Breast cancer is the most common malignancy among women in the United States. It often affects relatively young women with underlying good health, who are working outside the home and/or are caring for children or grandchildren at home. The diagnosis of breast cancer is often the most serious medical condition the patient has encountered, and she and her oncologist regard their decisions about systemic adjuvant therapy as some of the most important they will make in her lifetime. Data from tens of thousands of breast cancer patients allows some of the most informed decision-making in the treatment of cancer. Several new tools used to assess risk and predict response to specific treatments are available or are being studied.
Drs. Henry and Hayes give an excellent overview of decision-making tools presently used and specifically discuss gene-expression profiles, outlining how they are performed, their validation, and plans for future validation. This thorough review evenly informs the reader of the deficiencies of these profiles in decision-making but also highlights the potential such tools have for the clinic. This commentary will address some aspects of clinical decision-making for adjuvant treatment of breast cancer and speculate on the types of tools and information that could improve the process.
That Assess Risk
Clinical and pathologic staging remains the foundation for assessing risk of early breast cancer. Staging is limited to describing extent of disease but remains the best prognostic tool available. Despite the long and extensive use of staging for prognostication, important questions, such as the implication of micrometastases in nodes, remain.
Tumor grade is certainly considered when evaluating a tumor, but variability among pathologists in assessing grade is a limitation of this approach. Some studies suggest that occult tumor cells in the bone marrow or blood may also describe extent of disease and may add to the understanding of outcome.[2,3]
Tumor Characteristics That Assess Risk and Predict Response to Therapy
Other important tumor features are the presence of hormone receptors and the overexpression or amplification of HER2/neu. These features not only impact prognosis but also predict response to treatment. A major concern in present practice is the variability or inaccuracies in measurements of both steroid hormone receptors and HER2/neu.[4,5] Although efforts are being made to standardize these practices, more precise measurements of hormone receptors, HER2/neu, and tumor growth rates may be possible with gene-expression profiling, which may allow us to optimally apply information from these already fairly well-understood tumor characteristics.
A retrospective trial in patients with node-negative, estrogen receptor–positive breast cancer found that a high recurrence score by Oncotype DX predicted a large benefit from chemotherapy, while patients with low-scoring tumors had minimal benefit from chemotherapy. The results from the prospective TAILORx and MINDACT trials will better speak to the predictive value of Oncotype DX and MammaPrint, respectively. Several other candidates for predicting response to treatment are being studied—for example, the prediction of benefit from anthraclines in tumors with topoisomerase II alpha protein overexpression or sensitivity to taxanes and downregulation of tau.
The other essential evaluation when choosing adjuvant therapy is the assessment of the patient's ability to tolerate the various therapeutic options. Although we routinely assess age, comorbid conditions, and performance status, our understanding of interactions among patients, tumors, and therapies is limited. More research in the area of patient characteristics such as polymorphisms that predict outcome, therapeutic response or toxicity, is needed. As more therapeutic options become available, the choice for therapy may depend on toxicity, both acute and long-lasting . Variability in the toxicity of drugs from patient to patient remains largely unexplained. Recent emphasis on survivorship may promote a better understanding of long-term toxicity and improve therapeutic decisions.