Predictive/Prognostic Value.

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University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.


In breast cancer, prognostic and predictive information has traditionally been ascertained using clinicopathologic measures, such as tumor size and grade, lymph node involvement, and the presence of protein biomarkers, including the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)/neu. These parameters provide important prognostic and predictive information. Conventional clinicopathologic factors also help guide the use of adjuvant therapy. However, standard clinicopathologic factors have a limited ability to estimate prognosis, predict responses to chemotherapy, and help guide the selection of chemotherapeutic agents. In addition, clinical factors alone can be misleading in that they do not fully indicate whether chemotherapy is needed or if hormonal treatment in ER-positive patients is sufficient. Multianalyte assays with algorithmic analyses (MAAA) were developed toward the goal of personalized medicine, to supplement existing techniques and further inform the decision of whether to treat with adjuvant chemotherapy. Several gene expression profiling tests are now available for use in patients with breast cancer. In general, these tests provide an estimate of prognosis. In select instances, they may also be predictive for benefit from chemotherapy. This article reviews the latest studies evaluating the use of these tests.

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