Breast cancer and hormone replacement therapy: collaborative reanalysis of data from 51 epidemiological studies of 52 705 women with breast cancer and 108 411 women without breast cancer

Calle, Eugenia E.; Heath, Clark W.; Coates, Ralph J.; Liff, Jonathan M.; Franceschi, Silvia; Talamini, Renato; Chantarakul, N; Koetsawang, Suporn; Rachawat, Daungdao; Morabia, Alfredo; Schuman, Leonard M.; Stewart, Walter F.; Szklo, Moysés; Bain, Christopher; Schofield, F. D.; Siskind, Victor; Band, Pierre R.; Coldman, A.; Gallagher, R; Hislop, T. Gregory; Yang, Po‐Sheng; Duffy, Stephen W.; Kolonel, L. M.; Nomura, Abraham M. Y.; Oberle, M. W.; Ory, H. W.; Hb, Peterson; Wilson, Hoyt G.; Wingo, Phyllis A.; Ebeling, K; Kunde, D; Nishan, P.; Colditz, Graham A.; Martin, Nimit; Pardthaisong, Tieng; Silpisornkosol, Suporn; Theetranont, Choti; Boosiri, Banpot; Chutivongse, Supawat; Jimakorn, P; Virutamasen, Pramuan; Wongsrichanalai, Chansuda; McMichael, A J; Rohan, Thomas E.; Ewertz, Marianne; Paul, Charlotte; Skegg, D. C. G.; Spears, G. F. S.; Boyle, Peter; Evstifeeva, M.; Daling, Janet R.; Hutchinson, W. B.; Malone, Kathi; Noonan, EA; Stanford, Janet L.; Thomas, David B.; Weiss, Noel S.; White, Emily; Andrieu, Nadine; Bràmond, A.; Clavel, F; Gairard, B.; Lansac, J.; Piana, L; Rénaud, R; Fine, S. R.P.; Cuevas, Héctor Rodriguez; Ontiveros, Patricia; Palet, A.; Salazar, S. B.; Aristizabel, N.; Cuadros, Alvaro; Bachelot, Anne; Lê, Monique G.; Deacon, Judith; Peto, Julian; Taylor, C; Alfandary, Esther; Modan, Baruch; Ron, Elaine; Friedman, Gary D.; Hiatt, Robert A.; Bishop, D. Timothy; Kosmelj, J.; Primic-Žakelj, Maja; Ravnihar, B; Stare, Janez; Beeson, W L; Fraser, Gary E.; Bulbrook, R. D.; Cuzick, Jack; Fentiman, IS; Hayward, J. L.; Wang, D. Y.; Hanson, R. L.; Leske, M. C.; Mahoney, M. C.; Nasca, P. C.; Varma, A. O.; Weinstein, A. L.; Möller, Torgil; Olsson, Håkan; Ranstam, Jonas; Goldbohm, R. Alexandra; Brandt, Piet A. van den; Apelo, R. A.; Baens, J.; Cruz, J. R. de la; Benı́tez, Javier; Lacaya, Lyndon B.; Ngelangel, Corazon A.; Vecchia, Carlo La; Negri, Eva; Marubini, Ettore; Ferraroni, Monica; Gerber, Mariette; Richardson, Sylvia; Ségala, Claire; Gatei, D.; Kenya, Patrick; Kung'u, A; Mati, J. G.; Brinton, Louise A.; Hoover, Robert N.; Schairer, Catherine; Spirtas, Robert; Lee, H. P.; Rookus, Matti A.; Leeuwen, Flora E. van; Schoenberg, J. A.; Gammon, Marilie D.; Clarke, E. Aileen; Jones, Lesley; McPherson, Klim; Neil, Andrew; Vessey, Martin; Yeates, David; Beral, Valerie; Bull, Diana; Crossley, Barbara; Hermon, C; Jones, Stephanie; Key, Timothy J.; Lewis, Claire E.; Reeves, Gillian; Collins, Rory; Doll, Richard; Pető, Richárd; Pc, Hannaford; Kay, CliffordR.; Rosero-Bixby, Luis; Yuan, Jian‐Min; Wei, Hu; Yun, Tae-Jin; Chen, Zhiheng; Berry, Geoffrey; Booth, J. Cooper; Jelihovsky, Tatiana; MacLennan, Robert; Shearman, Rodney P.; Wang, Q. S.; Baines, Cornelia J.; Ab, Miller; Wall, Claus; Lund, Eiliv; Stalsberg, Helge; Dabancens, Alfredo; Martinez, L.; Molina, Ramiro; Salas, O; Alexander, Freda E.; Hulka, Barbara S.; Chilvers, Clair; Bernstein, Leslie; Haile, Robert W.; Paganini-Hill, A; Pike, Malcolm C.; Ross, Ronald K.; Ursin, Giske; Yu, Mimi C.; Longnecker, Matthew P.; Newcomb, Polly A.; Farley, T. M.N.; Holck, S; Meirik, Olav; Adami, Hans‐Olov; Bergkvist, R.; Persson, Ingemar; Levi, Fabio; Mabuchi, Kiyóhiko; Preston, Dale L.; Katsouyanni, Klea; Trichopoulou, Antonia; Trichopoulos, Dimitrios; Bergkvist, Leif

doi:10.1016/s0140-6736(97)08233-0

Cited by 2,109 publications

(67 citation statements)

References 57 publications

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“…This association of late age at menopause is now largely supported by data from 51 epidemiological studies conducted by the collaborative group on hormonal factors in breast cancer [10], which suggested that late menopause increases the risk of breast cancer by prolonged ovarian hormone production.…”

Section: Discussionmentioning

confidence: 84%

Risk Factors for Breast Cancer of Different Age Groups: Moroccan Data?

Laamırı¹,

Bouayad²,

Hasswane³

et al. 2015

OJOG

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Background: Several epidemiological studies have been carried to identify the risk factors for developing breast cancer. The aim of this study was to evaluate the risk factors associated with breast cancer in different age groups in the Moroccan women and to determine susceptible and protective factors. Methods: This was a case control study including 400 women with breast cancer and 400 controls of the National Institute of Oncology Sidi Mohamed Ben Abdallah, Rabat, Morocco from 2008 to 2010. These women were interviewed for epidemiological information and risk factor for breast cancer. Results: Early age at menarche, late menopause, oral contraceptive and family history with first degree were positively associated with breast cancer in all the series and in the age group between 22 and 34 years. In the age group between 44 and 34 years, a risk of breast cancer was associated only with a family history of breast cancer. In the age group of 45 years or older, the factors which seem to influence a breast cancer are: late menopause, oral contraceptive and family history in the first degree. In contrast, early childbearing, multiparity and lactation were negatively associated with breast cancer. Conclusion: This study is rather in favour of positive association between hormonal factors and breast cancer, and confirms the protective role of multiparity and lactation.

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Section: Discussionmentioning

confidence: 84%

Risk Factors for Breast Cancer of Different Age Groups: Moroccan Data?

Laamırı¹,

Bouayad²,

Hasswane³

et al. 2015

OJOG

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“…14 In a large meta-analysis, 15 the risk appeared to increase cumulatively by 1-2% per year, but disappear within 5 years of cessation. It is becoming clear that the risk from combined oestrogen/ progesterone HRT is greater than that from oestrogen only.…”

Section: Other Risk Factorsmentioning

confidence: 99%

“…Early menarche and late menopause increase breast cancer risk, as they prolong exposure to oestrogen and progesterone. [13][14][15][16][17][18][19][20][21][22] Long-term combined hormone replacement therapy (HRT) treatment (> 5 years) after the menopause is associated with a significant increase in risk. However, shorter-term treatments may still be associated with risk to those with a family history of breast cancer.…”

Section: Hormonal and Reproductive Risk Factorsmentioning

confidence: 99%

“…14 In a large meta-analysis, the risk appeared to increase cumulatively by 1-2% per year but disappear within 5 years of cessation. 15 Oestrogen-only HRT has a risk that appears much lower, and it may be risk neutral. [16][17][18][19] A meta-analysis also suggested that both during current use of the combined oral contraceptive and 10 years post use, there may be a 24% increase in risk of breast cancer.…”

Section: Hormonal and Reproductive Risk Factorsmentioning

confidence: 99%

“…[16][17][18][19] The results of a meta-analysis suggest that HRT appears to increase risk cumulatively by 1-2% per year, although the risk disappears within 5 years of cessation. 15 A meta-analysis suggested that both during current use of the combined oral contraceptive and 10 years post use, there may be a 24% increase in risk of breast cancer. 13 The age at first full-term pregnancy influences the RR of breast cancer, with early first full-term pregnancy offering some protection by transforming breast parenchymal cells into a more stable state, potentially resulting in less proliferation in the second half of the menstrual cycle.…”

Section: -21mentioning

confidence: 99%

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Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Evans

Astley

Stavrinos³

et al. 2016

Programme Grants Appl Res

117

126

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BackgroundIn the UK, women are invited for 3-yearly mammography screening, through the NHS Breast Screening Programme (NHSBSP), from the ages of 47–50 years to the ages of 69–73 years. Women with family histories of breast cancer can, from the age of 40 years, obtain enhanced surveillance and, in exceptionally high-risk cases, magnetic resonance imaging. However, no NHSBSP risk assessment is undertaken. Risk prediction models are able to categorise women by risk using known risk factors, although accurate individual risk prediction remains elusive. The identification of mammographic breast density (MD) and common genetic risk variants [single nucleotide polymorphisms (SNPs)] has presaged the improved precision of risk models.ObjectivesTo (1) identify the best performing model to assess breast cancer risk in family history clinic (FHC) and population settings; (2) use information from MD/SNPs to improve risk prediction; (3) assess the acceptability and feasibility of offering risk assessment in the NHSBSP; and (4) identify the incremental costs and benefits of risk stratified screening in a preliminary cost-effectiveness analysis.DesignTwo cohort studies assessing breast cancer incidence.SettingHigh-risk FHC and the NHSBSP Greater Manchester, UK.ParticipantsA total of 10,000 women aged 20–79 years [Family History Risk Study (FH-Risk); UK Clinical Research Network identification number (UKCRN-ID) 8611] and 53,000 women from the NHSBSP [aged 46–73 years; Predicting the Risk of Cancer At Screening (PROCAS) study; UKCRN-ID 8080].InterventionsQuestionnaires collected standard risk information, and mammograms were assessed for breast density by a number of techniques. All FH-Risk and 10,000 PROCAS participants participated in deoxyribonucleic acid (DNA) studies. The risk prediction models Manual method, Tyrer–Cuzick (TC), BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and Gail were used to assess risk, with modelling based on MD and SNPs. A preliminary model-based cost-effectiveness analysis of risk stratified screening was conducted.Main outcome measuresBreast cancer incidence.Data sourcesThe NHSBSP; cancer registration.ResultsA total of 446 women developed incident breast cancers in FH-Risk in 97,958 years of follow-up. All risk models accurately stratified women into risk categories. TC had better risk precision than Gail, and BOADICEA accurately predicted risk in the 6268 single probands. The Manual model was also accurate in the whole cohort. In PROCAS, TC had better risk precision than Gail [area under the curve (AUC) 0.58 vs. 0.54], identifying 547 prospective breast cancers. The addition of SNPs in the FH-Risk case–control study improved risk precision but was not useful inBRCA1(breast cancer 1 gene) families. Risk modelling of SNPs in PROCAS showed an incremental improvement from using SNP18 used in PROCAS to SNP67. MD measured by visual assessment score provided better risk stratification than automatic measures, despite wide intra- and inter-reader variability. Using a MD-adjusted TC model in PROCAS improved risk stratification (AUC = 0.6) and identified significantly higher rates (4.7 per 10,000 vs. 1.3 per 10,000;p < 0.001) of high-stage cancers in women with above-average breast cancer risks. It is not possible to provide estimates of the incremental costs and benefits of risk stratified screening because of lack of data inputs for key parameters in the model-based cost-effectiveness analysis.ConclusionsRisk precision can be improved by using DNA and MD, and can potentially be used to stratify NHSBSP screening. It may also identify those at greater risk of high-stage cancers for enhanced screening. The cost-effectiveness of risk stratified screening is currently associated with extensive uncertainty. Additional research is needed to identify data needed for key inputs into model-based cost-effectiveness analyses to identify the impact on health-care resource use and patient benefits.Future workA pilot of real-time NHSBSP risk prediction to identify women for chemoprevention and enhanced screening is required.FundingThe National Institute for Health Research Programme Grants for Applied Research programme. The DNA saliva collection for SNP analysis for PROCAS was funded by the Genesis Breast Cancer Prevention Appeal.

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