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Early detection and improvement in adjuvant systemic therapy has resulted in a decrease in breast cancer mortality. However, each year 500,000 women still die of breast cancer worldwide. It is clear that more work is needed to identify which breast cancers have a high propensity to metastasize, and to guide clinicians in selecting the most appropriate therapies.


Prognostic factors reflect the biology of the cancer and are defined as those factors associated with outcome without consideration of treatment. Predictive factors reflect the tumor sensitivity or resistance to a particular treatment, and are defined as those factors that predict which patients are likely to respond to a specific therapy. The relative worth of predictive factors will obviously differ with varying treatments. While some factors are primarily prognostic, some tumor markers are both prognostic and predictive. The classic example is the overexpression of HER-2/neu, which is associated with a worse outcome (prognostic), but also associated with response to treatment with trastuzumab (predictive).


The relative benefit of a predictive or prognostic factor can be classified as weak, moderate, or strong. For prognostic factors, the relative strength is related to the difference in the likelihood of an adverse event (recurrence, death) between a patient with the prognostic factor and one who is negative. An example of a strong prognostic factor is lymph node status, as a node-positive patient is 2 to 3 times more likely to have an event than a lymph node-negative patient. An example of a weak prognostic factor is estrogen receptor (ER) expression, as patients with ER-positive cancers have only slightly better outcomes than ER-negative patients. Predictive factors can also be classified as weak, moderate, and strong based on the likelihood that a patient with the factor will respond to treatment compared to that of a patient without the factor. While ER expression is a weak prognostic factor, it is a strong predictive factor for hormonal therapy, as ER-positive patients may realize a 50% reduction in the risk of recurrence with hormonal therapy, while ER-negative patients obtain almost no benefit from hormonal therapy.


When discussing prognostic and predictive factors, it is crucial to separate the statistical significance of a factor from the clinical significance. As an independent factor, many measurable factors may correlate with outcome. However, when examined in the context of other prognostic or predictive factors (multifactorial analysis), they may lose statistical significance. So while there may be biological significance to the factor it may provide little help to the clinician in estimating the likelihood of death or the benefit of therapy. Moreover, even if a factor retains statistical significance on multifactorial analysis, this still does not guarantee clinical significance. If the absence or presence of the factor is relatively rare among breast cancer patients, its clinical utility is severely limited. One example is tumor grade. While grade III (poorly differentiated) patients may have worse outcomes than grade I (well-differentiated) patients, the great majority of patients are either ...

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