Search tips
Search criteria 


Logo of jnciLink to Publisher's site
J Natl Cancer Inst. 2013 February 6; 105(3): 166–174.
Published online 2012 December 21. doi:  10.1093/jnci/djs505
PMCID: PMC3611853

Postpartum Remodeling, Lactation, and Breast Cancer Risk: Summary of a National Cancer Institute–Sponsored Workshop


The pregnancy–lactation cycle (PLC) is a period in which the breast is transformed from a less-developed, nonfunctional organ into a mature, milk-producing gland that has evolved to meet the nutritional, developmental, and immune protection needs of the newborn. Cessation of lactation initiates a process whereby the breast reverts to a resting state until the next pregnancy. Changes during this period permanently alter the morphology and molecular characteristics of the breast (molecular histology) and produce important, yet poorly understood, effects on breast cancer risk. To provide a state-of-the-science summary of this topic, the National Cancer Institute invited a multidisciplinary group of experts to participate in a workshop in Rockville, Maryland, on March 2, 2012. Topics discussed included: 1) the epidemiology of the PLC in relation to breast cancer risk, 2) breast milk as a biospecimen for molecular epidemiological and translational research, and 3) use of animal models to gain mechanistic insights into the effects of the PLC on breast carcinogenesis. This report summarizes conclusions of the workshop, proposes avenues for future research on the PLC and its relationship with breast cancer risk, and identifies opportunities to translate this knowledge to improve breast cancer outcomes.

Beginning with pregnancy, the breast undergoes a cyclical transformation during which it matures from a resting, nonfunctional gland to a milk-producing organ, which then gradually reverts back to quiescence after cessation of lactation. Data suggest that the pregnancy–lactation cycle (PLC) permanently alters the molecular histology of the breast (1,2) and influences breast cancer risk. In contrast with the extensive literature on breastfeeding and improved infant health (3–6), data related to molecular mechanisms and biomarkers linking the PLC to breast carcinogenesis are sparse. Separating the intertwined, dynamic effects of pregnancy, breastfeeding, pregnancy-related interruption of ovulation and postweaning remodeling of the breast is challenging, which suggests that novel research strategies are needed to study the PLC. To summarize scientific knowledge in this area and identify research priorities, the National Cancer Institute conducted a workshop entitled “Postpartum Remodeling, Lactation and Breast Cancer Risk: Towards Improved Risk Assessment and Prevention.” The workshop was designed to foster multidisciplinary discussions among attendees, including epidemiologists and other public health researchers, basic and translational scientists, lactation consultants, obstetricians, and pediatricians. This commentary summarizes findings from the workshop, which was held on March 2, 2012, in Rockville, Maryland.

The PLC: Epidemiological Associations With Breast Cancer Risk

The PLC and Overall Breast Cancer Risk

The hypothesis that lactation reduces breast cancer risk has been assessed in many case–control studies and in a limited number of large cohort investigations. Results have been summarized in two meta-analyses, which included approximately 60 individual studies. Bernier et al., who combined case–control studies, found that compared with parous women who never breastfed, women who had breastfed were at reduced risk of breast cancer (odds ratio [OR] = 0.90, 95% confidence interval [CI] = 0.86 to 0.94) (7). Similarly, another meta-analysis found that lactation conferred a marginal reduction in breast cancer risk (8), which was apparent only among women with four or more births and associated long durations of lifetime lactation (Figure 1). Thus, distinguishing the effects of breastfeeding and parity on breast cancer risk is difficult because the strongest effects of breastfeeding are found among multiparous women. In addition, some large cohort studies have not found an association between lactation and breast cancer (9). Overall, epidemiological evidence suggests that lactation-related protection in the general population is marginal and restricted to long lifetime durations of breastfeeding.

Figure 1.
Relative risk of breast cancer in parous women according to breastfeeding history and number of births. Estimates from reanalysis of 47 epidemiological studies (cohort and case–control) conducted in 30 different countries, with parity, lactation, ...

The PLC and Breast Cancer Risk Related to Specific Populations and Tumor Types

Breast cancer is an etiologically heterogeneous disease; risk factor associations, including those for breastfeeding, vary with patient and tumor characteristics (10–12). Early onset tumors occur more frequently among black women than among white women (13), and such tumors are often aggressive and difficult to treat (14). Black women have higher rates of estrogen receptor–negative breast cancers, and more specifically, they may be at heightened risk for basal-like cancers (15). In the Carolina Breast Cancer Study, not breastfeeding and elevated waist-to-hip ratio were the strongest risk factors for basal-like breast cancers, with an attributable fraction of 68% among premenopausal black women (15). Not breastfeeding was also linked to increased risk of estrogen receptor–negative/progesterone receptor–negative breast cancers in the Black Women’s Health Study (16) and to elevated risk of triple-negative tumors (which include many basal-like cancers) in a largely white population (17). In the Women’s Health Initiative study, breastfeeding was not significantly related to risk of estrogen receptor positive or triple-negative cancers, based on 176 cases of the latter (18).

Women who are carriers of BRCA1 mutations are at increased risk of early onset breast cancers, particularly of the basal subtype. Kotsopoulos et al. reported that BRCA1 carriers who breastfed for more than 1 year had a 32% reduction in breast cancer risk (OR = 0.68, 95% CI = 0.52 to 0.91), with a potentially greater effect among those who breastfed for more than 2 years (OR = 0.51, 95% CI = 0.35 to 0.74) (19). Breastfeeding was protective for both early- and late-onset cancers. In contrast, breastfeeding was not statistically significantly protective among BRCA2 mutation carriers, a group that is predisposed to develop molecular subtypes of breast cancers similar to those found in the general population (ie, mainly estrogen receptor–positive).

In another analysis, breastfeeding was modestly protective for breast cancer among premenopausal women, but it was highly protective among those with a first-degree relative with breast cancer (OR = 0.41, 95% CI = 0.22 to 0.75) (20). Risk was unrelated to total duration of breastfeeding, exclusive breastfeeding, or lactation-associated amenorrhea. In this same report, women who never breastfed and used lactation suppressants were at lower risk.

Terminal duct lobular units (TDLUs) are the functional structures of the breast and the site at which most breast cancers arise. Data suggest that the PLC affects the morphology of TDLUs, as observed later in life. Specifically, an analysis of tissues donated by healthy nonpregnant women found that breasts of nulliparous women contain fewer TDLUs per unit area than those of parous women (2). However, individual TDLUs of parous women show greater involution (ie, loss of acini and reduced size) compared with those of nulliparous women (2). Greater levels of TDLU involution have been linked to reduced breast cancer risk (21), and lack of involution may be particularly associated with basal-like breast cancers (22). Thus, one mechanism by which long-term breastfeeding could reduce risk of breast cancer, especially basal-like breast cancers, is by promotion of patterns of TDLU involution that are protective. However, this hypothesis is difficult to test because it requires access to breast tissues donated by women who breastfed for lengthy periods. Understanding effects of breastfeeding on breast cancer risk also adds complexity to addressing other issues in breast cancer epidemiology, including whether rates increase following pregnancy [a controversy reviewed in (23,24)], why early onset tumors may behave aggressively (14), and why some exposures may produce opposite effects on risk at different ages (25).

The PLC and Hormones Related to Breast Cancer Risk

Among premenopausal women, elevated circulating testosterone concentrations are associated with increased breast cancer risk (26); however, clear associations between premenopausal levels of other androgens or estrogens and breast cancer are lacking [reviewed in (27)]. Among postmenopausal women, higher circulating levels of several estrogens and androgens are associated with progressively increased breast cancer risk, independent of other factors, with strongest effects for free estradiol (relative risk = 2.58, 95% CI = 1.76 to 3.78, comparing highest with lowest quintile) (28). Limited prospective data also suggest that higher prolactin levels are associated with a slight increase in breast cancer risk, irrespective of menopausal status (29).

During pregnancy, blood levels of estrogens and progesterone rise linearly, reaching peak concentrations far higher than those observed among nonpregnant women (30). Levels of these hormones vary with multiple factors, such as parity, maternal age, smoking, and fetal gender (30,31). After delivery, circulating estrogen and progesterone levels fall dramatically. Among both pre- and postmenopausal women, pregnancy history is associated with subsequent lower prolactin levels (32,33), whereas neither breastfeeding nor increasing number of children is associated with prolactin levels later in life (32,34). Estrogens and androgens in postmenopausal women do not appear to be strongly related to the PLC (35); few studies have examined these associations in premenopausal women. Women with high levels of prolactin and low total estradiol during late pregnancy tend to have lengthier periods of postpartum amenorrhea (36). High androgen levels during pregnancy have also been associated with shorter breastfeeding duration (37).

The PLC and Breast Cancer Risk: Conclusions and Future Directions

Evidence suggests that breastfeeding is modestly protective for breast cancer overall but may be substantially protective for basal-like breast cancers. Thus, data on breastfeeding provide further evidence for etiologic heterogeneity in breast cancer. In addition, this interpretation suggests that increasing breastfeeding may reduce the incidence of aggressive subtypes, and thus mortality, especially among black women, a group that lags in progress toward meeting breastfeeding goals outlined in the Healthy People 2020 initiative (6).

Conducting epidemiological studies of the PLC is inherently difficult. Women diagnosed with breast cancer may report breastfeeding history differently than healthy women. Reasons for not breastfeeding may bias analyses if these factors are not considered. There are many reasons why mothers do not breastfeed (lactate) or do not breastfeed exclusively, including personal choice, lack of support or information about breastfeeding/lactation, and health issues affecting the mother or infant [reviewed in (4,38,39) and related references]. Women living in wealthier nations, especially if employed outside the home, are more likely to breastfeed for a short period after birth and/or pump milk at convenient intervals rather than to breastfeed in response to infant demands (40). It is unknown whether the timing or frequency of breastfeeding is associated with breast cancer risk. Additional knowledge about breastfeeding practices that most strongly reduce breast cancer risk would aid personal and public health decision making. Whether women breastfeed or not, the breast undergoes dramatic alterations during pregnancy that prepare it for future lactation, and these changes are difficult to separate from postpartum events.

Among parous women, increasing numbers of births are associated with reduced risk of estrogen receptor–positive breast cancer, the most common tumor subtype (10–12). Multiparity is also related to greater attained age and lifetime duration of lactation, which makes separating these effects difficult. Furthermore, early age at first birth, an established protective factor for breast cancer (41), may be related to multiparity, creating additional analytical complexity. For these reasons, an analysis of women following a first singleton pregnancy may offer advantages, but older primiparous women will include a subset of women who have used infertility treatments and therefore may have a distinctive set of breast cancer risk factors.

The Nurses’ Health Study II has developed a questionnaire module to assess lactation history in detail by pregnancy, and the Black Women’s Health Study has implemented an online module for pregnancy history and lactation. If feasible given priorities, questionnaires should collect data by pregnancy, including duration of exclusive and total breastfeeding, reasons for stopping or not starting breastfeeding, and use of lactation suppressive medication.

A fundamental unanswered question is whether the protective effects associated with the PLC reflect indirect systemic effects, such as lowering blood levels of circulating hormones, direct effects that occur in the nursed breast, as previously suggested (42), or both. In addition, the high hormone levels during pregnancy could increase breast cancer risk in the short term, especially for some molecular subtypes, whereas the long-term protective effects of pregnancy might reflect different mechanisms, such as greater TDLU involution.

Apart from sex-steroid hormones, few circulating factors have been assessed in relation to time since delivery, and most available data are from postmenopausal women. In considering how the PLC might affect breast cancer risk through modulation of hormones or growth factor levels, it will be important to account for the time of the measurement, changes over time, and cumulative exposure. In addition, improved methods for measuring hormones and other circulating factors among young women and for assessing gene expression in the breast are needed.

Milk as a Biospecimen for Studying the PLC

Noncellular Components in Breast Milk

Milk is rich in growth factors, chemokines, and immunomodulatory, anti-inflammatory and proinflammatory mediators, which are variably derived from leukocytes and breast epithelial cells (43–48). Several of these factors have suggested roles in breast development and/or breast cancer, including interleukins, tumor necrosis factor alpha, epidermal growth factor, and others. Many of these markers show substantial interindividual differences and temporal patterns related to parturition and the health status of the mother–infant dyad. Dvorak et al. found that epidermal growth factor levels are remarkably elevated in milk after extremely preterm deliveries (23–27 weeks) (45), and limited data suggest that these mothers are at elevated breast cancer risk (49,50), which is of interest given the critical role of epidermal growth factor in breast cancer, including the basal subtype (51).

Milk contains oligosaccharides and glycosylated proteins that are generally stable and can be assessed using mass spectrometry (52). Glycosylation can alter the molecular function of some proteins, which may have implications for breast carcinogenesis. Ongoing studies seek to determine whether specific glycosylation patterns are predictive of diseases (eg, diabetes), some of which are related to cancer risk. Recent technological advances have made it possible to profile glycosylation patterns in large sample sets (53–56). For example, mass spectroscopy can be applied to analyze glycosylation patterns in dried milk spots, thus allowing flexible sample collection approaches (57).

Cellular Components in Breast Milk

Milk contains mature epithelial cells, leukocytes, and potentially putative stem-like or progenitor cells (58,59). Therefore, breast milk can serve as a noninvasive source for studying cells of the mature gland. In an analysis of milk donated by 102 women, Wong et al. found that milk contained, on average, approximately 2×105 cells/mL, although counts ranged widely and were largely independent of mothers’ and infants’ ages and mothers’ histories of prior pregnancies (60). These researchers also isolated an epithelial cell-enriched fraction, which contained approximately 3×104 cells/mL, resulting in approximately 2.5 μg of DNA per donation, which is a favorable yield compared with nipple aspiration and ductal lavage.

Thompson et al. (61) and Ambrosone et al. (62) expanded milk research by analyzing DNA adducts in milk. Recently, Arcaro et al. have used epithelial cell-enriched milk fractions to detect DNA methylation in tumor suppressor genes by pyrosequencing (60,63). Additional research is needed to assess whether DNA methylation of tumor suppressor genes in milk is associated with breast cancer risk. Toward that end, preliminary comparisons show that levels of DNA methylation are similar in milk from the left and right breasts of women who have had a benign biopsy, whereas among women with cancer, levels are higher in the affected breast. (64).

Optimization and standardization of the timing of milk collection for research with regard to date of delivery, time of day, and breastfeeding is needed to advance the use of this specimen. Milk may be expressed by hand or by pumping with devices that vary in suction strength and the speed with which they empty the breast (65). Data suggest that these methods collect similar volumes of milk but composition may vary. Women whose breasts produce and store greater volumes of milk secrete milk of more variable content, potentially reflecting the influence of incomplete emptying between feeds; furthermore, a woman’s milk production may be asymmetric (66). Milk composition, milk fat, and cell content may vary not only within the first year of feeding but also within a single feeding, with higher fat and cell content in the “hindmilk” expelled at the end of feeding, as opposed to the initially expelled “foremilk” (67). A more recent study showed that changes in the milk fat and cell content continue after the end of feeding, peaking within 30 minutes following a substantial volume feed (68). Therefore, diurnal variation in milk fat and cell contents seems to be strongly associated with the degree of fullness of the breast. Given that patterns of breastfeeding affect both milk production and composition, they represent a consideration in measuring biomarkers (65).

Milk as a Biospecimen for Studying the PLC: Conclusions and Future Directions

Future research could leverage analysis of breast milk as an important plentiful, noninvasive, and easily accessed source of various cell populations within the lactating breast. Improved techniques for isolating both epithelial and nonepithelial cells from milk are needed, and progress may occur in tandem with development of better methods of preserving milk because cell fragility may be limiting. Freezing and thawing fresh milk leads to cell lysis, which limits analysis of epithelial cell-enriched fractions, whereas shipping chilled liquid milk is complicated and expensive. Furthermore, optimal methods for temporarily storing milk before processing are needed. Conducting in-person milk collections for research is challenging, especially where postdelivery care is decentralized. Improved methods of preserving cells in milk could enable more flexible means of shipping to laboratories, and improved fractionation would increase sensitivity for detecting cancer-related markers and mechanistic studies. Improved preparation may also facilitate a wide range of cytological analyses using immunohistochemistry, in situ hybridization, flow cytometry, and related methods that can potentially identify rare events with cellular localization, which may provide biological insights about the PLC and breast cancer.

The ability to analyze soluble macromolecules should advance quickly as technologies improve. A critical challenge for this research is identifying normative patterns of change with time since birth, given that collecting milk donations in narrowly defined intervals is impractical. Patterns of change over time may be highly informative. Methods for ensuring that factors influencing milk volume are not drivers of measured concentrations are needed to optimize the utility of these assays.

Animal Models and Mechanistic Studies of the PLC

In murine models, activation of an involution gene signature occurs in the mammary gland within a few hours of weaning. Various patterns of gene expression are observed, with some genes being maximally expressed within 12 hours and others gradually rising during the first 4 days of involution in the mouse. However, with the exception of dying cells that detach into the alveolar (acinar) lumen, morphological changes are not observed until 48 hours of involution. Stat3 has emerged as a key mediator of the involution process in the mouse mammary gland (69–73). Involution proceeds in two phases: 1) a potentially reversible phase, lasting until approximately 48 hours postweaning in mice, and 2) an irreversible phase beginning in mice at 48 hours postweaning that results in tissue remodeling (Figure 2). In mice, most of the alveolar epithelium is removed by 6 days postweaning, and prepregnancy gland morphology is reestablished by 10 days of involution, albeit with subtle changes to branching morphology and altered gene expression profiles. Stat3 activation mediates involution by induction of cell death in alveolar epithelium and by recruitment and alternative activation of macrophages (70). Mammary glands from conditionally deleted Stat3 mice demonstrate a “failed involution” phenotype marked by reduced cell death and inflammatory signaling, delayed remodeling, and a prolonged reversible phase (74).

Figure 2.
Schematic representation of the involution process. Involution occurs in two phases. The first phase is reversible phase, lasting up to 48 hours in the mouse, that is characterized by extensive cell death, which results in detachment of dying cells into ...

In animals, postweaning remodeling of the mammary gland is a proinflammatory process, which has led to the proposal of the “involution hypothesis” (75–84). This ascribes a tumor- and metastasis-promoting character to the postpartum micro environment. In support of this hypothesis, when human mammary tumor MCF10DCIS cells are injected into the mouse mammary gland 1 day postweaning, tumor size and lung infiltration of the cells were statistically significantly greater than in nulliparous controls (75). Characterization of the involuting mouse mammary gland suggests that increased collagen deposition, occurring specifically during postpartum gland involution, leads to upregulation of COX-2 and enhanced migration of tumor cells (75). Injecting ibuprofen (which inhibits COX-2 signaling) concurrent with tumor cells at 1 day postweaning reduced collagen deposition and tumor growth to levels similar to that in nulliparous controls, which suggests the importance of a collagen and COX-2 proinflammatory pathway in the promotion of tumor aggressiveness in the involuting mammary gland (75,84) (Figure 3). Importantly, macrophage infiltration and collagen deposition have been reported in human breasts undergoing postpartum involution.

Figure 3.
Proinflammatory involution environment promoting breast carcinogenesis. A model depicting COX-2, derived from the involuting mammary gland, mediating collagen fibrillogenesis and COX-2 promoting invasion (brown cells) of tumor cells exposed to involution ...

Data pointing to involution-associated inflammation as a factor in the development of aggressive breast cancer is also consistent with observational data from humans. Among women aged less than 40 years who develop breast cancer, survival is worse if the breast cancer is diagnosed between 4 to 7 months postpartum compared with diagnosis during pregnancy or at other times outside of this interval (85–87). This poorer prognosis may extend to breast cancers diagnosed within 5 years of pregnancy (88). Data also suggest that use of aspirin or nonsteroidal anti-inflammatory drugs may improve breast cancer survival [(89) and reviewed therein].

Animal Models and Mechanistic Studies of the PLC: Conclusions and Future Directions

Animal models may improve our knowledge of the mechanisms by which the PLC may influence breast cancer risk. A major challenge, however, is making findings from animal studies relevant to human biology. Weaning in animal models is generally a rapid process, whether physiologically or experimentally induced (eg, teat sealing or removal of pups from mother). However, a prolonged weaning model may more accurately reflect the more gradual weaning process that occurs in women. The timing of weaning (prolonged vs rapid) may influence the duration of involution, and, therefore, affect the gene expression program and components of the microenvironment. One report described an expansion of murine mammary stem cells during pregnancy and then a drop to or below baseline levels after weaning (90). Similar rules may apply to the human breast; however, the scarcity of human breast tissue specimens during the PLC poses considerable challenges to conducting such studies in humans. To this end, human breast milk may provide a source of cells for study.


The PLC is a complex period that permanently alters breast biology and influences breast cancer risk. The major research conclusions and future directions emerging through discussions among the multidisciplinary attendees at the National Cancer Institute workshop entitled “Postpartum Remodeling, Lactation and Breast Cancer Risk: Towards Improved Risk Assessment and Prevention” are summarized in Box 1.

Box 1.
Summary and research recommendations from the National Cancer Institute workshop “Postpartum Breast Remodeling, Lactation, and Breast Cancer Risk: Towards Improved Risk Assessment and Prevention”

The relationship of breastfeeding to breast cancer risk, particularly with regard to specific tumor subtypes, requires further study. Human studies suggest that pregnancy/lactation may offer strong protection against certain types of breast cancer in the long term but may be associated with increased breast cancer risk for other types in the short term. The latter is in agreement with studies in animal models that demonstrate that breast remodeling in the postweaning period may be associated with breast cancer growth and metastases. The protective effects of longer breastfeeding durations against aggressive breast cancer are now supported by a number of studies, particularly for BRCA1 mutation carriers, but our understanding of the underlying mechanisms is limited. Improving knowledge of PLC effects on the breast stem and/progenitor cell pools and the breast microenvironment in relation to breast cancer subtypes is a priority. Identification of mechanisms and markers related to postpartum breast changes among humans may have value for risk assessment and prevention. As animal research progresses, human studies could be designed to understand breast involution in relation to cancer risk and to explore the importance of inflammatory pathways in driving tumor aggressiveness. Human breast milk may provide a biospecimen for understanding the mechanisms that are important in the PLC and identifying biomarkers with potential value for risk assessment or early breast cancer detection. Given the importance of the PLC in breast carcinogenesis, research in this area may lead to important translational advances in risk assessment, early detection, or prevention of breast cancer.


This work was funded in part by the National Cancer Institute (NCI), Intramural Research Program, and through competitive awards from the Office of Research on Women’s Health (to MES) and Funding to Advance Research on Cancers in Women administrative supplement from the NCI Office of Science Planning and Assessment.


The funders have had no input on the writing or the decision to publish the commentary.

The authors thank Tricia Wilkerson and Patricia Madigan for assistance in preparing this report and Professors Peter Hartmann and Lars Bode and Dr Jackie Kent for comments on the manuscript.


1. Sherman ME, Figueroa JD, Henry JE, et al. The Susan G. Komen for the Cure Tissue Bank at the IU Simon Cancer Center: a unique resource for defining the “molecular histology” of the breast. Cancer Prev Res (Phila). 2012; 5(4):528–535 [PubMed]
2. Figueroa JD, Linville L, Brinton LA, et al. Breast cancer risk factor associations with breast tissue morphometry: results from the Komen for the Cure Tissue Bank. Paper presented at the American Association for Cancer Research Annual Meeting; April 2012; Chicago, IL
3. Ip S, Chung M, Raman G, et al. Breastfeeding and maternal and infant health outcomes in developed countries. Evid Rep Technol Assess (Full Rep). 2007;XX(153):1–186 [PubMed]
4. Stuebe A. The risks of not breastfeeding for mothers and infants. Rev Obstet Gynecol. 2009; 2(4):222–231 [PubMed]
5. Tse A, Michels KB. Maternal and offspring benefits of breast-feeding. In Symonds M, Ramsay MM, editors. eds. Maternal-Fetal Nutrition During Pregnancy and Lactation. Cambridge: Cambridge University Press; 2010; 106–118
6. Fielding J, Kumanyika S. Recommendations for the concepts and form of Healthy People 2020. Am J Prev Med. 2009; 37(3):255–257 [PubMed]
7. Bernier MO, Plu-Bureau G, Bossard N, et al. Breastfeeding and risk of breast cancer: a metaanalysis of published studies. Hum Reprod Update. 2000; 6(4):374–386 [PubMed]
8. Collaborative Group on Hormonal Factors in Breast Cancer Breast cancer and breastfeeding: collaborative reanalysis of individual data from 47 epidemiological studies in 30 countries, including 50302 women with breast cancer and 96973 women without the disease. Lancet. 2002; 360(9328): 187–195 [PubMed]
9. Michels KB, Willett WC, Rosner BA, et al. Prospective assessment of breastfeeding and breast cancer incidence among 89,887 women. Lancet. 1996; 347(8999):431–436 [PubMed]
10. Althuis MD, Fergenbaum JH, Garcia-Closas M, et al. Etiology of hormone receptor-defined breast cancer: a systematic review of the literature. Cancer Epidemiol Biomarkers Prev. 2004; 13(10):1558–1568 [PubMed]
11. Ma H, Bernstein L, Pike MC, et al. Reproductive factors and breast cancer risk according to joint estrogen and progesterone receptor status: a meta-analysis of epidemiological studies. Breast Cancer Res. 2006; 8(4):R43 [PMC free article] [PubMed]
12. Yang XR, Chang-Claude J, Goode EL, et al. Associations of breast cancer risk factors with tumor subtypes: a pooled analysis from the Breast Cancer Association Consortium studies. J Natl Cancer Inst. 2011; 103(3):250–263 [PMC free article] [PubMed]
13. Brinton LA, Sherman ME, Carreon JD, et al. Recent trends in breast cancer among younger women in the United States. J Natl Cancer Inst. 2008; 100(22):1643–1648 [PMC free article] [PubMed]
14. Anderson WF, Jatoi I, Sherman ME. Qualitative age interactions in breast cancer studies: mind the gap. J Clin Oncol. 2009; 27(32):5308–5311 [PMC free article] [PubMed]
15. Millikan RC, Newman B, Tse CK, et al. Epidemiology of basal-like breast cancer. Breast Cancer Res Treat. 2008; 109(1):123–139 [PMC free article] [PubMed]
16. Palmer JR, Boggs DA, Wise LA, et al. Parity and lactation in relation to estrogen receptor negative breast cancer in African American women. Cancer Epidemiol Biomarkers Prev. 2011; 20(9):1883–1891 [PMC free article] [PubMed]
17. Gaudet MM, Press MF, Haile RW, et al. Risk factors by molecular subtypes of breast cancer across a population-based study of women 56 years or younger. Breast Cancer Res Treat. 2011; 130(2):587–597 [PMC free article] [PubMed]
Phipps AI, Chlebowski RT, Prentice R, et al. Reproductive history and oral contraceptive use in relation to risk of triple-negative breast cancer. J Natl Cancer Inst. 2011;103(6):470–477 [PMC free article] [PubMed]
19. Kotsopoulos J, Lubinski J, Salmena L, et al. Breastfeeding and the risk of breast cancer in BRCA1 and BRCA2 mutation carriers. Breast Cancer Res. 2012; 14(2):R42 [PMC free article] [PubMed]
20. Stuebe AM, Willett WC, Xue F, et al. Lactation and incidence of premenopausal breast cancer: a longitudinal study. Arch Intern Med. 2009; 169(15):1364–1571 [PMC free article] [PubMed]
21. Milanese TR, Hartmann LC, Sellers TA, et al. Age-related lobular involution and risk of breast cancer. J Natl Cancer Inst. 2006; 98(22):1600–1607 [PubMed]
22. Yang XR, Figueroa JD, Falk RT, et al. Analysis of terminal duct lobular unit involution in luminal A and basal breast cancers. Breast Cancer Res. 2012; 14(2):R64 [PMC free article] [PubMed]
23. Cummings P, Weiss NS, McKnight B, et al. Estimating the risk of breast cancer in relation to the interval since last term pregnancy. Epidemiology. 1997; 8(5):488–494 [PubMed]
24. Thompson WD. Age at and time since: modeling temporal aspects of exposure. Epidemiology. 1997; 8(5):471–473 [PubMed]
25. Anderson WF, Matsuno RK, Sherman ME, et al. Estimating age-specific breast cancer risks: a descriptive tool to identify age interactions. Cancer Causes Control. 2007; 18(4):439–447 [PubMed]
26. Zeleniuch-Jacquotte A, Afanasyeva Y, Kaaks R, et al. Premenopausal serum androgens and breast cancer risk: a nested case-control study. Breast Cancer Res. 2012; 14(1):R32 [PMC free article] [PubMed]
27. Hankinson SE, Eliassen AH. Circulating sex steroids and breast cancer risk in premenopausal women. Horm Cancer. 2010; 1(1):2–10 [PMC free article] [PubMed]
28. Key T, Appleby P, Barnes I, et al. Endogenous sex hormones and breast cancer in postmenopausal women: reanalysis of nine prospective studies. J Natl Cancer Inst. 2002; 94(8):606–616 [PubMed]
29. Tworoger SS, Eliassen AH, Sluss P, et al. A prospective study of plasma prolactin concentrations and risk of premenopausal and postmenopausal breast cancer. J Clin Oncol. 2007; 25(12):1482–1488 [PubMed]
30. Toriola AT, Vaarasmaki M, Lehtinen M, et al. Determinants of maternal sex steroids during the first half of pregnancy. Obstet Gynecol. 2011; 118(5):1029–1036 [PMC free article] [PubMed]
31. Troisi R, Hoover RN, Thadhani R, et al. Maternal, prenatal and perinatal characteristics and first trimester maternal serum hormone concentrations. Br J Cancer. 2008; 99(7):1161–1164 [PMC free article] [PubMed]
32. Eliassen AH, Tworoger SS, Hankinson SE. Reproductive factors and family history of breast cancer in relation to plasma prolactin levels in premenopausal and postmenopausal women. Int J Cancer. 2007; 120(7):1536–1541 [PubMed]
33. Faupel-Badger JM, Sherman ME, Garcia-Closas M, et al. Prolactin serum levels and breast cancer: relationships with risk factors and tumour characteristics among pre- and postmenopausal women in a population-based case-control study from Poland. Br J Cancer. 2010; 103(7):1097–1102 [PMC free article] [PubMed]
34. Nagata C, Wada K, Nakamura K, et al. Associations of body size and reproductive factors with circulating levels of sex hormones and prolactin in premenopausal Japanese women. Cancer Causes Control. 2011; 22(4):581–588 [PubMed]
35. Endogenous Hormones and Breast Cancer Collaborative Group. Key TJ, Appleby PN, et al. Circulating sex hormones and breast cancer risk factors in postmenopausal women: reanalysis of 13 studies. Br J Cancer. 2011; 105(5):709–722 [PMC free article] [PubMed]
36. Campino C, Torres C, Rioseco A, et al. Plasma prolactin/oestradiol ratio at 38 weeks gestation predicts the duration of lactational amenorrhoea. Hum Reprod. 2001; 16(12):2540–2545 [PubMed]
37. Carlsen SM, Jacobsen G, Vanky E. Mid-pregnancy androgen levels are negatively associated with breastfeeding. Acta Obstet Gynecol Scand. 2010; 89(1):87–94 [PubMed]
38. Stuebe AM, Bonuck K. What predicts intent to breastfeed exclusively? Breastfeeding knowledge, attitudes, and beliefs in a diverse urban population. Breastfeed Med. 2011; 6(6):413–420 [PMC free article] [PubMed]
39. Philipp BL, Merewood A. The baby-friendly way: the best breastfeeding start. Pediatr Clin North Am. 2004; 51(3):761–783, xi [PubMed]
40. Murtagh L, Moulton AD. Working mothers, breastfeeding, and the law. Am J Public Health. 2011; 101(2):217–223 [PubMed]
41. Merrill RM, Fugal S, Novilla LB, et al. Cancer risk associated with early and late maternal age at first birth. Gynecol Oncol. 2005; 96(3):583–593 [PubMed]
42. Ing R, Petrakis NL, Ho JH. Unilateral breast-feeding and breast cancer. Lancet. 1977; 2(8029):124–127 [PubMed]
43. Garofalo R. Cytokines in human milk. J Pediatr. 2010; 156(2 Suppl):S36–S40 [PubMed]
44. Tomicic S, Johansson G, Voor T, et al. Breast milk cytokine and IgA composition differ in Estonian and Swedish mothers—relationship to microbial pressure and infant allergy. Pediatr Res. 2010; 68(4):330–334 [PubMed]
45. Dvorak B, Fituch CC, Williams CS, et al. Increased epidermal growth factor levels in human milk of mothers with extremely premature infants. Pediatr Res. 2003; 54(1):15–19 [PubMed]
46. Castellote C, Casillas R, Ramirez-Santana C, et al. Premature delivery influences the immunological composition of colostrum and transitional and mature human milk. J Nutr. 2011; 141(6):1181–1187 [PubMed]
47. Ustundag B, Yilmaz E, Dogan Y, et al. Levels of cytokines (IL-1beta, IL-2, IL-6, IL-8, TNF-alpha) and trace elements (Zn, Cu) in breast milk from mothers of preterm and term infants. Mediators Inflamm. 2005; 2005(6):331–336 [PMC free article] [PubMed]
48. Kverka M, Burianova J, Lodinova-Zadnikova R, et al. Cytokine profiling in human colostrum and milk by protein array. Clin Chem. 2007; 53(5):955–962 [PubMed]
49. Innes KE, Byers TE. First pregnancy characteristics and subsequent breast cancer risk among young women. Int J Cancer. 2004; 112(2):306–311 [PubMed]
50. Melbye M, Wohlfahrt J, Andersen AM, et al. Preterm delivery and risk of breast cancer. Br J Cancer. 1999; 80(3–4):609–613 [PMC free article] [PubMed]
51. Eccles SA. The epidermal growth factor receptor/Erb-B/HER family in normal and malignant breast biology. Int J Dev Biol. 2011; 55(7–9):685–696 [PubMed]
52. Zivkovic AM, German JB, Lebrilla CB, et al. Human milk glycobiome and its impact on the infant gastrointestinal microbiota. Proc Natl Acad Sci U S A.. 2011;108(Suppl 1):4653–4658 [PubMed]
53. An HJ, Miyamoto S, Lancaster KS, et al. Profiling of glycans in serum for the discovery of potential biomarkers for ovarian cancer. J Proteome Res. 2006; 5(7):1626–1635 [PubMed]
54. Kirmiz C, Li B, An HJ, et al. A serum glycomics approach to breast cancer biomarkers. Mol Cell Proteomics. 2007; 6(1):43–55 [PubMed]
55. Leiserowitz GS, Lebrilla C, Miyamoto S, et al. Glycomics analysis of serum: a potential new biomarker for ovarian cancer?. Int J Gynecol Cancer. 2008; 18(3):470–475 [PMC free article] [PubMed]
56. Li B, An HJ, Kirmiz C, et al. Glycoproteomic analyses of ovarian cancer cell lines and sera from ovarian cancer patients show distinct glycosylation changes in individual proteins. J Proteome Res. 2008; 7(9):3776–3788 [PubMed]
57. Ruhaak LR, Miyamoto S, Kelly K, et al. N-glycan profiling of dried blood spots. Anal Chem. 2012; 84(1):396–402 [PMC free article] [PubMed]
58. Cregan MD, Fan Y, Appelbee A, et al. Identification of nestin-positive putative mammary stem cells in human breastmilk. Cell Tissue Res. 2007; 329(1):129–136 [PubMed]
59. Hassiotou F, Beltran A, Chetwynd E, et al. Breastmilk is a novel source of stem cells with multi-lineage differentiation potential. Stem Cells. 2012; 30(10):2164–2174 [PMC free article] [PubMed]
60. Wong CM, Anderton DL, Smith-Schneider S, et al. Quantitative analysis of promoter methylation in exfoliated epithelial cells isolated from breast milk of healthy women. Epigenetics. 2010; 5(7):645–655 [PMC free article] [PubMed]
61. Thompson PA, Kadlubar FF, Vena SM, et al. Exfoliated ductal epithelial cells in human breast milk: a source of target tissue DNA for molecular epidemiologic studies of breast cancer. Cancer Epidemiol Biomarkers Prev. 1998; 7(1):37–42 [PubMed]
62. Ambrosone CB, Abrams SM, Gorlewska-Roberts K, et al. Hair dye use, meat intake, and tobacco exposure and presence of carcinogen-DNA adducts in exfoliated breast ductal epithelial cells. Arch Biochem Biophys. 2007; 464(2):169–175 [PubMed]
63. Browne EP, Punska EC, Lenington S, et al. Increased promoter methylation in exfoliated breast epithelial cells in women with a previous breast biopsy. Epigenetics. 2011; 6(12):1425–1435 [PubMed]
64. Browne EP Punska EC Anderton DL Lenington S Arcaro KFMethylation in exfoliated epithelial cells from lactating women at increased risk for developing breast cancer. Paper presented at Annual Conference American Association for Cancer Research; April 2–6, 2011; Orlando, FL.
65. Becker GE, Cooney F, Smith HA. Methods of milk expression for lactating women. Cochrane Database Syst Rev. 2011;12CD006170 [PubMed]
66. Mitoulas LR, Kent JC, Cox DB, et al. Variation in fat, lactose and protein in human milk over 24h and throughout the first year of lactation. Br J Nutr. 2002; 88(1):29–37 [PubMed]
67. Kent JC, Mitoulas LR, Cregan MD, et al. Volume and frequency of breastfeedings and fat content of breast milk throughout the day. Pediatrics. 2006; 117(3):e387–e395 [PubMed]
68. Hassiotou F Hepworth A Trengrove N Lai CT Hartmann PECoordinated response of the fat and cellular content of breastmilk to the degree of fullness of the breast. Paper presented at Experimental Biology; April 19–25, 2012; San Diego, CA.
69. Humphreys RC, Bierie B, Zhao L, et al. Deletion of Stat3 blocks mammary gland involution and extends functional competence of the secretory epithelium in the absence of lactogenic stimuli. Endocrinology. 2002; 143(9):3641–3650 [PubMed]
70. Hughes K, Wickenden JA, Allen JE, et al. Conditional deletion of Stat3 in mammary epithelium impairs the acute phase response and modulates immune cell numbers during post-lactational regression. J Pathol. 2012; 227(1): 106–117 [PMC free article] [PubMed]
71. Kreuzaler PA, Staniszewska AD, Li W, et al. Stat3 controls lysosomal-mediated cell death in vivo. Nat Cell Biol. 2011; 13(3):303–309 [PubMed]
72. Watson CJ, Kreuzaler PA. Remodeling mechanisms of the mammary gland during involution. Int J Dev Biol. 2011; 55(7–9):757–762 [PubMed]
73. Watson, CJ. Involution: apoptosis and tissue remodelling that convert the mammary gland from milk factory to a quiescent organ. Breast Cancer Res 2006;8(2):203–207. [PMC free article] [PubMed]
74. Hennighausen L, Robinson GW. Signaling pathways in mammary gland development. Dev Cell. 2001; 1(4):467–475 [PubMed]
75. Lyons TR, O’Brien J, Borges VF, et al. Postpartum mammary gland involution drives progression of ductal carcinoma in situ through collagen and COX-2. Nat Med.. 2011;17(9):1109–1115 [PMC free article] [PubMed]
76. Maller O, Martinson H, Schedin P. Extracellular matrix composition reveals complex and dynamic stromal-epithelial interactions in the mammary gland. J Mammary Gland Biol Neoplasia.. 2010;15(3):301–318 [PubMed]
77. Schedin PJ, Thackray LB, Malone P, et al. Programmed cell death and mammary neoplasia. Cancer Treat Res. 1996; 83:3–22 [PubMed]
78. Bemis LT, Schedin P. Reproductive state of rat mammary gland stroma modulates human breast cancer cell migration and invasion. Cancer Res. 2000; 60(13):3414–3418 [PubMed]
79. Schedin P, Strange R, Mitrenga T, et al. Fibronectin fragments induce MMP activity in mouse mammary epithelial cells: evidence for a role in mammary tissue remodeling. J Cell Sci. 2000; 113(Pt 5):795–806 [PubMed]
80. Schedin P, Mitrenga T, McDaniel S, et al. Mammary ECM composition and function are altered by reproductive state. Mol Carcinog. 2004; 41(4):207–220 [PubMed]
81. McDaniel SM, Rumer KK, Biroc SL, et al. Remodeling of the mammary microenvironment after lactation promotes breast tumor cell metastasis. Am J Pathol. 2006; 168(2):608–620 [PubMed]
82. Stein T, Morris JS, Davies CR, et al. Involution of the mouse mammary gland is associated with an immune cascade and an acute-phase response, involving LBP, CD14 and STAT3 . Breast Cancer Res. 2004; 6(2):R75–R91 [PMC free article] [PubMed]
83. Clarkson RW, Wayland MT, Lee J, et al. Gene expression profiling of mammary gland development reveals putative roles for death receptors and immune mediators in post-lactational regression. Breast Cancer Res. 2004; 6(2):R92–R109 [PMC free article] [PubMed]
84. O’Brien J, Lyons T, Monks J, et al. Alternatively activated macrophages and collagen remodeling characterize the postpartum involuting mammary gland across species. Am J Pathol. 2010; 176(3):1241–1255 [PubMed]
85. Bladstrom A, Anderson H, Olsson H. Worse survival in breast cancer among women with recent childbirth: results from a Swedish population-based register study. Clin Breast Cancer. 2003; 4(4):280–285 [PubMed]
86. Johansson AL, Andersson TM, Hsieh CC, et al. Increased mortality in women with breast cancer detected during pregnancy and different periods postpartum. Cancer Epidemiol Biomarkers Prev.. 2011;20(9):1865–1872 [PubMed]
87. Stensheim H, Moller B, van Dijk T, et al. Cause-specific survival for women diagnosed with cancer during pregnancy or lactation: a registry-based cohort study. J Clin Oncol. 2009; 27(1):45–51 [PubMed]
88. Schedin P. Pregnancy-associated breast cancer and metastasis. Nat Rev Cancer. 2006; 6(4):281–291 [PubMed]
89. Holmes MD, Chen WY, Li L, et al. Aspirin intake and survival after breast cancer. J Clin Oncol. 2010; 28(9):1467–1472 [PMC free article] [PubMed]
90. Tiede B, Kang Y. From milk to malignancy: the role of mammary stem cells in development, pregnancy and breast cancer. Cell Res. 2011; 21(2):245–257 [PMC free article] [PubMed]

Articles from JNCI Journal of the National Cancer Institute are provided here courtesy of Oxford University Press