Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.


Associations between circulating proteins and risk of breast cancer by intrinsic subtypes: a Mendelian randomisation analysis



The aetiologic role of circulating proteins in the development of breast cancer subtypes is not clear. We aimed to examine the potential causal effects of circulating proteins on the risk of breast cancer by intrinsic-like subtypes within the Mendelian randomisation (MR) framework.


MR was performed using summary statistics from two sources: the INTERVAL protein quantitative trait loci (pQTL) Study (1890 circulating proteins and 3301 healthy individuals) and the Breast Cancer Association Consortium (BCAC; 106,278 invasive cases and 91,477 controls). The inverse-variance (IVW)-weighted method was used as the main analysis to evaluate the associations between genetically predicted proteins and the risk of five different intrinsic-like breast cancer subtypes and the weighted median MR method, the Egger regression, the MR-PRESSO, and the MRLocus method were performed as secondary analysis.


We identified 98 unique proteins significantly associated with the risk of one or more subtypes (Benjamini–Hochberg false discovery rate < 0.05). Among them, 51 were potentially specific to luminal A-like subtype, 14 to luminal B/Her2-negative-like, 11 to triple negative, 3 to luminal B-like, and 2 to Her2-enriched-like breast cancer (ntotal = 81). Associations for three proteins (ICAM1, PLA2R1 and TXNDC12) showed evident heterogeneity across the subtypes. For example, higher levels of genetically predicted ICAM1 (per unit of increase) were associated with an increased risk of luminal B/HER2-negative-like cancer (OR = 1.06, 95% CI = 1.03–1.08, BH-FDR = 2.43 × 10−4) while inversely associated with triple-negative breast cancer with borderline significance (OR = 0.97, 95% CI = 0.95–0.99, BH-FDR = 0.065, Pheterogeneity < 0.005).


Our study found potential causal associations with the risk of subtypes of breast cancer for 98 proteins. Associations of ICAM1, PLA2R1 and TXNDC12 varied substantially across the subtypes. The identified proteins may partly explain the heterogeneity in the aetiology of distinct subtypes of breast cancer and facilitate the personalised risk assessment of the malignancy.

Your institute does not have access to this article

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: The flowchart of the current study.
Fig. 2: Heatmaps of MR estimates of proteins significantly associated with risk of one or more breast cancer subtypes.
Fig. 3: Scatter plots of MR associations of TXNDC12, PLA2R1 and ICAM1 with risk of five breast cancer intrinsic-like subtypes.

Data availability

All data used in this study are publicly available summary-level data, with the relevant studies cited.


  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49.

    PubMed  Google Scholar 

  2. Prat A, Perou CM. Deconstructing the molecular portraits of breast cancer. Mol Oncol. 2011;5:5–23.

    CAS  Article  Google Scholar 

  3. Endogenous H, Breast Cancer Collaborative, G., Key TJ, Appleby PN, Reeves GK, Roddam AW. Insulin-like growth factor 1 (IGF1), IGF binding protein 3 (IGFBP3), and breast cancer risk: pooled individual data analysis of 17 prospective studies. Lancet Oncol. 2010;11:530–42.

    Article  Google Scholar 

  4. Christopoulos PF, Msaouel P, Koutsilieris M. The role of the insulin-like growth factor-1 system in breast cancer. Mol Cancer. 2015;14:43.

    Article  Google Scholar 

  5. Shu X, Bao J, Wu L, Long J, Shu XO, Guo X, et al. Evaluation of associations between genetically predicted circulating protein biomarkers and breast cancer risk. Int J Cancer. 2020;146:2130–8.

    CAS  Article  Google Scholar 

  6. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.

    Article  Google Scholar 

  7. Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, et al. Genomic atlas of the human plasma proteome. Nature. 2018;558:73–9.

    CAS  Article  Google Scholar 

  8. Zhang H, Ahearn TU, Lecarpentier J, Barnes D, Beesley J, Qi G, et al. Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses. Nat Genet. 2020;52:572–81.

    CAS  Article  Google Scholar 

  9. Michailidou K, Hall P, Gonzalez-Neira A, Ghoussaini M, Dennis J, Milne RL, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013;45:353–61.361e351–52.

    CAS  Article  Google Scholar 

  10. Michailidou K, Lindstrom S, Dennis J, Beesley J, Hui S, Kar S, et al. Association analysis identifies 65 new breast cancer risk loci. Nature. 2017;551:92–4.

    Article  Google Scholar 

  11. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5:e1000529.

    Article  Google Scholar 

  12. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65.

    Article  Google Scholar 

  13. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:304–14.

    Article  Google Scholar 

  14. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25.

    Article  Google Scholar 

  15. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8.

    CAS  Article  Google Scholar 

  16. Carreras-Torres R, Johansson M, Haycock PC, Relton CL, Davey Smith G, Brennan P, et al. Role of obesity in smoking behaviour: Mendelian randomisation study in UK Biobank. BMJ. 2018;361:k1767.

    Article  Google Scholar 

  17. Mi H, Muruganujan A, Thomas PD. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res. 2013;41:D377–86.

    CAS  Article  Google Scholar 

  18. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–13.

    CAS  Article  Google Scholar 

  19. Milne RL, Kuchenbaecker KB, Michailidou K, Beesley J, Kar S, Lindstrom S, et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet. 2017;49:1767–78.

    CAS  Article  Google Scholar 

  20. Coignard J, Lush M, Beesley J, O’Mara TA, Dennis J, Tyrer JP, et al. A case-only study to identify genetic modifiers of breast cancer risk for BRCA1/BRCA2 mutation carriers. Nat Commun. 2021;12:1078.

    CAS  Article  Google Scholar 

  21. Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. N Engl J Med. 2009;360:790–800.

    CAS  Article  Google Scholar 

  22. Reis-Filho JS, Weigelt B, Fumagalli D, Sotiriou C. Molecular profiling: moving away from tumor philately. Sci Transl Med. 2010;2:47ps43.

    Article  Google Scholar 

  23. Yang XR, Sherman ME, Rimm DL, Lissowska J, Brinton LA, Peplonska B, et al. Differences in risk factors for breast cancer molecular subtypes in a population-based study. Cancer Epidemiol Biomark Prev. 2007;16:439–43.

    CAS  Article  Google Scholar 

  24. Tamimi RM, Colditz GA, Hazra A, Baer HJ, Hankinson SE, Rosner B, et al. Traditional breast cancer risk factors in relation to molecular subtypes of breast cancer. Breast Cancer Res Treat. 2012;131:159–67.

    CAS  Article  Google Scholar 

  25. Anderson KN, Schwab RB, Martinez ME. Reproductive risk factors and breast cancer subtypes: a review of the literature. Breast Cancer Res Treat. 2014;144:1–10.

    Article  Google Scholar 

  26. Gaudet MM, Gierach GL, Carter BD, Luo J, Milne RL, Weiderpass E, et al. Pooled analysis of nine cohorts reveals breast cancer risk factors by tumor molecular subtype. Cancer Res. 2018;78:6011–21.

    CAS  Article  Google Scholar 

  27. Siddiq A, Couch FJ, Chen GK, Lindstrom S, Eccles D, Millikan RC, et al. A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11. Hum Mol Genet. 2012;21:5373–84.

    CAS  Article  Google Scholar 

  28. Gudbjartsson DF, Sulem P, Stacey SN, Goldstein AM, Rafnar T, Sigurgeirsson B, et al. ASIP and TYR pigmentation variants associate with cutaneous melanoma and basal cell carcinoma. Nat Genet. 2008;40:886–91.

    CAS  Article  Google Scholar 

  29. Blank C, Brown I, Kacha AK, Markiewicz MA, Gajewski TF. ICAM-1 contributes to but is not essential for tumor antigen cross-priming and CD8+ T cell-mediated tumor rejection in vivo. J Immunol. 2005;174:3416–20.

    CAS  Article  Google Scholar 

  30. Ogawa Y, Hirakawa K, Nakata B, Fujihara T, Sawada T, Kato Y, et al. Expression of intercellular adhesion molecule-1 in invasive breast cancer reflects low growth potential, negative lymph node involvement, and good prognosis. Clin Cancer Res. 1998;4:31–6.

    CAS  PubMed  Google Scholar 

  31. Di D, Chen L, Wang L, Sun P, Liu Y, Xu Z, et al. Downregulation of human intercellular adhesion molecule-1 attenuates the metastatic ability in human breast cancer cell lines. Oncol Rep. 2016;35:1541–8.

    CAS  Article  Google Scholar 

  32. Figenschau SL, Knutsen E, Urbarova I, Fenton C, Elston B, Perander M, et al. ICAM1 expression is induced by proinflammatory cytokines and associated with TLS formation in aggressive breast cancer subtypes. Sci Rep. 2018;8:1–12.

    CAS  Article  Google Scholar 

  33. Guo P, Yang J, Di Jia MAM, Auguste DT. ICAM-1-targeted, Lcn2 siRNA-encapsulating liposomes are potent anti-angiogenic agents for triple negative breast cancer. Theranostics. 2016;6:1.

    Article  Google Scholar 

  34. Dai X, Li T, Bai Z, Yang Y, Liu X, Zhan J, et al. Breast cancer intrinsic subtype classification, clinical use and future trends. Am J Cancer Res. 2015;5:2929–43.

    CAS  PubMed  PubMed Central  Google Scholar 

Download references


The authors would like to thank the researchers of the Breast Cancer Association Consortium (BCAC), as well as Benjamin B Sun and colleagues for sharing summary statistics of GWAS.


XS (Xiang Shu) is supported, in part, by R00CA230205; XS (Xiaohui Sun) is supported by R00CA230205 & China Scholarship Council (CSC) (202108330197); MF is supported by the Quantitative Sciences Undergraduate Research Experience through R25CA214255; WZ is supported, in part by R01CA202981 and R01CA235553 for breast cancer research. We also acknowledge the Memorial Sloan Kettering P30 Cancer Center Support Grant (P30CA008748) for the statistical support.

Author information

Authors and Affiliations



XS (Xiang Shu) designed the study. QZ performed the statistical analyses. XS (Xiaohui Sun) created the figures. XS (Xiang Shu) drafted the manuscript. XS (Xiang Shu), MF, XG, JL, MER, X-OS, WZ and JLB interpreted the data and edited the manuscript. All authors have given final approval of the version to be published.

Corresponding author

Correspondence to Xiang Shu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

All included datasets were approved by respective ethics/institutional review committees, in accordance with the Declaration of Helsinki.

Consent to publish

Not applicable.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

The top 20 enriched pathways among 1890 tested proteins from overrepresentation test


Previously reported proteins, comparisons between the associations with risk of overall breast cancer and associations with risk of breast cancer subtypes: Full results from five MR approaches


Significant associations with risk of breast cancer subtypes (not previously reported for risk of overall breast cancer): full results from five MR approaches.

Bidirectional MR analysis for the identified proteins in the main analysis: from breast cancer subtype to proteins.


The pQTL of eight proteins showed a moderate linkage disequilibrium (LD) with previously identified breast cancer variants

Supplementary Figures

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shu, X., Zhou, Q., Sun, X. et al. Associations between circulating proteins and risk of breast cancer by intrinsic subtypes: a Mendelian randomisation analysis. Br J Cancer (2022).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI:


Quick links