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

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 (PRSER-/ER+). Furthermore, its effect was minimally attenuated when adjusted for PRSER-/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.

Characterizing the Relationship between Expression Quantitative Trait Loci (eQTLs), DNA Methylation Quantitative Trait Loci (mQTLs), and Breast Cancer Risk Variants

“…Genome-wide association studies (GWASs) have revealed a plethora of genetic variations influencing susceptibility to diverse human diseases and traits [ 22 ]. These effects of these variants have been aggregated into PRSs, which are powerful tools for predicting risk [ 4 ]. Not all GWAS-derived variants are necessarily functional; many occur in non-coding regions of the genome or in regions that do not directly affect gene function [ 23 , 24 ].…”

“…In the past decade, genome-wide association studies (GWASs), employing single-nucleotide polymorphism (SNP) arrays and extensive case–control samples, have facilitated the discovery of common low-risk variants associated with breast cancer [ 1 , 2 , 3 ]. Collaborative entities like the Breast Cancer Association Consortium (BCAC) have pinpointed over 200 SNPs exhibiting significant associations with breast cancer [ 4 ]. While the known SNPs account for 18% of the familial relative risk associated with breast cancer, the inclusion of variants reliably imputed from the OncoArray data can substantially increase this proportion to approximately 40% [ 3 , 5 , 6 ].…”