Circulating growth factor concentrations and breast cancer risk: a nested case-control study of IGF-1, IGFBP-3, and breast cancer in a family-based cohort

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Abstract

Background

Insulin-like growth factor 1 (IGF-1) and binding protein 3 (IGFBP-3) are associated with breast cancer in women at average risk of cancer. Less is known whether these biomarkers also predict risk in women with breast cancer family history.

Methods

We conducted a nested case-control study within the New York site of the Breast Cancer Family Registry (BCFR, n = 80 cases, 156 controls), a cohort enriched for breast cancer family history. Using conditional logistic regression, we estimated the association between IGF-1 and IGFBP-3 levels and breast cancer risk and examined whether this risk differed by predicted absolute breast cancer risk based on pedigree models.

Results

The overall association between IGF-1 or IGFBP-3 elevation (≥ median in controls) and breast cancer risk was elevated, but not statistically significant (IGF-1 OR = 1.37, 95% CI = 0.66–2.85; IGFBP-3 OR = 1.62, 95% CI = 0.81–3.24). Women with elevated predicted absolute 10-year risk ≥ 3.4% and elevated IGFBP-3 (≥ median) had more than a 3-fold increased risk compared to women with lower predicted absolute 10-year risk (< 3.4%) and low IGFBP-3 (OR = 3.47 95% CI = 1.04–11.6).

Conclusions

These data offer some support that the overall magnitude of the associations between IGF-1 and IGFBP3 seen in average risk cohorts may be similar in women enriched with a strong breast cancer family history.

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Most cited references 13

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Insulin-like growth factor 1 (IGF1), IGF binding protein 3 (IGFBP3), and breast cancer risk: pooled individual data analysis of 17 prospective studies

Kim Overvad (2010)

Summary Background Insulin-like growth factor 1 (IGF1) stimulates mitosis and inhibits apoptosis. Some published results have shown an association between circulating IGF1 and breast-cancer risk, but it has been unclear whether this relationship is consistent or whether it is modified by IGF binding protein 3 (IGFBP3), menopausal status, oestrogen receptor status or other factors. The relationship of IGF1 (and IGFBP3) with breast-cancer risk factors is also unclear. The Endogenous Hormones and Breast Cancer Collaborative Group was established to analyse pooled individual data from prospective studies to increase the precision of the estimated associations of endogenous hormones with breast-cancer risk. Methods Individual data on prediagnostic IGF1 and IGFBP3 concentrations were obtained from 17 prospective studies in 12 countries. The associations of IGF1 with risk factors for breast cancer in controls were examined by calculating geometric mean concentrations in categories of these factors. The odds ratios (ORs) with 95% CIs of breast cancer associated with increasing IGF1 concentrations were estimated by conditional logistic regression in 4790 cases and 9428 matched controls, with stratification by study, age at baseline, and date of baseline. All statistical tests were two-sided, and a p value of less than 0·05 was considered significant. Findings IGF1 concentrations, adjusted for age, were positively associated with height and age at first pregnancy, inversely associated with age at menarche and years since menopause, and were higher in moderately overweight women and moderate alcohol consumers than in other women. The OR for breast cancer for women in the highest versus the lowest fifth of IGF1 concentration was 1·28 (95% CI 1·14–1·44; p<0·0001). This association was not altered by adjusting for IGFBP3, and did not vary significantly by menopausal status at blood collection. The ORs for a difference in IGF1 concentration between the highest and lowest fifth were 1·38 (95% CI 1·14–1·68) for oestrogen-receptor-positive tumours and 0·80 (0·57–1·13) for oestrogen-receptor-negative tumours (p for heterogeneity=0·007). Interpretation Circulating IGF1 is positively associated with breast-cancer risk. The association is not substantially modified by IGFBP3, and does not differ markedly by menopausal status, but seems to be confined to oestrogen-receptor-positive tumours. Funding Cancer Research UK.

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The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions

A Antoniou, A P Cunningham, Tom Peto … (2008)

Multiple genetic loci confer susceptibility to breast and ovarian cancers. We have previously developed a model (BOADICEA) under which susceptibility to breast cancer is explained by mutations in BRCA1 and BRCA2, as well as by the joint multiplicative effects of many genes (polygenic component). We have now updated BOADICEA using additional family data from two UK population-based studies of breast cancer and family data from BRCA1 and BRCA2 carriers identified by 22 population-based studies of breast or ovarian cancer. The combined data set includes 2785 families (301 BRCA1 positive and 236 BRCA2 positive). Incidences were smoothed using locally weighted regression techniques to avoid large variations between adjacent intervals. A birth cohort effect on the cancer risks was implemented, whereby each individual was assumed to develop cancer according to calendar period-specific incidences. The fitted model predicts that the average breast cancer risks in carriers increase in more recent birth cohorts. For example, the average cumulative breast cancer risk to age 70 years among BRCA1 carriers is 50% for women born in 1920–1929 and 58% among women born after 1950. The model was further extended to take into account the risks of male breast, prostate and pancreatic cancer, and to allow for the risk of multiple cancers. BOADICEA can be used to predict carrier probabilities and cancer risks to individuals with any family history, and has been implemented in a user-friendly Web-based program (http://www.srl.cam.ac.uk/genepi/boadicea/boadicea_home.html).

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Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance

Anne Quante, Alice S. Whittemore, Tom Shriver … (2012)

Introduction Clinicians use different breast cancer risk models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used models that include nongenetic factors - BCRAT, also called Gail model, generally used for patients with average risk and IBIS, also called Tyrer Cuzick model, generally used for patients with above-average risk. Methods We evaluated the performance of the BCRAT and IBIS models as currently applied in clinical settings for 10-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide variation in individual risk makes it an interesting population to examine model performance across subgroups of women. For model calibration, we divided the cohort into quartiles of model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs). Results The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRAT- and IBIS-assigned risks of 3.18% and 5.49%, respectively, were lower than the cohort's 10-year cumulative probability of developing breast cancer (6.25%; 95% confidence interval (CI) = 5.0 to 7.8%). Agreement between assigned and observed risks was better for IBIS (HL X4 2 = 7.2, P value 0.13) than BCRAT (HL X4 2 = 22.0, P value <0.001). The IBIS model also showed better discrimination (AUC = 69.5%, CI = 63.8% to 75.2%) than did the BCRAT model (AUC = 63.2%, CI = 57.6% to 68.9%). In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Conclusions Models developed using extended family history and genetic data, such as the IBIS model, also perform well in women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Extending such models to include additional nongenetic information may improve performance in women across the breast cancer risk continuum.

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Author and article information

Contributors

Mary Beth Terry:

ORCID: http://orcid.org/0000-0002-4106-5033

mt146@columbia.edu

Journal

Journal ID (nlm-ta): Breast Cancer Res

Journal ID (iso-abbrev): Breast Cancer Res

Title: Breast Cancer Research : BCR

Publisher: BioMed Central (London )

ISSN (Print): 1465-5411

ISSN (Electronic): 1465-542X

Publication date (Electronic): 22 October 2020

Publication date PMC-release: 22 October 2020

Publication date (Print): 2020

Volume: 22

Electronic Location Identifier: 109

Affiliations

[1 ]GRID grid.21729.3f, ISNI 0000000419368729, Department of Epidemiology, , Mailman School of Public Health, Columbia University, ; 722 West 168th Street, New York, NY 10032 USA

[2 ]GRID grid.21729.3f, ISNI 0000000419368729, Department of Environmental Health Sciences, , Mailman School of Public Health, Columbia University, ; 722 West 168th Street, New York, NY 10032 USA

[3 ]GRID grid.239585.0, ISNI 0000 0001 2285 2675, Herbert Irving Comprehensive Cancer Center, , Columbia University Medical Center, ; 1130 St. Nicholas Avenue, New York, NY 10032 USA

[4 ]GRID grid.21729.3f, ISNI 0000000419368729, Department of Pediatrics, , Columbia University, ; 622 West 168th Street, New York, NY USA

[5 ]GRID grid.239585.0, ISNI 0000 0001 2285 2675, Department of Medicine, , Columbia University Medical Center, ; 630 West 168th Street, New York, NY USA

Author information

Mary Beth Terry http://orcid.org/0000-0002-4106-5033

Article

Publisher ID: 1352

DOI: 10.1186/s13058-020-01352-0

PMC ID: 7579807

PubMed ID: 33092613

SO-VID: 93cd3dba-7c5d-4035-b229-0eb754c94b95

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 11 June 2020

Date accepted : 7 October 2020

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;

Award ID: R01 CA 159868

Award ID: UM1 CA164920

Award Recipient : Mary Beth Terry

Funded by: FundRef http://dx.doi.org/10.13039/100000066, National Institute of Environmental Health Sciences;

Award ID: ES009089

Award Recipient : Regina M. Santella

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ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: igf-1,igfbp-3,breast cancer,risk model,family history

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ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: igf-1, igfbp-3, breast cancer, risk model, family history

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Circulating growth factor concentrations and breast cancer risk: a nested case-control study of IGF-1, IGFBP-3, and breast cancer in a family-based cohort

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Karger: Oncology

Most cited references 13

Insulin-like growth factor 1 (IGF1), IGF binding protein 3 (IGFBP3), and breast cancer risk: pooled individual data analysis of 17 prospective studies

The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions

Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance

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