Skip to main content
  • AACR Publications
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

AACR logo

  • Register
  • Log in
  • Log out
  • My Cart
Advertisement

Main menu

  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • CEBP Focus Archive
    • Meeting Abstracts
    • Progress and Priorities
    • Collections
      • COVID-19 & Cancer Resource Center
      • Disparities Collection
      • Editors' Picks
      • "Best of" Collection
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Informing Public Health Policy
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

  • AACR Publications
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

User menu

  • Register
  • Log in
  • Log out
  • My Cart

Search

  • Advanced search
Cancer Epidemiology, Biomarkers & Prevention
Cancer Epidemiology, Biomarkers & Prevention
  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • CEBP Focus Archive
    • Meeting Abstracts
    • Progress and Priorities
    • Collections
      • COVID-19 & Cancer Resource Center
      • Disparities Collection
      • Editors' Picks
      • "Best of" Collection
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Informing Public Health Policy
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

Research Articles

Anti-MUC1 Antibodies and Ovarian Cancer Risk: Prospective Data from the Nurses' Health Studies

Simone P. Pinheiro, Susan E. Hankinson, Shelley S. Tworoger, Bernard A. Rosner, John R. McKolanis, Olivera J. Finn and Daniel W. Cramer
Simone P. Pinheiro
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Susan E. Hankinson
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shelley S. Tworoger
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Bernard A. Rosner
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
John R. McKolanis
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Olivera J. Finn
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Daniel W. Cramer
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
DOI: 10.1158/1055-9965.EPI-10-0068 Published June 2010
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Abstract

Background: The surface epithelial glycoprotein MUC1 becomes overexpressed and hypoglycosylated in adenocarcinomas; similar changes occur during nonmalignant inflammatory events. Antibodies developed against tumor-like MUC1 in response to such events could be one way through which ovarian cancer risk factors operate.

Methods: We evaluated the association between anti-MUC1 antibodies and risk of ovarian cancer in a prospective nested case-control study in the Nurses' Health Studies. We used an ELISA to measure plasma anti-MUC1 antibodies in 117 ovarian cancer cases collected at least 3 years before diagnosis and 339 matched controls.

Results: In controls, younger women (P-trend = 0.03), those with a tubal ligation (P = 0.03), and those with fewer ovulatory cycles (P-trend = 0.04) had higher antibody levels. In cases, women with late-stage disease (P = 0.04) and those whose specimen was >11 years remote from diagnosis (P = 0.01) had higher antibody levels. Overall, increasing anti-MUC1 antibody levels were associated with a nonsignificant trend for lower risk for ovarian cancer, but there was highly significant heterogeneity by age (P-heterogeneity = 0.005). In women <64 years, the antibody level in quartiles 2 to 4 versus quartile 1 were associated with reduced risk (relative risk = 0.53; 95% confidence interval, 0.31-0.93; P-trend = 0.03), whereas in women ≥64 years, the corresponding relative risk was 2.11 (95% confidence interval, 0.73-6.04); P-trend = 0.05).

Conclusion: Anti-MUC1 antibodies evaluated several years before diagnosis may be associated with lower risk of subsequent ovarian cancer in women <64 years old at assessment.

Impact: Key elements of an “immune model” to explain ovarian cancer risk factors are confirmed and should be evaluated in larger prospective studies. Cancer Epidemiol Biomarkers Prev; 19(6); 1595–601. ©2010 AACR.

Introduction

The human mucin family member MUC1 is a heavily glycosylated protein expressed in low levels by many normal epithelial cells, whereas a poorly glycosylated form is overexpressed by many adenocarcinomas, including breast and ovarian cancers (1). Circulating anti-MUC1 antibodies have been identified in patients at the time of cancer diagnosis and generally predict better survival (2-4). Antibodies also have been found in healthy individuals, especially in women who are pregnant or lactating (4-7), leading to the hypothesis that natural immunity against MUC1 might develop and account for the long-term protective effects of pregnancy and breastfeeding on breast cancer risk (8). In addition, several other factors that reduce risk of ovarian cancer, including oral contraceptive use, tubal ligation, and mastitis during breastfeeding, have been associated with higher antibody levels (9). These findings led to the hypothesis that immunity developed against MUC1 could account, at least in part, for the long-term protective effect of certain reproductive and hormonal factors on ovarian cancer risk (9).

Because ovarian tumors can express MUC1 and lead to antibody development, it is important to evaluate antibody levels well before diagnosis in a prospective study design. Thus, we measured anti-MUC1 antibodies in plasma samples collected from Nurses' Health Study (NHS) participants in a nested case-control study of ovarian cancer, in which cases provided blood samples at least 3 years before a diagnosis, to evaluate the relationship between circulating anti-MUC1 antibodies and risk of ovarian cancer.

Materials and Methods

Study population

The NHS was established in 1976 among 121,700 U.S. female registered nurses ages 30 to 55 years at entry, and the NHSII in 1989 among 116,430 U.S. female registered nurses ages 25 to 42 years at enrollment (10, 11). Information on potential risk factors for ovarian cancer, including reproductive history, was obtained through baseline and follow-up questionnaires sent every 2 years. Heparinized blood specimens were provided by 32,826 NHS participants between 1989 and 1990 (12), and by 29,611 NHSII participants between 1996 and 1999 (13). Samples were shipped through overnight courier to the laboratory, where they were separated into plasma, RBC, and WBC components, and stored in liquid nitrogen freezers. Follow-up of NHS and NHSII blood cohorts was 98% in 2004 and 2003, respectively. NHS and NHSII protocols have been reviewed and approved by the Committee on the Use of Human Subjects in Research at the Brigham and Women's Hospital.

Incident cases of epithelial ovarian cancer diagnosed at least 3 years after blood collection but before June 1, 2004 (NHS) or June 1, 2003 (NHSII) were identified. A total of 117 cases (111 from NHS, 6 from NHSII) were confirmed by medical record review and matched to 339 controls (∼3 per case) on age (±1 y), menopausal status at blood collection and diagnosis, postmenopausal hormone use at blood collection, and month (±1 mo), time of the day (±2 h), and fasting status at the blood draw. All controls had intact ovaries at the time of case diagnosis. Except for nonmelanoma skin cancer, cases and controls had no history of cancer before blood collection.

Laboratory assays

Anti-MUC1 antibody assays were conducted at the Department of Immunology, University of Pittsburgh. Antibodies were measured against a synthetic 100-mer MUC1 peptide corresponding to five tandem repeats of the MUC1 polypeptide core repeat region (9, 14). Briefly, MUC1-coated Immulon wells (Dynax) and peptide-negative plates were incubated overnight and washed thrice with PBS before the addition of 100 μL of 2.5% bovine serum albumin in PBS. Serially diluted plasma (1:40-1:80 in PBS) was added to MUC1-coated plates and incubated at room temperature. Plates were washed 5 times with 100 μL PBS and 0.1% Tween 20 detergent. Alkaline phosphatase–labeled goat anti-human polyvalent IgM, IgG, and IgA (50 μL; Sigma-Aldrich) diluted 1:1,000 were added before plates were again washed 5 times with PBS-Tween. Alkaline phosphatase substrate pNPP (100 μL; Sigma-Aldrich) was added. Plates were incubated before the stop solution (0.5 mol/L NaOH) was added. We used the MRX Revelation plate reader (Thermo Labsystems) to read absorbance values at 405 to 410 nm, which were subtracted from the absorbance values obtained from antigen-negative plates to account for nonspecific binding.

Laboratory personnel were blinded to case/control status of the samples. Blood specimens from cases and their matched controls were assayed on the same plate, and each plate included two to eight quality control samples. The within-plate coefficient of variation was 9%, showing excellent reproducibility, and the between-plate coefficient of variation was 23% for absorbance values at the 1:40 dilution, and 9% and 24% at the 1:80 dilution. The Spearman rank correlation between the two dilutions was high (ρ = 0.93; P < 0.001); readings at the 1:40 dilution were used in this analysis. Cases and their matched controls were assayed together and on the same plate.

Statistical analysis

The distribution of anti-MUC1 antibody levels among subjects was skewed right; therefore, values were natural-log transformed for all analyses. No antibody values were identified as outliers (15). Generalized linear models were used to compare mean anti-MUC1 antibody levels across categories of factors possibly associated with antibodies, adjusting for age and assay plate, among controls and characteristics of the cancer among cases. The estimated number of ovulatory cycles for each woman was calculated by subtracting age at menarche, 1 year for each term pregnancy, duration of oral contraceptive use, and breastfeeding from age at menopause (or age if premenopausal), and multiplying by 12, as previously described (16).

We used conditional logistic regression to estimate relative risks (RR) and 95% confidence intervals (95% CI) in comparing quartiles (based on the control distribution) of anti-MUC1 antibody levels. To examine whether the association between antibody levels and ovarian cancer risk differed according to categories of age at blood collection, we compared the likelihood of models with and without an interaction term between quartiles of antibody levels weighted by median antibody levels (modeled continuously) and age (in years). All P-values were two-sided. Analyses were conducted on SAS 9.1 version (SAS Institute).

Results

The average age at blood collection was 57 years for both cases and controls, and the average age at ovarian cancer diagnosis for cases was 65 years (Table 1). Compared with controls, women who developed ovarian cancer were more likely to be nulliparous, have a family history of ovarian cancer, and have relatively shorter durations of oral contraceptive use.

View this table:
  • View inline
  • View popup
Table 1.

Characteristics of cases and controls at time of blood collection

Among controls, women in the top quartile of age at blood collection (i.e., ≥64 y) had significantly lower levels of antibodies compared with younger women (P = 0.03; Table 2). Antibody levels also decreased with increasing number of estimated ovulatory cycles (P-trend = 0.04) but were higher among controls with a history of tubal ligation (P = 0.03). Antibody levels were slightly but not significantly lower among controls whose age at menopause was ≥51 years or had regularly used talc in genital hygiene.

View this table:
  • View inline
  • View popup
Table 2.

Association between ovarian cancer risk factors and anti-MUC1 antibodies among 339 controls from the Nurses' Health Studies

Among cases, women whose age at blood collection was ≥64 years had a nonsignificantly higher level of antibodies than those whose age at collection was younger (P = 0.28; Table 3). No significant differences in antibodies were observed between invasive or borderline tumors, or by histology. Women who went on to develop late-stage versus early-stage disease tended to have higher antibody levels (P = 0.04), and women who became cases >11 years after their blood was drawn had higher antibody levels than those diagnosed earlier in the follow-up period (P = 0.01). As might be expected, only 5 (17%) of the 29 women with the shortest interval to diagnosis (and lowest anti-MUC1 antibody levels) were diagnosed after age 65 years compared with 20 (67%) of the 30 women with the longest interval to diagnosis (and highest antibody levels; P < 0.0001). None of the factors found to affect antibody levels in controls were significant in the cases (data not shown).

View this table:
  • View inline
  • View popup
Table 3.

Association between ovarian cancer characteristics and anti-MUC1 antibodies among 117 cases from the Nurses' Health Studies

Among all women, those with antibody levels in the top quartile had a nonsignificant 28% lower risk of developing ovarian cancer compared with those in the lowest quartile (95% CI, 0.39-1.31); there was no significant trend across quartiles (P-trend = 0.30; Table 4). However, there was a statistically significant difference in the association by age at blood draw (P-interaction = 0.005). Because the RRs in quartiles 2 through 4 were similar, we collapsed these quartiles to increase power in the analysis stratified by age. Among those <64 years at blood collection (n = 90 cases and 257 controls), the RR for ovarian cancer comparing quartiles 2 to 4 with quartile 1 was 0.53 (95% CI, 0.31-0.93); P-trend = 0.03). The comparable RR in women ≥64 years at blood draw (n = 27 cases and 82 controls) was 2.11 (95% CI, 0.73-6.04; P-trend = 0.05). Neither adjusting for known risk factors of ovarian cancer (e.g., oral contraceptive use, parity, and tubal ligation) nor excluding NHSII participants (6 cases and 8 controls) altered the results (data not shown).

View this table:
  • View inline
  • View popup
Table 4.

RR of ovarian cancer by quartiles of anti-MUC1 antibodies in the Nurses' Health Studies

Discussion

In this first-of-its-kind study, we prospectively evaluated the relationship between circulating anti-MUC1 antibody levels and risk of ovarian cancer. We observed a statistically significant interaction by age for the association between anti-MUC1 antibodies and risk of ovarian cancer, with an inverse association among women <64 years at assessment (i.e., the controls had higher antibody levels on average than the cases) and a positive association in those ≥64 years (i.e., the controls had lower antibody levels on average than the cases). A decline in anti-MUC1 antibodies in healthy individuals with increasing age has been observed previously (4, 9). This may be due to a mechanism called immunosenescence, which reflects a declining immune response toward many common antigens with increasing age and time since antigen presentation, such as after immunization (17, 18). Many of the exposures, that we have postulated could lead to protective antibodies, occur during the reproductive years, and their effect could wane with an increasing time interval from those events. This interpretation would be compatible with data from published epidemiologic studies indicating that the protective effect of oral contraceptive use (19, 20) and, possibly, with breastfeeding (21) and tubal ligation (22), attenuates over time. In addition, the drop in antibody levels among controls matches age-specific increases in risk for ovarian cancer in the population; that is, about a 2-fold change between women <64 years compared with those ≥64 years (27 versus 56 cases per 100,000 in NHS data).

The events associated with antibody levels among controls found in our study include known risk factors for ovarian cancer. Notably, women who had undergone a tubal ligation had ∼20% higher antibody levels compared with those who had not had one; this is similar to a prior finding among 705 healthy women (9). Explanations for the protective effect of tubal ligation on ovarian cancer risk have focused on blockage of potentially harmful contaminants from reaching the ovaries; however, our results suggest the injury associated with tubal ligation may trigger the release of MUC1 into the circulation and the development of protective anti-MUC1 antibodies.

Increasing number of ovulatory cycles, which has been linked with higher risk for ovarian cancer, was associated with lower antibody levels in this study and in one prior study examining this association (23). The ability of uninterrupted ovulations to lower anti-MUC1 antibody levels may be due to an absence of reproductive events, such as oral contraceptive (OC) use and breastfeeding, which may have otherwise elicited antibody production. In addition, MUC1 has higher expression in secretory endometrium (24), and circulating levels are highest during the luteal phase (25). Thus, it is possible that repeated cyclic variation of MUC1 in blood over many years could lead to an immune tolerance that ultimately lowers anti-MUC1 antibody levels.

A prior study of 705 women without ovarian cancer noted associations between anti-MUC1 antibody levels and other factors, including OC use, hysterectomy, breastfeeding, bone fracture, and talc use (9). Although we did not observe statistically significant associations for these exposures in this study (possibly due to the smaller number of controls), the direction of these relationships were similar to the prior study. For example, talc use was associated with lower anti-MUC1 antibody levels in both studies, fitting with its role as a factor that has been shown to increase risk for ovarian cancer (26-28). Like ovulatory cycles, regular talc use may represent an exposure that can trigger a chronic inflammatory response, lead to MUC1 antigenemia, and result in MUC1-specific immune tolerance. Clearly, additional research is necessary to fully understand how “acute” events translate into protection and “chronic” events into increased risk, and how these may relate to the difference in the association by age. We believe this will require an understanding of the effects of the events and age on cellular immune reactions that may be most critical for cancer elimination or escape.

We suggested that the decline in anti-MUC1 antibodies in controls reflected immunosenescene. Clearly, the higher antibody levels observed among cases who were ≥age 64 years at blood draw (Table 3) requires a different interpretation. First, we point out that the level of antibodies in the oldest age group was about the same as in the younger controls. This may indicate that there is a subset of women who do not have the expected decline in antibody levels as they age, perhaps due to chronic antigen challenge provided by an ongoing chronic inflammatory condition or a developing cancer. In addition, we note that cases who were ≥64 years at blood draw had an age at diagnosis of 75 years and overlapped with the group in Table 3, which had the greatest lag between age at blood draw and cancer diagnosis, and the highest antibody levels. Unfortunately, our data cannot clarify whether the higher antibody levels in older cases represent a persistence of high antibodies that may delay the onset of disease or new appearance of antibodies reflecting a chronic inflammatory response. Sorting out the possibilities will require larger studies with serial measurements of anti-MUC1 antibody levels across the important mid-60s age period. Besides anti-MUC1 antibodies, it may be necessary to measure free MUC1 antigen and immune complexes involving the antigen and antibodies. These have been described and could interfere with assays for either free antigen or free antibody (29). A comprehensive study might also establish whether the appearance (or persistence) of high levels of anti-MUC1 antibodies in later life might represent an early detection signal many years in advance of ovarian cancer.

In the current study, we focused our attention on MUC1 because of the wealth of data that exists on its expression in normal tissues versus cancer, inflammation, and other pathologic conditions (1, 30). We measured antibodies against the unglycosylated protein core of MUC1, commonly found in tumors. However, there are other aberrantly glycosylated MUC1 epitopes closely associated with tumor MUC1 that might be even more immunogenic, eliciting additional protective immunity (31, 32). It is also possible that additional ovarian cancer antigens can be presented to the immune system through other nonmalignant events, and lead to the creation of antibodies and cellular immunity that would also be associated with a reduced risk for ovarian cancer. MUC1 thus represents one of many targets of immunosurveillance that can collectively elicit protective immunity and lower cancer risk. The development of new high-throughput technologies may allow identification of antibodies that are present in circulation before ovarian cancer diagnosis, and the exposures or events that are associated with increased antibody levels and define serologic signatures of cancer risk reduction. Further, understanding how antibodies against MUC1 or other antigens are formed during exposures or events that predate ovarian cancer development may suggest ways to duplicate or amplify these protective effects through vaccination.

A limitation of this study is that we were unable to measure MUC1-specific memory T cells due to the lack of archived viable white cells. However, T-cell activation is indirectly measured through the presence of various isotypes of anti-MUC1 antibodies, all of which require antigen-specific helper T cells for their generation. We also lacked information on certain exposures that have previously been associated with anti-MUC1 antibody levels, such as mastitis, mumps parotitis, and endometriosis. The relatively small number of cases in this study clearly limited our power to examine the effect of antibodies by histologic type of ovarian cancer or by other potential effect modifiers, and the relatively high between-plate coefficients of variation, often seen in immune-based assays like the one used here, also contributed to a lack of precision.

Although ours is the first prospective study investigating anti-MUC1 antibodies in relation to ovarian cancer, one study examined humoral immune responses to MUC1 among BRCA carriers and related it to subsequent breast cancer, another MUC1-expressing cancer (33). Women who carried a mutation had significantly lower anti-MUC1 immunoglobulin antibodies compared with controls, and carriers who went on to develop breast cancer had lower (although not significantly so) antibody levels than carriers not found to have cancer at prophylactic mastectomy. Thus, this study fits with our observations in suggesting that anti-MUC1 antibodies correlate with a known risk factor for the cancer and risk for developing disease.

In conclusion, our prospective study provides important evidence that circulating anti-MUC1 antibody, either as an effector mechanism or as a marker of a more comprehensive immune response, may be associated with a lower risk of ovarian cancer in women <64 years and offers an explanation for the mechanism of action of several factors known to alter risk of ovarian cancer. These data further suggest a new paradigm that immunosurveillance may play an important role in ovarian carcinogenesis. Larger studies are necessary to clarify the role of MUC1 immunity in ovarian cancer, particularly in relation to the potential differential association by age at antibody assessment. Ultimately, this avenue of research could lead to new methods for ovarian cancer prevention or early detection.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Acknowledgments

We thank the participants in the Nurses' Health Studies for their continued participation and dedication.

Grant Support: NIH grants [grant numbers CA105009 and CA123170 (D.W. Cramer), CA49449 and CA67262 (SEH), and P01 CA73743 (O.J. Finn).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Footnotes

    • Received January 15, 2010.
    • Revision received March 24, 2010.
    • Accepted March 26, 2010.

    References

    1. ↵
      1. Vlad AM,
      2. Kettel JC,
      3. Alajez NM,
      4. Carlos CA,
      5. Finn OJ
      . MUC1 immunobiology: from discovery to clinical applications. Adv Immunol 2004;82:249–93.
      OpenUrlCrossRefPubMed
    2. ↵
      1. von Mensdorff-Pouilly S,
      2. Gourevitch MM,
      3. Kenemans P,
      4. et al
      . Humoral immune response to polymorphic epithelial mucin (MUC-1) in patients with benign and malignant breast tumours. Eur J Cancer 1996;32A:1325–31.
      OpenUrlPubMed
      1. Hamanaka Y,
      2. Suehiro Y,
      3. Fukui M,
      4. et al
      . Circulating anti-MUC1 IgG antibodies as a favorable prognostic factor for pancreatic cancer. Int J Cancer 2003;103:97–100.
      OpenUrlCrossRefPubMed
    3. ↵
      1. Richards ER,
      2. Devine PL,
      3. Quin RJ,
      4. et al
      . Antibodies reactive with the protein core of MUC1 mucin are present in ovarian cancer patients and healthy women. Cancer Immunol Immunother 1998;46:245–52.
      OpenUrlCrossRefPubMed
      1. Bon GG,
      2. Kenemans P,
      3. Verstraeten AA,
      4. et al
      . Maternal serum Ca125 and Ca15-3 antigen levels in normal and pathological pregnancy. Fetal Diagn Ther 2001;16:166–72.
      OpenUrlCrossRefPubMed
      1. Croce MV,
      2. Isla-Larrain MT,
      3. Price MR,
      4. Segal-Eiras A
      . Detection of circulating mammary mucin (Muc1) and MUC1 immune complexes (Muc1-CIC) in healthy women. Int J Biol Markers 2001;16:112–20.
      OpenUrlPubMed
    4. ↵
      1. Croce MV,
      2. Isla-Larrain MT,
      3. Capafons A,
      4. Price MR,
      5. Segal-Eiras A
      . Humoral immune response induced by the protein core of MUC1 mucin in pregnant and healthy women. Breast Cancer Res Treat 2001;69:1–11.
      OpenUrlPubMed
    5. ↵
      1. Agrawal B,
      2. Reddish MA,
      3. Krantz MJ,
      4. Longenecker BM
      . Does pregnancy immunize against breast cancer?. Cancer Res 1995;55:2257–61.
      OpenUrlAbstract/FREE Full Text
    6. ↵
      1. Cramer DW,
      2. Titus-Ernstoff L,
      3. McKolanis JR,
      4. et al
      . Conditions associated with antibodies against the tumor-associated antigen MUC1 and their relationship to risk for ovarian cancer. Cancer Epidemiol Biomarkers Prev 2005;14:1125–31.
      OpenUrlAbstract/FREE Full Text
    7. ↵
      1. Colditz GA,
      2. Hankinson SE
      . The Nurses' Health Study: lifestyle and health among women. Nat Rev Cancer 2005;5:388–96.
      OpenUrlCrossRefPubMed
    8. ↵
      1. Rockhill B,
      2. Willett WC,
      3. Hunter DJ,
      4. et al
      . Physical activity and breast cancer risk in a cohort of young women. J Natl Cancer Inst 1998;90:1155–60.
      OpenUrlCrossRefPubMed
    9. ↵
      1. Hankinson SE,
      2. Willett WC,
      3. Manson JE,
      4. et al
      . Plasma sex steroid hormone levels and risk of breast cancer in postmenopausal women. J Natl Cancer Inst 1998;90:1292–9.
      OpenUrlAbstract/FREE Full Text
    10. ↵
      1. Tworoger SS,
      2. Sluss P,
      3. Hankinson SE
      . Association between plasma prolactin concentrations and risk of breast cancer among predominately premenopausal women. Cancer Res 2006;66:2476–82.
      OpenUrlAbstract/FREE Full Text
    11. ↵
      1. Kotera Y,
      2. Fontenot JD,
      3. Pecher G,
      4. Metzgar RS,
      5. Finn OJ
      . Humoral immunity against a tandem repeat epitope of human mucin MUC-1 in sera from breast, pancreatic, and colon cancer patients. Cancer Res 1994;54:2856–60.
      OpenUrlAbstract/FREE Full Text
    12. ↵
      1. Rosner B
      . Percentage points for generalized ESD many-outlier procedure. Technometrics 1983;25:165–72.
      OpenUrlCrossRef
    13. ↵
      1. Rosner BA,
      2. Colditz GA,
      3. Webb PM,
      4. Hankinson SE
      . Mathematical models of ovarian cancer incidence. Epidemiology 2005;16:508–15.
      OpenUrlCrossRefPubMed
    14. ↵
      1. Weinberger B,
      2. Herndler-Brandstetter D,
      3. Schwanninger A,
      4. Weiskopf D,
      5. Grubeck-Loebenstein B
      . Biology of immune responses to vaccines in elderly persons. Clin Infect Dis 2008;46:1078–84.
      OpenUrlAbstract/FREE Full Text
    15. ↵
      1. Kumar R,
      2. Burns EA
      . Age-related decline in immunity: implications for vaccine responsiveness. Expert Rev Vaccines 2008;7:467–79.
      OpenUrlCrossRefPubMed
    16. ↵
      1. Willett WC,
      2. Bain C,
      3. Hennekens CH,
      4. Rosner B,
      5. Speizer FE
      . Oral contraceptives and risk of ovarian cancer. Cancer 1981;48:1684–7.
      OpenUrl
    17. ↵
      1. Beral V,
      2. Doll R,
      3. et al.
      , Collaborative Group on Epidemiological Studies of Ovarian Cancer. Ovarian cancer and oral contraceptives: collaborative reanalysis of data from 45 epidemiological studies including 23,257 women with ovarian cancer and 87,303 controls. Lancet 2008;371:303–14.
      OpenUrlCrossRefPubMed
    18. ↵
      1. Siskind V,
      2. Green A,
      3. Bain C,
      4. Purdie D
      . Breastfeeding, menopause, and epithelial ovarian cancer. Epidemiology 1997;8:188–91.
      OpenUrlPubMed
    19. ↵
      1. Miracle-McMahill HL,
      2. Calle EE,
      3. Kosinski AS,
      4. et al
      . Tubal ligation and fatal ovarian cancer in a large prospective cohort study. Am J Epidemiol 1997;145:349–57.
      OpenUrlAbstract/FREE Full Text
    20. ↵
      1. Terry KL,
      2. Titus-Ernstoff L,
      3. McKolanis JR,
      4. et al
      . Incessant ovulation, mucin 1 immunity, and risk for ovarian cancer. Cancer Epidemiol Biomarkers Prev 2007;16:30–5.
      OpenUrlAbstract/FREE Full Text
    21. ↵
      1. Hey NA,
      2. Graham RA,
      3. Seif MW,
      4. Aplin JD
      . The polymorphic epithelial mucin MUC1 in human endometrium is regulated with maximal expression in the implantation phase. J Clin Endocrinol Metab 1994;78:337–42.
      OpenUrlCrossRefPubMed
    22. ↵
      1. Erbagci AB,
      2. Yilmaz N,
      3. Kutlar I
      . Menstrual cycle dependent variability for serum tumor markers CEA, AFP, CA 19-9, CA 125 and CA 15-3 in healthy women. Dis Markers 1999;15:259–67.
      OpenUrlPubMed
    23. ↵
      1. Cook LS,
      2. Kamb ML,
      3. Weiss NS
      . Perineal powder exposure and the risk of ovarian cancer. Am J Epidemiol 1997;145:459–65.
      OpenUrlAbstract/FREE Full Text
      1. Cramer DW,
      2. Liberman RF,
      3. Titus-Ernstoff L,
      4. et al
      . Genital talc exposure and risk of ovarian cancer. Int J Cancer 1999;81:351–6.
      OpenUrlCrossRefPubMed
    24. ↵
      1. Gertig DM,
      2. Hunter DJ,
      3. Cramer DW,
      4. et al
      . Prospective study of talc use and ovarian cancer. J Natl Cancer Inst 2000;92:249–52.
      OpenUrlAbstract/FREE Full Text
    25. ↵
      1. Vlad AM,
      2. Muller S,
      3. Cudic M,
      4. et al
      . Complex carbohydrates are not removed during processing of glycoproteins by dendritic cells: processing of tumor antigen MUC1 glycopeptides for presentation to major histocompatibility complex class II-restricted T cells. J Exp Med 2002;196:1435–46.
      OpenUrlAbstract/FREE Full Text
    26. ↵
      1. Andrianifahanana M,
      2. Moniaux N,
      3. Batra SK
      . Regulation of mucin expression: mechanistic aspects and implications for cancer and inflammatory diseases. Biochim Biophys Acta 2006;1765:189–222.
      OpenUrlPubMed
    27. ↵
      1. Sorensen AL,
      2. Reis CA,
      3. Tarp MA,
      4. et al
      . Chemoenzymatically synthesized multimeric Tn/STn MUC1 glycopeptides elicit cancer-specific anti-MUC1 antibody responses and override tolerance. Glycobiology 2006;16:96–107.
      OpenUrlAbstract/FREE Full Text
    28. ↵
      1. Tarp MA,
      2. Sorensen AL,
      3. Mandel U,
      4. et al
      . Identification of a novel cancer-specific immunodominant glycopeptide epitope in the MUC1 tandem repeat. Glycobiology 2007;17:197–209.
      OpenUrlAbstract/FREE Full Text
    29. ↵
      1. Hermsen BB,
      2. Verheijen RH,
      3. Menko FH,
      4. et al
      . Humoral immune responses to MUC1 in women with a BRCA1 or BRCA2 mutation. Eur J Cancer 2007;43:1556–63.
      OpenUrlPubMed
    PreviousNext
    Back to top
    Cancer Epidemiology Biomarkers & Prevention: 19 (6)
    June 2010
    Volume 19, Issue 6
    • Table of Contents
    • Table of Contents (PDF)

    Sign up for alerts

    View this article with LENS

    Open full page PDF
    Article Alerts
    Sign In to Email Alerts with your Email Address
    Email Article

    Thank you for sharing this Cancer Epidemiology, Biomarkers & Prevention article.

    NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

    Enter multiple addresses on separate lines or separate them with commas.
    Anti-MUC1 Antibodies and Ovarian Cancer Risk: Prospective Data from the Nurses' Health Studies
    (Your Name) has forwarded a page to you from Cancer Epidemiology, Biomarkers & Prevention
    (Your Name) thought you would be interested in this article in Cancer Epidemiology, Biomarkers & Prevention.
    CAPTCHA
    This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
    Citation Tools
    Anti-MUC1 Antibodies and Ovarian Cancer Risk: Prospective Data from the Nurses' Health Studies
    Simone P. Pinheiro, Susan E. Hankinson, Shelley S. Tworoger, Bernard A. Rosner, John R. McKolanis, Olivera J. Finn and Daniel W. Cramer
    Cancer Epidemiol Biomarkers Prev June 1 2010 (19) (6) 1595-1601; DOI: 10.1158/1055-9965.EPI-10-0068

    Citation Manager Formats

    • BibTeX
    • Bookends
    • EasyBib
    • EndNote (tagged)
    • EndNote 8 (xml)
    • Medlars
    • Mendeley
    • Papers
    • RefWorks Tagged
    • Ref Manager
    • RIS
    • Zotero
    Share
    Anti-MUC1 Antibodies and Ovarian Cancer Risk: Prospective Data from the Nurses' Health Studies
    Simone P. Pinheiro, Susan E. Hankinson, Shelley S. Tworoger, Bernard A. Rosner, John R. McKolanis, Olivera J. Finn and Daniel W. Cramer
    Cancer Epidemiol Biomarkers Prev June 1 2010 (19) (6) 1595-1601; DOI: 10.1158/1055-9965.EPI-10-0068
    del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
    • Tweet Widget
    • Facebook Like
    • Google Plus One

    Jump to section

    • Article
      • Abstract
      • Introduction
      • Materials and Methods
      • Results
      • Discussion
      • Disclosure of Potential Conflicts of Interest
      • Acknowledgments
      • Footnotes
      • References
    • Figures & Data
    • Info & Metrics
    • PDF
    Advertisement

    Related Articles

    Cited By...

    More in this TOC Section

    • Estimated Effects of Multi-Cancer Screening
    • Gene–Environment Interaction Portfolio Analysis
    • Risk Prediction for Renal Cell Carcinoma
    Show more Research Articles
    • Home
    • Alerts
    • Feedback
    • Privacy Policy
    Facebook   Twitter   LinkedIn   YouTube   RSS

    Articles

    • Online First
    • Current Issue
    • Past Issues

    Info for

    • Authors
    • Subscribers
    • Advertisers
    • Librarians

    About Cancer Epidemiology, Biomarkers & Prevention

    • About the Journal
    • Editorial Board
    • Permissions
    • Submit a Manuscript
    AACR logo

    Copyright © 2021 by the American Association for Cancer Research.

    Cancer Epidemiology, Biomarkers & Prevention
    eISSN: 1538-7755
    ISSN: 1055-9965

    Advertisement