|
|||||
|
|
||||||
© 2001 American Society for Clinical Oncology Tumor Variants by Hormone Receptor Expression in White Patients With Node-Negative Breast Cancer From the Surveillance, Epidemiology, and End Results DatabaseFrom the Division of Cancer Prevention, Office of Special Population Research, and Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD. Address reprint requests to William F. Anderson, MD, MPH, National Cancer Institute Division of Cancer Prevention, Room EPN 211L, MSC-7322, 6130 Executive Blvd, Bethesda, MD 20892-7161; email wanderso@ mail.nih.gov.
PURPOSE: Hormone receptor expression (presence-positive or absence-negative) may reflect different stages of one disease or different breast cancer types. Determining whether hormone receptor expression represents one or more breast cancer phenotypes would have important paradigmatic and practical implications. METHODS: Breast cancer records were obtained from the National Cancer Institutes Surveillance, Epidemiology, and End Results (SEER) database. The study included 19,541 non-Hispanic white women with node-negative breast cancer. Standard tumor cell characteristics and breast cancer-specific survival were analyzed by independent estrogen receptor (ER+ and ER-), independent progesterone receptor (PR+ and PR-), and joint ERPR expression (ER+PR+, ER+PR-, ER-PR+, and ER-PR-). RESULTS: Age frequency density plots by hormone receptor expression showed two overlapping breast cancer populations with early-onset and/or late-onset etiologies. Independent ER+ and PR+ phenotype were associated with smaller tumor sizes, better grade, and better cancer-specific survival than ER- and PR- breast cancer types. Joint ERPR phenotype exhibited biologic gradients for tumor size, grade, and cancer-specific survival, which ranked from good to worse for ER+PR+ to ER+PR- to ER-PR+ to ER-PR-. CONCLUSION: Variations of standard tumor cell characteristics and breast cancerspecific survival by hormone receptor expression in white patients with node-negative breast cancer suggested two breast cancer phenotypes with overlapping etiologies and distinct clinical features.
CONVENTIONAL WISDOM views breast cancer as a multistep process with a spectrum of proclivities (or stages); that is, one disease along a linear biologic pathway from early to late tumor stages.1-5 Albeit the etiologic mechanisms of breast carcinogenesis are not fully understood, decades of research suggest an important role for the reproductive hormones (especially estrogen) and their nuclear receptors.6-10 Presumably, the carcinogenic insult initiates genomic alterations in estrogen-sensitive breast epithelium (estrogen receptorpositive, or ER+), and this insult is promoted by estrogen. ER+ tumor cells then drift to estrogen insensitive (ER-) tumor cells.11 In this model, ER+ to ER- phenotypic drift is a product of clonal evolution and expansion, reflecting different tumor stages rather than different breast cancer types.12 However, ER expression may be a stable phenotype.13 Sequential ER assays generally do not show ER+ to ER- phenotypic drift from primary to metastatic breast carcinoma.14 Additionally, if tumor cells did drift from ER+ to ER-, it is counterintuitive for ER+ breast cancer to increase with patient aging.15,16 Taken to its logical conclusion, the spectrum model predicts that younger (not older) women should have ER+ disease. Absent ER+ to ER- phenotypic drift would suggest that variations in hormone receptor expression represent different breast cancer types rather than different tumor stages.17-20 Establishing whether hormone receptor expression represents one or more breast cancer types (or variants) has important paradigmatic and practical implications.5,21-26 To investigate putative breast cancer variants, we examined patient age at diagnosis, tumor size, histologic grade, and breast cancer survival by independent ER, independent progesterone receptor (PR), and joint ERPR phenotype. Results suggested that hormone receptor phenotype reflects two types of breast cancer with early-onset and/or late-onset variants.
Breast cancer records were obtained from the National Cancer Institutes Surveillance, Epidemiology, and End Results (SEER) Cancer Incidence Public-Use CD-ROM, 1973 through 1996, August 1998 submission. The original breast.txt raw data file was imported from the Public-Use CD-ROM to SAS for Windows (Version 6.12, SAS Institute, Cary, NC), S-Plus 2000 for Windows (Statistical Sciences, Seattle, WA), and Statistica for Windows 99 edition (Version 5.5A, StatSoft, Tulsa, OK). Collected from nine population-based cancer registries, the SEER database includes 2.3 million cancer cases from representative American subsets comprising 9.5% of the United States population.27 SEER did not gather hormone receptor data before 1990. This analysis was restricted to white women with node-negative breast cancer who were accrued during the years of hormone receptor collection.
From 1990 through 1996, there were 132,159 breast cancer records in the Public-Use CD-ROM. This breast cancer cohort was sequentially filtered for the following: (1) female sex (n = 131,306); (2) infiltrating ductal carcinoma not otherwise specified (histopathologic code 8500; n = 79,768); (3) one or first primary breast cancer only (n = 68,801); (4) microscopic confirmation (n = 68,775); (5) American Joint Committee on Cancer tumor sizes T1-T328 corresponding to SEER extent of disease codes 10 through 30 (n = 63,309); (6) axillary lymph nodenegative breast cancer cases (n = 40,491); (7) tumor size
Study variables included standard tumor cell characteristics, patient age at diagnosis, tumor size, histologic grade, and hormone receptor expression. All study variables were stratified by independent ER, independent PR, and joint ERPR expression. Tumor size and age were analyzed as continuous and categoric variables. Age less than 50 years versus 50+ years was a surrogate measure for menopausal status. Tumor size was divided into two groups: more than 2.0 centimeters versus
Students t test for independent samples and one-way analysis of variance were used to detect differences in mean ages at diagnosis between groups defined by hormone receptor expression.30 Continuous 1-year age frequency distributions were generated with density plots, which were constructed with a smoothing method of the corresponding age-at-diagnosis frequency histogram.31 Using the density function in S-Plus 2000,32 the density plot used a filter width of 10. The vertical axis for each density plot represented smoothed estimates of the proportion (or density) of patients who developed breast cancer at the corresponding age at diagnosis on the horizontal axis. Kolmogorov-Smirnov statistics tested statistically significant differences between age frequency distributions.33 Kolmogorov-Smirnov statistics define the maximum difference in the cumulative proportions of two nonparametric distributions. Univariate and multivariate associations between hormone receptor expression and study variables were estimated with odds ratios and P values. Logistic regression was used to derive adjusted odds.34 All P values were two-sided. P values Outcome measures included overall survival and breast cancerspecific survival. SEERs vital status code established whether the patient was alive or dead. Cause of death was categorized as either breast cancerspecific or nonbreast cancer death. Overall survival was defined as the interval between date of breast cancer diagnosis and date of death from any cause. Breast cancerspecific survival was measured from the date of diagnosis to the date of breast cancerspecific death. The Kaplan-Meier product-limit method estimated overall and breast cancerspecific survivals from 1990 to 1995.35 Stratified log-rank test compared time to overall and cancer-specific survivals between groups by independent and/or joint hormone receptor expression.36
Descriptive statistics by independent ER and independent PR phenotypes yielded similar results (Tables 1 and 2, respectively). ER- compared with ER+ and PR- compared with PR+ were both associated with younger age at diagnosis, surrogate premenopausal status, larger tumor diameter, and poor histologic grade. Age frequency density plots showed bimodal distribution with early-onset mode (or peak frequency) and/or late-onset mode (or peak frequency; Fig 1). The vertical axis of each density plot represented smoothed estimates of the proportion of patients who had breast cancer at the corresponding age at diagnosis on the horizontal axis. Early-onset peak frequency approximated the premenopausal age of 40 to 50 years, whereas late-onset peak frequency occurred close to the postmenopausal age of 70 years. The postmenopausal peak predominated in ER+, PR+, and PR- phenotypes, whereas the premenopausal peak was dominant with ER- breast cancer.
Descriptive statistics by joint ERPR phenotypes are listed in Tables 3 and 4. A total of 66% (n = 12,811) were ER+PR+, 12.5% (n = 2,436) were ER+PR-, 3.4% (n = 663) were ER-PR+, and 18.6% (n = 3,631) were ER-PR-. Mean age at diagnosis was significantly different (P < .001) by one-way analysis of variance for joint ERPR profiles: 62.7 years for ER+PR+, 65.1 years for ER+PR-, 55.2 years for ER-PR+, and 57.0 years for ER-PR-. All possible pairs of mean age were also significantly different by Students t test for independent samples, after adjusting for multiple comparisons. Age frequency distribution density plots by joint ERPR status are shown in Fig 2. The concordant ERPR pair (ER+PR+ and ER-PR-) demonstrated bimodal premenopausal and postmenopausal peaks. The postmenopausal peak dominated in the ER+PR+ phenotype, whereas the premenopausal peak was dominant in ER-PR- expression. The discordant ERPR pair (ER+PR- and ER-PR+) had unimodal age frequency density plots. Frequency distribution was shifted to the right (towards late-onset or postmenopausal ages) for ER+PR-; peak distribution approximated 70 years of age. ER-PR+ was the reciprocal of ER+PR-; that is, peak frequency distribution was shifted to the left (towards early-onset or premenopausal ages) with peak frequency distribution between 40 and 50 years.
To further compare the age frequency distribution curves by independent and joint hormone receptor expression, we examined the Kolmogorov-Smirnov nonparametric test statistics. The largest test statistic was observed when ER+PR- was compared with ER-PR+ (Kolmogorov-Smirnov test statistic of 0.3366). The Kolmogorov-Smirnov test statistic between ER+ and ER- was larger than the test statistic between PR+ and PR- (0.1996 and 0.0601, respectively). All Kolmogorov-Smirnov test statistics were statistically significant (P < .001). Tumor size and histologic grade showed a type of dose response (or biologic gradient) with joint hormone receptor expression, which were ranked from good to worse for ER+PR+ to ER+PR- to ER-PR+ to ER-PR- (Tables 3 and 4). Age at diagnosis and surrogate menopausal status showed no biologic gradient by ERPR phenotype. With multivariate modeling, all relationships remained statistically significant except for tumor size in the ER-PR+ group (P = .314) (Table 4).
From 1990 to 1995, the median duration of follow-up was 31 months. Crude unadjusted overall survival was 92.7%. There were 18,114 living and 1,427 deceased patients: 904 nonbreast cancer deaths and 523 breast cancer deaths. Kaplan-Meier product-limit analysis demonstrated significant differences (log-rank test, P < .001) for both overall and breast cancerspecific survival by independent ER, independent PR, and joint ERPR profiles. Log-rank 2 results were greater for cause-specific than for overall survival, demonstrating that hormone receptor expression had a greater impact on breast cancerspecific than overall survival. Kaplan-Meier plots for breast cancerspecific survival by hormone receptor expression are shown in Fig 3. ER+ compared with ER- and PR+ compared with PR- showed improved survival. ER+ and PR+ had identical cancer-specific survival. There was a biologic gradient by joint ERPR phenotypes for cancer-specific survival, with worsening cumulative proportion surviving from ER+PR+ to ER+PR- to ER-PR+ to ER-PR-.
Although there is abundant information concerning independent ER and PR expression, comparatively little is known concerning joint ERPR phenotype. In part, this is because there are relatively few ER+PR- tumors and even fewer ER-PR+ cancers. Clark et al15 and others37,38 have noted varied hormonal expression by ER and PR phenotype in both early-stage and late-stage breast cancers. To further evaluate the importance of independent as well as joint hormone receptor expression in early-stage breast cancer, we examined the National Cancer Institutes SEER population-based database. Independent ER+ and independent PR+ expression were associated with older age at diagnosis, smaller tumor sizes, better histologic grade, and better breast cancerspecific survival than ER- and PR- disease. Age frequency density plots showed mixed breast cancer populations, with overlapping early-onset (premenopausal) and late-onset (postmenopausal) breast cancer types. ER+, PR+, and PR- had dominant postmenopausal peaks, whereas ER- had a dominant premenopausal peak (Fig 1). The trough between the bimodal peaks may represent the so-called Clemmesens hook, the characteristic midlife dip in age-specific breast cancer incidence that is attributed to the female climacteric.39,40 Purportedly, Clemmesens hook occurs at the junction of declining premenopausal breast cancer incidence and increasing postmenopausal breast cancer incidence. The midlife drop approximated 58 years of age in this analysis. It has been suggested that joint ERPR expression identifies breast cancer variants better than either independent ER or PR expression.18,20,26 There may be general agreement concerning concordant joint profiles (ER+PR+ and ER-PR-), but the discordant pair (ER+PR- and ER-PR+) has been problematic. ER+PR+ represents hormone-responsive breast cancer, whereas ER-PR- reflects hormone-insensitive tumors.18,41 In contrast, ER+PR- and/or ER-PR+ have been characterized as dubious discordant subsets,18 mutant pairs,42,43 laboratory artifacts,44 and imaginary.45 However, in this analysis, the discordant joint ERPR phenotypes had distinct age frequency density plots and prognostic factor profiles. The purest postmenopausal age frequency distribution was in the ER+PR- group, whereas the purest premenopausal pattern had ER-PR+ expression (Fig 2). for ER+PR- expression, the mean age at diagnosis was 65.1 years, with a single peak frequency distribution of 70 years, similar to the age of greatest risk for sporadic breast cancer.27,46 For ER-PR+ expression, peak age frequency distribution was between 40 and 50 years of age. Consequently, the oldest patients had a joint ER+PR- profile, whereas the youngest women were in the ER-PR+ group, consistent with the association of increased ER concentrations with aging and increased PR concentrations with premenopausal status.16,17,47-49 Greater premenopausal levels of endogenous estrogens presumably induce PR expression. Therefore, because increasing ER level is associated with increasing age and increasing PR level is associated with premenopausal status, it makes sense for ER+PR- to include the oldest women while ER-PR+ contains the youngest patients. Intermediate mean ages and age frequency distribution patterns are in the concordant groups, with ER+PR+ women being older than are ER-PR- women. There is thus a complicated relationship between age at diagnosis and menopausal status, which is reflected by joint ERPR phenotypes. Menopausal status was a surrogate measure in this analysis, but the Iowa Womens Health Study collected reproductive history as well as other epidemiologic risk factors from a self-reported questionnaire.26 Sporadic breast cancer was highly associated with ER+PR-, whereas family history of breast cancer had its strongest association with ER-PR+ profile. Similarly, a case-control analysis in Japan reported that family history was not associated with ER+PR- expression.50 Loman et al51 also suggested that familial tumors with high levels of PR might compose a distinct subgroup of hereditary breast carcinomas that are not related to BRCA1 and/or BRCA2. All three studies are consistent with our observation for the oldest and youngest patients to be in the ER+PR- and ER-PR+ groups, given that sporadic and familial breast cancers tend to occur in older and younger women, respectively.27,52,53 The Iowa Womens Health Study described joint ER+PR- expression as true sporadic breast cancers. The ER-PR+ profile could be a familial equivalent. Future etiologic studies should possibly focus on discordant ERPR phenotypes for analyzing sporadic and familial breast cancer.
Tumor size, grade, and breast cancer survival demonstrated biologic gradients, whereas age at diagnosis and menopausal status showed no biologic gradients by joint ERPR expression. Tumor size (> 2.0 v This analysis has several potential sources of error that could effect internal and/or external validity. First, hormone receptor assays were not carried out in a centralized laboratory. However, ER and PR assays are now obtained on virtually every breast cancer patient and assay technology is becoming standardized.54 Overall conclusions from a variety of different laboratories using different assays have usually been consistent.55 It also seems highly unlikely that nine SEER sites would have differential hormone receptor misclassification across the four ERPR phenotypes. We derived some comfort from the fact that our joint ERPR distribution was very similar to other American studies; that is, ER+PR+, ER+PR-, ER-PR+, and ER-PR- are approximately 60%, 15% to 20%, less than 5%, and 15% to 20%, respectively.18,26,41 In contrast, our hormone receptor distribution is strikingly different from a Japanese case-control study, which noted that ER+PR+, ER+PR-, ER-PR+, and ER-PR- were 39%, 25%, 5%, and 31%, respectively.50,56 However, joint ERPR distribution by Japanese ethnicity in the SEER database is also different from the Japanese case-control study in Japan. There were 595 Japanese women with lymph nodenegative breast cancer in the SEER 1973 through 1996 CD-ROM, and ERPR profiles were 69.6%, 12.1%, 5.2%, and 13.1% for ER+PR+, ER+PR-, ER-PR+, and ER-PR-, respectively. Japanese women in Japan and in America may have different breast cancers, or different ERPR phenotypes could have been due to selection bias, because joint receptor status was unknown in 60% of the Japanese cases in Japan. Second, our results may not be generalizable to the global breast cancer population, because we examined a lymph nodenegative subset from the SEER population-based database containing only non-Hispanic white women with infiltrating ductal carcinoma. We reasoned that early-stage breast cancer in a single ethnic group would reduce late-stage confounding of interrelated study variables such as race, delayed breast cancer detection, socioeconomic status, and so on.57-65 A preliminary analysis has confirmed racial variation by joint ERPR phenotype especially for black versus white women, but this will be the subject of another report. A future report could also incorporate node-positive breast cancer patients. A third concern relates to the fact that the SEER Public-Use CD-ROM does not include information pertaining to postoperative adjuvant treatment. Therefore, breast cancer outcome could not be adjusted for postoperative treatment. However, the observed biologic gradient (ER+PR+ to ER+PR- to ER-PR+ to ER-PR-) is not only biologically plausible but also consistent with previous studies. Wenger et al44 reported in 1993 that S-phase fraction increased from ER+PR+, ER+PR-, ER-PR+, and ER-PR-. S-phase fraction is strongly correlated with poor breast cancer prognosis.66 Early Breast Cancer Trialists Collaborative Group also observed relative improvements in early-stage breast cancer survival associated with adjuvant tamoxifen therapy, which were ranked from good to worse for ER+PR+ to ER+PR- to ER-PR+ to ER-PR-.67 Additionally, hormone receptor status was first noted to be a predictor of cancer-specific survival more than 20 years ago, before routine adjuvant systemic treatment.16,68 Hormone receptor status is also known to have prognostic value in node-negative patients who did not receive systemic treatment.69,70 Fourth, the short median follow-up time of 31 months may not be adequate to detect long-term survival differences in node-negative breast cancer. However, ER studies with short-term follow-up (2 to 4 years) can yield valid conclusions concerning breast cancer etiology and early outcome effects, but late outcome results will require long-term follow-up.16,71 Therefore, our early survival results must be verified with longer follow-up. Notwithstanding these theoretical limitations, this is the largest node-negative population-based breast cancer analysis (n = 19,451) to ever simultaneously examine independent ER, independent PR, and joint ERPR expression. Our results provide potentially important etiologic clues, clinical insights, and caveats:
In conclusion, variations of patient age at diagnosis, tumor size, grade, and cancer-specific survival by independent and joint hormone receptor expression posit two breast cancer variants with overlapping early or late-onset etiologies and distinct clinical features. The contemporary spectrum paradigm postulates that breast cancer is one disease along a continuous biologic pathway.1-4 Our large-scale population-based observations suggest that breast cancer is two diseases rather than one.
1. Heimann R, Hellman S: Aging, progression, and phenotype in breast cancer. J Clin Oncol 16: 2686-2692, 1998[Abstract]
2.
Hellman S, Harris JR: The appropriate breast cancer paradigm. Cancer Res 47: 339-342, 1987
3.
Hellman S: Natural history of small breast cancers. J Clin Oncol 12: 2229-2234, 1994 4. Hellman S: Natural history of breast cancer, in Harris JR, Lippman ME, Morrow M et al (eds): Diseases of the Breast, ed 2. Philidelphia, PA, Lippincott Williams &Wilkins, 2000, pp 407-423 5. Mueller CB: Stage II breast cancer is not simply a late stage I. Surgery 104: 631-638, 1988[Medline] 6. Folca PJ, Glascock RF, Irvine WT: Studies with tritium-labelled hexoestrol in advanced breast cancer. Lancet 2: 796-798, 1961[Medline] 7. Gorski J, Toft D, Shyamala G, et al: Hormone receptors: Studies on the interaction of estrogen with the uterus. Recent Prog Horm Res 24: 45-80, 1968 8. Hertz R: The estrogen-cancer hypothesis. Cancer 38: 534-540, 1976[Medline] 9. Jensen EV, DeSombre ER: Mechanism of action of the female sex hormones. Ann Rev Biochem 41: 203-230, 1972[Medline] 10. Kirschner MA: The role of hormones in the etiology of human breast cancer. Cancer 39: 2716-2726, 1977[Medline] 11. Jordan VC, Costa AF: Chemoprevention, in Harris JR (ed): Diseases of the Breast, ed 2. Philadelphia, PA, Lippincott Williams & Wilkins, 2000, pp 265-279 12. Moolgavkar SH, Day NE, Stevens RG: Two-stage model for carcinogenesis: Epidemiology of breast cancer in females. J Natl Cancer Inst 65: 559-569, 1980 13. Robertson JF: Oestrogen receptor: A stable phenotype in breast cancer. Br J Cancer 73: 5-12, 1996[Medline]
14.
Hull DF, Clark GM, Osborne CK, et al: Multiple estrogen receptor assays in human breast cancer. Cancer Res 43: 413-416, 1983 15. Clark GM, Osborne CK, McGuire WL: Correlations between estrogen receptor, progesterone receptor, and patient characteristics in human breast cancer. J Clin Oncol 2: 1102-1109, 1984[Abstract] 16. Osborne CK: Steroid hormone receptors in breast cancer management. Breast Cancer Res Treat 51: 227-238, 1998[Medline] 17. Cooper JA, Rohan TE, Cant EL, et al: Risk factors for breast cancer by oestrogen receptor status: A population-based case-control study. Br J Cancer 59: 119-125, 1989[Medline] 18. Thorpe SM: Estrogen and progesterone receptor determinations in breast cancer: Technology, biology and clinical significance. Acta Oncol 27: 1-19, 1988[Medline] 19. Velentgas P, Daling JR: Risk factors for breast cancer in younger women. J Natl Cancer Inst Monogr 16: 15-24, 1994 20. Wittliff J: Hormone and growth factor receptors, in Donegan WL, Spratt JS (eds): Cancer of the Breast, ed 4. Philadelphia, PA, W.B. Saunders, 1995, pp 346-374 21. De Waard F: Premenopausal and postmenopausal breast cancer: One disease or two? J Natl Cancer Inst 63: 549-552, 1979 22. Fitzpatrick PJ: A tale of two women: The concept of good and bad disease in breast cancer. Can J Surg 29: 78-79, 1986[Medline] 23. Fox MS: On the diagnosis and treatment of breast cancer. JAMA 241: 489-494, 1979[Abstract]
24.
Glass AG, Hoover RN: Rising incidence of breast cancer: Relationship to stage and receptor status. J Natl Cancer Inst 82: 693-696, 1990 25. Manton KG, Stallard E: A two-disease model of female breast cancer: Mortality in 1969 among white females in the United States. J Natl Cancer Inst 64: 9-16, 1980 26. Potter JD, Cerhan JR, Sellers TA, et al: Progesterone and estrogen receptors and mammary neoplasia in the Iowa Womens Health Study: How many kinds of breast cancer are there? Cancer Epidemiol Biomarkers Prev 4: 319-26, 1995[Abstract] 27. SEER: SEER Cancer Statistic Review, 1973-1996 "Initial Content." Bethesda, MD, National Cancer Institute, 1999 28. Fleming ID, Cooper JS, Henson DE, et al: Breast: AJCC Cancer Staging Manual, ed 5. Philadelphia, PA, Lippincott-Raven, 1997, pp 171-180 29. Percy C, Holten VV, Muir C: International Classification of Diseases for Oncology, ed 2. Geneva, Switzerland, World Health Organization, 1990, pp 144 30. Fisher RA: The Design of Experiments, ed 8. Edinburgh, Scotland, Oliver and Boyd, 1966 31. Silverman BW: Density Estimation for Statistics and Data Analysis. London, United Kingdom, Chapman and Hall, 1986 32. Mathsoft : Plotting one-dimensional data: S-Plus 2000 Users guide. Seattle, WA, Mathsoft, Inc, pp 72-78, 1999 33. Randles RH, Wolfe DA: Introduction to the theory of non-parametric statistics. New York, NY, John Wiley & Sons, 1979 34. Kleinbaum DG, Kupper LL, Morgenstern H: Epidemiologic Research: Principles and Quantitative Methods. New York, NY, John Wiley & Sons, 1982 35. Kaplan EL, Meier P: Nonparametric estimation from incomplete observations. J Am Stat Assoc 53: 457-481, 1958 36. Peto R, Peto J: Asymptomatically efficient rank invariant test procedures. J R Stat Soc A 135: 185-198, 1972 37. Osborne CK, Yochmowitz MG, Knight WAd, et al: The value of estrogen and progesterone receptors in the treatment of breast cancer. Cancer 46: 2884-2888, 1980[Medline]
38.
Ravdin PM, Green S, Dorr TM, et al: Prognostic significance of progesterone receptor levels in estrogen receptor-positive patients with metastatic breast cancer treated with tamoxifen: Results of a prospective Southwest Oncology Group study. J Clin Oncol 10: 1284-1291, 1992
39.
Thomas DB: Breast cancer in men. Epidemiol Rev 15: 220-231, 1993 40. Clemmesen J: Carcinoma of the breast. Br J Radiol 21: 583-590, 1948[Medline] 41. Wittliff JL: Steroid-hormone receptors in breast cancer. Cancer 53: 630-643, 1984[Medline]
42.
Fuqua SA, Fitzgerald SD, Chamness GC, et al: Variant human breast tumor estrogen receptor with constitutive transcriptional activity. Cancer Res 51: 105-109, 1991 43. Fuqua SA, Chamness GC, McGuire WL: Estrogen receptor mutations in breast cancer. J Cell Biochem 51: 135-139, 1993[Medline] 44. Wenger CR, Beardslee S, Owens MA, et al: DNA ploidy: S-phase, and steroid receptors in more than 127,000 breast cancer patients. Breast Cancer Res Treat 28: 9-20, 1993[Medline] 45. Vagundova M, Vagunda V, Jandakova E, et al: Assessment of estrogen and progesterone receptors and their combined phenotypes in 287 breast carcinomas: Good correlation between immunohistochemical and ligand saturation analyses for ER but not PR; frequency of ER- PR+ phenotype greatly reduced by immunohistochemistry. Breast Cancer Res Treat 57: 97, 1999 (abstr) 46. PDQ: Screening for breast cancer. NCI PDQ Screening & Prevention-Health Professionals, 1999, pp 1-23 47. Kreiger N, King WD, Rosenberg L, et al: Steroid receptor status and the epidemiology of breast cancer. Ann Epidemiol 1: 513-523, 1991[Medline]
48.
Stanford JL, Szklo M, Boring CC, et al: A case-control study of breast cancer stratified by estrogen receptor status. Am J Epidemiol 125: 184-194, 1987
49.
Stanford JL, Greenberg RS: Breast cancer incidence in young women by estrogen receptor status and race. Am J Public Health 79: 71-73, 1989
50.
Yoo KY, Tajima K, Miura S, et al: Breast cancer risk factors according to combined estrogen and progesterone receptor status: A case-control analysis. Am J Epidemiol 146: 307-314, 1997 51. Loman N, Johannsson O, Bendahl PO, et al: Steroid receptors in hereditary breast carcinomas associated with BRCA1 or BRCA2 mutations or unknown susceptibility genes [see comments]. Cancer 83: 310-319, 1998[Medline]
52.
Bain C, Speizer FE, Rosner B, et al: Family history of breast cancer as a risk indicator for the disease. Am J Epidemiol 111: 301-308, 1980
53.
Claus EB, Schildkraut J, Iversen ES Jr, et al: Effect of BRCA1 and BRCA2 on the association between breast cancer risk and family history [see comments]. J Natl Cancer Inst 90: 1824-1829, 1998
54.
Elledge RM, Osborne CK: Oestrogen receptors and breast cancer. BMJ 314: 1843-1844, 1997 55. Jensen EV: Steroid receptors in breast cancer: Historical perspective. Cancer 46: 2759-2761, 1980 56. Yoo KY, Tajima K, Miura S, et al: A hospital-based case-control study of breast-cancer risk factors by estrogen and progesterone receptor status. Cancer Causes Control 4: 39-44, 1993[Medline] 57. Berg JW, Ross R, Latourette HB: Economic status and survival of cancer patients. Cancer 39: 467-477, 1977[Medline] 58. Bloom HJG: The influence of delay on the natural history and prognosis of breast cancer. Br J Cancer 19: 228-262, 1965 59. Dayal HH, Power RN, Chiu C: Race and socio-economic status in survival from breast cancer. J Chron Dis 35: 675-683, 1982[Medline]
60.
Farley TA, Flannery JT: Late-stage diagnosis of breast cancer in women of lower socioeconomic status: Public health implications. Am J Public Health 79: 1508-1512, 1989 61. Freeman HP, Wasfie TJ: Cancer of the breast in poor black women. Cancer 63: 2562-2569, 1989[Medline] 62. Kerner JF, Trock BJ, Mandelblatt JS: Breast cancer in minority women, in Harris JR, Lippman ME, Morrow M et al (eds): Diseases of the Breast. Philadelphia, PA, Lippincott Williams & Wilkins, 2000, pp 955-966
63.
Mandelblatt J, Andrews H, Kerner J, et al: Determinants of late stage diagnosis of breast and cervical cancer: The impact of age, race, social class, and hospital type. Am J Public Health 81: 646-649, 1991 64. Swanson GM, Lin CS: Survival patterns among younger women with breast cancer: The effects of age, race, stage, and treatment. J Natl Cancer Inst Monogr 16: 69-77, 1994 65. Wilkinson GS, Edgerton F, Wallace HJ Jr, et al: Delay, stage of disease and survival from breast cancer. J Chronic Dis 32: 365-373, 1979[Medline] 66. Clark GM: Prognostic and predictive factors, in Harris JR, Lippman ME, Morrow M et al (eds): Diseases of the Breast. Phildelphia, PA, Lippincott Williams and Wilkins, 1999, pp 489-514 67. Early Breast Cancer Trialists Collaborative Group: Tamoxifen for early breast cancer: An overview of the randomised trials. Lancet 351:1451-1467, 1998
68.
Knight WA, Livingston RB, Gregory EJ, et al: Estrogen receptor as an independent prognostic factor for early recurrence in breast cancer. Cancer Res 37: 4669-4671, 1977 69. Crowe JP, Hubay CA, Pearson OH, et al: Estrogen receptor status as a prognostic indicator for stage I breast cancer patients. Breast Cancer Res Treat 2: 171-176, 1982[Medline] 70. Crowe JP Jr, Gordon NH, Hubay CA, et al: The interaction of estrogen receptor status and race in predicting prognosis for stage I breast cancer patients. Surgery 100: 599-605, 1986[Medline] 71. Hilsenbeck SG, Ravdin PM, de Moor CA, et al: Time-dependence of hazard ratios for prognostic factors in primary breast cancer. Breast Cancer Res Treat 52: 227-237, 1998[Medline] Submitted February 28, 2000; accepted July 18, 2000. This article has been cited by other articles:
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|||||||||||
|
Copyright © 2001 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
|