Affiliations of authors: Cancer Research UK, Department of Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, Queen Mary College, University of London, London, U.K. (JC, JW, EP, SWD); Cambridge Breast Unit, Addenbrookes Hospital, Cambridge, U.K. (RMLW)
Correspondence to: Jack Cuzick, PhD, Cancer Research UK, Department of Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, Charterhouse Square, London, EC1M 6BQ, U.K. (e-mail: jack.cuzick{at}cancer.org.uk)
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ABSTRACT |
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INTRODUCTION |
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During the International Breast Cancer Intervention Study I (IBIS-I), a trial of tamoxifen for breast cancer prevention, mammograms were obtained from all subjects at baseline and every 18 months during treatment. Although these mammograms were performed for screening purposes, they also documented changes in mammographic breast density during the course of treatment. In this study, we investigated the influence of tamoxifen treatment and other hormonal factors, family history of breast cancer, and anthropometric measures on mammographic density at baseline and during treatment in a subset of 818 healthy women at high risk of breast cancer from the IBIS-I chemoprevention trial. The important question of whether changes in density predict changes in risk is not addressed in this study but will be the subject of a later report.
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SUBJECTS AND METHODS |
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Baseline mammograms were required before randomization for all women. Additional mammograms were scheduled for 18, 36, and 54 months later and were read locally by radiologists at the participating centers. The primary aim of the mammography was early detection of breast cancer, so the quantitative assessments of breast density needed for this analysis were not immediately available. Mammograms from the eight largest recruiting centers were retrieved for density reading by a single consultant radiologist (R. M. L. Warren) and were digitized for future studies. The centers were in Aberdeen, Bristol, Cardiff, Edinburgh, London, Manchester, Nottingham, and Southampton. To be eligible for this study, women needed to have baseline, 18-month, and 54-month mammograms and be in full compliance with study medication. About 35% of the subjects were not eligible because their baseline mammographic examination was no longer available (usually because the screening service had destroyed it). We analyzed data from all 818 women (388 in the tamoxifen group and 430 in the placebo group; test for imbalance, P = .14) from these centers who were free of breast cancer at the completion of treatment and who satisfied the above criteria. Written informed consent to use mammograms and medical records for research on breast cancer risk factors was obtained from each subject, and local ethics committee approval was obtained from all participating centers.
Mammographic breast density was assessed visually by a consultant radiologist (R. M. L. Warren) and classified according to the criteria set out by Wolfe (8), Boyd et al. (9), and Gram et al. (10). The proportion of the breast composed of dense tissue, termed breast density, was also estimated and expressed as a percentage of total breast area (to the nearest 5%). Thus, in effect, a 21-point categorical scale was used. In this report, we used only the raw percent density and the Boyd classification scale (6), which categorizes percent density into the following groups: A = 0%, B = 1%10%, C = 11%25%, D = 26%50%, E = 51%75%, and F = 76%100%. The mammograms for each woman were sorted by date and were read in batches for 20 women at a time. The mammograms for each woman were viewed consecutively, commencing with the baseline film, in sessions lasting approximately 40 minutes per batch. This mammographic examination reading was done without knowledge of treatment group or other potential predictive factors. To assess the reproducibility of these readings, a subset of mammograms from approximately 70 women from Manchester were read separately by a specially trained research nurse (E. Pinney). The two sets of readings showed very good agreement, with correlation coefficients of .91 for the baseline readings, .88 for readings at 18 months, and .74 for readings at 54 months. There was a substantial difference in opinion in only one case, which resulted from an underexposed film. Measures of the change in density over the first 18 and 54 months of treatment also showed good correlation between readers ( = .66 and
= .89, respectively).
Statistical Analysis
Breast density was not normally distributed. The distribution was bimodal with observations being concentrated around 0 and Boyd class E (51%75% dense). Breast density was also not a truly continuous variable because it was measured on a 21-point scale quantized by 5% increments in density. Because we were primarily interested in the effect of tamoxifen on reduction in breast density, we excluded women with baseline densities at or below 10% at entry from those analyses involving changes in breast density.
The univariate and multivariable logistic regression analyses examined associations between baseline breast density [dichotomized into breast density groups of <50% or 50% (4)] and age at first birth, body mass index, age at entry to the study, age at menarche, menopausal status at entry, predicted familial relative risk (RR) of developing breast cancer within the next 10 years, history of previous breast biopsy, history of benign breast disease (proliferative disease with or without atypical hyperplasia), hormone replacement therapy use, and smoking status. The predicted familial relative risk of death from breast cancer within 10 years (relative to the corresponding population risk) was calculated with a model for predicting individual breast cancer risk developed by Tyrer et al. (11) and divided into three categories: low (RR
2), moderate (RR from >2 to
3), and high (RR >3). The importance of each variable was assessed with the likelihood ratio test (deviance) in univariate and multivariable models. We also investigated whether the absolute change in breast density (between entry and the end of the trial) was related to the above variables (including treatment group) by use of linear regression. In this case, model fit was assessed quantitatively with the r2 measure (i.e., the proportion of the variation in the data explained by the model) and visually with appropriate residual plots. This analysis was repeated with an adjustment for breast density at the start of the trial.
A series of Students t tests were carried out to investigate whether the magnitude and strength of the tamoxifen effect on breast density varied for specific subgroups of women. All statistical tests were two-sided.
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RESULTS |
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Fig. 2 shows baseline breast density by age. The mean breast density expected 5 years after trial entry in the absence of treatment was estimated from the baseline data; the mean density at entry within each of the four older age groups (4650 years, 5155 years, 5660 years, or >60 years) was weighted by the number of women in the preceding 5-year age group to give
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where M is the expected density after 5 years, i is the ith 5-year age group, P is the population, and D is the density by 5-year age group. Breast density in the placebo group at the end of the study was, on average, 35%, which is consistent with the effect of aging 5 years, leading to a breast density of 37%.
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The results from the univariate and multivariable logistic regression models for breast density at entry to the trial are summarized in Table 3. Body mass index, age at entry to the trial, menopausal status, predicted familial relative risk of developing breast cancer by the CuzickTyrer method (11), previous breast biopsy examination, and smoking status were statistically significant variables in the univariate and multivariable models. Body mass index is inversely related to the risk of having high breast density (in the 25.0130 kg/m2 category, odds ratio [OR] = 0.40, 95% CI = 0.27 to 0.61; in the >30 kg/m2 category, OR = 0.22, 95% CI = 0.14 to 0.35). Age at entry had an independent effect that, even after adjusting for menopausal status, was particularly apparent for women older than 55 years whose breast densities were substantially lower than those of younger postmenopausal women. Surprisingly, women with the highest predicted familial relative risk of developing breast cancer were less likely to have high breast density than those with low-to-moderate predicted risks. We also found that high breast density was less common in current (OR = 0.60, 95% CI = 0.40 to 0.91) and former (OR = 0.79, 95% CI = 0.56 to 1.13) smokers compared with never smokers, which has been observed before and is in keeping with the known antiestrogenic action of smoking (13). A history of breast biopsy examination was associated with increased breast density. This result was strongly influenced by women with atypical hyperplasia (OR = 20.2, 95% CI = 2.35 to 174; P = .006), but women with nonproliferative benign breast disease (OR = 1.88, 95% CI = 1.23 to 2.77) or proliferative disease without atypical hyperplasia (OR = 1.56, 95% CI = 0.65 to 3.73) have a moderately increased risk of high breast density as well.
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Additional Students t tests were carried out to determine whether the observed effects of tamoxifen on density applied equally to all subgroups of women. Estimates of the mean reduction in breast density with tamoxifen in different subgroups and the corresponding confidence intervals are shown in Fig. 3. In all cases, tamoxifen treatment was associated with reductions in density, but such reductions tended to be smaller in older and postmenopausal women. The interaction of breast density with age was statistically significant (P<.001). In women aged 45 years or younger at entry, the net reduction with tamoxifen was 13.4% (95% CI = 8.6% to 18.1%), whereas in women older than 55 years, it was 1.1% (95% CI = 3.0% to 5.1%). The differences in breast density associated with other subgroups showed no consistent patterns.
Although the identified breast cancer risk factors appear to have a clear influence on breast density, their inclusion in the multivariable model jointly account for just 17% of the total variation in the baseline density. It therefore seems likely that other unknown factors also influence breast density.
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DISCUSSION |
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Postmenopausal status, increased age, and smoking status were associated with lower breast density, a result consistent with the effects of these variables on estrogen levels (13,15,16). In keeping with other reports (17), we also observed reduced density in women with a high body mass index, which is probably the result of greater fatty replacement in breasts of women with more adipose tissue. An unexpected observation was the association between a high familial risk of developing breast cancer within the next 10 years and a lower breast density (P = .02), which was not lost when adjustments for other factors were made in the multivariable model. High breast density was associated more strongly with a familial risk of breast cancer in the lowest tertile than with a familial risk of breast cancer in the highest tertile (OR = 0.45, 95% CI = 0.25 to 0.80; P = .006). This result is surprising because a family history of breast cancer and high breast density are well established risk factors for developing breast cancer and because dense breasts are often observed in women with BRCA1 mutations (18). It is possible that women with a family history of breast cancer may develop cancer by a different molecular pathway (e.g., ER-negative tumors in BRCA1 cancers) that is unrelated to breast density (19,20) or to estrogen levels. This hypothesis is highly speculative, however, and these results require further confirmation.
The increase in density associated with a previous biopsy is consistent with the increased risk of breast cancer associated with such a history, especially because the highest densities were found in women with atypical hyperplasia (21,22). Women with atypia had a relatively large drop in density on follow-up, but there was no apparent interaction with tamoxifen.
The association of density with other risk factors for breast cancer, notably age at menarche and age at first birth, was less marked in our study than in other studies (some of which were larger than our study) that looked at a population at normal risk for breast cancer (10,15). The lack of association in our study may be the result of insufficient power. Age at menarche showed no association with breast density at baseline, but the reduction in density was greater in women who underwent menarche when younger than 13 years.
The marked reduction in breast density associated with tamoxifen treatment is of particular interest for future chemoprevention studies. It also provides justification for the use of breast density as an entry criterion for breast cancer chemoprevention trials (23). The marked reduction in breast density associated with tamoxifen treatment also suggests the possibility of using a change in breast density as an intermediate or surrogate end point for breast cancer in chemoprevention trials. Our results indicate that approximately two-thirds of the total reduction in breast density attained with tamoxifen over the 5-year period occurred within the first 18 months, which suggests that breast density may be a potential early marker of efficacy. It may be that failure of a chemopreventive agent to change breast density will provide an early indication that the preventive strategy for the women in question is not effective. However, the change in density associated with tamoxifen predicted only one-third of the reduction in the incidence of breast cancer seen in the prevention trials, suggesting that tamoxifen has additional preventive activity not reflected in its effect on breast density.
The observed changes in breast density associated with tamoxifen were greatest in women who were premenopausal at entry. Premenopausal estrogen levels are higher, so the relative antiestrogenic effect of tamoxifen is greater, thus explaining its effect on density. However, the prevention trials indicate that the ability of tamoxifen to reduce breast cancer risk appears to be independent of age (2), so the clinical significance of greater reductions in breast density in younger women is unclear. They may be related to bringing about an earlier menopause or may not be permanent. Further studies are needed to establish whether breast density reverts to its age-specific population level after cessation of tamoxifen treatment.
Two important questions remain to be answered: First, is the effect of tamoxifen on breast density reversed when treatment stops? Second, is the tamoxifen-induced reduction in density associated with a reduction in breast cancer risk at the individual level? Both of these questions are the subject of ongoing research, the first by determining the mammographic density 12 years after the completion of treatment and the second by use of a nested casecontrol study within IBIS-I.
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NOTES |
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We thank the following IBIS investigators and local staff at participating centers for their time and assistance in obtaining the mammograms for this study: Fiona Gilbert, Heather Deans, Elspeth Singleton, Linda Gunn, and Moira Ayrton (Grampian Primary Care NHS Trust, Aberdeen); Simon Cawthorn, Nicola Slack, Ruth Illingworth, and Bernadette Thompson (Frenchay Hospital, Bristol); Dorothy Goddard (Royal United Hospital, Bath); Liz Kutt (Bristol Royal Infirmary, Bristol); Robert Mansel, Kathleen Lyons, Amanda Pickersgill, and Samantha Morris (University Hospital Wales, Cardiff); Elaine Anderson, Lesley Smart, and Sarah Drummond (Scottish Breast Screening Programme, Edinburgh); Hisham Hamed, Sheila Rankin, and Diane Ridley (Guys Hospital, London); Anthony Howell, Caroline Boggis, Mary Wilson, and Louise Harrop (Nightingale Breast Centre, Manchester); Roger Blamey, John Robertson, Robin Wilson, Nicky Scott, and Kay James (City Hospital, Nottingham) and Diana Eccles, Mary Briley, and Mary Cross (Royal South Hants Hospital, Southampton).
We also thank Natasha Warsi for her help with the collection and digitizing of mammograms, and we gratefully acknowledge the women who have participated in the IBIS-I trial, without whom none of this would be possible.
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REFERENCES |
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1 Cuzick J, Forbes J, Edwards R, Baum M, Cawthorn S, Coates A, et al. First results from the International Breast Cancer Intervention Study (IBIS-I): a randomised prevention trial. Lancet 2002;360:81724.[CrossRef][ISI][Medline]
2 Cuzick J, Powles T, Veronesi U, Forbes J, Edwards R, Ashley S, et al. Overview of the main outcomes in breast-cancer prevention trials. Lancet 2003;361:296300.[CrossRef][ISI][Medline]
3 Warner E, Lockwood G, Tritchler D, Boyd NF. The risk of breast cancer associated with mammographic parenchymal patterns: a meta-analysis of the published literature to examine the effect of method of classification. Cancer Detect Prev 1992;16:6772.[ISI][Medline]
4 Byng JW, Yaffe MJ, Jong RA, Shumak RS, Lockwood GA, Tritchler DL, et al. Analysis of mammographic density and breast cancer risk from digitized mammograms. Radiographics 1998;18:158798.[Abstract]
5 Hemminki K, Granstrom C, Czene K. Attributable risks for familial breast cancer by proband status and morphology: a nationwide epidemiologic study in Sweden. Int J Cancer 2002;100:2149.[CrossRef][ISI][Medline]
6 Greendale GA, Reboussin BA, Slone S, Wasilauskas C, Pike MC, Ursin G. Postmenopausal hormone therapy and change in mammographic density. J Natl Cancer Inst 2003;95:307.
7 Atkinson C, Warren R, Bingham SA, Day NE. Mammographic patterns as a predictive biomarker of breast cancer risk: effect of tamoxifen. Cancer Epidemiol Biomarkers Prev 1999;8:8636.
8 Wolfe JN. Breast patterns as an index of risk for developing breast cancer. Am J Roentgenol 1976;126:11307.[ISI]
9 Boyd NF, Byng JW, Jong RA, Fishell EK, Little LE, Miller AB, et al. Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian national breast screening study. J Natl Cancer Inst 1995;87:6705.[Abstract]
10 Gram IT, Funkhouser E, Tabar L. The Tabar classification of mammographic parenchymal patterns. Eur J Radiol 1997;24:1316.[CrossRef][ISI][Medline]
11 Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 2004;23:111120.[CrossRef][Medline]
12 Ursin G, Ma H, Wu AH, Berstein L, Salane M, Parisky Y, et al. Mammographic density and breast cancer in three ethnic groups. Cancer Epidemiol Biomarkers Prev 2003;12:3328.
13 Sala E, Warren RM, McCann J, Duffy SW, Luben R, Day NE. Smoking and high-risk mammographic parenchymal patterns. Breast Cancer Res 2000;2:5963.[ISI][Medline]
14 Atkinson C, Bingham SA. Mammographic breast density as a biomarker of effects of isoflavones on the female breast. Breast Cancer Res 2002;4:14.[ISI][Medline]
15 Jakes RW, Duffy SW, Ng FC, Gao F, Ng EH, Seow A, et al. Mammographic parenchymal patterns and self-reported soy intake in Singapore Chinese women. Cancer Epidemiol Biomarkers Prev 2002;11:60813.
16 Duffy SW, Tabar L, Smith RA, Krusemo UB, Prevost TC, Chen HH. Risk of breast cancer and risks with breast cancer; the relationship between histologic type with epidemiology, disease progression and survival. Semin Breast Dis 1999;2:292300.
17 Sala E, Warren R, McCann J, Duffy S, Luben R, Day N. High-risk mammographic parenchymal patterns and anthropometric measures: a case-control study. Br J Cancer 1999;81:125761.[CrossRef][ISI][Medline]
18 Huo Z, Giger ML, Olopade OI, Wolverton DE, Weber BL, Metz CE, et al. Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers. Radiology 2002;225:51926.
19 Pankow J, Vachon C, Kuni C, King R, Arnett D, Grabrick D, et al. Genetic analysis of mammographic breast density in adult women: evidence of a gene effect. J Natl Cancer Inst 1997;89:54956.
20 Boyd N, Dite G, Stone J, Gunasekara A, English D, McCredie M, et al. Heritability of mammographic density, a risk factor for breast cancer. N Engl J Med 2002;347:88694.
21 Dupont WD, Page DL. Risk factors for breast cancer in women with proliferative breast disease. N Engl J Med 1985;312:14551.
22 Page DL, Dupont WD. Indicators of increased breast cancer risk in humans. J Cell Biochem Suppl 1992;16G:17582.[Medline]
23 Cuzick J, Berridge D, Whitehead J. Mammographic dysplasia as entry criterion for breast cancer prevention trials. Lancet 1991;337:1225.
Manuscript received October 17, 2003; revised February 17, 2004; accepted February 27, 2004.
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