Development and testing of a polygenic risk score for breast cancer aggressiveness

Yiwey Shieh; Jacquelyn Roger; Christina Yau; Denise M Wolf; Gillian L Hirst; Lamorna Brown Swigart; Scott Huntsman; Donglei Hu; Jovia L Nierenberg; Pooja Middha; Rachel S Heise; Yushu Shi; Linda Kachuri; Qianqian Zhu; Song Yao; Christine B Ambrosone; Marilyn L Kwan; Bette J Caan; John S Witte; Lawrence H Kushi; Laura van 't Veer; Laura J Esserman; Elad Ziv

doi:10.1038/s41698-023-00382-z

Development and testing of a polygenic risk score for breast cancer aggressiveness

NPJ Precis Oncol. 2023 May 15;7(1):42. doi: 10.1038/s41698-023-00382-z.

Authors

Yiwey Shieh¹, Jacquelyn Roger², Christina Yau³, Denise M Wolf⁴, Gillian L Hirst³, Lamorna Brown Swigart⁴, Scott Huntsman⁵, Donglei Hu⁵, Jovia L Nierenberg^{5

6}, Pooja Middha⁵, Rachel S Heise⁷, Yushu Shi⁷, Linda Kachuri⁸, Qianqian Zhu⁹, Song Yao¹⁰, Christine B Ambrosone¹⁰, Marilyn L Kwan¹¹, Bette J Caan¹¹, John S Witte⁸, Lawrence H Kushi¹¹, Laura van 't Veer⁴, Laura J Esserman³, Elad Ziv⁵

Affiliations

¹ Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA. yis4001@med.cornell.edu.
² PhD Program in Biological and Medical Informatics, University of California, San Francisco, San Francisco, CA, USA.
³ Department of Surgery, University of California, San Francisco, San Francisco, CA, USA.
⁴ Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA.
⁵ Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
⁶ Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
⁷ Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
⁸ Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA.
⁹ Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, USA.
¹⁰ Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
¹¹ Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.

Abstract

Aggressive breast cancers portend a poor prognosis, but current polygenic risk scores (PRSs) for breast cancer do not reliably predict aggressive cancers. Aggressiveness can be effectively recapitulated using tumor gene expression profiling. Thus, we sought to develop a PRS for the risk of recurrence score weighted on proliferation (ROR-P), an established prognostic signature. Using 2363 breast cancers with tumor gene expression data and single nucleotide polymorphism (SNP) genotypes, we examined the associations between ROR-P and known breast cancer susceptibility SNPs using linear regression models. We constructed PRSs based on varying p-value thresholds and selected the optimal PRS based on model r² in 5-fold cross-validation. We then used Cox proportional hazards regression to test the ROR-P PRS's association with breast cancer-specific survival in two independent cohorts totaling 10,196 breast cancers and 785 events. In meta-analysis of these cohorts, higher ROR-P PRS was associated with worse survival, HR per SD = 1.13 (95% CI 1.06-1.21, p = 4.0 × 10^-4). The ROR-P PRS had a similar magnitude of effect on survival as a comparator PRS for estrogen receptor (ER)-negative versus positive cancer risk (PRS_ER-/ER+)_. Furthermore, its effect was minimally attenuated when adjusted for PRS_ER-/ER+, suggesting that the ROR-P PRS provides additional prognostic information beyond ER status. In summary, we used integrated analysis of germline SNP and tumor gene expression data to construct a PRS associated with aggressive tumor biology and worse survival. These findings could potentially enhance risk stratification for breast cancer screening and prevention.

Abstract

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