Molecular profiling in breast cancer

The worldwide incidence of breast cancer is approximately 500,000 patients per year. It is the leading cause of death in women between the ages of 40 and 79 years, and the second leading cause of cancer death for women of all ages. Breast cancer mortality has declined over the past 10 years due largely to improved education and earlier detection by mammographic screening, but also in part due to the increasing use of adjuvant systemic therapy. Decisions related to the use of adjuvant systemic therapies have relied on traditional clinicopathologic staging, including histologic appearance, identification of specific tumor subtype, tumor grading, assessment of lymph-node status, and presence of metastases. These are useful for the initial workup and general information related to prognosis, but are limited in their ability to predict response to treatment and/or risk of adverse events to such therapies. Clinically validated predictive tools have classically included evaluation of hormonal receptors (estrogen and progesterone receptors, ER and PR, respectively) and most recently HER2. Although histologic grade is generally considered prognostic, it has not been added to staging systems due to concern over reproducibility. Data to support the inclusion of special newer techniques (such as serial sectioning, immunohistochemistry (IHC), and/or reverse transcriptase-polymerase chain reaction, RTPCR) to detect micrometastases in hematoxylin-and-eosin-negative lymph nodes also serve to provide general ideas related to prognosis, but their independent contribution is a matter of debate. Models to assist physicians in estimating the absolute benefit for an individual patient have been developed, but they are based on clinical-histopathologic data and have inherent limitations [45, 68]. An International Expert Consensus Panel [26] defined "minimal-risk" and "average-risk" groups for endocrine-responsive disease, and suggested that adjuvant chemotherapy be considered for women in the average-risk group (Table 45.1) who are less than 70 years of age; by definition, average risk does not include patients with pure tubular or colloid histology, histologic patterns associated with a very favorable prognosis. Although adjuvant chemotherapy is of benefit even in average-risk populations, the majority of women are treated unnecessarily to benefit a few. The Oxford Overview analyses showed a significant proportion of long-term survivors among untreated patients [15, 16]. Assuming a relative risk reduction of 25% from the addi tion of adjuvant chemotherapy to hormonal therapy, the absolute benefit derived from the addition of chemotherapy is low, even in individuals at higher risk for relapse (Table 45.2). For an average-risk individual, for example, approximately 100 women must be treated in order to benefit only 3 or 4 patients. These calculations are based upon the Adjuvant Online model (www.adjuvantonline.com) described by Ravdin et al. [68]. (Table presented) The need to correctly identify that subgroup of patients, in order to maximize our efficacy and spare unnecessary toxicity, has fueled active research in the field of predictive and prognostic markers. A prognostic factor can be defined as a clinical or biological feature that correlates with survival (or disease-free survival, DFS). It gives information about the course of the disease (ideally) in the absence of treatment. However, a pure prognostic factor does not provide information about the benefit of treatment for the patient. In contrast, a predictive factor is a clinical or biological feature that correlates with response to a certain treatment. It gives information about the efficacy of treatment, according to the characteristics of the tumor, regardless of prognosis [33]. The ideal prognostic factor will allow us to accurately predict which patients do not need treatment because of the excellent prognosis, while the ideal predictive factor, by selecting responsive patients, will increase the response rate to 100% in those patients who need treatment. (Table presented) No clinical and biologic parameters completely fulfill these requirements; however, we use in our practice clinical biological information with the goal of providing tailored recommendations to our patients. A Tumor Marker Utility Grading System (TMUGS) has been proposed by Hayes and colleagues [32, 34, 35]. The idea behind such a system includes standardization of those parameters to be included in the evaluation of clinical utility for tumor markers. The initial portion of such a grading system includes clarification of the characteristics of the marker in question (designation, relevant alteration from normalcy, assay format and reagents, specimen type, and the neoplastic disease for which the marker is being evaluated). The clinical utility includes identification of potential user risk assessment, screening, differential diagnosis, prognosis, prediction, and/or monitoring clinical course. A level of evidence is then assigned before considering the general incorporation of marker testing for clinical practice: level I - a highly powered prospective randomized trial in which the marker is the primary objective of the study, or a critically performed meta-analysis of lesser-level-of-evidence studies; level II - the marker in question is a secondary objective within a prospective clinical trial that is performed to address a therapeutic question; level III - studies that consist of hypothesis-generating investigations. Further expansion of these levels of evidence extends to level V, and include not only the level of evidence, but also a proposed utility scale [32]. In this TMUGS (Table 45.3), level I evidence with a utility scale of +++ provides the best combination of parameters for clinical use.