Abstract 2271: Pathway analysis suggests biological processes driven by germline genetic associations with breast cancer prognosis
Authors: Garcia Maria Escala , Guo Qi , Wessels Lodewyk , Bader Gary , Pharoah Paul , Chenevix-Trench Georgia , Easton Douglas , Canisius Sander , Schmidt Marjanka 
Published: August 2018

Finding the impact of germline variants on breast cancer survival could provide novel insights in its etiology and help identify new therapeutic targets. While genome-wide association studies (GWAS) have made considerable progress in identifying germline variants associated with diverse risk phenotypes, survival analysis has been hampered by a lack of power. To overcome this limitation, we aimed to assess whether biological networks operating in breast cancer prognosis can be inferred by integrating patient genotypes and the associated survival data in a pathway analysis.

We analyzed ~7.3 million genotyped and imputed variants from 84,457 breast cancer patients in all, estrogen receptor (ER) positive and negative breast cancers and validated the results in 12,381 independent samples. First, a Cox model was fitted to obtain summary statistics for each variant. We then integrated the summary statistics into gene scores using the Pascal algorithm. Finally, we constructed pathways based on the gene scores and a protein-protein interaction network with the HotNet2 tool. To assess the validity of the significant networks (P&lt;0.01) found by Hotnet2, we tested the association of the germline variants forming the subnetworks with prognosis. We selected genetic variants that best represented the genetic association of each subnetwork with survival by using a Lasso-penalized Cox model. Then we computed a polygenic hazard score (PHS) on the independent validation set and used it to run a univariate Cox model that allowed selecting the networks significantly associated with prognosis (P&lt;0.05).

We found significant subnetworks in the ER-specific analyses, but not in those including all breast cancers. We then clustered the networks into functional pathways to identify significant (Padj&lt;0.05) functional groups. The enriched processes included growth factor signalling, DNA repair and cell cycle functions. These pathways overlapped with similar biological processes obtained in a downstream characterization analysis based on the genotypes and mRNA expression data of the TCGA breast cancer study and were complemented by key enriched transcription factors (Padj&lt;0.01).

The approach developed is novel in studying germline variants and breast cancer-specific mortality and shows an alternative method to handle underpowered datasets. The networks and the posterior functional characterization suggest that there is some genetic regulation of biological processes associated with breast cancer prognosis specifically for each ER-status subgroup.

Citation Format: Maria Escala Garcia, Qi Guo, Lodewyk Wessels, Gary Bader, Paul Pharoah, Georgia Chenevix-Trench, Douglas Easton, Sander Canisius, Marjanka Schmidt. Pathway analysis suggests biological processes driven by germline genetic associations with breast cancer prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2271.