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Evaluation of associations between genetically predicted circulating protein biomarkers and breast cancer risk

Shu, Xiang ; Bao, Jiandong ; Wu, Lang ; Long, Jirong ; Shu, Xiao Ou ; Guo, Xingyi ; Yang, Yaohua ; Michailidou, Kyriaki ; Bolla, Manjeet K. and Wang, Qin , et al. (2020) In International Journal of Cancer 146(8). p.2130-2138
Abstract

A small number of circulating proteins have been reported to be associated with breast cancer risk, with inconsistent results. Herein, we attempted to identify novel protein biomarkers for breast cancer via the integration of genomics and proteomics data. In the Breast Cancer Association Consortium (BCAC), with 122,977 cases and 105,974 controls of European descendants, we evaluated the associations of the genetically predicted concentrations of >1,400 circulating proteins with breast cancer risk. We used data from a large-scale protein quantitative trait loci (pQTL) analysis as our study instrument. Summary statistics for these pQTL variants related to breast cancer risk were obtained from the BCAC and used to estimate odds ratios... (More)

A small number of circulating proteins have been reported to be associated with breast cancer risk, with inconsistent results. Herein, we attempted to identify novel protein biomarkers for breast cancer via the integration of genomics and proteomics data. In the Breast Cancer Association Consortium (BCAC), with 122,977 cases and 105,974 controls of European descendants, we evaluated the associations of the genetically predicted concentrations of >1,400 circulating proteins with breast cancer risk. We used data from a large-scale protein quantitative trait loci (pQTL) analysis as our study instrument. Summary statistics for these pQTL variants related to breast cancer risk were obtained from the BCAC and used to estimate odds ratios (OR) for each protein using the inverse-variance weighted method. We identified 56 proteins significantly associated with breast cancer risk by instrumental analysis (false discovery rate <0.05). Of these, the concentrations of 32 were influenced by variants close to a breast cancer susceptibility locus (ABO, 9q34.2). Many of these proteins, such as insulin receptor, insulin-like growth factor receptor 1 and other membrane receptors (OR: 0.82–1.18, p values: 6.96 × 10−4–3.28 × 10−8), are linked to insulin resistance and estrogen receptor signaling pathways. Proteins identified at other loci include those involved in biological processes such as alcohol and lipid metabolism, proteolysis, apoptosis, immune regulation and cell motility and proliferation. Consistent associations were observed for 22 proteins in the UK Biobank data (p < 0.05). The study identifies potential novel biomarkers for breast cancer, but further investigation is needed to replicate our findings.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
breast cancer, circulating protein biomarkers, genetics, instruments
in
International Journal of Cancer
volume
146
issue
8
pages
9 pages
publisher
John Wiley & Sons Inc.
external identifiers
  • pmid:31265136
  • scopus:85069909795
ISSN
0020-7136
DOI
10.1002/ijc.32542
language
English
LU publication?
yes
id
6f4065f3-7ebd-45a3-943b-c52f29543c43
date added to LUP
2019-08-29 11:48:33
date last changed
2024-03-19 19:08:03
@article{6f4065f3-7ebd-45a3-943b-c52f29543c43,
  abstract     = {{<p>A small number of circulating proteins have been reported to be associated with breast cancer risk, with inconsistent results. Herein, we attempted to identify novel protein biomarkers for breast cancer via the integration of genomics and proteomics data. In the Breast Cancer Association Consortium (BCAC), with 122,977 cases and 105,974 controls of European descendants, we evaluated the associations of the genetically predicted concentrations of &gt;1,400 circulating proteins with breast cancer risk. We used data from a large-scale protein quantitative trait loci (pQTL) analysis as our study instrument. Summary statistics for these pQTL variants related to breast cancer risk were obtained from the BCAC and used to estimate odds ratios (OR) for each protein using the inverse-variance weighted method. We identified 56 proteins significantly associated with breast cancer risk by instrumental analysis (false discovery rate &lt;0.05). Of these, the concentrations of 32 were influenced by variants close to a breast cancer susceptibility locus (ABO, 9q34.2). Many of these proteins, such as insulin receptor, insulin-like growth factor receptor 1 and other membrane receptors (OR: 0.82–1.18, p values: 6.96 × 10<sup>−4</sup>–3.28 × 10<sup>−8</sup>), are linked to insulin resistance and estrogen receptor signaling pathways. Proteins identified at other loci include those involved in biological processes such as alcohol and lipid metabolism, proteolysis, apoptosis, immune regulation and cell motility and proliferation. Consistent associations were observed for 22 proteins in the UK Biobank data (p &lt; 0.05). The study identifies potential novel biomarkers for breast cancer, but further investigation is needed to replicate our findings.</p>}},
  author       = {{Shu, Xiang and Bao, Jiandong and Wu, Lang and Long, Jirong and Shu, Xiao Ou and Guo, Xingyi and Yang, Yaohua and Michailidou, Kyriaki and Bolla, Manjeet K. and Wang, Qin and Dennis, Joe and Andrulis, Irene L. and Castelao, Jose E. and Dörk, Thilo and Gago-Dominguez, Manuela and García-Closas, Montserrat and Giles, Graham G. and Lophatananon, Artitaya and Muir, Kenneth and Olsson, Håkan and Rennert, Gadi and Saloustros, Emmanouil and Scott, Rodney J. and Southey, Melissa C. and Pharoah, Paul D.P. and Milne, Roger L. and Kraft, Peter and Simard, Jacques and Easton, Douglas F. and Zheng, Wei}},
  issn         = {{0020-7136}},
  keywords     = {{breast cancer; circulating protein biomarkers; genetics; instruments}},
  language     = {{eng}},
  month        = {{04}},
  number       = {{8}},
  pages        = {{2130--2138}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{International Journal of Cancer}},
  title        = {{Evaluation of associations between genetically predicted circulating protein biomarkers and breast cancer risk}},
  url          = {{http://dx.doi.org/10.1002/ijc.32542}},
  doi          = {{10.1002/ijc.32542}},
  volume       = {{146}},
  year         = {{2020}},
}