One of the primary challenges in the treatment of patients with early-stage breast cancer is determining which patients will benefit from adjuvant chemotherapy. Traditionally, treatment decisions have been made based on a combination of tumor characteristics and patient and physician perspectives regarding risks and benefits. Recent technologic advances, including the development of gene-expression arrays, have led to the identification of molecular signatures that provide prognostic information in addition to the basic clinicopathologic features of individual tumors. While these new methods allow for more refined determination of prognosis for an individual patient, few data are available to support use of these new technologies in the clinic for treatment decision-making. At present, data from a single retrospective study are available to support the use of one assay, the 21-gene recurrence score, for decision-making regarding adjuvant chemotherapy. Large, multinational clinical trials are currently ongoing to evaluate the use of two of the multiparameter assays, although it will be many years before oncologists can apply the results of these trials in the clinic.
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.
Drs. Henry and Hayes have done an excellent job of describing and comparing the various gene-expression profiles and rightly point out the limitations for present clinical use while educating oncologists about the possibilities for the future. Gene-expression profiling has the potential to better quantitate and relate prognostic and predictive tumor characteristics for predicting outcome and therapeutic response. Such tests will be particularly useful if they can define groups that need less therapy and groups at high risk or likely to benefit from some specific intervention.
Almost certainly, the most informative signatures have yet to be developed. Although powerful, these tests will likely be most useful when considered with our more traditional decision-making tools and supplemented by new assessments of patient characteristics that predict outcome and toxicity. Additionally, good decision-making by patients with their oncologists will be improved by models that incorporate all of these factors and present the data in ways that are easily understood by patients.
-Elizabeth Reed, MD
The author has no significant financial interest or other relationship with the manufacturers of any products or providers of any service mentioned in this article.
1. Colleoni M, Rotmensz N, Peruzzotti G, et al: Size of breast cancer metastases in axillary lymph nodes: Clinical relevance of minimal lymph node involvement. J Clin Oncol 23:1379-1389, 2005.
2. Rack BK, Schindlbeck C, Janni WJ, et al: Circulating tumor cells in peripheral blood of primary breast cancer patients (abstract 5007). Br Cancer Res Treat 100(1 suppl):S215, 2006.
3. Braun S, Pantel K, Muller P, et al: Cytokeratin-positive cells in the bone marrow and survival of patients with stage I, II,or III breast cancer. N Engl J Med 342:525-533, 2000.
4. Phillips T, Marray G, Wakamiya K, et al: Development of standard estrogen and progesterone receptor immunohistochemical assays for selection of patients for antihormonal therapy. Appl Immunohistochem Mol Morphol 15:325-331, 2007.
5. Carlson RW, Moench SJ, Hammond EH, et al: HER2 testing in breast cancer: NCCN task force report and recommendations. JNCCN 4(suppl 3):S1-S22, 2006.
6. Paik S, Tang G, Shak S, et al: Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24:3726-3734, 2006.
7. O'Malley FP, Chia S, Tu D, et al: Topoisomerase II alpha protein overexpression has predictive utility in a randomized trial comparing CMF to CEF in premenopausal women with node positive breast cancer (abstract 38). Br Cancer Res Treat 100(1 suppl):S18, 2006.
8. Rouzier R, Rajan R, Wagner P, et al: Microtubule-associated protein tau: A marker of paclitaxel sensitivity in breast cancer. Proc Natl Acad Sci U S A 102:8315-8320, 2005.