Molecular Signatures Predict Outcomes of Breast Cancer
http://www.100md.com
《新英格兰医药杂志》
Breast cancer is classified and managed largely on the basis of anatomy — in contrast with lymphoma, which has been classified and treated according to grade for more than 20 years. Tumor size and the degree of involvement of the axillary nodes are used to estimate the risk of systemic micrometastases at diagnosis and, accordingly, whether systemic adjuvant therapy, which improves overall survival in largely unselected populations, is needed.1
A routine question faced by oncologists is, which of the two thirds of patients with hormone-receptor–positive breast cancer require systemic adjuvant chemotherapy to decrease their chance of recurrence? Although there are substantial differences in the prognosis and natural history between histologically defined low-grade and high-grade breast cancers that express hormone receptors, national consensus guidelines currently recommend the consideration of adjuvant chemotherapy for estrogen receptor (ER)-positive, node-negative tumors that are more than 1 cm in diameter.2 However, retrospective analyses suggest that adjuvant chemotherapy does not benefit patients with highly ER–positive breast cancer (regardless of nodal status), whereas it does appear to benefit patients with lower levels of ER expression.3,4 This finding suggests that biology trumps anatomy in the determination of prognosis and the benefit of chemotherapy.
Accordingly, a sea change is under way with subtypes of breast cancer increasingly being recognized as separate diseases that require biologically based therapies. One type of breast cancer, HER2-positive disease, was definitively identified as a separate entity when it was found that survival among patients with early-stage breast cancer is substantially improved by trastuzumab, a monoclonal antibody that interrupts HER2 signaling, when combined with standard chemohormonal therapy.5,6 The large magnitude of the benefit seen with the addition of trastuzumab heralds the advances to come, as other biologically defined subtypes become the focus of adjuvant-therapy trials.
Gene-expression profiling has contributed to this evolving realization that the biologic heterogeneity of breast cancer has implications for treatment. There are now several predictors based on this method. One such predictor is the intrinsic-subtype classifier, which uses gene-expression profiles to distinguish among breast cancers on the basis of either their cell type of origin — the luminal cell (which is ER-positive) or the basal cell (which lacks expression of ER, the progesterone receptor, and HER2) — or whether the tumor is HER2-positive.7
A second microarray-based predictor, specifically based on the levels of expression of 70 genes, discriminates between a good and a poor outcome (risk of recurrence) in patients with early-stage breast cancer. The signature associated with a poor prognosis demonstrates overexpression of genes regulating the cell cycle, invasion, metastasis, and angiogenesis.8,9
A third predictor calculates a recurrence score on the basis of the expression of 21 genes, with the use of reverse transcriptase–polymerase chain reaction (RT-PCR) in formalin-fixed, paraffin-embedded tissue. The predictor separates node-negative, ER-positive breast cancers into categories of high risk, intermediate risk, and low risk of recurrence.10,11
A fourth predictor, based on a wound-response gene-expression signature derived from the transcriptional response of normal fibroblasts to serum in cell culture, has also been shown to improve the risk stratification of early breast cancer over that provided by standard clinicopathological features, in that the development of distant metastases is more likely among patients whose breast cancers have activated pathways for matrix remodeling, cell motility, and angiogenesis than among those whose cancers do not.12
A fifth predictor uses a ratio of the levels of expression of two genes, one encoding homeobox 13 and the other encoding the interleukin-17B receptor. This predictor, which is based on assays using RT-PCR in formalin-fixed, paraffin-embedded tissue, was developed to determine the risk of recurrence in women with node-negative, ER–positive breast cancers who had received treatment with tamoxifen.13
These diagnostic advances have galvanized the international breast-cancer research community and have led to the launch of the Microarray in Node-Negative Disease May Avoid Chemotherapy (MINDACT) and the Trial Assigning Individualized Options for Treatment (Rx) (TAILORx) studies. These trials will use the 70-gene profile and the recurrence score, respectively, to determine prospectively which patients with ER-positive, node-negative breast cancer benefit from adjuvant chemotherapy and which patients have a risk of recurrence sufficiently low that chemotherapy is unlikely to change their outcome. The results will probably alter standard medical practice such that, in the future, 30 to 50 percent fewer patients with ER-positive breast cancer will receive adjuvant chemotherapy.
In this issue of the Journal, Fan et al.14 report on the extent to which the five predictors are concordant in their classification of the risk of recurrence. They applied the predictors to a single data set that included both gene-expression data and clinical-outcome data for 295 patients. Four of the predictors were highly concordant in the prediction of recurrence and death. The predictor based on the two-gene ratio was not concordant with the other predictors; however, it was designed to predict the benefit from tamoxifen rather than to establish the prognosis for patients with ER–positive disease who had received local therapy only, and only 40 patients in the data set had received tamoxifen.
The study was limited by its inclusion in the 295-patient data set of both patients who had received local therapy only — whose prognosis can be clearly discerned — and patients who had received tamoxifen, chemotherapy, or both — whose natural history was potentially perturbed by one or more interventions. Thus, the ability of the gene-expression assays to predict prognosis was somewhat confounded. Another limitation was that a subgroup of the gene-expression data, derived from the 295 patients to test the prognostic power of the multigene assays, was first used as the training set for the intrinsic-subtype, 70-gene, and wound-response predictors (i.e., the set in which gene-expression cutoff points were selected). As the authors point out, having the training set embedded within the test set positively biases the performance of the predictive assays in their estimates of the recurrence-free survival in multivariate analyses.
To what extent are these concordant gene-expression predictors useful in the management of early-stage breast cancer? Do they add value over that provided by standard prognostic factors and factors predictive of the response to treatment? There was excellent concordance among the predictors in the identification of patients at high risk for recurrence, such that each predictor indicated a poor prognosis for almost all the patients with ER-negative, HER2–positive, or ER-positive and high-grade cancers. Most patients with these high-grade cancers associated with an elevated risk of recurrence routinely receive adjuvant chemotherapy, and multigene classifiers are therefore not needed to identify them. Moreover, the multivariate analyses conducted by Fan et al. showed that the gene-expression assays had a prognostic value independent of that of some standard prognostic factors, including grade. However, the assays did not include quantitative assessments of ER status and progesterone-receptor status, or an evaluation of the HER2 status, mitotic rate, or presence of lymphovascular invasion — other tumor characteristics that are available on routine histopathological assessment and that provide important prognostic information (especially for intermediate-grade breast cancers, whose natural history is the most variable within-grade). At present, therefore, it is not clear that the quantification of the level of expression of dozens or hundreds of genes provides more information about the potential of a cancer for metastasis, virulence,15 and response to therapy for an individual patient than does an optimal analysis of the standard and readily available histopathological prognostic factors.
However, the final judgment about the clinical usefulness of gene-expression profiling may ultimately be practical: if the recommendation for potentially life-saving adjuvant endocrine therapy, chemotherapy, or both is to be based on biologic factors, then the assessment of those factors must be reproducible and reliable. The literature is replete with documentation that assessments of hormone-receptor and HER2 status are highly variable, with substantial rates of false negative and false positive results, and that expert pathologists disagree in their assignments of breast-cancer grade, especially for intermediate-grade cancers. Gene-expression assays that can be performed on formalin-fixed, paraffin-embedded tissue and that provide highly reproducible prognostic information in prospective studies will be of great clinical utility, even if they do not independently predict the prognosis in multivariate analyses that include all the standard clinicopathological prognostic factors. To this end, the high degree of prognostic concordance among four of the predictors, including the recurrence score, is encouraging — especially because the recurrence-score predictor uses formalin-fixed, paraffin-embedded tissue (thus avoiding the need for the processing and storing of fresh tissue) and RT-PCR (an established, specific, and reproducible technique with a wide dynamic range).
Perhaps most important, molecular-expression profiles have the potential to identify the dominant growth and survival networks (networks of proteins in the breast-cancer cell that enable its growth and survival) assessed by the various predictive assays. There is a great, unmet need in the treatment of ER-negative and HER2-negative basal cancers and ER-positive, high-grade cancers, because the major molecular networks that sustain these cancers are not yet known. With the abundance of molecularly targeted inhibitors available either commercially or within clinical trials, the identification of these survival networks is an urgent research priority.
Dr. O'Shaughnessy reports having received consulting fees from the Molecular Profiling Institute. No other potential conflict of interest relevant to this article was reported.
Source Information
From the Baylor Sammons Cancer Center, Texas Oncology, and US Oncology — all in Dallas.
References
Early Breast Cancer Trialists' Collaborative Group (EBCTCG). Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 2005;365:1687-1717.
Carlson RW, Brown E, Burstein HJ, et al. NCCN task force report: adjuvant therapy for breast cancer. J Natl Compr Canc Netw 2006;4:Suppl 1:S1-S26.
International Breast Cancer Study Group. Endocrine responsiveness and tailoring adjuvant therapy for postmenopausal lymph node-negative breast cancer: a randomized trial. J Natl Cancer Inst 2002;94:1054-1065.
Albain K, Barlow W, O'Malley F, et al. Concurrent (CAFT) versus sequential (CAF-T) chemohormonal therapy (cyclophosphamide, doxorubicin, 5-fluorouracil, tamoxifen) versus T alone for postmenopausal, node-positive estrogen (ER) and/or progesterone (PgR) receptor-positive breast cancer: mature outcomes and new biologic correlates on phase III Intergroup trial 0100-(SWOG 8814). San Antonio Breast Cancer Symposium, 2004. (Accessed July 14, 2006, at http://www.SABCS.org.)
Romond EH, Perez EA, Bryant J, et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med 2005;353:1673-1684.
Piccart-Gebhart MJ, Procter M, Leyland-Jones B, et al. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N Engl J Med 2005;353:1659-1672.
Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature 2000;406:747-752.
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 2002;347:1999-2009.
Wang Y, Klijn JGM, Zhang Y, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005;365:671-679.
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 (in press).
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 2004;351:2817-2826.
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 U S A 2005;102:3738-3743.
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 2006;12:2080-2087.
Fan C, Oh DS, Wessels L, et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 2006;355:560-569.
Heimann R, Hellman S. Clinical progression of breast cancer malignant behavior: what to expect and when to expect it. J Clin Oncol 2000;18:591-599.(Joyce A. O'Shaughnessy, M)
A routine question faced by oncologists is, which of the two thirds of patients with hormone-receptor–positive breast cancer require systemic adjuvant chemotherapy to decrease their chance of recurrence? Although there are substantial differences in the prognosis and natural history between histologically defined low-grade and high-grade breast cancers that express hormone receptors, national consensus guidelines currently recommend the consideration of adjuvant chemotherapy for estrogen receptor (ER)-positive, node-negative tumors that are more than 1 cm in diameter.2 However, retrospective analyses suggest that adjuvant chemotherapy does not benefit patients with highly ER–positive breast cancer (regardless of nodal status), whereas it does appear to benefit patients with lower levels of ER expression.3,4 This finding suggests that biology trumps anatomy in the determination of prognosis and the benefit of chemotherapy.
Accordingly, a sea change is under way with subtypes of breast cancer increasingly being recognized as separate diseases that require biologically based therapies. One type of breast cancer, HER2-positive disease, was definitively identified as a separate entity when it was found that survival among patients with early-stage breast cancer is substantially improved by trastuzumab, a monoclonal antibody that interrupts HER2 signaling, when combined with standard chemohormonal therapy.5,6 The large magnitude of the benefit seen with the addition of trastuzumab heralds the advances to come, as other biologically defined subtypes become the focus of adjuvant-therapy trials.
Gene-expression profiling has contributed to this evolving realization that the biologic heterogeneity of breast cancer has implications for treatment. There are now several predictors based on this method. One such predictor is the intrinsic-subtype classifier, which uses gene-expression profiles to distinguish among breast cancers on the basis of either their cell type of origin — the luminal cell (which is ER-positive) or the basal cell (which lacks expression of ER, the progesterone receptor, and HER2) — or whether the tumor is HER2-positive.7
A second microarray-based predictor, specifically based on the levels of expression of 70 genes, discriminates between a good and a poor outcome (risk of recurrence) in patients with early-stage breast cancer. The signature associated with a poor prognosis demonstrates overexpression of genes regulating the cell cycle, invasion, metastasis, and angiogenesis.8,9
A third predictor calculates a recurrence score on the basis of the expression of 21 genes, with the use of reverse transcriptase–polymerase chain reaction (RT-PCR) in formalin-fixed, paraffin-embedded tissue. The predictor separates node-negative, ER-positive breast cancers into categories of high risk, intermediate risk, and low risk of recurrence.10,11
A fourth predictor, based on a wound-response gene-expression signature derived from the transcriptional response of normal fibroblasts to serum in cell culture, has also been shown to improve the risk stratification of early breast cancer over that provided by standard clinicopathological features, in that the development of distant metastases is more likely among patients whose breast cancers have activated pathways for matrix remodeling, cell motility, and angiogenesis than among those whose cancers do not.12
A fifth predictor uses a ratio of the levels of expression of two genes, one encoding homeobox 13 and the other encoding the interleukin-17B receptor. This predictor, which is based on assays using RT-PCR in formalin-fixed, paraffin-embedded tissue, was developed to determine the risk of recurrence in women with node-negative, ER–positive breast cancers who had received treatment with tamoxifen.13
These diagnostic advances have galvanized the international breast-cancer research community and have led to the launch of the Microarray in Node-Negative Disease May Avoid Chemotherapy (MINDACT) and the Trial Assigning Individualized Options for Treatment (Rx) (TAILORx) studies. These trials will use the 70-gene profile and the recurrence score, respectively, to determine prospectively which patients with ER-positive, node-negative breast cancer benefit from adjuvant chemotherapy and which patients have a risk of recurrence sufficiently low that chemotherapy is unlikely to change their outcome. The results will probably alter standard medical practice such that, in the future, 30 to 50 percent fewer patients with ER-positive breast cancer will receive adjuvant chemotherapy.
In this issue of the Journal, Fan et al.14 report on the extent to which the five predictors are concordant in their classification of the risk of recurrence. They applied the predictors to a single data set that included both gene-expression data and clinical-outcome data for 295 patients. Four of the predictors were highly concordant in the prediction of recurrence and death. The predictor based on the two-gene ratio was not concordant with the other predictors; however, it was designed to predict the benefit from tamoxifen rather than to establish the prognosis for patients with ER–positive disease who had received local therapy only, and only 40 patients in the data set had received tamoxifen.
The study was limited by its inclusion in the 295-patient data set of both patients who had received local therapy only — whose prognosis can be clearly discerned — and patients who had received tamoxifen, chemotherapy, or both — whose natural history was potentially perturbed by one or more interventions. Thus, the ability of the gene-expression assays to predict prognosis was somewhat confounded. Another limitation was that a subgroup of the gene-expression data, derived from the 295 patients to test the prognostic power of the multigene assays, was first used as the training set for the intrinsic-subtype, 70-gene, and wound-response predictors (i.e., the set in which gene-expression cutoff points were selected). As the authors point out, having the training set embedded within the test set positively biases the performance of the predictive assays in their estimates of the recurrence-free survival in multivariate analyses.
To what extent are these concordant gene-expression predictors useful in the management of early-stage breast cancer? Do they add value over that provided by standard prognostic factors and factors predictive of the response to treatment? There was excellent concordance among the predictors in the identification of patients at high risk for recurrence, such that each predictor indicated a poor prognosis for almost all the patients with ER-negative, HER2–positive, or ER-positive and high-grade cancers. Most patients with these high-grade cancers associated with an elevated risk of recurrence routinely receive adjuvant chemotherapy, and multigene classifiers are therefore not needed to identify them. Moreover, the multivariate analyses conducted by Fan et al. showed that the gene-expression assays had a prognostic value independent of that of some standard prognostic factors, including grade. However, the assays did not include quantitative assessments of ER status and progesterone-receptor status, or an evaluation of the HER2 status, mitotic rate, or presence of lymphovascular invasion — other tumor characteristics that are available on routine histopathological assessment and that provide important prognostic information (especially for intermediate-grade breast cancers, whose natural history is the most variable within-grade). At present, therefore, it is not clear that the quantification of the level of expression of dozens or hundreds of genes provides more information about the potential of a cancer for metastasis, virulence,15 and response to therapy for an individual patient than does an optimal analysis of the standard and readily available histopathological prognostic factors.
However, the final judgment about the clinical usefulness of gene-expression profiling may ultimately be practical: if the recommendation for potentially life-saving adjuvant endocrine therapy, chemotherapy, or both is to be based on biologic factors, then the assessment of those factors must be reproducible and reliable. The literature is replete with documentation that assessments of hormone-receptor and HER2 status are highly variable, with substantial rates of false negative and false positive results, and that expert pathologists disagree in their assignments of breast-cancer grade, especially for intermediate-grade cancers. Gene-expression assays that can be performed on formalin-fixed, paraffin-embedded tissue and that provide highly reproducible prognostic information in prospective studies will be of great clinical utility, even if they do not independently predict the prognosis in multivariate analyses that include all the standard clinicopathological prognostic factors. To this end, the high degree of prognostic concordance among four of the predictors, including the recurrence score, is encouraging — especially because the recurrence-score predictor uses formalin-fixed, paraffin-embedded tissue (thus avoiding the need for the processing and storing of fresh tissue) and RT-PCR (an established, specific, and reproducible technique with a wide dynamic range).
Perhaps most important, molecular-expression profiles have the potential to identify the dominant growth and survival networks (networks of proteins in the breast-cancer cell that enable its growth and survival) assessed by the various predictive assays. There is a great, unmet need in the treatment of ER-negative and HER2-negative basal cancers and ER-positive, high-grade cancers, because the major molecular networks that sustain these cancers are not yet known. With the abundance of molecularly targeted inhibitors available either commercially or within clinical trials, the identification of these survival networks is an urgent research priority.
Dr. O'Shaughnessy reports having received consulting fees from the Molecular Profiling Institute. No other potential conflict of interest relevant to this article was reported.
Source Information
From the Baylor Sammons Cancer Center, Texas Oncology, and US Oncology — all in Dallas.
References
Early Breast Cancer Trialists' Collaborative Group (EBCTCG). Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 2005;365:1687-1717.
Carlson RW, Brown E, Burstein HJ, et al. NCCN task force report: adjuvant therapy for breast cancer. J Natl Compr Canc Netw 2006;4:Suppl 1:S1-S26.
International Breast Cancer Study Group. Endocrine responsiveness and tailoring adjuvant therapy for postmenopausal lymph node-negative breast cancer: a randomized trial. J Natl Cancer Inst 2002;94:1054-1065.
Albain K, Barlow W, O'Malley F, et al. Concurrent (CAFT) versus sequential (CAF-T) chemohormonal therapy (cyclophosphamide, doxorubicin, 5-fluorouracil, tamoxifen) versus T alone for postmenopausal, node-positive estrogen (ER) and/or progesterone (PgR) receptor-positive breast cancer: mature outcomes and new biologic correlates on phase III Intergroup trial 0100-(SWOG 8814). San Antonio Breast Cancer Symposium, 2004. (Accessed July 14, 2006, at http://www.SABCS.org.)
Romond EH, Perez EA, Bryant J, et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med 2005;353:1673-1684.
Piccart-Gebhart MJ, Procter M, Leyland-Jones B, et al. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N Engl J Med 2005;353:1659-1672.
Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature 2000;406:747-752.
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 2002;347:1999-2009.
Wang Y, Klijn JGM, Zhang Y, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005;365:671-679.
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 (in press).
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 2004;351:2817-2826.
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 U S A 2005;102:3738-3743.
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 2006;12:2080-2087.
Fan C, Oh DS, Wessels L, et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 2006;355:560-569.
Heimann R, Hellman S. Clinical progression of breast cancer malignant behavior: what to expect and when to expect it. J Clin Oncol 2000;18:591-599.(Joyce A. O'Shaughnessy, M)