Skip to main content
Top
Published in: Cancer Causes & Control 6/2019

01-06-2019 | Breast Cancer | Original Paper

Population-based relative risks for specific family history constellations of breast cancer

Authors: Frederick S. Albright, Wendy Kohlmann, Leigh Neumayer, Saundra S. Buys, Cindy B. Matsen, Kimberly A. Kaphingst, Lisa A. Cannon-Albright

Published in: Cancer Causes & Control | Issue 6/2019

Login to get access

Abstract

Purpose

Using a large resource linking genealogy with decades of cancer data, a non-traditional approach was used to estimate individualized risk for breast cancer (BC) based on specific family history extending to first cousins, providing a clearer picture of the contribution of various aspects of both close and distant combinations of affected relatives.

Methods

RRs for BC were estimated in 640,366 females for a representative set of breast cancer family history constellations that included number of first- (FDR), second-(SDR), and third-degree relatives (TDR), maternal and paternal relatives, and age at earliest diagnosis in a relative.

Results

RRs for first-degree relatives of BC cases ranged from 1.61 (= 1 FDR affected, CI 1.56, 1.67) to 5.00 (≥ 4 FDRs affected, CI 3.35, 7.18). RRs for second-degree relatives of probands with 0 affected FDRs ranged from 1.04 (= 1 SDR affected, CI 1.00, 1.08) to 1.71 (≥ 4 SDRs affected, CI 1.26, 2.27) and for second-degree relatives of probands with exactly 1 FDR from 1.54 (0 SDRs affected, CI 1.47, 1.61) to 4.78 (≥ 5 SDRs; CI 2.47, 8.35). RRs for third-degree relatives with no closer relatives affected were significantly elevated over population risk for probands with ≥ 5 affected TDRs RR = 1.32, CI 1.11, 1.57).

Conclusions

The majority of females in the Utah resource had a positive family history of BC in FDRs to TDRs. Presence of any number of affected FDRs or SDRs significantly increased risk for BC over population risk; and more than four TDRs, even with no affected FDRs or SDRs, significantly increased risk over population risk. Risk prediction derived from the specific and extended family history constellation of affected relatives allows identification of females at increased risk even when they do not have a conventionally defined high-risk family; these risks could be a powerful, efficient tool to individualize cancer screening and prevention.
Literature
1.
go back to reference Nelson HD, Pappas M, Zakher B, Mitchell JP et al (2014) Risk assessment, genetic counseling, and genetic testing for BRCA-related cancer in women: a systematic review to update the U.S. Preventive Services Task Force recommendation. Ann Intern Med 160(4):255–266CrossRefPubMed Nelson HD, Pappas M, Zakher B, Mitchell JP et al (2014) Risk assessment, genetic counseling, and genetic testing for BRCA-related cancer in women: a systematic review to update the U.S. Preventive Services Task Force recommendation. Ann Intern Med 160(4):255–266CrossRefPubMed
2.
go back to reference Daly MB, Pilarski R, Berry M, Buys SS, Farmer M et al (2017) NCCN guidelines insights: genetic/familial high-risk assessment: breast and ovarian, version 2. 2017. J Natl Compr Canc Network 15(1):9–20CrossRef Daly MB, Pilarski R, Berry M, Buys SS, Farmer M et al (2017) NCCN guidelines insights: genetic/familial high-risk assessment: breast and ovarian, version 2. 2017. J Natl Compr Canc Network 15(1):9–20CrossRef
3.
go back to reference Kraus C, Hoyer J, Vasileiou G, Wunderle M et al (2017) Gene panel sequencing in familial breast/ovarian cancer patients identifies multiple novel mutation also in genes other than BRCA1/2. Int J Cancer 140(1):95–102CrossRefPubMed Kraus C, Hoyer J, Vasileiou G, Wunderle M et al (2017) Gene panel sequencing in familial breast/ovarian cancer patients identifies multiple novel mutation also in genes other than BRCA1/2. Int J Cancer 140(1):95–102CrossRefPubMed
4.
go back to reference Saslow D, Boetes C, Burke W et al (2007) American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin 57(2):75–89CrossRef Saslow D, Boetes C, Burke W et al (2007) American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin 57(2):75–89CrossRef
6.
go back to reference Cintolo-Gonzalez JA, Braun D, Blackford AL et al (2017) Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications. Breast Cancer Res Treat 164(2):263–284CrossRefPubMed Cintolo-Gonzalez JA, Braun D, Blackford AL et al (2017) Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications. Breast Cancer Res Treat 164(2):263–284CrossRefPubMed
7.
go back to reference Narod SA (2018) Personalised medicine and population health: breast and ovarian cancer. Hum Genet 137(10):769–778CrossRefPubMed Narod SA (2018) Personalised medicine and population health: breast and ovarian cancer. Hum Genet 137(10):769–778CrossRefPubMed
8.
go back to reference Skolnick M (1980) The Utah geneological database: a resourse for genetic epidemiology Banbury Report No 4. Cancer Incidence Defined Populations 4:285–297 Skolnick M (1980) The Utah geneological database: a resourse for genetic epidemiology Banbury Report No 4. Cancer Incidence Defined Populations 4:285–297
9.
go back to reference Cannon-Albright LA (2008) Utah family-based analysis: past, present and future. Hum Hered 65(4):209–220CrossRefPubMed Cannon-Albright LA (2008) Utah family-based analysis: past, present and future. Hum Hered 65(4):209–220CrossRefPubMed
10.
go back to reference Agresti A (1990) Categorical data analysis. Wiley, New York Agresti A (1990) Categorical data analysis. Wiley, New York
14.
go back to reference Lee AJ, Cunningham AP, Kuchenbaecker KB, Mavaddat N, Easton DF, Antoniou AC, The Consortium of Investigators of Modifiers of BRCA1/21 and The Breast Cancer Association Consortium, BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface, British Journal of Cancer, online publication 17 December 2013. https://doi.org/10.1038/bjc.2013.730 Lee AJ, Cunningham AP, Kuchenbaecker KB, Mavaddat N, Easton DF, Antoniou AC, The Consortium of Investigators of Modifiers of BRCA1/21 and The Breast Cancer Association Consortium, BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface, British Journal of Cancer, online publication 17 December 2013. https://​doi.​org/​10.​1038/​bjc.​2013.​730
15.
go back to reference Lu KH, Wood ME, Daniels M, Burke C, Ford J et al (2014) American Society of Clinical Oncology Expert Statement: collection and use of a cancer family history for oncology providers. J Clin Oncol 32(8):833–840CrossRefPubMedPubMedCentral Lu KH, Wood ME, Daniels M, Burke C, Ford J et al (2014) American Society of Clinical Oncology Expert Statement: collection and use of a cancer family history for oncology providers. J Clin Oncol 32(8):833–840CrossRefPubMedPubMedCentral
17.
18.
go back to reference Ziogas A, Anton C (2003) Validation of family history data in cancer family registries. Am J Prev Med 24(4):190–198CrossRefPubMed Ziogas A, Anton C (2003) Validation of family history data in cancer family registries. Am J Prev Med 24(4):190–198CrossRefPubMed
20.
go back to reference Doerr M, Teng K (2012) Family history: still relevant in the genomics era. Cleve Clin J Med 79(5):331–336CrossRefPubMed Doerr M, Teng K (2012) Family history: still relevant in the genomics era. Cleve Clin J Med 79(5):331–336CrossRefPubMed
21.
go back to reference Aiyar L, Shuman C, Hayeems R, Dupuis A, Pu S, Wodak S, Chitayat D, Velsher L, Davies J (2014) Risk estimates for complex disorders: comparing personal genome testing and family history. Gen in Med 16(3):231–237 Aiyar L, Shuman C, Hayeems R, Dupuis A, Pu S, Wodak S, Chitayat D, Velsher L, Davies J (2014) Risk estimates for complex disorders: comparing personal genome testing and family history. Gen in Med 16(3):231–237
22.
go back to reference Heald B, Edelman E, Eng C (2012) Prospective comparison of family medical history with personal genome screening for risk assessment of common cancers. EJHG 20:547–551CrossRefPubMed Heald B, Edelman E, Eng C (2012) Prospective comparison of family medical history with personal genome screening for risk assessment of common cancers. EJHG 20:547–551CrossRefPubMed
23.
go back to reference Bloss CS, Topol EJ, Schork NJ (2012) Association of direct-to-consumer genome-wide disease risk estimates and self-reported disease. Genet Epidemiol 36(1):66–70CrossRefPubMed Bloss CS, Topol EJ, Schork NJ (2012) Association of direct-to-consumer genome-wide disease risk estimates and self-reported disease. Genet Epidemiol 36(1):66–70CrossRefPubMed
24.
go back to reference Brewer HR, Jones ME, Schoemaker MJ, Ashworth A, Swerdlow AJ (2017) Family history and risk of breast cancer: an analysis accounting for family structure. Breast Cancer Res Treat 165:193–200CrossRefPubMedPubMedCentral Brewer HR, Jones ME, Schoemaker MJ, Ashworth A, Swerdlow AJ (2017) Family history and risk of breast cancer: an analysis accounting for family structure. Breast Cancer Res Treat 165:193–200CrossRefPubMedPubMedCentral
25.
go back to reference Collaborative Group on Hormonal Factors in Breast Cancer (2001) Familial breast cancer: collaborative reanalysis of individual data from 52 epidemiological studies including 58,209 women with breast cancer and 101,986 women without the disease. Lancet 358(9291):1389–1399CrossRef Collaborative Group on Hormonal Factors in Breast Cancer (2001) Familial breast cancer: collaborative reanalysis of individual data from 52 epidemiological studies including 58,209 women with breast cancer and 101,986 women without the disease. Lancet 358(9291):1389–1399CrossRef
26.
go back to reference Hemminiki K, Vaittinen P (1998) Familial breast cancer in the family-cancer database. Int J Cancer 77(3):386–391CrossRef Hemminiki K, Vaittinen P (1998) Familial breast cancer in the family-cancer database. Int J Cancer 77(3):386–391CrossRef
27.
go back to reference McLellan T, Jorde LB, Skolnick MH (1984) Genetic distances between the utah mormons and related populations. Am J Hum Genet 36(4):836–857PubMedPubMedCentral McLellan T, Jorde LB, Skolnick MH (1984) Genetic distances between the utah mormons and related populations. Am J Hum Genet 36(4):836–857PubMedPubMedCentral
29.
go back to reference Katanis N (2016) The continuum of causality in human genetic disorders. Genome Biol 17:233CrossRef Katanis N (2016) The continuum of causality in human genetic disorders. Genome Biol 17:233CrossRef
Metadata
Title
Population-based relative risks for specific family history constellations of breast cancer
Authors
Frederick S. Albright
Wendy Kohlmann
Leigh Neumayer
Saundra S. Buys
Cindy B. Matsen
Kimberly A. Kaphingst
Lisa A. Cannon-Albright
Publication date
01-06-2019
Publisher
Springer International Publishing
Published in
Cancer Causes & Control / Issue 6/2019
Print ISSN: 0957-5243
Electronic ISSN: 1573-7225
DOI
https://doi.org/10.1007/s10552-019-01171-5

Other articles of this Issue 6/2019

Cancer Causes & Control 6/2019 Go to the issue
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine