Utilizing prognostic and predictive factors in breast cancer View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2005-04

AUTHORS

Deepa S. Subramaniam, Claudine Isaacs

ABSTRACT

Opinion statementIn order to make optimal treatment recommendations for patients with early-stage breast cancer, it is essential to accurately determine the patient’s underlying risk of disease recurrence and choose a therapy to which the individual is most likely to respond. Lymph node status, tumor size, histopathologic features including tumor type and grade, and hormone receptor status are well-accepted prognostic factors related to breast cancer. In addition, hormone receptor status is a very strong predictor of response to hormonal therapy. However, our currently accepted prognostic and predictive factors fall short and there is a critical need to more accurately identify those most likely to require or benefit from particular therapies. Attention has therefore focused on the determination of novel prognostic and predictive factors. The most promising new factor is the level of urokinase plasminogen activator and its inhibitor plasminogen activator inhibitor. Other putative factors include proliferative rate, the presence of lymphatic or vascular invasion, human epidermal growth factor receptor 2 (HER-2/neu or erbB-2) positivity, the presence of micrometastases in lymph nodes or bone marrow, and gene expression profile by microarray analysis, and by RNA-based methodology. Data regarding potential new prognostic factors are constantly emerging. These studies are frequently challenging to interpret as they are often retrospective, based on relatively small numbers of patients, include a mix of treated and untreated women, and often do not control for other known prognostic factors. Therefore, new data must be interpreted with caution. More... »

PAGES

147-159

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11864-005-0022-1

DOI

http://dx.doi.org/10.1007/s11864-005-0022-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050656043

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/15717996


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