A primary challenge for medical oncologists treating women with early-stage breast cancer is deciding which patients should receive adjuvant chemotherapy. It would be helpful to identify which patients either will not relapse at all or will relapse without adjuvant chemotherapy (prognosis) and which therapies are most likely to work against specific cancers (prediction), so that only patients who will obtain benefit are exposed to the inherent toxicities. Historically, this assessment of risk of distant metastatic disease has been based on clinicopathologic features of the tumor, such as lymph node involvement and histologic grade. More recently, the development of multiparameter assays has permitted more detailed evaluation of the biology—as opposed to the anatomy—of tumors. This review will discuss the development of these tools, and whether they are ready for use in the clinics to make individual treatment decisions regarding adjuvant chemotherapy.
Adjuvant Systemic Therapy
Breast cancer remains the most common malignancy in women in the United States. However, breast cancer–specific mortality has been decreasing, due in part to earlier detection of disease with screening mammography and to improved treatment of disease with adjuvant systemic therapies.[1,2] Surgery and radiation therapy are curative for the majority of patients, who obtain no additional benefit from adjuvant systemic therapy. Conversely, for those patients at high risk for recurrence, systemic therapy does not necessarily prevent development of distant metastases.
Unfortunately, no prognostic or predictive factor is 100% perfect for determining benefit from a given therapy for an individual patient. Because of this uncertainty, should all patients receive all available therapies? If a patient is willing to accept any toxicity for any benefit then perhaps she should be treated with everything. In reality, however, the majority of patients are willing to forgo some benefit in order to avoid some toxicity, so therapy must be carefully selected.
By querying breast cancer patients who had previously received chemotherapy, several investigators have attempted to determine the smallest absolute chemotherapy benefit that patients are willing to accept, based on their prior treatment experiences.[3-5] For example, one study evaluated breast cancer survivors previously treated with CMF (cyclophosphamide, methotrexate, fluorouracil [5-FU]) for 6 months. As expected, when asked if they would accept treatment with chemotherapy in a number of hypothetical scenarios, most patients stated that they would opt for therapy again when the gains were large (> 10% absolute benefit), and fewer were willing to do so as the odds of benefit decreased. However, more than 50% of patients said they would undergo chemotherapy for as little as a 3% to 5% absolute improvement in outcome, and nearly 50% stated that they would be willing to accept therapy for as little as a 1% absolute benefit.
These results are remarkably similar to two other studies, suggesting that although these results may be confounded since subjects had survived their breast cancer, a large number of women seem to be willing to take chemotherapy for very small potential chances of benefit. Even so, a substantial proportion of patients would not accept therapy for an absolute benefit of less than 10%, and therefore it remains critically important to determine prognosis and prediction as accurately as possible.
Guidelines and Algorithms
Multiple methods are commonly used by clinicians to determine prognosis. Traditionally the key factors used to estimate the risk of distant metastatic disease include lymph node involvement, histologic grade, tumor size, and expression of hormone receptors (HR) and HER2 (erbB2). The TNM staging system, which combines tumor size (T), lymph node status (N), and presence or absence of distant metastasis (M), has been correlated with prognosis.[6,7]
In order to use these factors for clinical decision-making, numerous guidelines have been developed by agencies in both the United States and Europe. The National Comprehensive Cancer Network (NCCN) guidelines recommend adjuvant chemotherapy for patients with lymph node involvement, for those with HR-negative breast cancer and tumor size > 1 cm, and for those with HR- and HER2-positive disease and tumor size > 1 cm. The NCCN recommends consideration of chemotherapy for patients with tumor size 0.6 to 1.0 cm regardless of HR status, and those with HR-positive and HER2-negative disease and tumor size > 1 cm.
The St. Gallen guidelines differ slightly from those of the NCCN. They recommend adjuvant chemotherapy for patients with HR-positive, high-risk disease or HR-negative, intermediate- or high-risk disease, and recommend consideration of chemotherapy for patients with HR-positive, intermediate-risk disease. High-risk disease is defined as four or more nodes positive with any HER2 status, or one to three nodes positive and HER2 overexpressed. Low risk is considered age ≥ 35, tumor size ≤ 2 cm, grade 1, no angiolymphatic invasion, and HER2-negative.
For example, consider a 55-year-old woman with a 3-cm, grade 1, ER-positive, HER2-negative tumor with four positive lymph nodes. Using the standard prognostic factors outlined above, in the absence of systemic therapy she has an approximately 50% chance of recurrence over the succeeding 10 to 15 years after diagnosis. Using either the NCCN or St. Gallen guidelines, adjuvant chemotherapy followed by hormonal therapy would be recommended for this patient. Now suppose the patient's tumor was 1.5 cm with no lymph node involvement. In the absence of systemic therapy, her 10-year risk of recurrence is approximately 15%. In this situation, adjuvant chemotherapy should be considered in addition to hormonal therapy according to the NCCN guidelines, but only hormonal therapy should be offered according to the St. Gallen guidelines.
In an attempt to make it easier for physicians to apply the guidelines to individual patients, multiple computer-based algorithms and decision aides have been developed.[10,11] With Adjuvant! Online, the most widely used and highly validated program, the treating oncologist inputs information including patient age, comorbidities, tumor size, histologic grade, HR status, and number of positive nodes, and the computer program predicts the 10-year risk of relapse and mortality for the patient. Based on the physician's choice of chemotherapy and/or endocrine therapy regimen, estimated benefit from each systemic therapy modality is given. The algorithm was validated in a large breast cancer registry, and performed well except in very young women. In the example given above, depending on choice of chemotherapy, the estimated absolute benefit from chemotherapy in the first scenario is 6% to 12%, whereas in the second scenario it is only 2% to 4%.
While these guidelines and computer algorithms allow for individualized decision-making for adjuvant therapy based on the clinicopathologic characteristics of a patient's tumor, they are imperfect. The oncologist still lacks the ability to predict for an individual patient whether her disease will relapse, and whether she will obtain benefit from adjuvant chemotherapy. Therefore, newer methods of assessing breast cancer prognosis and need for systemic therapy are required.
Prognosis and Prediction
Taken together, the considerations above highlight the importance of prognostic and predictive factors. Prognostic factors reflect the metastatic potential and/or growth rate of the tumor, and are used to determine patient outcomes without consideration of treatment administered. For example, ER expression is generally considered a favorable prognostic factor, whereas Ki67 is unfavorable. Conversely, predictive factors reflect the sensitivity or resistance of a tumor to a therapeutic agent, and therefore are used to predict which patients are likely to respond to a specific treatment. ER expression is predictive for response to endocrine therapy, whereas HER2 overexpression is predictive for response to HER2-directed therapies, such as trastuzumab (Herceptin).
Indeed, a factor may be favorable for both prognosis and prediction (as in the ER example given above), or may be mixed. For example, HER2 is an unfavorable prognostic factor, but is a favorable predictive factor for response to HER2-directed therapies (Table 1). Until appropriate studies have been performed, however, it can be difficult to know how to use prognostic and predictive factors appropriately in the clinical setting. Prognostic and predictive properties based on currently available data for three common tumor markers are given in Table 1.
Dr. Hayes is a consultant or advisory panel participant/lecturer for and/or has received an honorarium from StemCapture, American Biosciences, Abraxis, Cytogen Corp, AviaraDx, Precision Therapeutics, Inc, Monogram Bioscience, Siemens Medical Solutions Diagnostics, Pfizer, and Predictive Biosciences; and is the principle or co-investigator for sponsored clinical research for GlaxoSmithKline, Pfizer, Novartis, and the Wyeth-Ayerst Genetics Institute.
1. Peto R, Boreham J, Clarke M, et al: UK and USA breast cancer deaths down 25% in year 2000 at ages 20-69 years. Lancet 355:1822, 2000.
2. Berry DA, Cronin KA, Plevritis SK, et al: Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med 353:1784-1792, 2005.
3. Coates AS, Simes RJ: Patient assessment of adjuvant treatment in operable breast cancer; in Williams CJ (ed): Introducing New Treatments for Cancer: Practical, Ethical, and Legal Problems, pp 447-458. New York, John Wiley & Sons Ltd, 1992.
4. Ravdin P, Siminoff I, Harvey J: Survey of breast cancer patients concerning their knowledge and expectations of adjuvant therapy. J Clin Oncol 16:515-521, 1998.
5. Lindley C, Vasa S, Sawyer T, et al: Quality of life and preferences for treatment following systemic adjuvant therapy for early stage breast cancer. J Clin Oncol 16:1380-1387, 1998.
6. Greene FL, Page DL, Fleming I, et al (eds): AJCC Cancer Staging Manual, 6th ed. New York, Springer-Verlag, 2002.
7. Woodward WA, Strom EA, Tucker SL, et al: Changes in the 2003 American Joint Committee on Cancer staging for breast cancer dramatically affect stage-specific survival. J Clin Oncol 21:3244-3248, 2003.
8. Carlson RW, Anderson BO, Burstein HJ, et al: Breast cancer. J Natl Compr Canc Netw 3:238-289, 2005.
9. Goldhirsch A, Glick JH, Gelber RD, et al: Meeting highlights: International expert consensus on the primary therapy of early breast cancer 2005. Ann Oncol 16:1569-1583, 2005.
10. Ravdin PM, Siminoff LA, Davis GJ, et al: Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol 19:980-991, 2001.
11. Whelan TJ, Loprinzi C: Physician/patient decision aids for adjuvant therapy. J Clin Oncol 23:1627-1630, 2005.
12. Olivotto IA, Bajdik CD, Ravdin PM, et al: Population-based validation of the prognostic model Adjuvant! for early breast cancer. J Clin Oncol 23:2716-2725, 2005.
13. McGuire WL, Clark GM: Prognostic factors and treatment decisions in axillary-node-negative breast cancer. N Engl J Med 326:1756-1761, 1992.
14. Gasparini G, Pozza F, Harris AL: Evaluating the potential usefulness of new prognostic and predictive indicators in node-negative breast cancer patients. J Natl Cancer Inst 85:1206-1219, 1993.
15. Patterson SD, Aebersold RH: Proteomics: The first decade and beyond. Nature Genetics 33(suppl):311-323, 2003.
16. Quackenbush J: Microarray analysis and tumor classification. N Engl J Med 354:2463-2472, 2006.
17. Fuller AP, Palmer-Toy D, Erlander MG, et al: Laser capture microdissection and advanced molecular analysis of human breast cancer. J Mammary Gland Biol Neoplasia 8:335-345, 2003.
18. Paik S, Shak S, Tang G, et al: A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351:2817-2826, 2004.
19. Ma XJ, Patel R, Wang X, et al: Molecular classification of human cancers using a 92-gene real-time quantitative polymerase chain reaction assay. Arch Pathol Lab Med 130:465-473, 2006.
20. Perou CM, Sorlie T, Eisen MB, et al: Molecular portraits of human breast tumours. Nature 406:747-752, 2000.
21. Sorlie T, Perou CM, Tibshirani R, et al: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 98:10869-10874, 2001.
22. Sorlie T, Tibshirani R, Parker J, et al: Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100:8418-8423, 2003.
23. van de Vijver MJ, He YD, van't Veer LJ, et al: A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347:1999-2009, 2002.
24. van 't Veer LJ, Dai H, van de Vijver MJ, et al: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530-536, 2002.
25. Buyse M, Loi S, van't Veer L, et al: Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst 98:1183-1192, 2006.
26. Wang Y, Klijn JG, Zhang Y, et al: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365:671-679, 2005.
27. Desmedt C, Piette F, Loi S, et al: Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the transbig multicenter independent validation series. Clin Cancer Res 13:3207-3214, 2007.
28. Foekens JA, Atkins D, Zhang Y, et al: Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer. J Clin Oncol 24:1665-1671, 2006.
29. 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.
30. Chang HY, Sneddon JB, Alizadeh AA, et al: Gene expression signature of fibroblast serum response predicts human cancer progression: Similarities between tumors and wounds. PLoS Biology 2:E7, 2004.
31. Chang HY, Nuyten DS, Sneddon JB, et al: Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci USA 102:3738-3743, 2005.
32. Liu R, Wang X, Chen GY, et al: The prognostic role of a gene signature from tumorigenic breast-cancer cells. N Engl J Med 356:217-226, 2007.
33. Glinsky GV, Berezovska O, Glinskii AB: Microarray analysis identifies a death-from-cancer signature predicting therapy failure in patients with multiple types of cancer. J Clin Invest 115:1503-1521, 2005.
34. Ma XJ, Wang Z, Ryan PD, et al: A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 5:607-616, 2004.
35. Goetz MP, Suman VJ, Ingle JN, et al: A two-gene expression ratio of homeobox 13 and interleukin-17b receptor for prediction of recurrence and survival in women receiving adjuvant tamoxifen. Clin Cancer Res 12:2080-2087, 2006.
36. Miller LD, Smeds J, George J, et al: An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci USA 102:13550-13555, 2005.
37. Sotiriou C, Wirapati P, Loi S, et al: Comprehensive analysis integrating both clinicopathological and gene expression data in more than 1,500 samples: Proliferation captured by gene expression grade index appears to be the strongest prognostic factor in breast cancer. J Clin Oncol 24:abst 507, 2006.
38. Loi S, Haibe-Kains B, Desmedt C, et al: Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade. J Clin Oncol 25:1239-1246, 2007.
39. Sotiriou C, Wirapati P, Loi S, et al: Gene expression profiling in breast cancer: Understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 98:262-272, 2006.
40. Hedenfalk I, Duggan D, Chen Y, et al: Gene-expression profiles in hereditary breast cancer. N Engl J Med 344:539-548, 2001.
41. Shen R, Ghosh D, Chinnaiyan AM: Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data. BMC Genomics 5:94, 2004.
42. Henry NL, Hayes DF: Uses and abuses of tumor markers in the diagnosis, monitoring, and treatment of primary and metastatic breast cancer. Oncologist 11:541-552, 2006.
43. Simon R: Development and evaluation of therapeutically relevant predictive classifiers using gene expression profiling. J Natl Cancer Inst 98:1169-1171, 2006.
44. Habel LA, Shak S, Jacobs MK, et al: A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients. Breast Cancer Res 8:R25, 2006.
45. Paik S: Development and clinical utility of a 21-gene recurrence score prognostic assay in patients with early breast cancer treated with tamoxifen. Oncologist 12:631-635, 2007.
46. Fan C, Oh DS, Wessels L, et al: Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 355:560-569, 2006.
47. Goetz MP, Ingle JN, Couch FJ, et al: Gene-expression-based predictors for breast cancer. N Engl J Med 356:752-753, 2007.
48. Gianni L, Zambetti M, Clark K, et al: Gene expression profiles in paraffin-embedded core biopsy tissue predict response to chemotherapy in women with locally advanced breast cancer. J Clin Oncol 23:7265-7277, 2005.
49. Sparano JA: TAILORx: Trial assigning individualized options for treatment. Clin Breast Cancer 7:347-350, 2006.
50. Bogaerts J, Cardoso F, Buyse M, et al: Gene signature evaluation as a prognostic tool: Challenges in the design of the MINDACT trial. Nature Clin Pract Oncol 3:540-551, 2006.